Bioethanol Production by Carbohydrate-Enriched Biomass of Arthrospira (Spirulina) platensis

In the present study the potential of bioethanol production using carbohydrate-enriched biomass of the cyanobacterium Arthrospira platensis was studied. For the saccharification of the carbohydrate-enriched biomass, four acids (H2SO4, HNO3, HCl and H3PO4) were investigated. Each acid were used at four concentrations, 2.5 N, 1 N, 0.5 N and 0.25 N, and for each acid concentration the saccharification was conducted under four temperatures (40 °C, 60 °C, 80 °C and 100 °C). Higher acid concentrations gave in general higher reducing sugars (RS) yields (%, gRS/gTotal sugars) with higher rates, while the increase in temperature lead to higher rates at lower acid concentration. The hydrolysates then were used as substrate for ethanolic fermentation by a salt stress-adapted Saccharomyces cerevisiae strain. The bioethanol yield (%, gEtOH/gBiomass) was significantly affected by the acid concentration used for the saccharification of the carbohydrates. The highest bioethanol yields of 16.32% ± 0.90% (gEtOH/gBiomass) and 16.27% ± 0.97% (gEtOH/gBiomass) were obtained in hydrolysates produced with HNO3 0.5 N and H2SO4 0.5 N, respectively

Breakthroughs in bioalcohol production from microalgae: Solving the hurdles

Bioethanol production from microalgae biomass has been proposed as an innovative alternative to substitute fossil fuel sources. Unlike other renewable sources (e.g., lignocellulosic materials), microalgae biomass has no lignin, which makes the carbohydrate extraction process easier and eventually it should help to develop cleaner and safer bioethanol production processes. Carbohydrates in microalgae can be present in a variety of forms (cellulose, starch, and/or glycogen) and located in different regions of the cells (inner, inside, outside). Carbohydrate type, location, and concentration will strongly depend on cultivation and operation conditions with concentrations ranging from 15% to 50%. Several steps must be applied to obtain bioethanol from this biomass. First, different methods can be employed to disrupt the cell wall and release the carbohydrates such as physical-mechanicals, chemicals, and/or a combination of them. After that, enzymatic hydrolysis could be required to convert the carbohydrates into simple sugars. Finally, a yeast or bacteria fermentation stage is performed to transform these sugars into ethanol. However, it is imperative that the principal parameters of these different steps should be optimized during the bioethanol production before industrial implementation, and more research on economic and life cycle analysis is needed to ensure the economic feasibility of the process.COS

Removal of phosphate from aqueous solutions by adsorption onto Ca(OH)2 treated natural clinoptilolite

Phosphorus (P) recovery from wastewater is of great interest especially when the loaded adsorbent can be used in the agriculture as slow-release fertilizer. The application depends on environmental concerns related to the chemical modification of the adsorbent and the release of toxic compounds from the loaded material to the soil or the water during adsorption. The present work focused on the phosphate (PO4-P) removal from aqueous solutions under low P concentrations (0.5–10mg/L) by using Ca(OH)2-pretreated natural zeolite (CaT-Z). As activation agent, Ca(OH)2 presents benefits in terms of pretreatment costs and environmental impact of the applied adsorbent. The pretreatment of natural zeolite (clinoptilolite) with 0.25mol/L Ca(OH)2 led to an increase of P removal from 1.7 to 97.6% at initial P concentration of 10mg/L, pH 7 and 298K. Low residual concentrations of 81–238μg P/L were achieved at 298K rendering CaT-Z a promising sorbent for tertiary wastewater treatment. At 200mg P/L, the adsorption capacity was 7.57mg P/g CaT-Z. The P removal efficiency was pH-independent suggesting a beneficial use of CaT-Z under acidic and alkaline conditions. Adsorption was found to be an endothermic and slow process reaching equilibrium after 120h, whereas the half of the PO4-P was adsorbed in the first 8h. The applied kinetic models showed that both film and intraparticle diffusion contributed to phosphate removal. Phosphate sorption decreased in the presence of the anionic surfactant SDS, Fe2+, HCO3−, acetate and citrate anion. The predominant mechanisms of ligand exchange and Ca-P surface precipitation were confirmed by the IR-ATR and SEM-EDS analyses, respectively

Atmospheric neutrinos with the first KM3NeT/ORCA data and prospects for measuring the atmospheric neutrino flux

KM3NeT is a research infrastructure aiming to study astrophysical sources as well as to perform particle physics studies, through the detection of neutrinos in the abyssal depths of the Mediterranean Sea. The KM3NeT/ORCA detector (Oscillation Research with Cosmics in the Abyss), currently under construction, is deployed at 2450 m depth near Toulon, France. It consists of vertical structures (Detection Units) equipped with spherical Digital Optical Modules, each hosting a set of photomultiplier tubes capable of detecting neutrino events from the Cherenkov radiation induced by the daughter particles. In this contribution, an analysis of data collected with the first 6 Detection Units (ORCA6) leading to a sample of atmospheric neutrino events is described. The angular resolution and the energy reconstruction performance for this event selection, which is a key factor for measuring the atmospheric neutrino flux, are also presented.Article signat per 297 autors/es: M.Ageron, S. Aiello, A. Albert, M. Alshamsi, S. Alves Garre, Z. Aly, A. Ambrosone, F. Ameli, M. Andre, G. Androulakis, M. Anghinolfi, M. Anguita, G. Anton, M. Ardid, S. Ardid, W. Assal, J. Aublin, C. Bagatelas, B. Baret, S. Basegmez du Pree, M. Bendahman, F. Benfenati, E. Berbee, A. M. van den Berg, V. Bertin, S. Beurthey, V. van Beveren, S. Biagi, M. Billault, M. Bissinger, M. Boettcher, M. Bou Cabo, J. Boumaaza, M. Bouta, C. Boutonnet, G. Bouvet, M. Bouwhuis, C. Bozza, H.Brânzas, R. Bruijn, J. Brunner, R. Bruno, E. Buis, R. Buompane, J. Busto, B. Caiffi, L. Caillat, D. Calvo, S. Campion, A. Capone, H. Carduner, V. Carretero, P. Castaldi, S. Celli;, R. Cereseto, M. Chabab, C. Champion, N. Chau, A. Chen, S. Cherubini, V. Chiarella, T. Chiarusi, M. Circella, R. Cocimano, J. A. B. Coelho, A. Coleiro, M. Colomer Molla, S. Colonges, R. Coniglione, A. Cosquer, P. Coyle, M. Cresta, A. Creuso, A. Cruz, G. Cuttone, A. D’Amico, R. Dallier, B. De Martino, M. De Palma, I. Di Palma, A. F. Díaz, D. Diego- Tortosa, C. Distefano, A. Domi, C. Donzaud, D. Dornic, M. Dörr, D. Drouhin, T. Eberl, A. Eddyamoui, T. van Eeden, D. van Eijk, I. El Bojaddaini, H. Eljarrari, D. Elsaesser, A. Enzenhöfer, V. Espinosa, P. Fermani, G. Ferrara, M. D. Filipovic, F. Filippini, J. Fransen, L. A. Fusco, D. Gajanana, T. Gal, J. García Méndez, A. Garcia Soto, E. Garçon, F. Garufi, C. Gatius, N. Geißelbrecht, L. Gialanella, E. Giorgio, S. R. Gozzini, R. Gracia, K. Graf, G. Grella, D. Guderian, C. Guidi, B. Guillon, M. Gutiérrez, J. Haefner, S. Hallmann, H. Hamdaoui, H. van Haren, A. Heijboer, A. Hekalo, L. Hennig, S. Henry, J. J. Hernández-Rey, J. Hofestädt, F. Huang,W. Idrissi Ibnsalih, A. Ilioni, G. Illuminati, C.W. James, D. Janezashvili, P. Jansweijer, M. de Jong, P. de Jong, B. J. Jung, M. Kadler, P. Kalaczynski, O. Kalekin,U. F. Katz, F. Kayzel, P.Keller, N. R. Khan Chowdhury, G. Kistauri, F. van der Knaap, P. Kooijman, A. Kouchner, M. Kreter, V. Kulikovskiy, M. Labalme, P. Lagier, R. Lahmann, P. Lamare, M. Lamoureux, G. Larosa, C. Lastoria, J. Laurence, A. Lazo, R. Le Breton, E. Le Guirriec, S. Le Stum, G. Lehaut, O. Leonardi, F. Leone, E. Leonora, C. Lerouvillois, J. Lesrel, N. Lessing, G. Levi, M. Lincetto, M. Lindsey Clark, T. Lipreau, C. LLorens Alvarez, A. Lonardo, F. Longhitano, D. Lopez-Coto, N. Lumb, L. Maderer, J. Majumdar, J. Manczak, A. Margiotta, A. Marinelli, A. Marini, C. Markou, L. Martin, J. A. Martínez-Mora, A. Martini, F. Marzaioli, S. Mastroianni, K.W. Melis, G. Miele, P. Migliozzi, E. Migneco, P. Mijakowski, L. S. Miranda, C. M. Mollo, M. Mongelli, A. Moussa, R. Muller, P. Musico, M. Musumeci, L. Nauta, S. Navas, C. A. Nicolau, B. Nkosi, B. Ó Fearraigh, M. O’Sullivan, A. Orlando, G. Ottonello, S. Ottonello, J. Palacios González5, G. Papalashvili, R. Papaleo, C. Pastore, A. M. Paun, G. E. Pavalas, G. Pellegrini, C. Pellegrino, M. Perrin-Terrin, V. Pestel, P. Piattelli, C. Pieterse, O. Pisanti, C. Poirè, V. Popa, T. Pradier, F. Pratolongo, I. Probst, G. Pühlhofer, S. Pulvirenti, G. Quéméner, N. Randazzo, A. Rapicavoli, S. Razzaque, D. Real, S. Reck, G. Riccobene, L. Rigalleau, A. Romanov, A. Rovelli, J. Royon, F. Salesa Greus, D. F. E. Samtleben, A. Sánchez Losa, M. Sanguineti, A. Santangelo, D. Santonocito, P. Sapienza, J. Schmelling, J. Schnabel, M. F. Schneider, J. Schumann, H. M. Schutte, J. Seneca, I. Sgura, R. Shanidze, A. Sharma, A. Sinopoulou, B. Spisso, M. Spurio, D. Stavropoulos, J. Steijger, S. M. Stellacci, M. Taiuti, F. Tatone, Y. Tayalati, E. Tenllado, D. Tézier, T. Thakore, S. Theraube, H. Thiersen, P. Timmer, S. Tingay, S. Tsagkli, V. Tsourapis, E. Tzamariudaki, D. Tzanetatos, C. Valieri, V. Van Elewyck, G. Vasileiadis, F. Versari, S. Viola, D. Vivolo, G. de Wasseige, J.Wilms, R.Wojaczynski, E. deWolf, T. Yousfi, S. Zavatarelli, A. Zegarelli, D. Zito, J. D. Zornoza, J. Zúñiga, N. Zywucka.Postprint (published version

Atmospheric neutrinos with the first detection units of KM3NeT/ARCA

The KM3NeT Collaboration is constructing two deep-sea Cherenkov detectors in the Mediterranean Sea, aiming at neutrino oscillation measurements with the ORCA array, while the ARCA array aims at neutrino astronomy in the TeV range. In April 2021, 5 additional detection units were deployed in the ARCA site. The KM3NeT/ARCA instrumented volume is currently similar to the one of the ANTARES neutrino telescope. In this contribution, an analysis of the data obtained with the detector before April 2021 is presented as well as the analysis of the very first data from the new KM3NeT/ARCA configuration. The performance is demonstrated using atmospheric muons and the first atmospheric neutrinos are shown.Article signat per 297 autors/es: M.Ageron, S. Aiello, A. Albert, M. Alshamsi, S. Alves Garre, Z. Aly, A. Ambrosone, F. Ameli, M. Andre, G. Androulakis, M. Anghinolfi, M. Anguita, G. Anton, M. Ardid, S. Ardid, W. Assal, J. Aublin, C. Bagatelas, B. Baret, S. Basegmez du Pree, M. Bendahman, F. Benfenati, E. Berbee, A. M. van den Berg, V. Bertin, S. Beurthey, V. van Beveren, S. Biagi, M. Billault, M. Bissinger, M. Boettcher, M. Bou Cabo, J. Boumaaza, M. Bouta, C. Boutonnet, G. Bouvet, M. Bouwhuis, C. Bozza, H.Brânzas, R. Bruijn, J. Brunner, R. Bruno, E. Buis, R. Buompane, J. Busto, B. Caiffi, L. Caillat, D. Calvo, S. Campion, A. Capone, H. Carduner, V. Carretero, P. Castaldi, S. Celli;, R. Cereseto, M. Chabab, C. Champion, N. Chau, A. Chen, S. Cherubini, V. Chiarella, T. Chiarusi, M. Circella, R. Cocimano, J. A. B. Coelho, A. Coleiro, M. Colomer Molla, S. Colonges, R. Coniglione, A. Cosquer, P. Coyle, M. Cresta, A. Creuso, A. Cruz, G. Cuttone, A. D’Amico, R. Dallier, B. De Martino, M. De Palma, I. Di Palma, A. F. Díaz, D. Diego- Tortosa, C. Distefano, A. Domi, C. Donzaud, D. Dornic, M. Dörr, D. Drouhin, T. Eberl, A. Eddyamoui, T. van Eeden, D. van Eijk, I. El Bojaddaini, H. Eljarrari, D. Elsaesser, A. Enzenhöfer, V. Espinosa, P. Fermani, G. Ferrara, M. D. Filipovic, F. Filippini, J. Fransen, L. A. Fusco, D. Gajanana, T. Gal, J. García Méndez, A. Garcia Soto, E. Garçon, F. Garufi, C. Gatius, N. Geißelbrecht, L. Gialanella, E. Giorgio, S. R. Gozzini, R. Gracia, K. Graf, G. Grella, D. Guderian, C. Guidi, B. Guillon, M. Gutiérrez, J. Haefner, S. Hallmann, H. Hamdaoui, H. van Haren, A. Heijboer, A. Hekalo, L. Hennig, S. Henry, J. J. Hernández-Rey, J. Hofestädt, F. Huang,W. Idrissi Ibnsalih, A. Ilioni, G. Illuminati, C.W. James, D. Janezashvili, P. Jansweijer, M. de Jong, P. de Jong, B. J. Jung, M. Kadler, P. Kalaczynski, O. Kalekin,U. F. Katz, F. Kayzel, P.Keller, N. R. Khan Chowdhury, G. Kistauri, F. van der Knaap, P. Kooijman, A. Kouchner, M. Kreter, V. Kulikovskiy, M. Labalme, P. Lagier, R. Lahmann, P. Lamare, M. Lamoureux, G. Larosa, C. Lastoria, J. Laurence, A. Lazo, R. Le Breton, E. Le Guirriec, S. Le Stum, G. Lehaut, O. Leonardi, F. Leone, E. Leonora, C. Lerouvillois, J. Lesrel, N. Lessing, G. Levi, M. Lincetto, M. Lindsey Clark, T. Lipreau, C. LLorens Alvarez, A. Lonardo, F. Longhitano, D. Lopez-Coto, N. Lumb, L. Maderer, J. Majumdar, J. Manczak, A. Margiotta, A. Marinelli, A. Marini, C. Markou, L. Martin, J. A. Martínez-Mora, A. Martini, F. Marzaioli, S. Mastroianni, K.W. Melis, G. Miele, P. Migliozzi, E. Migneco, P. Mijakowski, L. S. Miranda, C. M. Mollo, M. Mongelli, A. Moussa, R. Muller, P. Musico, M. Musumeci, L. Nauta, S. Navas, C. A. Nicolau, B. Nkosi, B. Ó Fearraigh, M. O’Sullivan, A. Orlando, G. Ottonello, S. Ottonello, J. Palacios González5, G. Papalashvili, R. Papaleo, C. Pastore, A. M. Paun, G. E. Pavalas, G. Pellegrini, C. Pellegrino, M. Perrin-Terrin, V. Pestel, P. Piattelli, C. Pieterse, O. Pisanti, C. Poirè, V. Popa, T. Pradier, F. Pratolongo, I. Probst, G. Pühlhofer, S. Pulvirenti, G. Quéméner, N. Randazzo, A. Rapicavoli, S. Razzaque, D. Real, S. Reck, G. Riccobene, L. Rigalleau, A. Romanov, A. Rovelli, J. Royon, F. Salesa Greus, D. F. E. Samtleben, A. Sánchez Losa, M. Sanguineti, A. Santangelo, D. Santonocito, P. Sapienza, J. Schmelling, J. Schnabel, M. F. Schneider, J. Schumann, H. M. Schutte, J. Seneca, I. Sgura, R. Shanidze, A. Sharma, A. Sinopoulou, B. Spisso, M. Spurio, D. Stavropoulos, J. Steijger, S. M. Stellacci, M. Taiuti, F. Tatone, Y. Tayalati, E. Tenllado, D. Tézier, T. Thakore, S. Theraube, H. Thiersen, P. Timmer, S. Tingay, S. Tsagkli, V. Tsourapis, E. Tzamariudaki, D. Tzanetatos, C. Valieri, V. Van Elewyck, G. Vasileiadis, F. Versari, S. Viola, D. Vivolo, G. de Wasseige, J.Wilms, R.Wojaczynski, E. deWolf, T. Yousfi, S. Zavatarelli, A. Zegarelli, D. Zito, J. D. Zornoza, J. Zúñiga, N. Zywucka. Postprint (published version

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Cultivation of Chlorella vulgaris and Arthrospira platensis with Recovered Phosphorus from Wastewater by Means of Zeolite Sorption

In this study, zeolite was employed for the separation and recovery of P from synthetic wastewater and its use as phosphorus (P) source for the cultivation of the green microalga Chlorella vulgaris and the cyanobacterium Arthrospira (Spirulina) platensis. At P-loaded zeolite concentration of 0.15–1 g/L, in which P was limited, the two species displayed quite different behavior regarding their growth and biomass composition. C. vulgaris preferred to increase the intracellular P and did not synthesize biomass, while A. platensis synthesized biomass keeping the intracellular P as low as possible. In addition under P limitation, C. vulgaris did display some little alteration of the biomass composition, while A. platensis did it significantly, accumulating carbohydrates around 70% from about 15%–20% (control). Both species could desorb P from zeolite biologically. A. platensis could recover over 65% and C. vulgaris 25% of the P bounded onto zeolite. When P-loaded zeolite concentration increased to 5 g/L, P was adequate to support growth for both species. Especially in the case of C. vulgaris, growth was stimulated from the presence of P-loaded zeolite and produced more biomass compared to the control

Using natural zeolite for ammonia sorption from wastewater and as nitrogen releaser for the cultivation of Arthrospira platensis

Herein a new approach for the application of wastewater nutrients for the cultivation of cyanobacteria or microalgae is described. Natural zeolite was used as medium for the sorption of ammonia from wastewater and subsequently as nitrogen releaser in cultures of Arthrospira. platensis. The main scope of the present approach was to isolate ammonia from the wastewater and to transfer it into the culture medium excluding thus the suspended solids, the dissolved colored compounds or any other possible contaminant of the wastewater. The results demonstrate that the indirect use of ammonia derived from wastewater using zeolite as sorption and releasing medium for the cultivation of A. platensis is promising. This is the first time that a medium was used for indirect application of wastewater nutrient for the production of cyanobacterial or microalgal biomass.publisher: Elsevier articletitle: Using natural zeolite for ammonia sorption from wastewater and as nitrogen releaser for the cultivation of Arthrospira platensis journaltitle: Bioresource Technology articlelink: http://dx.doi.org/10.1016/j.biortech.2013.12.122 content_type: article copyright: Copyright © 2014 Elsevier Ltd. All rights reserved.status: publishe