Development of predictive models for catalyst development

Abstract. This work was done as a part of the BioSPRINT project, which aims to improve biorefinery operations through process intensification and to replace fossil-based polymers with new bio-based products. The goal was to identify machine learned (ML) models that will accelerate the catalyst identification with high-throughput (HTP) screening methods, identify non-obvious formulations and allow catalyst tuning for different feedstock compositions. Maximum activity for conversion of complex sugar mixtures with optimal selectivity towards the key products of interest is desired. In the literature part of the thesis, ML was studied in general, where the focus was on different variable selection methods and modeling techniques, more specifically on data-driven modeling. Furthermore, modeling in catalysis was discussed with focus on ML in catalysis. Catalyst screening and selection, descriptor modeling and selection, and predictive modeling in catalysis were studied. In the experimental part, focus was on developing ML models that predict catalyst performance with relevant descriptors. Dataset for hydrogenation of 5-ethoxymethylfurfural with simple bimetal catalysts, including main metals and promoters, was used as ML model input with the addition of catalyst descriptors found in the literature. Four different responses were used in the experiments: selectivity and conversion with two different solvents. Methods used in the experimental part were discussed in detail, where data collection, preprocessing, variable selection, modeling and model validation were considered. Reference models without variable selection were first identified. Secondly, regularization algorithms were used to identify models. Finally, models with variable subsets obtained with regularization algorithms were identified. The effect of cross-validation was also studied. In general, good modeling results were obtained with boosted ensemble tree methods, support vector machine (SVM) methods and Gaussian process regression (GPR) methods. Lasso regression turned out to be the best variable selection method. Good results were obtained with the descriptors found in the literature. It was also shown, that fairly good results can be obtained with only two variables in the studied case. Promoter variables were not considered nearly as important as main metals with variable selection algorithms. Even though the modeling results were good, the variable selection methods were almost purely data-driven, and the actual relevance of the variables cannot be guaranteed. In the future work, optimization should be studied with the goal of finding catalysts that maximize catalyst performance values based on the model predictions. Also, extrapolation capabilities of the models need to be studied and improved. The studied methods can be easily implemented to other datasets. In the BioSPRINT project, experimental results related to the dehydration reaction of C5 and C6 sugars with simple metal catalysts will be obtained and used with the studied methods.Ennustavien mallien laatiminen katalyytin valmistuksen tehostamiseksi. Tiivistelmä. Tämä työ tehtiin osana BioSPRINT-projektia, jonka tavoitteena on kehittää biojalostamoiden toimintaa parantamalla niiden prosessitehokkuutta ja korvata fossiilipohjaiset polymeerit uusilla biopohjaisilla tuotteilla. Työn tavoitteena oli muodostaa koneoppimista hyödyntämällä mallit, jotka nopeuttavat optimaalisten katalyyttien löytämistä tehoseulonnan (high-throughput (HTP) screening) avulla, auttavat identifioimaan vaikeasti löydettäviä katalyyttiyhdistelmiä ja mahdollistavat katalyytin valinnan eri lähtöainekoostumuksilla. Tavoitteena on maksimoida monimutkaisten sokeriyhdisteiden konversio ja selektiivisyys halutuiksi tuotteiksi. Työn kirjallisuusosiossa perehdyttiin koneoppimiseen yleisellä tasolla, missä pääpaino oli muuttujanvalintamenetelmissä ja datapohjaisissa mallinnusmenetelmissä. Lisäksi kirjallisuusosassa tutkittiin mallinnuksen käyttöä katalyysissä, missä pääpaino oli koneoppimisen käytössä. Työssä tarkasteltiin myös katalyyttien seulontaa ja valintaa, laskennallisten muuttujien (deskriptorien) määrittelyä ja valintaa, sekä ennustavan mallinnuksen käyttöä katalyysissä. Kokeellisessa osiossa painopiste oli koneoppimista hyödyntävien mallien muodostuksessa, jotka ennustavat katalyyttien suorituskykyä oleellisilla deskriptoreilla. Data-aineistona käytettiin 5-etoksimetyylifurfuraalin hydrausreaktion tuloksia yksinkertaisilla kaksikomponenttisilla metallikatalyyteillä, jotka sisältävät päämetallin ja promoottorin. Data-aineistoa täydennettiin kirjallisuudesta löytyvillä katalyyttien deskriptoreilla ja käytettiin koneoppimista hyödyntävien mallien sisääntulona. Tutkimuksissa käytettiin neljää eri vastemuuttujaa: selektiivisyyttä ja konversiota kahdella eri liuottimella. Kokeellisessa osiossa käytetyt menetelmät käytiin läpi perusteellisesti huomioon ottaen data-aineiston keräämisen, esikäsittelyn, muuttujanvalinnan, mallinnuksen ja mallin validoinnin. Ensin referenssimallit identifioitiin. Tämän jälkeen regularisaatioalgoritmeilla suoritettiin mallinnus. Lopuksi mallinnus suoritettiin käyttämällä muuttujajoukkoja, jotka oli valittu käyttäen regularisaatioalgoritmeja. Myös ristivalidoinnin vaikutusta tutkittiin. Yleisesti hyvät mallinnustulokset saavutettiin boosted ensemble tree -tekniikalla, tukivektorikoneella ja Gaussian process -regressiolla. Lasso-menetelmä todettiin parhaaksi muuttujanvalinta-algoritmiksi. Hyvät tulokset saavutettiin kirjallisuudesta löytyvien deskriptorien avulla. Tutkimuksissa todettiin myös, että hyvät mallinnustulokset voidaan saavuttaa kyseisessä tutkimustapauksessa jopa vain kahdella muuttujalla. Päämetalleja kuvaavien muuttujien merkitsevyys todettiin paljon suuremmaksi kuin promoottorien vastaavien muuttujien. Saatavia mallinnustuloksia tarkasteltaessa täytyy huomioida, että muuttujanvalinta oli melkein täysin datapohjainen eikä muuttujien varsinaista merkitsevyyttä voida taata. Jatkossa mallien ennustuksia voidaan hyödyntää optimoinnissa, jossa tavoitteena on etsiä katalyyttiyhdistelmä, joka maksimoi katalyyttien suorituskyvyn. Myös mallin ekstrapolointikykyä täytyy tutkia ja kehittää. Tutkittavat menetelmät ovat helposti sovellettavissa myös muille samantyylisille data-aineistoille. BioSPRINT-projektista saadaan tulevaisuudessa käytettäväksi viisi- ja kuusihiilisten sokerien dehydraatioon perustuva data-aineisto yksinkertaisilla metallikatalyyteillä, jota tullaan käyttämään jatkotutkimuksissa

Shape coexistence at the proton drip-line: First identification of excited states in 180Pb

Excited states in the extremely neutron-deficient nucleus, 180Pb, have been identified for the first time using the JUROGAM II array in conjunction with the RITU recoil separator at the Accelerator Laboratory of the University of Jyvaskyla. This study lies at the limit of what is presently achievable with in-beam spectroscopy, with an estimated cross-section of only 10 nb for the 92Mo(90Zr,2n)180Pb reaction. A continuation of the trend observed in 182Pb and 184Pb is seen, where the prolate minimum continues to rise beyond the N=104 mid-shell with respect to the spherical ground state. Beyond mean-field calculations are in reasonable correspondence with the trends deduced from experiment.Comment: 5 pages, 4 figures, submitted to Phys.Rev.

Performance and flow dynamics studies of polymeric optofluidic sers sensors

We present a polymer-based optofluidic surface enhanced Raman scattering chip for biomolecule detection, serving as a disposable sensorchoice with cost-effective production. The SERS substrate is fabricated by using industrial roll-to-roll UV-nanoimprinting equipment andintegrated with adhesive-based polymeric microfluidics. The functioning of the SERS detection on-chip is confirmed and the effect of thepolymer lid on the obtainable Raman spectra is analysed. Rhodamine 6G is used as a model analyte to demonstrate continuous flowmeasurements on a planar SERS substrate in a microchannel. The relation between the temporal response of the sensors and sample flowdynamics is studied with varied flow velocities, using SERS and fluorescence detection. The response time of the surface-dependent SERSsignal is longer than the response time of the fluorescence signal of the bulk flow. This observation revealed the effect of convection on thetemporal SERS responses at 25 μl/min to 1000 μl/min flow velocities. The diffusion of analyte molecules from the bulk concentration intothe sensing surface induces about a 40-second lag time in the SERS detection. This lag time, and its rising trend with slower flow velocities, has to be taken into account in future trials of the optofluidic SERS sensor, with active analyte binding on the sensing surface

Bridging the gap between policy and science in assessing the health status of marine ecosystems

Human activities, both established and emerging, increasingly affect the provision of marine ecosystem services that deliver societal and economic benefits. Monitoring the status of marine ecosystems and determining how human activities change their capacity to sustain benefits for society requires an evidence-based Integrated Ecosystem Assessment approach that incorporates knowledge of ecosystem functioning and services). Although, there are diverse methods to assess the status of individual ecosystem components, none assesses the health of marine ecosystems holistically, integrating information from multiple ecosystem components. Similarly, while acknowledging the availability of several methods to measure single pressures and assess their impacts, evaluation of cumulative effects of multiple pressures remains scarce. Therefore, an integrative assessment requires us to first understand the response of marine ecosystems to human activities and their pressures and then develop innovative, cost-effective monitoring tools that enable collection of data to assess the health status of large marine areas. Conceptually, combining this knowledge of effective monitoring methods with cost-benefit analyses will help identify appropriate management measures to improve environmental status economically and efficiently. The European project DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status) specifically addressed t hese topics in order to support policy makers and managers in implementing the European Marine Strategy Framework Directive. Here, we synthesize our main innovative findings, placing these within the context of recent wider research, and identifying gaps and the major future challenges

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First observation of excited states in 173Hg

The neutron-deficient nucleus 173Hg has been studied following fusion-evaporation reactions. The observation of gamma rays decaying from excited states are reported for the first time and a tentative level scheme is proposed. The proposed level scheme is discussed within the context of the systematics of neighbouring neutron-deficient Hg nuclei. In addition to the gamma-ray spectroscopy, the alpha decay of this nucleus has been measured yielding superior precision to earlier measurements.Comment: 5 pages, 4 figure

Proton drip-line nuclei in relativistic mean-field theory

The position of the two-proton drip line has been calculated for even-even nuclei with $10 \leq Z \leq 82$ in the framework of the relativistic mean-field (RMF) theory. The current model uses the NL3 effective interaction in the mean-field Lagrangian and describes pairing correlations in the Bardeen-Cooper-Schrieffer (BCS) formalism. The predictions of the RMF theory are compared with those of the Hartree-Fock+BCS approach (with effective force Skyrme SIII) and the finite-range droplet model (FRDM) and with the available experimental information.Comment: 18 pages, RevTeX, 2 p.s figures, to appear in Phys. Rev.

Biodiversity in Marine Ecosystems—European Developments toward Robust Assessments

Sustainability of marine ecosystems and their services are dependent on marine biodiversity, which is threatened worldwide. Biodiversity protection is a major target of the EU Marine Strategy Framework Directive, requiring assessment of the status of biodiversity on the level of species, habitats, and ecosystems including genetic diversity and the role of biodiversity in food web functioning and structure. This paper provides a summary of the development of new indicators and refinement of existing ones in order to address some of the observed gaps in indicator availability for marine biodiversity assessments considering genetic, species, habitat, and ecosystem levels. Promising new indicators are available addressing genetic diversity of microbial and benthic communities. Novel indicators to assess biodiversity and food webs associated with habitats formed by keystone species (such as macroalgae) as well as to map benthic habitats (such as biogenic reefs) using high resolution habitat characterization were developed. We also discuss the advances made on indicators for detecting impacts of non-native invasive species and assessing the structure and functioning of marine food-webs. The latter are based on indicators showing the effects of fishing on trophic level and size distribution of fish and elasmobranch communities well as phytoplankton and zooplankton community structure as food web indicators. New and refined indicators are ranked based on quality criteria). Their applicability for various EU and global biodiversity assessments and the need for further development of new indicators and refinement of the existing ones is discussed

New gas-filled mode of the large-acceptance spectrometer VAMOS

Spectromètre VAMOSA new gas-filled operation mode of the large-acceptance spectrometer VAMOS at GANIL is reported. A beam rejection factor greater than 1010 is obtained for the 40Ca+150Sm system at 196 MeV. The unprecedented transmission efficiency for the evaporation residues produced in this reaction is estimated to be around 80% for ®xn channels and above 95% for xnyp channels. A detailed study of the performance of the gasfilled VAMOS and future developments are discussed. This new operation mode opens avenues to explore the potential of fusion reactions in various kinematics