Engineering Conferences International

Daza, Claudia

Engineering Conferences InternationalECI Digital ArchivesBioEnergy IV: Innovations in Biomass Conversionfor Heat, Power, Fuels and Chemicals ProceedingsSpring 6-12-2013Sustainability issues regarding bamboo as arenewable feedstock for fuels and materialsClaudia DazaECNFollow this and additional works at: http://dc.engconfintl.org/bioenergy_ivPart of the Chemical Engineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inBioEnergy IV: Innovations in Biomass Conversion for Heat, Power, Fuels and Chemicals by an authorized administrator of ECI Digital Archives. Formore information, please contact franco@bepress.com.Recommended CitationClaudia Daza, "Sustainability issues regarding bamboo as a renewable feedstock for fuels and materials" in "BioEnergy IV: Innovationsin Biomass Conversion for Heat, Power, Fuels and Chemicals", Manuel Garcia-Perez,Washington State University, USA DietrichMeier, Thünen Institute of Wood Research, Germany Raffaella Ocone, Heriot-Watt University, United Kingdom Paul de Wild,Biomass & Energy Efficiency, ECN, The Netherlands Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/bioenergy_iv/34www.ecn.nlSustainability issues regarding bamboo as a renewable feedstock for fuels and materialsClaudia Daza M.BioEnergy IV – Otranto – ItalyJune  12- 2013SustainabilitySustainability Themes•Greenhouse gas emissions•Competition with food and local applications•Biodiversity•Environment•Prosperity•Social well-being. CriteriaIndicatorsSustainable BiomassBamboo:An alternative sustainable feedstock for the biobased economyWhy bamboo?• +36 Million Hectares • Millions of tons could be harvested sustainably each year• Opportunities for sustainable development Source: Inbar.• Fast growing – 10-40 Ton/Ha-year• Regenerates itself after it has been responsibly cared for and harvested No replanting• Excellent reforesting crop– Low consumption of fertilizers (GHG)– Easy propagationno seeds – Water table preservation– Biodiversity preservation• CO2 sump• Opportunities for rural development– Product diversificationPhoto: Avellaneda J.Why bamboo?Current UsesPlant section Current useCulm Finished products: housing, flooring furniture, paper,charcoal.Leaves Left in the field as fertilizer, and/or collected foranimal feed.Branches Low value applications, low market (chopsticks)Roots Food7Major global supplier of products: China65%7%28%Contribution of world bamboo resources by continentAsiaAfricaAmericaSource: FAOBAMBUSA VULGARIS VAR. VULGARIS SCHRADER EX WENDLANDSource: G.H. Gutierrez, 2009.Lignocellulosic feedstock9Yields and composition depend on: specie and age of the plant, plant section, cultivation site and harvesting season.Feedstock Bamboo culmCane Bagasse Wheat strawWoodHHV (dry) MJ/kg 17-20 18-20 16-19 17-20Density kg/m3 500-700 150-200 160-300 200-500Yield Ton/Ha-year 20-40 7-10 6-12 10-20Overall composition (dwt %)Cellulose 40-60 35 38 50Hemicellulose 20-30 25 36 23Lignin 20-40 20 16 22Others** 2-10 20 10 5** Ash, resins, etc.LogisticsCostBiobased: Chemicals+ Materials+ Energy10Lignin(Enzymatic) hydrolysisFractionation(organosolv)Lignocellulosic biomassFermentationChemical conversionEthanolButanolLactic acidThermochemical depolymerisatonCHPHMFPhenolicsFuel additivesElectricityHeatCelluloseHemicellulosePrimary biorefinery Secondary biorefinery......…FurfuralFiller, adhesives, ...Paper & cellulosic materialsSolid Fuel in Power PlantsCoal & biomassFibersFertilizerBiocharLignocellulosic Biorefinery11Torrefied Pellets for Sustainable Biomass Export from ColombiaAssessing the whole chain of bamboo cultivation & collection via torrefaction upgrading to application as biofuelBamboo Project: Jan 2011-April 2013Bamboo as a biomass import chainAssessment of:• technical suitability• sustainability• economicalfeasibilityfor:• Co-firing torrefied Colombian bamboopellets in NLThe SBI scheme aims to give an impulse to the promotion of the sustainability of the biomass import chains for biobased energy and chemical applications/transport/electricity/heating and chemicals/materialsLooking for certificationThe Dutch technical agreement (NTA8080) describes the requirements for sustainably produced biomass for energy applications (power, heat & cold and transportation fuels). Biomass includes solid as well as liquid and gaseous biofuels.  The NTA8080 is intended to be applied at organizations that wish to sustainably: • Produce, • Convert,• Trade; or • Use biomass for energy generation or as transporting fuel. • Currently being revised and extended to bio-based productsBiomass chain assessmentSecurity of supplyTechnical specificationsSustainability criteriaTorrefaction for upgrading biomass• Process parameters– Temperature: 200-300 °C– Absence of oxygenTenacious and fibrousLHV = 9 - 12 MJ/kgHydrophilicBiodegradableHeterogeneousTorrefactionFriable and less fibrousLHV = 18 - 24 MJ/kgHydrophobicPreservedHomogeneousPelletisationBulk density = 650-800 kg/m3Bulk energy density = 12 - 19 GJ/m3Case study: Guadua angustifolia• Woody bamboo species native toLatin America. • Fast growing: 30 m in 6 months20-40 Ton/ha-yearLondonoGuadua a. development stages17  ResiduesNew shotYoung Mature          Overmature Dry0 1 2 3 4 5 6 7 8 9 10 11GuaduaangustifoliaYearMaturity stage  vs timeCelulloseHemicelluloseLigninConstruction FuelMajor componentsEnd Use BiorefiningSustainabilitycriteria includedFSC NTA8O8OBiodiversity Yes YesGHG No YesEnvironment Yes YesSocial Yes YesSustainability certification181 MJ of Electricity: Coal vs. Bamboo19102030405060708090100%Electricity, medium voltage, productionNL, at grid/NL SElectricity by torrefied bamboo fromplantation88 % GHG emission reductionNTA8080 = 70%Accounting methodology follows:EC recommendations for solid biomassDefault values/processes/referencesCarbon stock is not included01002003004005006000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20CO2t ha-1YearTotal CO2 t ha-1Living culmsCulmsharvestedDry culmsBelow ground biomassWhat if carbon stock is included?Living standsBelow ground biomas:  (soil organic carbon, roots)When carbon stock is included: CO2 storageEstimated GHG emissions:Reduction: 300 %  vs. 88%Harmonize methodologiesMeasure, monitor and demonstrateJ.C. CamargoConclusions• Bamboo has the potential to be a highly sustainable biomass source for the biobased economy species, location and practices dependent.– Fuels– Chemicals– Materials• Sustainability certification is possible• Regulatory framework in producing countries• Research and Development– New “traditional”  biomass chain– Multidisciplinary approachThank you for your attention23This work forms part of the project: “Torrefied bamboo pellets for sustainable biomass import from Colombia”. Financial support by NL Agency under the subsidy scheme Sustainable Biomass Import. Partners in the project were: Imperial College of London (UK), Technological University of de Pereira (Co), Colombian Bamboo Society (Co). For more information, please contact:  Claudia M. Daza Montaño Researcher Biomass & Energy Efficiency  T  +31 88 515 41 96        Westerduinweg 3, 1755 LE  PETTEN F  +31 88 515 84 88        P.O. Box 1, 1755 ZG  PETTEN                                         The Netherlands daza@ecn.nl                    www.ecn.nl Fuente: Amezquita et al. 2011WOOD / TREE1.It is Not auto-reproducible.2. Solid stalk and not segmented.3. Has bark and aged cells form the hardened heart of the tree which is in the center.4.Has cambium tissueand stalk increasesin diameter with age.GUADUA/ BAMBOO1. It is autoreproducible.2. Culm or stalk is hollow, cylindrical & segmented3. No bark or central heart. Thehardest part is the area of theperiphery and is in the outer.4. No cambium tissue and does not increase culm in diameter with age.DIFERENCS BETWEEN GUADUA/BAMBÚ VS. TREESResidues vs Bioenergy cropResiduesfromforestResiduesfromplantationsBioenergycropCommentsYield per ha + ++ +++ Forest explotaition needs permitCurrentpotential ++ + -- Existing area covered/ Species FuturePotential++ ++ +++ Suitable areaCost ++ ++ + Main production/management costare allocated to main productSmall holders ++ + + Nucleos forestalesGHGemissionsreduction+ + +++ Use of residues account for emissionsfrom the collection pointDoes notinclude carbon stock 27Alternative Species Selection1. Guadua angustifolia Kunth (500 - 1.600 masl)2. Guadua amplexifolia Presl. (0 - 800 masl)3. Chusquea subulata Clark (2.200 – 2.800 masl)4. Bambusa vulgaris var. vulgaris (0 – 1.500 masl)5. Dendrocalamus strictus (0 - 800 masl)Selection Criteriaa) Culm size.b) Productivityc) Climate and soil conditions(Londono et al 2011)G. angustifolia1 B. vulgaris1Leaves, 4,1Branches, 22,8Culm, 73,1Leaves, 22,1Branches, 59,9Culm, 57,8G. amplexifolia1Leaves, 6,7 Branches, 28,3Culm, 65,0D. strictus1Culm, 74,4Branches, 15,7Leaves, 9,9Above ground biomass(Camargo et al 2011)Logistics30Bamboo density is superior to other biomass (600 kg/m3 vs. 200-300 kg/m3)Transport €/TonOptions:• Flattened culms/chips/pellets• Shipping• Local pre-treatmentLife Cycle Assessment31Impact category Definition UnitAbiotic depletion Depletion of non-living natural resources, including energy resources kg Sb[1] eq.Global warming Contribution of a substance to the greenhouse effect kg CO2 eq.Ozone layer depletion Thinning of the stratospheric ozone layer as a result of anthropogenic emissionskg CFC[2]-11 eq.Human toxicity Impacts of toxic substances present in the environment on human health kg 1,4-DCB[3] eq.Fresh water aquatic ecotoxicityImpacts of toxic substances on freshwater aquatic ecosystems kg 1,4-DCB eq.Marine aquatic ecotoxicity Impacts of toxic substances on marine aquatic ecosystems kg 1,4-DCB eq.Terrestrial ecotoxicity Impacts of toxic substances on terrestrial ecosystems kg 1,4-DCB eq.Photochemical oxidation Capacity of volatile organic compounds and carbon monoxide to produce photo-oxidants such as ozonekg C2H4 eq.Acidification Impacts of acidifying pollutants (mainly SO2, NOx and NHx), through emissions to the air, on the natural and man-made environment, human health and natural resourceskg SO2 eq.Eutrophication Impacts of eutrofying substances (nutrients), through emissions to  air, water and soil, on the natural and man-made environment, and natural resourceskg PO43- eq.[1] Sb = antimony [2] CFC = Chlorofluorocarbons [3] DCB=DichlorobenzeneSimaPro Software + CML method (Centrum voor Milieukunde Leiden (CML)Impact distribution320.10.20.30.40.50.60.70.80.90.100.Transport, transoceanic freightship/OCE STransport, combination truck, dieselpowered/USTorrefied bamboo pellets fromplantation at plantFertilizersTransportBiomass production: FSC Certification• FSC standard for Guadua– Colombian national interpretation forguadua bamboo forest– 1st in the world Now used in Asia• Some forest of “Guadua angustifoliaKunth” are FSC certified  Small holders• Access to international market.34Torrefaction TorwashTested biomass: bamboo• TORWASHCombining torrefaction with a washing step in order to recover certain minerals from biomass in order to use it as a non-fossil fertiliser(Hydrothemal Torrefaction)Proximate &ultimate (% mass, dry fuel)Raw Torwashed(wet torrefaction)ash @ 815°C 6,3 4,5Ash composition (mg/kg fuel, dry fuel)K 23029 510Cl 568 120-98%-79%Fuel Analyses35012345678Raw Torwashed  Torrefied240oCTorrefied 255oCTorrefied 270oC%Guadua Ash (815 oC)175001800018500190001950020000205002100021500Raw Torwashed  Torrefied 240oC Torrefied 255 oC Torrefied 270 oCKJ/KgHHV Guadua angustifoliaGrindability36Janssen, A.01020304050607080900 0.1 0.2 0.3 0.4 0.5 0.6 0.7Power consumption (kWe/MWth)Average particle size (mm)Bamboe 5yr - UntreatedBamboe 5yr - Torrefied 240°CBamboe 5yr - Torrefied 255°CBamboe 5yr - Torrefied 270°CBamboe 5yr - TorwashedWillow - UntreatedWillow - Torrefied 260°CAU bituminous coalBamboo Pellets377008009001000110012001300140040 60 80 100 120 140 160 180Density (kg/m3)Temperature (°C)Batch - UntreatedBatch - Torrefied at 240°CBatch - Torrefied at 255°CBatch - Torrefied at 270°C7008009001000110012001300140040 60 80 100 120 140 160 180Density (kg/m3)Temperature (°C)Pilot - UntreatedPilot - Torrefied at 245°CPilot - Torrefied at 255°CPilot - Torrefied at 265°CVery rough estimates of the achieved densities were ± 1250 kg/m3 (pellet) and ± 610 kg/m3(bulk). Alternative potential species384.93.85.62.76.9012345678G. angustifolia G. amplexifolia B. strictus B. vulgaris ChusqueaSubulata%Bamboo speciesAsh (815 oC)0.110.090.040.020.120.000.020.040.060.080.100.120.14G. angustifolia G.amplexifoliaB. strictus B. vulgaris ChusqueaSubulata%Bamboo speciesChlorine 0200040006000800010000120001400016000G. angustifolia G. amplexifolia B. strictus B. vulgaris Chusquea SubulataGJ/m3HHV bamboo species0.70.81.20.60.80.000.501.001.50G. angustifolia G.amplexifoliaB. strictus B. vulgaris ChusqueaSubulata%Bamboo speciesNitrogenFuel properties39Untreated Treated 245°CTreated 255°C Treated 265°CFuel Russian Coal Wood GuaduaangustifoliaMoisture 10.4 7.1 16.4Proximate analysis (% mass, dry fuel basis)Ash @ 815°C 8 1.44 4.7Volatile matter 32 80 77HHV (KJ/kg) 27800 20093 18676Ultimate analysis (% mass, dry fuel basis)C 68 50.25 46.5H 4 6.13 5.9N 0.87 0.37 0.33S 0.35 0.026 0.09O by diff. 11.6 44.2 43Ash composition (mg/kg fuel, dry basis)Na (± 7) 405 191 3.4Si (± 90) 34841 1331 13492S 3500 260 868K (± 20) 2390 984 10539Cl(± 20) 100 253 13620%10%20%30%40%50%60%70%80%90%100%Si Na K Cl S Cr Cu Zn Others*Fraction of elements removed by TorwashCombustion tests400.000.050.100.150.200.250.30Colombian coalair80/20Colombian/torrairtorwashedbamboo (fine0.09 - 0.125)Deposition propensity0.00000.00020.00040.00060.00080.00100.00120.00140 0.2 0.4 0.6 0.8Fouling factorAsh fed (gr, cumulative)Untreated bamboo 5.1 (0.09 -0.125μm)Torr 5.1 270 (0.09 - 0.125μm) Colombian coal (0.09-0.125μm)Blend 80/20 coal/torr bamboo5.1WoodCynaratorwashed bamboo 5.1 (0.09 -0.125μm)• Similar to woodFryda et al41Torrefaction TorwashTested biomass: bamboo• TORWASHCombining torrefaction with a washing step in order to recover certain minerals from biomass in order to use it as a non-fossil fertiliser(Hydrothemal Torrefaction)Proximate &ultimate (% mass, dry fuel)Raw Torwashed(wet torrefaction)ash @ 815°C 6,3 4,5Ash composition (mg/kg fuel, dry fuel)K 23029 510Cl 568 120-98%-79%ECN 50 kg/h torrefaction pilot plant (Since February 2008)Torrefaction tests at ECNBamboo - Guaduatreated @ 245°CBamboo - Guaduatreated @ 255°CBamboo - Guaduatreated @ 265°CuntreatedBamboo - Guaduatorrefaction pelletization 1-10 tons of test batchesfeedingCombustion• Combustion simulator (LCS)– Combustion characteristics– Slagging and fouling– Emissions (NOx, SOx, Dioxins)• Fuel and Ash characterisation• Main result: Similar to wood

English

Sustainability issues regarding bamboo as a renewable feedstock for fuels and materials

Persson, Kristin

Engineering Conferences InternationalECI Digital ArchivesHarnessing The Materials Genome: AcceleratedMaterials Development via Computational andExperimental ToolsProceedingsFall 10-2-2012The Materials Project – A Public Materials DesignPlatformKristin PerssonLawrence Berkeley National LaboratoryFollow this and additional works at: http://dc.engconfintl.org/materials_genomePart of the Biomedical Engineering and Bioengineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inHarnessing The Materials Genome: Accelerated Materials Development via Computational and Experimental Tools by an authorized administrator ofECI Digital Archives. For more information, please contact franco@bepress.com.Recommended CitationKristin Persson, "The Materials Project – A Public Materials Design Platform" in "Harnessing The Materials Genome: AcceleratedMaterials Development via Computational and Experimental Tools", J.-C. Zhao, The Ohio State Univ.; M. Asta, Univ. of CaliforniaBerkeley; Peter Gumbsch Institutsleiter Fraunhofer-Institut fuer Werkstoffmechanik IWM; B. Huang, Central South University Eds,ECI Symposium Series, (2013). http://dc.engconfintl.org/materials_genome/11The Materials Project – A Public Materials Design PlatformKristin PerssonLawrence Berkeley National LaboratoryOutline• Materials Design for Cathodes• The Public Platform: www.materialsproject.org– Pymatgen– The Materials API– Example Collaborations• The VisionExample: Materials Design for CathodesZnFinding a better alkaline cathodeOAOAO2 BO2AB2O3ABO4ABOx in ABOxV ABO4+AO+AB2O34 23 1 0AO+AB2O3+ABOABO+A+BVcutoffMnO2MnO2+H2O+Zn ->MnOOH + ZnOH2SPECS: Higher energy density Both reactants and products stable in high molar KOHA BValidationCalculating soli  s ability in waterCalculatedPersson et al, PHYSICAL REVIEW B, 005400 (2012), Prediction of solid-aqueous equilibria: Scheme to combine first-principles calculations of solids with experimental aqueous statesExperimental – Pourbaix’s AtlasKnown compoundsNew compoundspermutation strategyDatabaseInitial screening (phenomenological)30,000 ~ 100,000~ 100,000 - 200’000Solid StabilitySolubility The Screening StrategyComputational ScreeningCandidate materialsData mining≈ 10,000 - 20’000VoltageCapacityHigh negative formation energyNegative formation energyHow to stabilize high-valentcompoundsHigh dissolution energyModerate dissolution energyNo dissolution energyHow to stabilize at pH = 15Positive formation energyHigh positive formation energyDesign rules: how to stabilize high energy compounds, which corrosion-resistant elements to add…Predicted alkaline cathode material performance > 130,000 compounds considered• 30,000 known from ICSD •> 100,000 new generated > 1500 compounds Capacity > 1 Ah/cc1.1 V < Ave voltage < 2.2 VTier 1 MnO2The winners Energy density > 1.7Wh/cc≈ 200 compounds Reactant stable in air Reactant stable in 9N KOH Product stable in 9N KOHTier 2 End result – 200 compounds predicted to outperform current cathode AND are predicted stable (through entire reaction) in 9 M KOHBi5+ Ni4+ Several patents filed by Duracell New Li-ion Cathode Materials Discovered @MITNew phosphate discovered @MIT through computations Completely new classof materials synthesized based on computational predictionsNo Li containing carbono phosphates AT ALL are known in nature !Real battery data !“Sidorenkites”: Li3M(CO3)(PO4)  (M = Fe, Mn, Ni, Co)Courtesy of Ceder at al, MITThe Public Platform:www.materialsproject.orgFill in ‘missing’ property data on inorganic compoundsAssist researchers in predicting new materialsProvide interactive analysis tools and codesEnable rapid screening and data miningAccuracyBenchmarking (dV, bond length, enthalpy, …)error checkinghttps://materialsproject.org/wiki/Change in bond length distributionData Compatibilitybetween data setsOxide region poorMetals OKOxide region OKMetals poorJain et al, Formation Enthalpies by mixing GGA and GGA+U Calculations, Physical Review B 84, 045115 (2011).Leveraging the community Over 26,000 compounds and growingmonthly Multiple tools based on computed data>35,000 REACTIONSReaction energiesalready available online:Phase diagrams with available data:28,300 SYSTEMSMost measured materials data is of order of 102 – elastic constants, band gaps etc…Launched Oct 2011> 7000 phase diagrams generated> 1,300 structure prediction requests >2,800 registered usersDane Morgan Stefano CurtaroloKristin PerssonAnubhav JainDaniel GunterMaciejHaranszykWei ChenGerbrandCederShyue Ping OngDavid SkinnerShreyasCholiaJack DeslippeAlan DozierRaphael FinkLarge Software Infrastructure StructureRockets & FireworksRockets & FireworksThe Materials APILarge Software Infrastructure StructureRockets & FireworksRockets & FireworksThe APIPymatgenpython materials genomicsis the codebase powering the Materials Project.Or sql☺pymatpro • Database interface• Run management (fireworks)Defines core extensible Python objects for materials data representation.Building robust software that lasts… :All pymatgen classes and methods come withunit testsAll code have proper documentationNah – Bob left in 2004….Does anyone remember how to run ‘GoBabyMonte.c’ ?????pymatgen is managed via          .Licensed under the MIT licenseStable versions are available on the popular Python Package Index (PyPI) at pymatgenis globalAccesses of pymatgen’s doc.The Materials APIThe Materials APIAn open platform for accessing Materials Project data based on REpresentational State Transfer (REST) principlesFlexible and scalable to cater to large number of users, with different access privilegesSimple to use and code agnosticImproved accessibility of dataMore developers of analyses and appsIncreased data valuehttps://www.materialsproject.org/rest/v1/materials/Fe2O3/vasp/energyPreambleIdentifier, typically a formula (Fe2O3), id (1234) or chemical system (Li-Fe-O)PropertyData type (vasp, exp, etc.)Request typeSecure accessAn individual API key provides secure access with defined privilegesAPI keys available at https://www.materialsproject.org/profileExample collaborationsScreening for CO2 sorbents (Grey et al)• High selectivity and absorption capacity for CO2 at elevated temperatures (400-900°C)• Good absorption/desorption kinetics - preferably under a wide range of p(CO2)• Good cyclability for absorption/desorption processes• Good hydrothermal and mechanical propertiesCalculating reaction energies of thousands of oxides with CO2Carbonator Calciner650°CFlue gases(FCO2)O2 +CoalGases “without CO2”CaCO3+ CaOCO2 concentrated900-930°C CaCO3 (F0, fresh sorbent)CaO (purge)Existing power plant HinCaO(FCaO)0 20 40 60 80Li2ZrO3Li2TiO3LiFeO2K2ORb2OCaOAverageE(calc) - E(exp) (kJ/mol)CO2 + Reactant Oxide  Carbonate + Product OxideAverage error in energy corresponds to the necessary correction in the empirical energy given to CO2 moleculeTesting new functional (Jacobsen et al)• Developed a new functional: GLLB-SC• GLLB-SC improves performance on band gaps• GLLB-SC requires much more testing• Optimized structures from the Materials Projectallow for testing  GLLB-SC over tens of thousands of compoundsI.E. Castelli, T. Olsen, S. Datta, D.D. Landis, S. Dahl, K.S. Thygesen, and K.W. Jacobsen, Energy & Environmental Science 1–6 (2012).FEFF (Dozier and Rehr)Adding • computed K-edge cross sections• L-edge in future…• XANES, EXAFS, EELS, …• cluster of atoms – not periodic solidvia                       and Rockets/ FireworksCu XANES spectrum from exp and FEFF;  Inelastic Losses in X-ray Absorption Theory,Thesis,  Luke Campbell (2002). Disseminate data via Materials Project Source (MPS)MPSaboutcrystalabout• Who authored the xtal?• What papers to cite?• Is the xtal linked to any known databases?• What codes operated on the xtal?• What metadata is associated with the Send to Materials Projectxtal?author, publication reference and link… 200+ relaxed structures from that last CEThe VisionFrom single entities…!050010001500200025003000350040004500-5-4.5-4-3.5-3-2.5-2-1.5-1-0.502 2.5 3 3.5 4 4.5 5 5.5Eq. Oxygen Release Temp.,Charged State (eV)Eq. Oxygen Release µO2,Charged State (eV)Voltage (V) oxides phosphates silicates borates sulfates… towards amaterials genomeThank you for your attentionAnd thanks to the team:Anubhav Jain         Alan Dozier       GerbrandCeder Stefano CurtaroloDaniel GunterJeff Grossman    Dane Morgan   Rafael FinkShyue Ping Ong David Skinner   Stefan Adams         Mark Asta Jack DeslippeAnthony Gamst Wei ChenGeoffroyHautierShreyasCholia Michael Kocher     MaciejHaranszyk Will Richards     Jeff NeatonThanks to sponsors:

The Materials Project – A Public Materials Design Platform

Tuncer, Mustafa

Engineering Conferences InternationalECI Digital ArchivesUltra-High Temperature Ceramics: Materials ForExtreme Environmental Applications II ProceedingsSpring 5-15-2012Low temperature synthesis and sintering ofmechanically activated ZrB2 powdersMustafa TuncerDumlupinar University, TurkeyFollow this and additional works at: http://dc.engconfintl.org/uhtcPart of the Materials Science and Engineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inUltra-High Temperature Ceramics: Materials For Extreme Environmental Applications II by an authorized administrator of ECI Digital Archives. Formore information, please contact franco@bepress.com.Recommended CitationMustafa Tuncer, "Low temperature synthesis and sintering of mechanically activated ZrB2 powders" in "Ultra-High TemperatureCeramics: Materials For Extreme Environmental Applications II", W. Fahrenholtz, Missouri Univ. of Science & Technology; W. Lee,Imperial College London; E.J. Wuchina, Naval Service Warfare Center; Y. Zhou, Aerospace Research Institute Eds, ECI SymposiumSeries, (2013). http://dc.engconfintl.org/uhtc/10Dumlupınar UniversityMaterial and Ceramic Engineering Dept.Kutahya / TURKEYMustafa TUNCERLow Temperature Synthesis and Sintering of Mechanically Activated ZrB2 PowdersContent• Purpose       • ZrB2 Synthesis: Citrat gel process • Mechanical activation of powders• ZrB2 materials containing second phase• Ongoing researches • ConclusionThe Aim of This Study Synthesis nanocrystalline ZrB2 powders using sol –gel method Densify ZrB ceramics at low temperature 2via using mechanical activation process Produce ZrB2 ceramics with addition of  Fe, ZrO2, B4C and SiCZrB2 ProductionDifferent methods for producing zirconiumdiborides have been proposed ;Borothermal /carbothermal reduction Solution-based methodsSelf-propagating high temperature (SHS)WaterCitric acidZirconium OxychlorideZrOCl2.8H2O Boric acidH3BO3Set the stirrer at 200 oC for 3-4 hGelation (highly viscous solution)How to prepare powders: Citrat gel methodkeep the gel at 400 oCfor 8 h in mantle heaterThermal treatment CharacterizationAdvantages-Homogenous compositions-High purity-Size and morphology control-Simple to apply ZrB2Mole ratio: [Zr+4 + B+4] / Citric acid= 4Solid resin / Polimeric precursorXRD pattern of ZrB2 at various temperaturesZrB2@ 1500 oCZrO2@ 1100 oC@ 1200 oC@ 1400 oCPowder characteristics for ZrB2 @ 1500 oC for 2 hDensity(g/cm3)Surface Area(m2/g)Crystalline sizedXRD (nm) Particle size, dBET (nm)Carbon Content (wt %)5.5 1.6 60 682 3XRD pattern of activated-solid precursor gelAs-pyrolysized (400 oC) solid precursor gelDry activation for 1 h1000 rpm, WC balls, WCvial@ 1400 oCThermal treatment at 1350 and 1400 oCOxide impuritiesZrBZrB22ZrBZrB22ZrBZrB22ZrBZrB22ZrBZrB22ZrBZrB22ZrBZrB22 ZrBZrB22ZrBZrB22@ 1350 oCActivation of solid precursor gel decreases the formation temperature of ZrB2 phase SEM images of ZrB2 crystallized @ 1500 Faced morphology of ZrB2 grainTEM images of ZrB2 crystallized @ 1500 Crystalline size ~ 45 from TEM~ 60 from XRDDTA / TG curves for solid precursor gel post-pyrolysized @ 400 oC680 oCCrystallization of zirconia Total weight loss ~20% for gel @ 25 – 1400 °CSolid state reactions by carbothermal reduction 1260 oCBoric acid dissociation1Citric acid dissociation1Estimated reaction pathway for formation of ZrB2ZrB2 formation21 Khanra, Bull.  Mater.  Sci., 20072 Huang, Am.,Cer.,Sc., 2006It is difficult to densify ZrB2 ceramics due tocovalent binding characteristic and high meltingpoint.Densification of ZrB2 CeramicsDifferent methods are used to densify ZrB2ceramics to use sintering agents advanced sintering techniques (e.g. SPS, HP) to synthesis the fine powders with highsurface areaIn this study, mechanical activation processwas used to lower formation and sinteringtemperature of ZrB2 BECAUSEMechanical activation enhance: reactivity,  structural defect sinterability at low temperatureSo it will be possible to densify boride ceramicsat lowered temperatures without externalpressureMechanical activation by planetary millMechanical energy supply deformed crystal structure under the milling actionPreparation of Powders for Sintering Step 1: SYNTHESIZED (1500 °C) ZrB2 POWDERS Step 2: ACTIVATION- 850 rpm rotation speed- Zirconia Balls (10 mm in diameter)- Zirconia Jar (80 ml capacity)- Milling time of 1 h Step 3: COMPACTION AND PRESSURELESS SINTERING- Dry pressing (40 MPa) and CIP (150 MPa)- @1775 °C for 5 hours in inert gas atmosphereSintering of ZrB2Produced from activated powdersρ = 86 %Hv = 3.9 GPa under the 9.8 NSEM images of  sintered material compacted from mechanical activated ZrB2-Fe (2.5 vol %) powdersρ = 99.9 %Hv = 14.9 GPa under the 9.8 NFePressureless Sintering @ 1775 for 5 hoursSEM images of  composite material compacted from mechanical activated ZrB2-SiC (14 vol %) powdersρ = 80 %Hv = 1.65 under the 9.8 NPressureless Sintering @ 1775 for 5 hoursSEM images of  composite material compacted from mechanical activated ZrB2-ZrO2 (7 vol %) powdersρ = 92 %Hv = 3.1 GPa under the 9.8 NPressureless Sintering @ 1775 for 5 hoursSEM images of  composite material compacted from mechanical activated ZrB2-B4C (3 vol %) powdersρ = 92 %Hv = 12.7 GPa under the 9.8 NPressureless Sintering @ 1775 for 5 hoursThe driving forces induce densification at lowtemperature;1- Wet Chemical SynthesisNanocrystalline ZrB2 powders via citrat gel metod2- Mechanical ActivationActivation process give broken surface (unbindingatoms), leading to high surface energy.CONCLUSIONSZrB2 powders were synthesized using citrat-gelmethod.Sintered ZrB2 ceramics from activatedpowders (ZrB -Fe, ZrB -ZrO , ZrB -BC ) were2 2 2 2obtained at lower temperature (1775 oC)without external pressureFuture Efforts on ZrB2• ZrB2 films by electroforetic deposition• Microwave-sintering of ZrB2 materials• Pressure slip-casting• Characterization of the electrical and mechanical properties of zirconium diboride filmsShear Roll-Milling Process for Coating of NanoZrB2 on CuZrB2-Cu-1 ZrB2-Cu-2Surface Microstructure of ZrB2 Coating for ZrB2-Cu-1 and ZrB2-Cu-2ZrB2-Cu-1 ZrB2-Cu-230-500 nmnanofiber like structures5-100 nmFE-SEM Images of ZrB2 Coating for ZrB2-Cu-1 and ZrB2-Cu-2(10-15×40-400 nm)ZrB2-Cu-120-25 µmZrB2 Coating Layers for ZrB2-Cu-1 and ZrB2-Cu-2ZrB2-Cu-217 µmZrB2-Cu-1ZrB2XRD Pattern(100)ZrB2-Cu-2Resistance-Time Graph at Different Relative Humidity Levels for ZrB2-Cu-1AcknowledgementsThis study is supported within the Project # 2009-Ç0232 by National Boron Research Institute (BOREN)Thank  you for your attention

Low temperature synthesis and sintering of mechanically activated ZrB2 powders

Zhao, Huimin

Engineering Conferences InternationalECI Digital ArchivesMetabolic Engineering IX ProceedingsSummer 6-6-2012Pathway Engineering via Synthetic BiologyHuimin ZhaoEnergy Biosciences InstituteFollow this and additional works at: http://dc.engconfintl.org/metabolic_ixPart of the Biomedical Engineering and Bioengineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inMetabolic Engineering IX by an authorized administrator of ECI Digital Archives. For more information, please contact franco@bepress.com.Recommended CitationHuimin Zhao, "Pathway Engineering via Synthetic Biology" in "Metabolic Engineering IX", E. Heinzle, Saarland Univ.; P. Soucaille,INSA; G. Whited, Danisco Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/metabolic_ix/14Huimin ZhaoDepartment of Chemical and Biomolecular EngineeringDepartment of ChemistryPathway Engineering viaSynthetic BiologyDepartment of BiochemistryDepartment of BioengineeringInstitute for Genomic BiologyECI Metabolic Engineering Conference IX, Biarritz, France, June 6, 2012Research Interests in Zhao GroupPathways and Genomes• Phloroglucinol biosynthesis• FR900098 biosynthesis• Xylitol biosynthesis• Advanced biofuels• Alkaloid biosynthesisSynthetic BiologyTool Development• Directed evolution• Rational design• High throughput methods• Biosynthesis• Genome engineeringSynthesisAnalysisGrand Challenge #1 (Energy & Sustainability): Urgent need for oil replacement Use renewable feedstocks to produce fuels, chemicals, and drugsGrand Challenge #2 (Health): Need for new therapeuticsProteins• Gene switches/circuits• Artificial nucleases• P450 enzymes• Type III PKS• N-OxygenasesModeling#1. Engineering Microbial Factories(Fuels and Natural Products @ UIUC) L-xylitol (biorefinery building block)D-XyloseCelluloseGlucoseCellulasesCellulosomesL-ArabinoseHemicellulose LigninHeat/ElectricityAcetyl-coAPPPEthanol(1st generation biofuel)Butanol, Hydrocarbons(2nd generation biofuels)PEPPyruvateFR900098(antimalarial drug)Phloroglucinol(specialty chemical)PolyketidesMetabolic Engineering Research Lab (MERL)  @ Singapore2 1Biomass utilizationBiochemical production SugarsAdipic acidn-butanolMetabolic EngineeringBiomass3Overall Goal: Develop and apply systems and synthetic biology approaches to engineer microorganisms capable of cost-effectively producing industrial chemicals from renewable feedstocks. Tool Development• Computational• Genetic manipulation&OthersBillionsofDollars102030400SemiconductorDiscovery IntegratedCircuit MOSTransistor MSICircuit VLSITechnology U.S. Factory Sales of Solid State Products Microelectronics and Biotechnology1950 1960 1970 1980 1990 2000BillionsofDollars10203040195001960 1970 1980 1990 2000Double HelixDiscovery RecombinantDNA technology Mabs and DNASequencing GeneTherapy GenomicsWorld Market for Health-Care Biotechnology Synthetic biologyTools Driving BiotechnologyFirst Generation BiotechRecombinant DNA Technology“Cut and paste”PCR“DNA Amplification”DNA Sequencing“Read out the genetic code”First product: human insulin, produced in E. coli in 1978.- Recombinant human growth hormone - Recombinant blood clotting factor VIII- ……Global market size for recombinant proteins: ~$60B in 2009human insulinTransformative Advances in DNA Sequencing and SynthesisCarlson, Nat. Biotech. 27, 1091 (2009)Carr and Church, Nat. Biotech. 27, 1151 (2009)?10-1000’s genescomplex chemicals and materialsorganisms as products ...Building Large DNA Molecules via One-step DNA Assembler  vectorura3amp2µn-1 in vivo assembly of DNA X12 nX Xchromosomeδδ ura3hisGhisG-1-fragmentX12n-1nX XX δ-2δ sitefragments in yeastShao et al. Nucleic Acids Res. (2009)aA3-fA6-rA4-fA7-r A8-rA6-fA2 (2477 bp)A3 (3705 bp)A7-f A8-fA4-r A5-rA7 (2769 bp)A8 (2293 bp)A6 (2838 bp)A5-fA5 (2125 bp)A4 (2303 bp)A3-rA2-rA2-fA1-rA1-fA1 (3263 bp)XR Delta2ADH1pPGK1pPYK1p TEF1pTEF2pPDC1pHXT7p FBA1pADH1tCYC1tADH2tTPI1tENO2tTDH2tPGIt FBA1tHisGXDHXKSCrtECrtBCrtICrtY Crt ZM     A1    A2     A3   A4    A5     A6    A7    A8combined pathway (p)M     A1     A2   A3     A4    A5     A6     A7    A8combined pathway (c)bEight-gene Pathway: A Combined Xylose and Zeaxanthin Pathwayc1 kb-2 kb-3 kb-4 kb-3 kb-1 kb-2 kb-4 kb-dBroad Application of DNA AssemblerPathway EngineeringPathway Discovery Synthetic BiologyProtein Engineering Genome Assembly… …123 n-2n-1nBiofuels ProductionWen et al. Current Opinion in Biotech. (2009)Gibson et al. PNASOnline publication date: Dec. 10, 2008Shao et al. Nucleic Acids Res. Online publication date: Dec. 12, 2008Discovering New DrugsActivating Cryptic Pathways from Sequenced Genomes and MetagenomesNews & Views: Cobb and Zhao, Nature Biotech 2012Engineering a Microbial Factory for Advanced Biofuels ProductionGlucose, Xylose, ArabinoseHemicellulose/CelluloseAcetyl-coAEthanol)Advanced biofuels(Butanol, Hydrocarbons)PEPPyruvateyeastL-Arabinose D-XyloseSugar TransportersL-Arabinose D-XyloseXylitol D-XyluloseArabitolL-XyluloseXRLAD LXR XDHXRNAD(P)H   NAD(P)+ NAD(P)H   NAD(P)+Pentose Utilization in YeastNAD(P)H   NAD(P)+NAD(P)+     NAD(P)H NAD(P)+     NAD(P)HSugar UptakeHeterologous PathwayRedox ImbalanceMetabolic FluxGlucose Repression in Mixed Sugar Fermentation Glucose repression occurs in S. cerevisiae Alternative carbon source fermentation is inhibited in the presence of glucose Lag time in xylose and arabinose consumption curveOlsson et al.  Appl Microbiol Biotechnol (2009) 82:909–919Coexpression of CellobioseTransporter and β-Glucosidasecellobioseβ-glucosidasecellobioseglucoseoutside cellglucoseinside cellethanolxylosepentoserepression repressionCoexpression of CellobioseTransporter and β-Glucosidase Cellodextrin transport system from Neurospora crassa Cellodextrin transporters: NCU00801 (cdt1), NCU00809, NCU08114(cdt2) β-glucosidase: NCU00130 (gh1-1)Galazka JM, et al. Science 330, 84 (2010) S. cerevisiae with a heterologous cellodextrin transport system showed improved growth rate. Coexpression of CellobioseTransporter and β-Glucosidase Genes 3 transporters: cdt-1, cdt-2, NCU00809 2 β-glucosidases: gh1-1 from N. crassa, bgl1 from A. aculeatuspRS425PYK1 promoterβ-glucosidaseTEF1 promotercellobiose transporterPGK1 terminator Plasmids Use DNA assembler method to integrate genes into pRS425 plasmid Strains 6 plasmids constructed were transformed into S. cerevisiaestrain with an integrated xyloseutilization pathwayADH1 terminatorStrain Transporter β-glucosidaseSL01 cdt1 gt1-1SL02 NCU00809 gt1-1SL03 cdt2 gt1-1SL04 cdt1 bgl1SL05 NCU00809 bgl1SL06 cdt2 bgl1SL00 - -Mixed Sugar Cultivation in Shake-flask: Cellobiose+XyloseSL01 SL00SL01 SL00Yield ethanol 0.28 0.22Productivity ethanol  (g/(L h)) 0.23 0.07cellobiose (■) , xylose  (▲), glucose(●), ethanol (▼) , Dry cell weight (□)Mixed Sugar Cultivation in Bioreactor: Cellobiose+XyloseSL01 SL00cellobiose (■) , xylose  (▲), glucose(●), ethanol (▼) , Dry cell weight (□)SL01 SL00Yield ethanol 0.39 0.24Productivity ethanol  (g/(L h)) 0.49 0.09Li et al. Mol Biosyst 2010Balancing Metabolic Flux Remains a Big Challenge Production of value-added compounds usually requires introduction of multi-step metabolic pathways Metabolic flux in multistep metabolic pathways need to be optimized to avoid metabolic burden  Overexpression of certain genes,  Redox imbalance from unmatched cofactor specificity Accumulation of unstable or toxic intermediates Traditional approaches Overexpression and deletion of certain genes in metabolic pathways Modulating the expression levels of individual enzymes Protein engineering to improve performance of rate limiting enzymes Targeting a specific enzyme instead of the overall pathway Simultaneous optimization of multiple metabolic genes remains a big challengeBalancing Metabolic Flux Remains a Big Challenge Perturbation of global transcription machinery Genome-scale mapping of fitness altering genes Multiplex genome engineering Balance metabolic flux within the target pathway Strengths of promoters Ribosome binding sitesIntergenic regions Synthetic scaffoldsPfleger et al.,Nat Biotechnol 24, 1027-1032 (2006)Dueber et al., Nat Biotechnol 27, 753-759 (2009)Alper et al.,  Metab Eng 9, 258-267 (2007)Warnecke et al., Metab Eng 12, 241-250 (2010)Warner et al., Nature Biotechnology 28, 856-U138 (2010)Wang et al., Nature 460, 894-898 (2009)Salis et al.,  Nat Biotechnol 27, 946-950 (2009)Alper et al., Proc Natl Acad Sci U S A 102, 12678-12683 (2005)Pathway Optimization by COMPACTERCustomized Optimization of Metabolic Pathways by Combinatorial Transcriptional Engineering (COMPACTER)Target pathwayGene3 Terminator3Gene2 Terminator2Gene1 Terminator1Promoter1 Promoter2 Promoter3Promoter1Promoter1Screening/selection of mutant pathways with desired phenotypeCombinatorial assembly of a library of mutant pathwaysGene1Terminator2Terminator2 Terminator3Gene3Terminator1Terminator1 Gene2Promoter1control Selected 6 yeast promoters Nucleotide analogue mutagenesis Isolating promoter mutants via FACSPromoter Mutants with Varying StrengthTEF1TPI1GPM1GPDFBA1PDC1ENO2PYK1TEF2PGK1HXT7TDH2ADH1PGI10 2000 4000 6000 8000 10000 12000Mean Fluorescence of GFPPromoters used in cassette Oxygen-limited Oxygen-abundantAlper et al. PNAS 102, 12678-12683 (2005)Sun et al. B&B, 2012Promoter Mutants with Varying Strength00.511.5Neg D11 C12 G7 A2 A5 B4 G10 E9Relative StrengthTEF1p Mutants-0.60.41.4B11 B8 B7 D3 A9Relative StrengthPDC1p mutants-0.10.40.91.4B10 B5 F8 B9 B4 A6Relative StrengthENO2p mutants01000200030004000500060007000FBA mutants0100020003000400050006000700036-1 34-1 39-1 45-1 38-1 41-1 49-1 47-1 43-1 PDC Neg 4-1GPM mutantsPathway Optimization by COMPACTERβ-glucosidaseExtra-cellobioseCellodextrin Transporter GlycolysisEthanolIntra-cellobioseXylose Utilizing Pathway Cellobiose Utilizing Pathway Host strain: INVSc1 (Invitrogen) Diploid, auxotrophic mutation available Control pRS416-PDC1p(WT)-csXR-TEF1p(WT)-ctXDH-ENO2p(WT)-ppXKS Backbone: pRS416  Single copy shuttle vector Library size: 104~105 Fermentation:Xylose concentration (g/L)14Ethanol concentration (g/L)INVSc1Optimization of the Xylose Utilizing Pathway in the INVSc1 Strain Initial OD~1 Oxygen limited condition YP mediaWT S3 UnitXylose consumption rate0.24 0.40 g/L/hrEthanol production rate0.04 0.10 g/L/hrEthanol yield 0.16 0.25g/g xylose 0 20 40 60 80 10012014016002040Xylose concentration (g/L)024681012Ethanol concentration (g/L)Time (hour)Optimization of the Xylose Utilizing Pathway in an Industrial Strain Host Strain Still Spirits (Classic) Turbo Distiller's Yeast Control pRS-KanMX-PDC1p(WT)-csXR-TEF1p(WT)-ctXDH-ENO2p(WT)-ppXKS Backbone:pRS-KanMX Single copy shuttle vector Library size: 103~104 Fermentation:4050Xylose concentration (g/L)Classic 15Ethanol concentration (g/L) Initial OD~10 Oxygen limited condition YP mediaYPD seed YPX seedUnitClassic WT Classic S7 Classic S7Xylose consumption rate0.06 0.74 0.92 g/L/hrEthanol production rate0 0.17 0.24 g/L/hrEthanol yield 0 0.24 0.26 g/g xylosexylose (■), ethanol (▼) 0 20 40 60 800102030Xylose concentration (g/L)Time (hour)0510Ethanol concentration (g/L)Host-specific Pathway Optimization Switching optimized xylose utilizing pathways between laboratory and industrial strains4050Xylose concentration (g/L)Classic (switched)1214Ethanol concentration (g/L)4050Xylose concentration (g/L)1214Ethanol concentration (g/L)INVSc1 (switched)xylose (■), ethanol (▼) 0 20 40 60 800102030Xylose concentration (g/L)Time (hour)0246810Ethanol concentration (g/L)0 20 40 60 800102030Xylose concentration (g/L)Time (hour)0246810Ethanol concentration (g/L)Host-specific Pathway Optimization qPCR analysis of the optimized xylose utilizing pathways between laboratory and industrial strainsOptimization of the CellobioseUtilizing PathwayOptimized Xylose Utilizing Pathways are Strain SpecificOpen symbol: pathway optimized in INVSc1 strain, Solid symbol: pathway optimized in Classic strain, Red circle: cellobiose, Black square: OD (A600), Blue down triangle: ethanol. Optimized Cellobiose Utilizing Pathways are Strain Specific qPCR analysis of the optimized cellobiose utilizing pathways between laboratory and industrial strainsDirected Evolution for Strain DevelopmentLocal OptimizationGlobal OptimizationDirected Evolution for Strain Development01020304050600102030405060 OD (A600)  024681012024681012 Glucose (g/L/h)  020406080020406080 Cellobiose (g/L)  Ethanol (g/L) OD (A600)  0 10 20 30 40 50 Time (h)0 10 20 30 40 50020406080020406080  Time (h)Cellobiose (g/L)   0 10 20 30 40 50 Time (h)0 10 20 30 40 500510152025303505101520253035 Time (h)  Ethanol (g/L/h)0 50 100 150 200 Time (h) #9,#91 and #9118 have same final OD, ethanol concentration and glucose accumulation A#9118 has lower OD and higher ethanol  A#9118 has much lower glucose accumulation No mutations were found in promoter regions in A#9118Directed Evolution for Strain DevelopmentCellobiose fermentation performance of evolved yeast strains #9, #9-1, #9-1-18 and A#9-1-18Consolidated Bioprocessing (CBP)Recombinant microbe:• Cellulolytic • Ethanogenic (yeast)Cellulosic biomassPretreatmentCellulasesproductionCellulose hydrolysis Fermentation Cellulosic biomassPretreatmentDevelopment towards  Consolidated bioprocessing (CBP): save ~10-20 cents/gallon of ethanolSSFFermentation of cellulosic sugarsof hemicellulosic sugarsEthanol EthanolDirect conversion of pretreated cellulosic biomass into ethanol more consolidated processL. Lynd et al. Curr Opin Biotechnol 577 (2005)Direct Conversion of Cellulose to Ethanol by Engineered Mini-cellulosomes•EG: Endogluconase•CBH: Exogluconase•BG: β-glucosidaseYeast cell wallmini-scaffoldinCBHBGLGlucose Cello-oligosaccharidesEG Yeast surface display of functional minicellulosomes Functional display of a mini-scaffoldin Successful assembly of minicellulosomes through cohesin-dockerin interaction Synergistic hydrolysis of cellulose Direct fermentation of hydrolysate (glucose) to ethanolDirect Ethanol Production from PASC15002000 CipA3-EGII-CBHII-BGL1 negativeEthanol (mg/L)681012 CipA3-EGII-CBHII-BGL1 negativeResidual PASC (g/L)Ethanol production Residual PASCWen, F., Sun, J. and Zhao, H. AEM (2010)Yield: 0.31 grams of ethanol per gram of PASC62% of theoretical yield0 20 40 60 8005001000Ethanol (mg/L)Time (hr)0 20 40 60 80024Residual PASC (g/L)Time (hr)Direct Conversion of Xylan to Ethanol by Engineered HemicellulosomesSun, J. et al. AEM (2012)Direct Conversion of Xylan to Ethanol by Engineered Hemicellulosomes80010001200 HZ3345: Ethanol    HZ3345: Residual total sugar HZ3001: Ethanol    HZ3001: Residual total sugar Ethanol (mg/L)6810Residual total sugar (g/L)Sun, J. et al. AEM (2012)0 20 40 60 800200400600Time (hr)Ethanol (mg/L)024Residual total sugar (g/L)Yield: 0.31 grams of ethanol per gram of birchwood xylanSummary• Developed a DNA assembler method for constructing large DNA molecules such as pathways, plasmids, and genomes. • Developed a DNA assembler based synthetic biology method (COMPACTER) for optimizing the metabolic flux in a heterologous pathway• Engineered a yeast strain capable of simultaneously and efficiently utilizing C5/C6 sugars• Engineered yeast strains for consolidated bioprocessing of cellulose and xylan respectively. The Zhao GroupCollaboratorsWilfred van der Donk, Bill Metcalf, Satish Nair, Neil Kelleher, Nathan Price, Steve LongFunding SupportNIH, Keck Foundation, BP EBI, ARPA-E Current Group MembersPostdocsZengyi Shao, Meng Wang, Xueyang Feng, Dan CoursolleGraduate studentsCarl Denard, Ning SunJie Sun, Ryan Cobb,AlumniDr. Hua Zhao, Dr. Byoung-jin Kim, Dr. Jing Du, Dr. Yongbo Yuan, Dr. Fei WenDawn Eriksen, Jing Liang, Yunzi Luo, Sijin Li, Luigi Chanco, Si Tong, Todd Freestone, Guodong RaoZhanar Abigil, Jiazhang LianJinglin Li, Sai Wen, Zehua Bao, Jonathan Ning, Chao Ran, Sujit JagtapUndergraduate studentsLu Lu, Amy OreskovicWei Yang, Patrick LynnMark your calendarsFor ECI’s Biochemical and Molecular Engineering Conference to be held in Beijing, ChinaJune 16 to 20, 2013 Beijing Pharmaand Biotech CenterCo-hosted by:BEIJING PHARMA AND BIOTECH CENTERANDENGINEERING CONFERENCES INTERNATIONALBiochemical and Molecular Engineering XVIIIFrontiers in Biological Design, Synthetic Biology and ProcessingEast Meets WestHonorary Co-Chairs: Professors Daniel I. C. Wang (MIT) and Pinkai Ouyang (Nanjing) Co-chairs: Huimin Zhao (U. Illinois), David Robinson (Merck), and Tianwei Tan (Beijing)Advisory Committee Co-Chairs:  Weichang Zhou (USA), and Guoping Zhao (China)

Pathway Engineering via Synthetic Biology

Abhyankar, Shrirang

Smith, Barry

Zhang, Hong

Flueck, Alexander

Engineering Conferences InternationalECI Digital ArchivesModeling, Simulation, And Optimization for the21st Century Electric Power Grid ProceedingsFall 10-24-2012Using PETSc as a Testbed for Developing andBenchmarking Parallel Power System ApplicationsShrirang AbhyankarArgonne National LaboratoryBarry SmithArgonne National LaboratoryHong ZhangArgonne National LaboratoryAlexander FlueckIllinois Institute of TechnologyFollow this and additional works at: http://dc.engconfintl.org/power_gridPart of the Electrical and Computer Engineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inModeling, Simulation, And Optimization for the 21st Century Electric Power Grid by an authorized administrator of ECI Digital Archives. For moreinformation, please contact franco@bepress.com.Recommended CitationShrirang Abhyankar, Barry Smith, Hong Zhang, and Alexander Flueck, "Using PETSc as a Testbed for Developing and BenchmarkingParallel Power System Applications" in "Modeling, Simulation, And Optimization for the 21st Century Electric Power Grid", M. Petri,Argonne National Laboratory; P. Myrda, Electric Power Research Institute Eds, ECI Symposium Series, (2013).http://dc.engconfintl.org/power_grid/28Using PETSc as a Testbed for Developing andBenchmarking Parallel Power System ApplicationsShrirang Abhyankar, Barry Smith, Hong ZhangArgonne National LaboratoryAlexander FlueckIllinois Institute of TechnologyOctober 24, 2012Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 1 / 22Algorithms ,computing resources, and softwaretools need to be upto date with the marketoperational demands.Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 2 / 22Algorithms ,computing resources, and softwaretools need to be upto date with the marketoperational demands.Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 2 / 22Need for parallelizing power system algorithmsNext-generation power gridPMUs, smart meters, Distributed Generation,Plug-in hybrid vehicles,Smart and Micro-grids, Power electronics, Increased communicationResultant Computational challengesData explosion, Real-time simulation requirements, Larger or densernetwork, Multi-scale (temporal, geographical)Next-generation computing architectureMulticore, Manycore machines.GPGPUsShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 3 / 22Parallel computing in Power SystemsResearch on parallel power system applicationsPower Flow, Optimal Power Flow, Transient Stability, ContingencyAnalysis, State Estimation, Electromagnetic transients simulation.Survey papers“Parallel processing in power systems computation” (IEEE Task Force)“High Performance Computing in Power Systems” (D. Falcao)Many parallel algorithms showed significant time savings yet hesitancyin commercial adoptionSpecific algorithms tested for on a specific architecture using a specifictopology and operating conditions.Parallel algorithms are hard to program:synchronization/communication, partitioning, parallel linear, nonlinearsolvers, reductions, debugging,...Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 4 / 22Parallel computing in Power SystemsResearch on parallel power system applicationsPower Flow, Optimal Power Flow, Transient Stability, ContingencyAnalysis, State Estimation, Electromagnetic transients simulation.Survey papers“Parallel processing in power systems computation” (IEEE Task Force)“High Performance Computing in Power Systems” (D. Falcao)Many parallel algorithms showed significant time savings yet hesitancyin commercial adoptionSpecific algorithms tested for on a specific architecture using a specifictopology and operating conditions.Parallel algorithms are hard to program:synchronization/communication, partitioning, parallel linear, nonlinearsolvers, reductions, debugging,...Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 4 / 22Parallel computing in Power SystemsNeed to develop and benchmark parallel algorithms on differentcomputing architectures on different topologies under different powersystem operating conditions.Benchmark existing parallel algorithms.Develop and benchmark new ones.This a lot of work!!!High performance libraries, such as PETSc, can aid in the rapiddevelopment and benchmarking process:Rapid development of parallel applications.Portable to variety of computing architectures.Wide array of tested for linear, nonlinear, and time-stepping solvers.Reduce the experimentation time and effort.Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 5 / 22Parallel computing in Power SystemsNeed to develop and benchmark parallel algorithms on differentcomputing architectures on different topologies under different powersystem operating conditions.Benchmark existing parallel algorithms.Develop and benchmark new ones.This a lot of work!!!High performance libraries, such as PETSc, can aid in the rapiddevelopment and benchmarking process:Rapid development of parallel applications.Portable to variety of computing architectures.Wide array of tested for linear, nonlinear, and time-stepping solvers.Reduce the experimentation time and effort.Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 5 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) IntroductionPortable Extensible Toolkit for Scientific ComputationWhat is PETSc?Library for developing large-scale parallel applications.Provides parallel numerical solvers (time-stepping, nonlinear, linear)and basic building blocks (parallel matrices, vectors, communicationobjects) for rapidly developing parallel applications.Mostly used by researchers in PDE applications.Free for anyone to use including industrial users.Top 100 R & D award in 2009, Cited as DOE’s top 10 advancements incomputational science accomplishments in 2008.What can PETSc handle ?PETSc has run implicit problems with 1 billion unknownsPFLOTRAN for flow in porous mediaPETSc has run on over 224, 000 cores efficientlyUNIC on the IBM BG/P at ANLPFLOTRAN on the Cray XT5 Jaguar at ORNLShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 6 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) IntroductionPortable Extensible Toolkit for Scientific ComputationWhat is PETSc?Library for developing large-scale parallel applications.Provides parallel numerical solvers (time-stepping, nonlinear, linear)and basic building blocks (parallel matrices, vectors, communicationobjects) for rapidly developing parallel applications.Mostly used by researchers in PDE applications.Free for anyone to use including industrial users.Top 100 R & D award in 2009, Cited as DOE’s top 10 advancements incomputational science accomplishments in 2008.What can PETSc handle ?PETSc has run implicit problems with 1 billion unknownsPFLOTRAN for flow in porous mediaPETSc has run on over 224, 000 cores efficientlyUNIC on the IBM BG/P at ANLPFLOTRAN on the Cray XT5 Jaguar at ORNLShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 6 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) IntroductionPETSc OrganizationShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 7 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) Application development using PETScFlow control of a PETSc applicationTimestepping Solvers (TS)Preconditioners (PC)Nonlinear Solvers (SNES)Linear Solvers (KSP)FunctionEvaluationPostprocessingJacobianEvaluationApplicationInitializationMain RoutinePETScShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 8 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) Application development using PETScPETSc Numerical ComponentsKrylov Subspace MethodsCG CGS OtherChebychevRichardsonTFQMRBi-CG-StabGMRESVectorsOtherStrideBlock IndicesIndex SetsIndicesBlock CompressedSparse Row(BAIJ)BlockDiagonal(BDiag)CompressedSparse Row(AIJ)OtherDenseMatricesBackwardEulerPseudo-TimeSteppingTime SteppersEuler OtherBlockJacobiAdditiveSchwarz (sequential only)LUParallel Numerical Components of PETScJacobi ILU ICC OtherPreconditionersNewton-based MethodsTrust RegionLine Search  OtherNonlinear SolversShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 9 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) FeaturesPETSc featuresFree for anyone, including industrial usersPortabilityUnix, Linux, MacOS, WindowsTightly/loosely couple architectures32/64 bit ints, single/double/quad precision, real/complexC, C++, Fortran, Matlab, Python (petsc4py)ExtensibilityBLAS, LAPACK, BLACS, ScaLAPACK, PLAPACKMPICH, MPE, Open MPIParMetis, Chaco, Jostle, Party, ScotchMUMPS, Spooles, SuperLU, SuperLU Dist, UMFPack, pARMSPaStiX, BLOPEX, FFTW, SPRNGHYPRE, ML, SPAISundialsHDF5, BoostPackages can be directly downloaded and installed at configure time--download-<packagename>=1Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 10 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) FeaturesPETSc featuresFree for anyone, including industrial usersPortabilityUnix, Linux, MacOS, WindowsTightly/loosely couple architectures32/64 bit ints, single/double/quad precision, real/complexC, C++, Fortran, Matlab, Python (petsc4py)ExtensibilityBLAS, LAPACK, BLACS, ScaLAPACK, PLAPACKMPICH, MPE, Open MPIParMetis, Chaco, Jostle, Party, ScotchMUMPS, Spooles, SuperLU, SuperLU Dist, UMFPack, pARMSPaStiX, BLOPEX, FFTW, SPRNGHYPRE, ML, SPAISundialsHDF5, BoostPackages can be directly downloaded and installed at configure time--download-<packagename>=1Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 10 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) FeaturesPETSc featuresAbstract linear algebra interface (Vectors, Matrices, Index Sets, etc.)Consistent user interface.Keep MPI opaque to the user.Flexible run time optionsOldModify. Make. Run. Modify. Make. RunNew./ex -snes_type <ls,tr,test> -ksp_type<gmres,cg,bicg,preonly> -pc_type <lu,ilu,icc,jacobi>-mat_type <aij,baij,sbaij>Debugging [gdb, dbx]Automatic generation of trace backAttach debugger at start, on error, to a subset of processesValgrindCheck jacobian correctness-snes_type test -snes_test_displayProfilingLogs Time, Memory usage, Calls, Flops, MPI messages, user code(stages, events)Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 11 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) FeaturesPETSc featuresAbstract linear algebra interface (Vectors, Matrices, Index Sets, etc.)Consistent user interface.Keep MPI opaque to the user.Flexible run time optionsOldModify. Make. Run. Modify. Make. RunNew./ex -snes_type <ls,tr,test> -ksp_type<gmres,cg,bicg,preonly> -pc_type <lu,ilu,icc,jacobi>-mat_type <aij,baij,sbaij>Debugging [gdb, dbx]Automatic generation of trace backAttach debugger at start, on error, to a subset of processesValgrindCheck jacobian correctness-snes_type test -snes_test_displayProfilingLogs Time, Memory usage, Calls, Flops, MPI messages, user code(stages, events)Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 11 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) FeaturesPETSc featuresAbstract linear algebra interface (Vectors, Matrices, Index Sets, etc.)Consistent user interface.Keep MPI opaque to the user.Flexible run time optionsOldModify. Make. Run. Modify. Make. RunNew./ex -snes_type <ls,tr,test> -ksp_type<gmres,cg,bicg,preonly> -pc_type <lu,ilu,icc,jacobi>-mat_type <aij,baij,sbaij>Debugging [gdb, dbx]Automatic generation of trace backAttach debugger at start, on error, to a subset of processesValgrindCheck jacobian correctness-snes_type test -snes_test_displayProfilingLogs Time, Memory usage, Calls, Flops, MPI messages, user code(stages, events)Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 11 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) FeaturesPETSc featuresAbstract linear algebra interface (Vectors, Matrices, Index Sets, etc.)Consistent user interface.Keep MPI opaque to the user.Flexible run time optionsOldModify. Make. Run. Modify. Make. RunNew./ex -snes_type <ls,tr,test> -ksp_type<gmres,cg,bicg,preonly> -pc_type <lu,ilu,icc,jacobi>-mat_type <aij,baij,sbaij>Debugging [gdb, dbx]Automatic generation of trace backAttach debugger at start, on error, to a subset of processesValgrindCheck jacobian correctness-snes_type test -snes_test_displayProfilingLogs Time, Memory usage, Calls, Flops, MPI messages, user code(stages, events)Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 11 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) What’s new?Support for GPGPUs and Multicore architecturesFigure: NViDia GTX 280 GPU architectureFigure: Intel Nehalem Figure: AMD BarcelonaShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 12 / 22Portable Extensible Toolkit for Scientific Computation (PETSc) What’s new?Splitting for MultiphysicsEfficient solvers for coupled multiphysics applications[A BC D] [xy]=[fg]Relaxation: -pc_fieldsplit_type[additive,multiplicative,symmetric_multiplicative][AD]−1 [AC D]−1 [A1]−1(1−[A B1] [AC D]−1)Gauss-Seidel inspired, works when fields are loosely coupledFactorization: -pc_fieldsplit_type schur[A BS]−1 [1CA−1 1]−1, S = D − CA−1BShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 13 / 22PS applications PSapp ListList of power system applications that can be developedusing PETScLinear (using KSP)DC Power Flow, Sensitivity factorsNonlinear (using SNES)AC Power Flow, Contingency analysis, Continuation power flowDistribution power flow, Combined Transmission-distribution powerflowTime-stepping (using TS)Transient stability, Electromagnetic transientsCombined transient stabilty-electromagnetic transients (hybridsimulation)Optimization (using TAO package)SCOPF, LMP calculationsEigen-value analysis (using SLEPc package)Small signal stability analysisShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 14 / 22PS applications PSdevReal-time electrical power system dynamicsNonlinear differential-algebraicpower system modelẋ = f (x , y)0 = g(x , y)Three-phase networkSpatial decomposition in parallel0 0.5 1 1.5 2 2.5 359.759.859.96060.160.260.360.460.560.6Generator speeds (Hz)Time(sec)TS3phPSS/E* Scalabilty results of 2360 bus, 4670 branches, 1080 generatorsystemShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 15 / 22PS applications PSdevCombined Electromechanical and ElectromagneticTransients SimulationCapture “global” slow dynamics and “local” fast dynamicsUse TS globally and EMT locallyNeed interface forTime stepNetwork modelingWaveformShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 16 / 22PS applications PSdevExisting “explicit” hybrid simulation approachMake separate TS and EMT programs talk to each otherExplicit approachNo iterations between TS and EMTDiverges for large changes in voltages/currentsLimited parallelismShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 17 / 22PS applications PSdevProposed “Implicitly-coupled” hybrid simulation approachCombine TS and EMT at theequation level rather than at theapplication levelSolve TS equations andcoupled-in-time EMT equationsfor each TS time step togetherMore robust than the explicitapproachAllows an integrated parallelimplementationShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 18 / 22PS applications PSdevMulti-scale dynamics simulation strategyOnly run the implicitly coupled simulator in the presence of fastdynamics, run TS for all other times!! "!"!#$!! %&'!Time-comparison of different dynamic analysesSystem size Simulated time (sec) TS3ph EMT Only TSEMT TS3ph-TSEMT9 bus 3 0.13 4.96 5.46 0.41118 bus 3 0.36 30.1 4.87 0.53Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 19 / 22PS applications PSdevParallel implementation and performance resultsPartition TS network in space and EMT network in timeEach processor gets equations forTS subnetworkEMT equations for multiple time-steps*2360 buses total, 4 buses,3 transmission lines and 4 loads in EMT network*Using GMRES + Block-Jacobi + LU + Very Dishonest preconditioningShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 20 / 22PS applications PSdevPETSc use in the example applicationsEasy parallel implementationPartitioning (using ParMetis)Tune Linear solvers (using KSP and PC libraries)Nonlinear solver (SNES library)Portable codeReduced experimentation timeSelecting different algorithms at run-time!!Shrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 21 / 22SummaryWrapping up...Developing parallel, nontrivial applications that deliver high performance isstill difficult and requires months (or even years) of concentrated effort.PETSc is a toolkit that can ease these difficulties and reduce thedevelopment time, but it is not a black-box solver, nor a silver bullet. –Barry SmithPETSc can help power system applicationsto solve algebraic and DAE problemsbenchmark with different numerical solvers.rapidly develop efficient parallel code, can start from examplesdevelop new solution methods and data structuresdebug and analyze performanceadvice on software design, solution algorithms, and performancepetsc-{users,dev,maint}@mcs.anl.govShrirang Abhyankar (ANL) MSO21P 2012 October 24, 2012 22 / 22

Using PETSc as a Testbed for Developing and Benchmarking Parallel Power System Applications

Lee, W.E.

Eakins, Emily

Jackson, Heather

Jayaseelan, Doni

Engineering Conferences InternationalECI Digital ArchivesUltra-High Temperature Ceramics: Materials ForExtreme Environmental Applications II ProceedingsSpring 5-17-2012Advanced characterization of composite ultra hightemperature ceramic systemsW.E. LeeImperial College LondonEmily EakinsImperial College LondonHeather JacksonImperial College LondonDoni JayaseelanImperial College LondonFollow this and additional works at: http://dc.engconfintl.org/uhtcPart of the Materials Science and Engineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inUltra-High Temperature Ceramics: Materials For Extreme Environmental Applications II by an authorized administrator of ECI Digital Archives. Formore information, please contact franco@bepress.com.Recommended CitationW.E. Lee, Emily Eakins, Heather Jackson, and Doni Jayaseelan, "Advanced characterization of composite ultra high temperatureceramic systems" in "Ultra-High Temperature Ceramics: Materials For Extreme Environmental Applications II", W. Fahrenholtz,Missouri Univ. of Science & Technology; W. Lee, Imperial College London; E.J. Wuchina, Naval Service Warfare Center; Y. Zhou,Aerospace Research Institute Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/uhtc/20Advanced Characterisation of Ultra-high Temperature Ceramic SystemsBill Lee, Emily Eakins, Heather Jackson and Doni JayaseelanDept. of Materials and Centre for Advanced Structural Ceramics, Imperial College London, UKUltra-High Temperature Ceramics: Materials for Extreme Environmental Applications II, Schloss Hernstein, Austria May 17th, 2012.Characterisation Techniques• Microstructures: SEM, FIB, TEM– Laser melting ZrC and ZrB2– Lessons from Refractories– Oxidation of ZrB2-SiC + La– Dislocations in ZrB2-SiC• Surfaces: ToF-SIMS, FIB-SIMS– Oxidation of ZrB2 + SiCMaterials and Processing.• ZrC– Carbothermal reduction ZrO2, Spark Plasma Sintered (SPS) at QMUL 1985oC, 50 MPa 6-8 min., >95% dense.• ZrB2 ceramics– ZrB2, ZrB2/20 vol.% SiC (ZS20) + 1-10wt.% LaB6or La2O3 (ZS20-La).– SPS at QMUL over range T, t and P (1750-1850oC, 6-10min., 50-70MPa) to typically >98% dense. 40 mmInert gas (2 bar)Laser Induced Melting at the Institute for TransUranium Elements (ITU)Laser ProfilesPeak Power 990 - 3800 WPulse Time 70 - 3600 msBeam Diameter 3 or 8 mmSapphire window (τ = 0.8539)ZrC emissivity ~0.6Pressure vesselSampleHeatingHigh-power 1064.5 nm CW laserTypical thermogram, commercial ZrC0.963 mm beam, 990 W, 70 msHeating 4.5 kW Nd-YAGAr Atmosphere800 µmIntensity (arbitrary units)100806040202θ (°) Laser melted ZrC ZrC powder diffraction peaksMolten pool consistent with ZrC by XRD1 mmCommercial ZrC0.963 mm beam990 W, 70 msMelted ZrC Microstructure.60 µm60 µmDendritic structure characteristic of melting and recrystallisationHF Jackson et al., “Laser Melting of Zirconium Carbide: Determination of Phase Transitions in Refractory Ceramic Systems,” J. Am. Ceram. Soc. 94 [10] 3561-69 (2011)Homogeneous, single phase ZrC at melted surfaceMelted ZrC Microstructure.Fast quenching produces metastable eutectic, but repeated melting homogenises the surface zone10 µm xZrCZrCy-C eutectic10 µmMetastable ZrCy+C eutectic forms sub-surface layerUnmelted bulk0.5 mmCommercial ZrC0.968 mm beam4 laser pulses of 2500-3800 W400 ms eachZrC5 µmFIB-milled sectionsGraphiteTEM of Melted ZrC Eutectica = 4.6-4.8 Å0.5 µmLinear streaks0000202200002 (graphite)Elliptical streaks000020220Graphite diffraction rings, spot pattern from nearby ZrCyDistinct Zr-rich and C-rich regionsA – PyrometerB – Laser collimating optics (4.0 kW Nd:YAG)C – Ceramic disc on Ta sheet on refractory brickLaser testing was carried out in airLaser ProfilesPeak Power 600 - 1800 WLaser Melting at TWIHeating Time 30 – 300 sBeam Diameter 10 mmsample (10 mm x 3 mm)Ta supportzirconia brick ∆T~500oC2343oC – recorded max temperature within 0.2s of laser offTemperature uncertaintyAir Atmosphere2.5MW/m2, 300sSurface of Laser Heated ZrB2.• All is ZrO2.• Low magnification shows uneven surface including radial flow of liquid. Burst bubbles, craters and porous structure fromo rapid volatilisation of boriao volume shrinkage  from ZrO2 transitions DD Jayaseelan, HF Jackson, E Eakins, P Brown and WE Lee, “Laser Modified Microstructures in ZrB2, ZrB2/SiC & ZrC,” J. Euro. Ceram. Soc.  30 [11] 2279-88 (2010).2.5MW/m2, 300sSurface of Laser Heated ZrB2.Multifaceted surface ZrO2 grains between fine web ZrO2 matrixFaceted smooth grains in fine web matrixHeterogeneous; surface features vary from region to region possibly due to differing local cooling conditions.  Dendritic microstructure in ZrC suggests direct melting of the carbide.  XRD, SEM and TEM confirm presence of ZrCx and metastable eutectic ZrCy-C where x and y have not been characterised. Laser modifies grain surface by heating, melting, molten zone solidification on cooling to give variety of grain Summary of Laser Melting Studies.morphologies, sizes and porosities.  Resulting microstructures are heterogeneous with range of morphologies. Mechanisms of microstructure formation are a complex function local t, T and atmosphere and extent to which solid, liquid or vapour involved.  Applying Refractories Concepts to UHTCs• Control of local equilibrium/environment.• Use of in situ reactions to provide protection, do-it-yourself coatings. WE Lee and RE Moore, “The Evolution of in situ Refractories in the 20th Century,” J. Am. Ceram. Soc. 81 [6] 1385-1410 (1998). • Natural graphite flakes.• Oxide ceramic aggregate.• Deoxidising Oxide-C Steelmaking Bricks: True Composite Systemsmetal additions.• Nanoscalecarbon bond derived from phenolicpolymer resin.Dense MgO Layer Formation in MgO-C Bricks.• Refractories may react with furnace atmosphere but local atmosphere in brick may be different.• Create reducing atmosphere internally within brick in an otherwise oxidising Basic Oxygen Steelmaking environment. • MgO/C reaction forms Mg vapour which travels to brick surface and reacts with available oxygen to form dense MgO layer which protects the brick from further slag penetration.  Local Liquid.• Composition of most penetrating liquid may be very different from bulk slag due to more rapid diffusion of certain cations in silicate slag (e.g. Fe, Cr, Mn, Ni) or reaction with refractory matrix. Local Equilibrium• By controlling this equilibrium e.g. using solids with open crystal structures able to accommodate these Spinelcations (e.g. MgO, MgAl2O4) can slow penetration by forming more viscous local liquid or less soluble reaction interlayers.PericlaseSingle Phase MgO Grain Corrosion.SILICATE SLAGMgO GRAIN• Fe,Mn from slag diffused into MgO to form (Mg,Fe,Mn)O layer.• Indirect attack.• Local slag thus silica-rich and viscous. In situ Refractories.• The product(s) of reaction within a refractory system or between the refractory and furnace contents leading to beneficial refractory behaviour.• Types I-IV.WE Lee, S Zhang and H Sarpoolaky, “Different Types of in situ Refractories,” pp.245-252 in Ceramic Transactions 125 (2001). Type I In Situ Refractories.• Arise from reactions between refractories components due to high temperature producing useful phases.• E.g. self forming spinels in castablesfrom fine MgO and Al2O3 powder.• Spinel formation in matrix of dry vibratable from reaction of fine MgO and Al2O3.Type II In Situ Refractories.• Reactions occur within the refractory but assisted by reaction with the (liquid or vapour) furnace contents.Type II in SituCeramics from Reaction of Al Metal Additives with Furnace Atmosphere.AlN WhiskersType III In Situ Refractories.Single Phase Al2O3 Grain Corrosion.SILICATE SLAGALUMINA GRAIN• White Fused Alumina (WFA) formed CA6 and complex spinel layers.• CA6 layer adjacent WFA is integral so direct dissolution WFA impossible.Type IV In Situ Refractories.Example: Slag splashing in BOS vessel extended lining lives from ~200 to >1000. SiO2ZrO2ZrB2 ZrB2/20% SiC (ZS20)ZrOLa2Zr2O7/ZrO2ZS20-10% LaB6Comparing oxidised ZrB2, ZrB2-SiC (ZS20) and ZrB2-SiC-LaB6 after oxidation at 1600oC/1h50 µmZrB210 µm2ZrB2/SiCZrB2/SiCPorous ZrO2 top layerUn protectiveSiO2 top layerProtective ( < 1800oC)La2Zr2O7 top layerProtective (~ 2000oC)La2Zr2O7/ZrO2ZS20-10% LaB6When adding RE to ZS20, thefollowing reactions occur duringoxidation.Formation of Refractory Coatings in ZrB2 + 20 vol% SiC +10% LaB6 after 1h at 1600oC10 µm ZrB2/SiCLight La2Zr2O7 is continuous anddark silicate liquid present asisolated drops.DD Jayaseelan, E Zapata-Solvas, P Brown and WE Lee, “In-situ Formation of Oxidation Resistant Refractory Coatings on SiC-reinforced ZrB2 Ultra High Temperature Ceramics,” J. Am. Ceram. Soc. 95 [4] 1247-54 (2012).Solid Oxidation Products.• Continuous solidrefractory layer formation, isolated liquid silica. • Form solid using rare earth additives and control of reaction between the UHTC+RE  system and the atmosphere.• Improved oxidation resistance in UHTCs may be derived from in situ generation of solid protective layers.E Eakins, DD Jayaseelan and WE Lee, “Toward Oxidation-resistant ZrB2-SiC Ultra High Temperature Ceramics,” Met. and Mats. Trans. 42A 878-887 (2011).WG Fahrenholtz and GE Hilmas, “Oxidation of Ultra-high Temperature Transition Metal Diboride Ceramics,” Int. Mater. Reviews 57 1 61-72 (2012).TEM of Dislocations in ZrB2- SiC.Samples Hot Pressed at ISTEC 50MPa, 1900oC 20-120minsZrB2 + 5vol%SiC ZrB2 +10vol% SiCZrB2 + 15vol% SiCAverage ZrB2 Dislocation Density versus SiC Content for SPS and HP Samples.More dislocations with more SiCdue to  αmismatch.More dislocations with SPS than HP?  Impact of internal stress on properties?Emily Eakins, Nanoscale characterisation of effect of SiC on microstructure and oxidation behaviour of ZrB2-based ceramics, PhD Thesis 2011, Dept. of Materials, Imperial College London.Microstructure Summary• Range of techniques needed for full characterisation of UHTCs including SEM, FIB and TEM.• We can learn lessons from over a century of refractories research and use.• Gaps in our knowledge of linear and planar defects in these systems which need linking to processing and properties.Surfaces• Surface analysis using Secondary Ion Mass Spectroscopy (SIMS).ION TOF TOF-SIMS/LEIS                               FEI FIB 200 SIMSSIMS Process• Typically Ga primary beam.• Mass analyse sputtered species via magnetic deflection/time of flight.• All elements and all isotopes can be analysed• Sensitivity: parts per million• Lateral resolution: beam width 5nm-50µm• Depth resolution: beam energy  (~ R ~ several nm)• Information depth (a few atomic layers)Mass spectrometryAll elements and isotopesDepth profilingDistribution of elements with Analysis Parameters:PI:Sample: Venetian GlassBi 3+Origin: Imperial CoFile:Energy: 25 keVCurrent: 0.40 pAComment:110210Composition / weight%Symbol SubstanceCsNaCsMgCsKCsCaCsSiOCs2HAdjacent Average: 10RX02129B.TFDPolarity: positive---SIMS Modes of operationdepth resolved to 0.3nmImagingLateral distribution of elements (100nm easy and 5nm with FIB)tascon GmbH · Münster, Germany  PI DD: 4.34E+013 Ions/cm²Area: 99.6x99.6 µm²Sputter Parameters:SpI: Cs+Area:Sp DD:300.0x300.0 µm²1.65E+018 Ions/cm²Energy: 1 keVCurrent: 138.00 nADepth/nm200 400 600010Composition / weight%O 2 flooding: -Polymer (PP)Melt StabiliserAntioxidant Polymer, Stabiliser, AntioxidantField of View: 284 x 284 µm2FIB-SIMS Analysis of Hot Pressed ZrB2+ 20vol% SiC 1h at 1500oC.• Layers:– Outer glass– Mixed layer.– Unoxidised bulk.Emily Eakins, Nanoscale characterisation of effect of SiC on microstructure and oxidation behaviour of ZrB2-based ceramics, PhD Thesis 2011, Dept. of Materials, Imperial College London.FIB-SIMS Analysis of Outer Glassy Layer.• All regions contain Si, O, B.• C, N, O containing species vary.• No other impurities.FIB-SIMS Analysis of Mixed Layer.• 4 phases detected:– ZrB2– ZrO2– Glass  – Phase 4.FIB-SIMS Analysis of Phase 4.• Phase 4 is a Zr-rich oxide + traces B, Si and C + impurities such as Na, Ca and K introduced during powder production or on oxidation. • Also contains hydroxide, H2O+ and H2O- presumably arising on removal of sample from furnace due to highly reactive new oxide. • Phase 4 is an impurity sink + pores/trapped glass.• Surface glass has lost  alkali impurities. SIMS Summary• Light element analysis (e.g.  B, O, C) crucial in examination of UHTCs (also to check stoichiometry in carbides).• Amount of light element may vary with location (again local environment) • Need to characterise O/OH levels from e.g. RT oxidation/hydration of powders. • Cannot ignore water, pick-up may have implications for re-use.• Minor impurities e.g. alkalis important.Important UHTC Issues.• Local environment/equilibria.• Beneficial use of in situ environment.• Need to understand impact of processing on internal stress and impact of internal stress on properties.• Need to use a range of characterisation techniques including for analysis of light elements.• Cannot ignore water.

Advanced characterization of composite ultra high temperature ceramic systems

Finney, Karen

James, Rob

Engineering Conferences InternationalECI Digital ArchivesFifty Years Of Watershed Modeling - Past, PresentAnd Future Proceedings2012Real Time High-Resolution Flood ForecastingSystem Using PCSWMM, EPA SWMM5 andNEXRAD RadarKaren FinneyCHI, CanadaRob JamesCHI, CanadaFollow this and additional works at: http://dc.engconfintl.org/watershedPart of the Civil and Environmental Engineering CommonsThis Article is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion in Fifty Years OfWatershed Modeling - Past, Present And Future by an authorized administrator of ECI Digital Archives. For more information, please contactfranco@bepress.com.Recommended CitationKaren Finney and Rob James, "Real Time High-Resolution Flood Forecasting System Using PCSWMM, EPA SWMM5 andNEXRAD Radar" in "Fifty Years Of Watershed Modeling - Past, Present And Future", A.S. Donigian, AQUA TERRA Consultants;Richard Field, US EPA (retired); Michael Baker Jr., Inc. Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/watershed/6

Real Time High-Resolution Flood Forecasting System Using PCSWMM, EPA SWMM5 and NEXRAD Radar

Gillen, Andrew

Edwards, Lyndon

Riley, Daniel

Franks, George

Engineering Conferences InternationalECI Digital ArchivesUltra-High Temperature Ceramics: Materials ForExtreme Environmental Applications II ProceedingsSpring 5-15-2012Solid state diffusion bonding of ZrC to Zr-basedalloysAndrew GillenInstitute of Materials Engineering, Australian Nuclear Science and Technology OrganisationLyndon EdwardsInstitute of Materials Engineering, Australian Nuclear Science and Technology OrganisationDaniel RileyInstitute of Materials Engineering, Australian Nuclear Science and Technology OrganisationGeorge FranksUniversity of MelbourneFollow this and additional works at: http://dc.engconfintl.org/uhtcPart of the Materials Science and Engineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inUltra-High Temperature Ceramics: Materials For Extreme Environmental Applications II by an authorized administrator of ECI Digital Archives. Formore information, please contact franco@bepress.com.Recommended CitationAndrew Gillen, Lyndon Edwards, Daniel Riley, and George Franks, "Solid state diffusion bonding of ZrC to Zr-based alloys" in "Ultra-High Temperature Ceramics: Materials For Extreme Environmental Applications II", W. Fahrenholtz, Missouri Univ. of Science &Technology; W. Lee, Imperial College London; E.J. Wuchina, Naval Service Warfare Center; Y. Zhou, Aerospace Research InstituteEds, ECI Symposium Series, (2013). http://dc.engconfintl.org/uhtc/12Solid state diffusion bonding of ZrC to Zr-based alloysAndrew L. Gillen1,2, George V. Franks2 , Lyndon Edwards1, and Daniel P. Riley1,21Institute of Materials Engineering, Australian Nuclear Science and Technology Organisation, Lucas Heights NSW 2234 AUSTRALIA2University of Melbourne, School of Engineering, Parkville, VIC 3010 AUSTRALIAEmail: andrew.gillen@ansto.gov.auIntroductionInstitute of Materials Engineering“A major centre of materials engineering and expertise in Australia with a multidisciplinary team of scientists and engineers”Materials for Extreme EnvironmentsRadiation, Elevated Temperature, High Stress and Corrosion Fission and Fusion Applications2• Nuclear Power will play a vital role in realising a clean energy future– Lowest cost, low emission technology for producing baseload power– Currently generates 30% of baseload power in EUIntroduction– Two nuclear power plants to be built at Vogtle, GA (First in US in 35 years)• Currently, worldwide, there are:– 433 nuclear reactors in operation– 63 nuclear reactors under construction– 160 nuclear reactors in planningSource: http://www.world-nuclear.org/info/reactors.htmlOlkiluoto-3, Finland Source: theage.com.au3Background• Generation IV initiative (GIF) lauched by US DOE in 2000– 10 Member Nations + Euratom– Aim: Design and develop a new generation, of energy efficient, sustainable, safe and reliable, proliferation resistant nuclear reactors by 20304BackgroundProposed Gen-IV Reactor DesignsReactor System Coolant Neutron SpectrumCore Outlet Temperature (ºC)Pressure(High = 7-15MPa)Very HighTemperature Reactor (VHTR)Gas (eg. He)Thermal >900 HighGas Cooled Fast Source: Murty et al., 2008ReactorGas (eg. He) Fast ~850 HighSodium Cooled Fast Reactor (SFR) Molten Salt (fluoride salts)Fast 700-800 LowLead Cooled Gas Reactor (LFR)Liquid Metal (eg. Pb, Pb-Bi)Fast 550-800 LowMolten Salt Reactor (MSR)Liquid Metal (Na)Thermal ~550 LowSupercritical water-cooled reactor (SCWR)Water Thermal/Fast 350-620 Very High5– Materials exposed to• Higher temperatures• Higher neutron doses• High stresses• Extremely corrosive BackgroundProposed Gen-IV Reactor Designsenvironments– Additional radiation-induced material effects• 1D/2D and 3D Defects • Segregation• Creep• Diffusion • Precipitation• Volumetric SwellingSource: Guerin et al., 20096Background• Reactor Technology Challenges– Availability of durable, radiation tolerant materials– Sustainability & waste disposal– EconomicsProposed Gen-IV Reactor DesignsSource: Buschow et. al, 2001– Safety and reliability– Proliferation resistance & physical protection• Radiation Tolerant Materials– Dimensional stability under irradiation– Large interfacial area– Low defect mobility– Good recovery from irradiation induced defectsDisplacement Cascade(○ -vacancies; ● interstitials)7Research Motivation• Many reactor components in proposed Generation IV systems will require high-strength interfacial bonding for maximised performance• Several refractory non-oxide ceramics are proposed for application in Gen-IV reactors– SiC, TiC, ZrC– Applications: heat exchangers, thermal insulations, core-reactor components and high thermal load components• A UHTC joined to a metallic material • exhibits high refractoriness• retains structural integrity of the underlying metallic substrateANSTO OPAL Reactor CoreSource: ansto.gov.au8• A promising UHTC Gen-IV material– Diffusion barrier layer on TRISO fuel in earlier Generation GFRs– Proposed as a fuel matrix component in Gen-IV GFR– Other potential applications in elevated temperature nuclear environments• Key Nuclear Properties– Low neutron absorption cross sectionZirconium Carbide– High melting point (~3420ºC)– Recovery from irradiation induced defects– Resistance to attack from fissile products• Crystal Structure– Interstitial carbide– NaCl, B1 FCC structure– Stoichiometry: ZrC0.55 to ZrC0.98• Major limitation: low fracture toughnessZrC NaCl Structure 9• Zircaloy-4, Zircaloy-2 (Zr+Sn), Zr-Nb• Nuclear Applications– Cladding, pressure tubes and structural components• Key Nuclear propertiesZirconium Alloys– Low neutron absorption cross section– Excellent corrosion resistance– Good mechanical properties• HCP Crystal Structure (@RT)– Strong anisotropy– α → β transformation ~850°C• Limited to operation at ~400°C in nuclear applications due to creep/embrittlementZircaloy Cladded Nuclear Reactor FuelSource: cameco.com10Joining ZrC/Zircaloy-4Mechanical ChemicalSolid State Liquid stateBoltingScrew threadShrinkfittingUltrasonicWeldingAnodicBondingDiffusionBondingGas-metalEutectic BondingGlass-ceramicsBrazing AdhesivesSource: Fernie et. al . 2009Method: Solid State Diffusion Bonding• Usually carried out in a HUP or HIP• Structure, CTE mismatch and surface roughness important• Zircaloy-4/ZrC CTE mismatch is relatively low• Key Steps(a) asperity contact(b) yielding under large localised stresses(c) deformation and diffusion mass transfer (d) removal of interfacial voids and bond formation11Joining ZrC/Zircaloy-4Mechanical ChemicalSolid State Liquid stateBoltingScrew threadShrinkfittingUltrasonicWeldingAnodicBondingDiffusionBondingGas-metalEutectic BondingGlass-ceramicsBrazing AdhesivesSource: Fernie et. al . 2009Method: Solid State Diffusion Bonding• Usually carried out in a HUP or HIP• Structure, CTE mismatch and surface roughness important• Zircaloy-4/ZrC CTE mismatch is relatively low• Key Steps(a) asperity contact(b) yielding under large localised stresses (c) deformation and diffusion mass transfer (d) removal of interfacial voids and bond formation11Joining ZrC/Zircaloy-4Mechanical ChemicalSolid State Liquid stateBoltingScrew threadShrinkfittingUltrasonicWeldingAnodicBondingDiffusionBondingGas-metalEutectic BondingGlass-ceramicsBrazing AdhesivesSource: Fernie et. al . 2009Method: Solid State Diffusion Bonding• Usually carried out in a HUP or HIP• Structure, CTE mismatch and surface roughness important• Zircaloy-4/ZrC CTE mismatch is relatively low• Key Steps(a) asperity contact(b) yielding under large localised stresses(c) deformation and diffusion mass transfer (d) removal of interfacial voids and bond formation11Joining ZrC/Zircaloy-4Mechanical ChemicalSolid State Liquid stateBoltingScrew threadShrinkfittingUltrasonicWeldingAnodicBondingDiffusionBondingGas-metalEutectic BondingGlass-ceramicsBrazing AdhesivesSource: Fernie et. al . 2009Method: Solid State Diffusion Bonding• Usually carried out in a HUP or HIP• Structure, CTE mismatch and surface roughness important• Zircaloy-4/ZrC CTE mismatch is relatively low• Key Steps(a) asperity contact(b) yielding under large localised stresses(c) deformation and diffusion mass transfer (d) removal of interfacial voids and bond formation11Joining ZrC/Zircaloy-4Mechanical ChemicalSolid State Liquid stateBoltingScrew threadShrinkfittingUltrasonicWeldingAnodicBondingDiffusionBondingGas-metalEutectic BondingGlass-ceramicsBrazing AdhesivesSource: Fernie et. al . 2009Method: Solid State Diffusion Bonding• Usually carried out in a HUP or HIP• Structure, CTE mismatch and surface roughness important• Zircaloy-4/ZrC CTE mismatch is relatively low• Key Steps(a) asperity contact(b) yielding under large localised stresses(c) deformation and diffusion mass transfer (d) removal of interfacial voids and bond formation11• Preparation– Zirconium Carbide– ~2% Hf, 3-5 micron APS– Cold Uniaxially Pressed, HUP, HIP– No sintering aids– Ø25mm size, 2mm thickStage 1 Stage 2Process HUPing HIPingTemperature 2000ºC 2000ºCJoining ZrC/Zircaloy-4ZrC Processing Steps– Grinding/polishing steps to 1µm diamond surface finish– Zircaloy-4– Zr-1.56Sn– Hot rolled, annealed, blasted & pickled– Ø25mm size, 3mm thick– Grinding polishing steps to 1µm surface diamond finishPressure 20MPa 100MPaBulk Density after Processing (ASTM C20)~96.5 ~99%12Joining ZrC/Zircaloy-4ZrCZircaloy-4ZrC• Samples joined in hot uniaxial press, 0.01Pa vacuum, no interlayer• Processing parameters: 1300ºC, 40MPa• Void-free solid-state diffusion bond produced• Macro-deformation of Zircaloy-4 during joining processZircaloy-4Zircaloy-413Zircaloy-4Zircaloy-4Diffusion CoupleResults• BSE SEM MicroscopyZrCZrC14ID Elemental wt%O Sn Hf1 1.99 0.75Zircaloy-4ResultsEDS: Minor Elements12loy-42 2.17 1.813 3.074 1.36 2.375 1.18 2.36ZrC15345BSE ImageZrCZircaloy-4Diffusion CoupleResultsA B• Observations: EDS LinescanBSE ImageZrCZrC– Higher [O], [Sn] in Zircaloy-4– Evidence of Hf diffusion into Zircaloy-4– Marked changes in [Sn], [Hf] near interlayer17Concentration (at%)Distance (µm) 6Counts100000200000 XP02505Results• X-ray Diffraction Position [°2Theta]10 20 30 40 50 60 700P o s i t i o n  [ °2 T h e t a ]1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 P e a k  L i s t 0 3 - 0 6 5 - 3 3 6 6 0 3 - 0 6 5 - 0 9 7 3 0 0 - 0 0 8 - 0 4 1 5ZirconiumZirconium CarbideGraphite– Zr and ZrC presence confirmed– Free carbon also detected– Need smaller beam size for more  accurate resolution of interface structurePEAK LIST17– Conclusions– Void-free ZrC/Zircaloy-4 solid-state diffusion bond achieved– Observations• Higher relative [O] in Zircaloy-4• Evidence of surface oxidation• Evidence of Sn segregation at interfaceConclusions & Future Work• Evidence of Hf diffusion into Zircaloy-4• Macro-deformation of Zircaloy-4 during joining process – Future Work• Optimise process temperature and pressure to minimise residual stresses and macro-deformation• C detection & mapping (eg. Raman)• Mechanical testing of diffusion couples• Irradiation of samples18PhD Advisory CommitteeDr Daniel Riley, Prof. George Franks, Prof. Lyndon Edwards, Sam MoriccaMaterial Fabrication & Facility Support – IME/Engineering, ANSTODr Ken Short, Neil Webb, Kerry Cruickshank, Stephen DeenAcknowledgementsMetallography – IME, ANSTOTim Palmer, Clint JennisonMicroscopy / X-ray Diffraction – IME, ANSTOJoel Davis, Dr Gordon Thorogood, Dr Tracey HanleyIME Workshop – IME, ANSTOGarrie Hammond, Patrick De Bono, Wilfred Crasta19

Solid state diffusion bonding of ZrC to Zr-based alloys

Antoniewicz, Maciek

Engineering Conferences InternationalECI Digital ArchivesMetabolic Engineering IX ProceedingsSummer 6-7-201213C Metabolic Flux Analysis of CHO Cells withParallel Labeling ExperimentsMaciek AntoniewiczUniversity of DelawareFollow this and additional works at: http://dc.engconfintl.org/metabolic_ixPart of the Biomedical Engineering and Bioengineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inMetabolic Engineering IX by an authorized administrator of ECI Digital Archives. For more information, please contact franco@bepress.com.Recommended CitationMaciek Antoniewicz, "13C Metabolic Flux Analysis of CHO Cells with Parallel Labeling Experiments" in "Metabolic Engineering IX",E. Heinzle, Saarland Univ.; P. Soucaille, INSA; G. Whited, Danisco Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/metabolic_ix/21Maciek Antoniewicz, Woo Suk AhnUniversity of Delaware13C Metabolic Flux Analysis of CHO Cells with Parallel Labeling ExperimentsMetabolic Engineering IXJune 7, 2012CHO cells67%2%9%5%13%CHOBHKHumanMurineMaciek Antoniewicz  - mranton@udel.edu Slide 14% HybridomaEtc• Most popular cell line to produce biotherapeutics• CHO metabolism is still poorly understood• New insights needed for medium optimization and cell line screeningFlux analysis in CHO cells10152010152025VCD (105cells/mL)Glucose, Lactate (mM)GlucoseLactateCellsMetabolism changes in timeMaciek Antoniewicz  - mranton@udel.edu Slide 205050 2 4 6VCD (10Glucose, Lactate (mM)Time (day)Flux analysis in CHO cells10152010152025VCD (105cells/mL)Glucose, Lactate (mM)oxPPPGlucoseGL3PLipidsG6PFBPDHAP GAP6PG Ru5PX5P R5PGAP S7PE4P F6PF6PGlycogenNTPSer Gly Gly.exThrThrALC1 + COGCS3PGBPGCHO CELL METABOLISMGLYCOLYSISPENTOSE PHOSPHATEAMINO ACID CellsMetabolism changes in time Metabolism is complexGlucoseLactateMaciek Antoniewicz  - mranton@udel.edu Slide 305050 2 4 6VCD (10Glucose, Lactate (mM)Time (day)PYRAcCoACITOAAMALPYRSucCoAICITAKGFUMMALCITICITAKGOAA PEPAcCoAOAAmitochondrioncytosolAmino Acids(Gln, Pro, Arg, His)SUCFatty AcidsPEPCKMEmPCGluAmino Acids(Met, Val, Ile, Thr)Amino Acids(Phe, Tyr)Amino Acids(Asp, Asn)Amino Acids(Leu, Ile, Phe, Tyr, Lys, Trp)GluProLactateAmino Acids(Ser, Cys)AlaACONcIDHcFAox22PGMEcACONmIDHmLactateTCA CYCLEMETABOLISMANAPLEROSISMetabolic Flux Analysis in CHO cellsMaciek Antoniewicz  - mranton@udel.edu Slide 4Ahn and Antoniewicz, Biotechnol J, 7: 61-74, 2012Metabolic Flux Analysis (MFA)S DA Pv1 v2 v7Bv4v3 C Ev5 Qv8Rv6v1 v2 v3 v4 v5 v6 v7 v8A 1 -1 -1 0 0 0 0 0                0 B 0 1 0 -1 0 0 0 0                0C 0 0 1 0 -1 0 0 0   *        =   0D 0 0 0 1 0 0 -1 0                0E 0 0 0 0 1 -1 0 -1                0Maciek Antoniewicz  - mranton@udel.edu Slide 5v1v2v3v4v5v6v7v8S * v = 0Metabolic Flux Analysis (MFA)S DA Pv1 v2 v7Bv4v3 C Ev5 Qv8Rv6S * v = 0R * v = rv1 v2 v3 v4 v5 v6 v7 v8A 1 -1 -1 0 0 0 0 0                0 B 0 1 0 -1 0 0 0 0       v1       0C 0 0 1 0 -1 0 0 0       v2       0D 0 0 0 1 0 0 -1 0       v3       0E 0 0 0 0 1 -1 0 -1   *   v4   =   0S -1 0 0 0 0 0 0 0       v5       rSP 0 0 0 0 0 0 1 0       v6       rPQ 0 0 0 0 0 0 0 1       v7       rQR 0 0 0 0 0 1 0 0       v8       rRMaciek Antoniewicz  - mranton@udel.edu Slide 6Metabolic Flux Analysis (MFA)Metabolic flux analysis is only as reliable as your data and modeling assumptions• Observability Problem– If rank(data)  =  number of fluxes       → all fluxes can be determined– If rank(data)  <  number of fluxes       → not all fluxes can be uniquely determined Maciek Antoniewicz  - mranton@udel.edu Slide 7Model of CHO metabolismoxPPPGlucoseGL3PLipidsG6PFBPDHAP GAP6PG Ru5PX5P R5PGAP S7PE4P F6PF6PGlycogenNTPAmino AcidsSer Gly Gly.ex2PG3PGBPG ThrThrALC1 + CO2GCSPEPCKAmino AcidsMaciek Antoniewicz  - mranton@udel.edu Slide 8PYRAcCoACITOAAMALPYRSucCoAICITAKGFUMMALCITPEPAcCoAOAAmitochondrioncytosolAmino Acids(Gln, Pro, Arg, His)SUCFatty AcidsMEmPCGluAmino Acids(Met, Val, Ile, Thr)Amino Acids(Phe, Tyr)Amino Acids(Leu, Ile, Phe, Tyr, Lys, Trp)Glu.exProLactate(Ser, Cys)Ala.exLipidsMEcACLOAA(Asp, Asn)TCA CycleModel of CHO metabolism• External Measurements– GlucoseGlucoseG6PFBPDHAP GAPF6P2PG3PGBPGMaciek Antoniewicz  - mranton@udel.edu Slide 9PYRPEPLactateGlucoseG6PFBPDHAP GAPF6P2PG3PGBPGModel of CHO metabolism• External Measurements– Glucose– LactatePYRAcCoACITOAAMALPYRSucCoAICITAKGFUMPEPmitochondrioncytosolSUCLactateTCA CycleMaciek Antoniewicz  - mranton@udel.edu Slide 10GlucoseG6PFBPDHAP GAPF6P2PG3PGBPGModel of CHO metabolism• External Measurements– Glucose– Lactate– GlutaminePYRAcCoACITOAAMALPYRSucCoAICITAKGFUMMALPEPmitochondrioncytosolGln.exSUCGluLactateMEcTCA CycleMaciek Antoniewicz  - mranton@udel.edu Slide 11oxPPPGlucoseGL3PLipidsG6PFBPDHAP GAP6PG Ru5PR5PF6PGlycogenNTP2PG3PGBPGModel of CHO metabolism• External Measurements– Glucose– Lactate– Glutamine– Growth ratePYRAcCoACITOAAMALPYRSucCoAICITAKGFUMMALCITPEPAcCoAOAAmitochondrioncytosolGln.exSUCFatty AcidsGluLactateLipidsMEcACLTCA CycleMaciek Antoniewicz  - mranton@udel.edu Slide 12oxPPPGlucoseGL3PLipidsG6PFBPDHAP GAP6PG Ru5PR5PF6PGlycogenNTPAmino AcidsSer Gly Gly.ex2PG3PGBPGModel of CHO metabolism• External Measurements– Glucose– Lactate– Glutamine– Growth ratePYRAcCoACITOAAMALPYRSucCoAICITAKGFUMMALCITPEPAcCoAOAAmitochondrioncytosolAmino Acids(Gln, Pro, Arg, His)SUCFatty AcidsGluAmino Acids(Met, Val, Ile, Thr)Amino Acids(Phe, Tyr)Amino Acids(Leu, Ile, Phe, Tyr, Lys, Trp)Glu.exProLactate(Ser, Cys)Ala.exLipidsMEcACLTCA Cycle– Amino acidsMaciek Antoniewicz  - mranton@udel.edu Slide 13oxPPPGlucoseGL3PLipidsG6PFBPDHAP GAP6PG Ru5PX5P R5PGAP S7PE4P F6PF6PGlycogenNTPAmino AcidsSer Gly Gly.ex2PG3PGBPG ThrThrALC1 + CO2GCSPEPCKAmino AcidsModel of CHO metabolism• External Measurements– Glucose– Lactate– Glutamine– Growth ratePYRAcCoACITOAAMALPYRSucCoAICITAKGFUMMALCITPEPAcCoAOAAmitochondrioncytosolAmino Acids(Gln, Pro, Arg, His)SUCFatty AcidsMEmPCGluAmino Acids(Met, Val, Ile, Thr)Amino Acids(Phe, Tyr)Amino Acids(Leu, Ile, Phe, Tyr, Lys, Trp)Glu.exProLactate(Ser, Cys)Ala.exLipidsMEcACLOAA(Asp, Asn)TCA Cycle– Amino acids• 13C-Labeling GC-MS– Typical 200+ mass isotopomers measuredMaciek Antoniewicz  - mranton@udel.edu Slide 14CHO Cell Culture• CHO cell fed-batch culture– CHO-K1 cells (ATCC CCL-61) in T-25 flasks– Humidified incubator, 5% CO2, 37 deg.C• Medium composition– DMEM + 10% FBS + 1% PS– 6.7 mM [1,2-13C]glucose• Measurements– Glucose, lactate (YSI)– Amino acid concentrations (GC-MS)– 13C-Labeling of intracellular metabolites (GC-MS)Maciek Antoniewicz  - mranton@udel.edu Slide 15CHO Fed-batch cultureMaciek Antoniewicz  - mranton@udel.edu Slide 16Ahn WS & Antoniewicz MR, Metab Eng, 2011Shift in Lactate MetabolismGrowth PhaseNon-growthMaciek Antoniewicz  - mranton@udel.edu Slide 17Ahn WS & Antoniewicz MR, Metab Eng, 2011CHO Fed-batch cultureMaciek Antoniewicz  - mranton@udel.edu Slide 18Ahn WS & Antoniewicz MR, Metab Eng, 2011CHO Fed-batch cultureExponentialPhaseStationaryPhaseMaciek Antoniewicz  - mranton@udel.edu Slide 19Ahn WS & Antoniewicz MR, Metab Eng, 2011[1,2-13C]glucose additionMetabolism quenching and GC-MS analysisExtracellular metabolite profilingMaciek Antoniewicz  - mranton@udel.edu Slide 20Ahn WS & Antoniewicz MR, Metab Eng, 2011Biomass specific fluxesBiomass specific uptake and excretion rates of extracellular metabolites (nmol/106cells/hr)Maciek Antoniewicz  - mranton@udel.edu Slide 21Ahn WS & Antoniewicz MR, Metab Eng, 2011Intracellular Metabolite AnalysisTracer experiments withCHO cell culture in T25 flasksCold methanolChloroform/waterRemoval of media1.5 mL cold methanol5 min in icepH 7~8, overnight in cold chamber, centrif.Mass isotopomer distribution quantification 1 v CHCl31 v H2OCellsMaciek Antoniewicz  - mranton@udel.edu Slide 22Non-polar metabolitesPolar metabolitesSPE separation Organic/Amino acidsLipids in CHCl3DryingAmino acids/organic acidsDrying; 37oC, 9hrfractionationTriglyceride/FFAGC-MS AnalysisGC-MS AnalysisDerivatizationLipid esters, TMS der. of FFADerivatizationMOX: 37oC, 90 minTBDMS; 60oC, 30 minMOX/TBDMS der. of metabolitesGC-MS MeasurementsMaciek Antoniewicz  - mranton@udel.edu Slide 23Ahn WS & Antoniewicz MR, Metab Eng, 201113C-Labeling in MetabolitesIsotopic steady state Isotopic non-steady stateC LabelingC LabelingMaciek Antoniewicz  - mranton@udel.edu Slide 24time13C Labelingtime13C LabelingStationary 13C-MFA Non-stationary 13C-MFAYoung et al., Biotech. Bioeng. 2008Wiechert et al., Met. Eng. 1999Labeling dynamics of intracellular metabolitesMaciek Antoniewicz  - mranton@udel.edu Slide 25Ahn WS & Antoniewicz MR, Metab Eng, 2011Labeling dynamics of intracellular metabolitesMaciek Antoniewicz  - mranton@udel.edu Slide 26Ahn WS & Antoniewicz MR, Metab Eng, 2011Isotopic non-stationary 13C-MFAMaciek Antoniewicz  - mranton@udel.edu Slide 27Ahn WS & Antoniewicz MR, Metab Eng, 2011Parallel Labeling ExperimentsGlucose and Lactate (mM)5101520Viable Cell Density (cells/mL)106107LactCellGluc [1,2-13C]GlucoseMaciek Antoniewicz  - mranton@udel.edu Slide 28Time (day)0 2 4 6Glutamine (mM)0123NH3 (mM)012345Glucose and Lactate (mM)0 Viable Cell Density (cells/mL)105GlnNH3[U-13C]GlutamineAhn WS & Antoniewicz MR, Metab Eng (Submitted)Isotopic steady state < 3 hrMaciek Antoniewicz  - mranton@udel.edu Slide 29Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Intracellular Mass Isotopomers[1,2-13C]Glucose [U-13C]GlutamineMaciek Antoniewicz  - mranton@udel.edu Slide 30Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Metabolic Fluxes Growth vs. StationaryMaciek Antoniewicz  - mranton@udel.edu Slide 31Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Metabolic Fluxes Growth vs. StationaryMaciek Antoniewicz  - mranton@udel.edu Slide 32Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Metabolic Fluxes Growth vs. StationaryMaciek Antoniewicz  - mranton@udel.edu Slide 33Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Combined 13C-MFA of parallel experimentsMaciek Antoniewicz  - mranton@udel.edu Slide 34Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Combined 13C-MFA of parallel experimentsMaciek Antoniewicz  - mranton@udel.edu Slide 35Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Validating loss of metabolites in glycolysisMaciek Antoniewicz  - mranton@udel.edu Slide 36Validating Lipid MetabolismMaciek Antoniewicz  - mranton@udel.edu Slide 37Ahn WS & Antoniewicz MR, Metab Eng (Submitted)13C-Labeling in LipidsMaciek Antoniewicz  - mranton@udel.edu Slide 38Ahn WS & Antoniewicz MR, Metab Eng (Submitted)De novo lipid biosynthesisMaciek Antoniewicz  - mranton@udel.edu Slide 39Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Validating Pentose Phosphate PathwayMaciek Antoniewicz  - mranton@udel.edu Slide 40Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Estimating oxPPP flux using 13C-MFA• [1,2-13C]glucose tracer– Glycolysis produces M2– oxPPP produces M1– non-oxPPP produces M3• Exponential growth phase– Low oxPPP (1% of glucose)• Stationary phase– Increased oxPPP (20% of glucose)Maciek Antoniewicz  - mranton@udel.edu Slide 41Ahn WS & Antoniewicz MR, Metab Eng (Submitted)Parallel Labeling Experiments for PPP[1]+[4,5,6]GlucG6PRu5PoxPPP[2]+[4,5,6]GlucG6PRu5PoxPPP[3]+[4,5,6]GlucG6PRu5PoxPPPGlucose Glucose Glucose12C 13C F6PGAPM1 M3F6PGAPM1 M3F6PGAPM1 M3M1M3High oxPPP< 1M1M3Control exp.≈ 1M1M3Control exp.≈ 13PGPEP3PGPEP3PGPEPAhn WS & Antoniewicz MR, J Biol Chem (Submitted)Mass Isotopomer DistributionsMaciek Antoniewicz  - mranton@udel.edu Slide 43Ahn WS & Antoniewicz MR, J Biol Chem (Submitted)oxPPP estimated with different tracersMaciek Antoniewicz  - mranton@udel.edu Slide 44Ahn WS & Antoniewicz MR, J Biol Chem (Submitted)Mass isotopomer analysisMaciek Antoniewicz  - mranton@udel.edu Slide 45Ahn WS & Antoniewicz MR, J Biol Chem (Submitted)Mass isotopomer analysisMaciek Antoniewicz  - mranton@udel.edu Slide 46Oxidative PPPAhn WS & Antoniewicz MR, J Biol Chem (Submitted)Mass isotopomer analysisLost C1-C3 ?Maciek Antoniewicz  - mranton@udel.edu Slide 47Oxidative PPPAhn WS & Antoniewicz MR, J Biol Chem (Submitted)Loss of carbon atoms in non-oxPPPMaciek Antoniewicz  - mranton@udel.edu Slide 48Lost C1-C3 atomsv18Ahn WS & Antoniewicz MR, J Biol Chem (Submitted)Validated oxPPP fluxMaciek Antoniewicz  - mranton@udel.edu Slide 49Ahn WS & Antoniewicz MR, J Biol Chem (Submitted)Reversible non-oxPPP fluxesMaciek Antoniewicz  - mranton@udel.edu Slide 50Reversible non-oxPPP fluxesMaciek Antoniewicz  - mranton@udel.edu Slide 51Conclusions CHO cells• Flux analysis is only as reliable as the model• 13C-MFA for model validation• In the transition from growth phase to stationary phase, CHO cells dramatically changed metabolism• New insights into CHO metabolism:– Oxidative Pentose Phosphate– Lipid metabolism– Loss of carbon atoms in glycolysisMaciek Antoniewicz  - mranton@udel.edu Slide 52AcknowledgmentsGraduate Students Undergrad Students FundingWoo Suk Ahn Nick Marze DuPont Young Professor GrantRob Leighty Carolyn Slusser UD Research FoundationAditi Swarup Matt Grossbach NIH COBREJungik Choi Casey DeWoody NSF Graduate FellowshipScott Crown Christine Smith NSF CAREER AwardJing Lu NSF MCBSlide 53Maciek Antoniewicz  - mranton@udel.edu

13C Metabolic Flux Analysis of CHO Cells with Parallel Labeling Experiments

Chabalgoity, Jose

Engineering Conferences InternationalECI Digital ArchivesVaccine Technology IV ProceedingsSpring 5-22-2012Functional genomics as a tool to define a molecularsignature of effective vaccination against foot andmouth disease virusJose ChabalgoityDepartamento de Desarrollo Biotecnologico, Facultad de Medicina, Universidad de la Republica, UruguayFollow this and additional works at: http://dc.engconfintl.org/vaccine_ivPart of the Biomedical Engineering and Bioengineering CommonsThis Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion inVaccine Technology IV by an authorized administrator of ECI Digital Archives. For more information, please contact franco@bepress.com.Recommended CitationJose Chabalgoity, "Functional genomics as a tool to define a molecular signature of effective vaccination against foot and mouth diseasevirus" in "Vaccine Technology IV", B. Buckland, University College London, UK; J. Aunins, Janis Biologics, LLC; P. Alves , ITQB/IBET; K. Jansen, Wyeth Vaccine Research Eds, ECI Symposium Series, (2013). http://dc.engconfintl.org/vaccine_iv/24Transcriptional biomarkers of effective vaccination against Foot and Mouth Disease José A. ChabalgoityInstituto de Higienehttp://www.higiene.edu.uy/ddbpLaboratory for Vaccine ResearchSalmonella as vaccinevectorSalmonella: geneticvariation & pathogenicityImmunotherapiesfor respiratoryinfectionsBiotechindustry VeterinaryvaccinesLaboratory for Vaccine ResearchBiotechindustry VeterinaryvaccinesFoot and Mouth Disease (FMD)• Highly contagious disease of mammals. Affects cattle, pigs, sheep, goats, etc. • Clinical signs can vary from mild to severe. Fatalities may occur, especially in young animals• Spreads rapidly• Seven serotypes of FMD virus (FMDV): no cross immunity between serotypes• Severe economic lossesOutbreaks have major impact in countries economyS. Levy Vaccine Technology III - 2010FMDV vaccines• Inactivated virus vaccines• No cross-protection between serotypes • Multivalent vaccines to provide protection against the different serotypes• Challenge tests are needed to establish a PD50 (50% protective dose) value or protection• Challenge experiments requires appropriate biosecurity containment (Group 4 pathogens)• Slow and expensive procedure O.I.E.- Manual of Diagnostic Tests and Vaccines for Terrestrial AnimalsPotency Test• PD50 test: The number of protective doses in a vaccine is estimated from the resistance to live virus challenge• Cattle of at least 6 months of age, obtained from areas free from FMD should be used.• Unprotected animals show lesions at sites other than the tongue• Vaccines of high potency will prevent the development of local tongue lesions at the site of challengeO.I.E.- Manual of Diagnostic Tests and Vaccines for Terrestrial AnimalsFMDV vaccines• The use of animals should be avoided where possible by the use of in vitro alternatives.• A serological test is considered to be satisfactory where a valid correlation between the observed protection, and the specific antibody response has been established.• Time consuming assayO.I.E.- Manual of Diagnostic Tests and Vaccines for Terrestrial Animals• Need to be delivered rapidly into the field for outbreaks attack• Short shelf lifeCooperación Comunidad Europea - MERCOSURCooperación Unión Europea - MERCOSURGenome-wide analysis of transcriptional responses against vaccinationTranscriptional signature of protection as surrogate of protectionNew technologies for development & control of Foot and Mouth vaccinesNew vaccinesdevelopmentNew analytical tools tovalidate productionprocessFunctional genomics as a tool to define a molecular signature of effective vaccination against FMDVWork implementationAnalysisNaive animalsVaccinationCell stimulationMany parameters to be adjusted• Time before processing (samples transport)• Antigen preparation• Time of stimulation (kinetics of the response)• RNA conservation (QC for microarray) • Housekeeping gene • Initial qRT-PCR for sample selectionGenome-wide analysis of transcriptional responsesB. Taurus (Bovine) Oligo Microarray v2 Design ID 023647 4 x 44K(Agilent Technologies)1109 differentially expressed genes344 genes upregulatedGenome-wide analysis of transcriptional responsesNaïve Vaccinated765 genes downregulatedMain altered biological processImmunological DiseaseCell-To-Cell Signaling and Interaction – Activation of LeukocytesCell-To-Cell Signaling and Interaction – Activation of Blood cellsAntigen Presentation – Activation of phagocytesAntigen Presentation – Activation of antigen presenting cellsVaccine Potency Test• PD50 test: estimated from the resistance to live virus challenge• Control animals must develop lesions on at least three feetUnprotected animals show lesions at sites •other than the tongue• Vaccine of high potency will prevent the development of local tongue lesions at the site of challenge.O.I.E.- Manual of Diagnostic Tests and Vaccines for Terrestrial Animals341 differentially expressed genes276 genes upregulatedGenome-wide analysis of transcriptional responsesSymptomaticHighly protected65 genes downregulatedA molecular signature of effective vaccination against FMDV?qRT-PCR for microarray validation• TLDA not suitable due to low numbers of inventoredgenes• Need to design appropriate set of primersGene Symbol Gene NameGenes from microarray vaccinated vs naive animalsC5AR1 complement component 5a receptor 1CCR1 chemokine (C-C motif) receptor 1CD14 CD14 moleculeCD180 CD180 moleculeCD36 CD36 molecule (thrombospondin receptor)CLEC7A C-type lectin domain family 7, member ACSF1R colony stimulating factor 1 receptorCXCL10 chemokine (C-X-C motif) ligand 10CXCL5 chemokine (C-X-C motif) ligand 5FCGR1A Fc fragment of IgG, high affinity Ia, receptor (CD64)FGF18 fibroblast growth factor 18HCK hemopoietic cell kinaseIL12A interleukin 12A (p35)IL12B interleukin 12B (p40)IL2RA interleukin 2 receptor, alphaLIF leukemia inhibitory factor (cholinergic differentiation factor)MMP9 matrix metallopeptidase 9PIGR polymeric immunoglobulin receptorS100A9 S100 calcium binding protein A9SLAMF1 signaling lymphocytic activation molecule family member 1TAP1 transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)TG thyroglobulinTHBS1 thrombospondin 1TNF tumor necrosis factorTNFRSF1A tumor necrosis factor receptor superfamily, member 1AGenes from microarray highly protected vssymptomatic animalsBLA-DQB MHC class II antigenBOLA-DQB major histocompatibility complex, class II, DQ betaBoLA-DRB3 major histocompatibility complex, class II, DRB3CCNB1 cyclin B1CD3G CD3g molecule, gamma (CD3-TCR complex)CDKN3 cyclin-dependent kinase inhibitor 3EIF2B1 eukaryotic translation initiation factor 2B, subunit 1 alpha, 26kDaEIF5A eukaryotic translation initiation factor 5AGAPDH glyceraldehyde-3-phosphate dehydrogenaseGBP4 guanylate binding protein 4IGJ immunoglobulin J polypeptide, linker protein for Ig α and µ polypeptidesIRF8 interferon regulatory factor 8KIR3DL2 killer cell immunoglobulin-like receptorRPL10 ribosomal protein L10RPS24 ribosomal protein S24THSD4 thrombospondin, type I, domain containing 4Gene Symbol Gene NameGenes present in both microarraysCLEC1A C-type lectin domain family 1, member ACCL4 chemokine (C-C motif) ligand 4CREB3 cAMP responsive element binding protein 3CXCR2 chemokine (C-X-C motif) receptor 2CXCR5 chemokine (C-X-C motif) receptor 5DNAJA1 DnaJ (Hsp40) homolog, subfamily A, member 1FTSJ2 FtsJ homolog 2 (E. coli)GADD45B growth arrest and DNA-damage-inducible, betaGPR128 G protein-coupled receptor 128GPR137B G protein-coupled receptor 137BIFN-γ interferon, gammaIGF2 insulin-like growth factor 2 (somatomedin A)IL23R interleukin 23 receptorP2RY6 pyrimidinergic receptor P2Y, G-protein coupled, 6RPL9 ribosomal protein L9SIX5 SIX homeobox 5UPP1 uridine phosphorylase 1GeneSymbolArrayVaccinated vsNaiveArrayProtected vs SymptomaticGenes in microarray vaccinated vsnaive animalsC5AR1CD180CLEC7ACXCL10FCGR1AFGF18IL12AIL12BLIFS100A9SLAMF1TGTHBS1qRT-PCR for microarray validationTNFGenes in microarray protected vssymptomatic animalsBLA-DQBCCNB1CD3GEIF5AGBP4IGJIRF8KIR3DL2Genes shared by both microarraysCLEC1ACCL4CREB3CXCR5DNAJA1FTSJ2GADD45BGPR137BIFNgP2RY6SIX5UPP1Transcriptional biomarker of vaccination ?SymptomaticTranscriptional biomarker of vaccination & high protection ?SymptomaticTranscriptional biomarker of high protection ?SymptomaticNon-validated genesSymptomatic + 2SymptomaticIFN-γ a suitable biomarker of Symptomaticvaccination & high protection ?Better than Neutralizing antibodies ?SymptomaticMolecular signature seems to better differentiate between highly protected and symptomatic animalsAnimal Symptoms Neutralizing antibodies (UA) Increase IFN-γ mRNA1 - 3,48 0,010342 - 2,26 0,016244 - 3,55 0,0115511 - 2,99 0,004785 - 2,35 0,0046310 - 3,29 0,002717 + 3,48 0,001238 + 2,26 0,001549 + 3,55 0,0017713 + 2,99 0,000153 + 2,17 0,0072812 + 1,79 0,00082Naïve 1 + 0,00086NaÏve 2 + 0,00058Conclusions & future directions• There is a need for proper subrogates of protection in vaccines• This is especially important when trying to translate research from preclinical studies to clinical trials• Genome-wide transcriptional profiling allowed the discovery of biomarkers that seem more accurate than neutralizing antibodies for FMD• Need to conduct large screenings to validate the process• This shall accelerate the release of new batches of vaccines into the fieldDesarrollo BiotecnologicoInstituto de HigieneUruguayJuan Martin MarquesAndrea RossiSebastian SasiasGustavo SarrocaInstituto LeloirArgentinaOsvaldo PodhajcerSantiago WerbahjEduardo CafferataBiogenesis BagoArgentinaSusana LevyMarcelo A. Spitteler Eliana Smitsaart Alejandro HamJorge Tito FilippiINTA CastelarArgentinaInstitut des Hautes Etudes Scientifiques FranceArndt BeneckeMariano Perez Institut Pasteur of MontevideoUruguayCarlos RobelloGonzalo GreifURUGUAYLargest beef consumers in the world: 53 kg per capitaThe biggest “asado”: Guinnes record 2008Beaches of UruguayBeaches of UruguayWorld cup South Africa 2010American Cup Argentina 2011

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