Deep learning design of functionally graded porous electrode of proton exchange membrane fuel cells

For the next generation of proton exchange membrane (PEM) fuel cells, the conventional electrode with uniform distribution of functional components is urged to be replaced by functional graded electrode for the prominent performance, efficiency and avoid exacerbated catalyst cost. Due to the complex and non-linear behaviours of PEM fuel cell system, rapid and effective computational model and optimisation algorithm are required to handle such a complex relationship between electrode design parameters and cell performance. In this work, a multi-physics model with multi-directionally graded electrode is developed, in which a deep machine learning approach is embedded, to create a surrogate model for multi-objective optimisation empowered by non-dominated sort genetic algorithm (NSGA-II). A robust prediction deep neural network model with the mean square error lower than 0.01 is obtained from training and then coupling with NSGA-II to evaluate and optimise the fuel cell performances and cost. Remarkably, the Pareto front is successfully defining the trade-off relationship between the objectives where it aids to identify an optimum point where it satisfies the cost effectiveness while maintaining relatively high cell performances. Our work presents a promising strategy to optimise the fuel cell system with underlying interaction and allow rapid and accurate prediction and optimisation.</p

MOESM2 of Recombinant production of medium- to large-sized peptides in Escherichia coli using a cleavable self-aggregating tag

Additional file 2. MALDI-TOF MS analyses of target peptides

MOESM1 of Recombinant production of medium- to large-sized peptides in Escherichia coli using a cleavable self-aggregating tag

Additional file 1. Peptide sequences

Anode partial flooding modelling of proton exchange membrane fuel cells: Model development and validation

A two-dimensional along-the-channel CFD (computational fluid dynamic) model, coupled with a two-phase flow model of liquid water and gas transport for a PEM (proton exchange membrane) fuel cell is described. The model considers non-isothermal operation and thus the non-uniform temperature distribution in the cell structure. Water phase-transfer between the vapour, liquid water and dissolved phase is modelled with the combinational transport mechanism through the membrane. Liquid water saturation is simulated inside the electrodes and channels at both the anode and cathode sides. Three types of models are compared for the HOR (hydrogen oxidation reaction) and ORR (oxygen reduction reaction) in catalyst layers, including Butler–Volmer (B–V), liquid water saturation corrected B–V and agglomerate mechanisms. Temperature changes in MEA (membrane electrode assembly) and channels due to electrochemical reaction, ohmic resistance and water phase-transfer are analysed as a function of current density. Nonlinear relations of liquid water saturations with respect to current densities at both the anode and cathode are regressed. At low and high current densities, liquid water saturation at the anode linearly increases as a consequence of the linear increase of liquid water saturation at the cathode. In contrast, exponential relation is found to be more accurate at medium current densities

Machine learning based techno-economic process optimisation for CO₂ capture via enhanced weathering

This work evaluated the practicability and economy of the enhanced weathering (EW)-based CO2 capture in series packed bubble column (S-PBC) contactors operated with different process configurations and conditions. The S-PBC contactors are designed to fully use the advantages of abundant seawater and highly efficient freshwater through a holistic M4 model, including multi-physics, machine learning, multi-variable and multi-objective optimisation. An economic analysis is then performed to investigate the cost of different S-PBC configurations. A data-driven surrogate model based on a novel machine learning algorithm, extended adaptive hybrid functions (E-AHF), is implemented and trained by the data generated by the physics-based models. GA and NSGA-II are applied to perform single- and multi-objective optimisation to achieve maximum CO2 capture rate (CR) and minimum energy consumption (EC) with the optimal values of eight design variables. The R2 for the prediction of CR and EC is higher than 0.96 and the relative errors are lower than 5%. The M4 model has proven to be an efficient way to perform multi-variable and multi-objective optimisation, that significantly reduces computational time and resources while maintaining high prediction accuracy. The trade-off of the maximum CR and minimum EC is presented by the Pareto front, with the optimal values of 0.1014 kg h−1 for CR and 6.1855 MJ kg−1CO2 for EC. The calculated net cost of the most promising S-PBC configuration is around 400 $t−1CO2, which is about 100$ t−1CO2 lower than the net cost of current direct air capture (DAC), but compromised by slower CO2 capture rate.</p

Oxygen Vacancy Enhanced Proton Transfer to Boost Carbamate Decomposition Kinetics with Tunable Heterostructure Ni/NiO

Catalytic carbamate decomposition is a feasible option for reducing the heat duty of amine solvent regeneration during the chemisorption of CO2 capture; advanced material with excellent proton transfer and exchange performance is crucial to boost the decomposition kinetics in an alkaline environment. Here, we prepared magnetic heterostructure Ni/NiO nanocatalysts with tunable Ni(0) nanoparticles and NiO support. The heterointerface of the proposed materials creates abundant surface oxygen vacancies (OVs) and offers abundant reactive active sites ascribed to the special electron transfer scheme of Ni0–NiO. The generated surface hydroxyls and unsaturated coordinated Ni, respectively, provide transferable protons and electrons, involved in the deprotonation of RNH3+ and C–N break of RNHCOO–. Thus, the obtained nanomaterials achieved considerably improved CO2 desorption of up to 3 mmol/min for a CO2-saturated monoethanolamine solvent, representing a substantial (approximately 50%) increase over the catalyst-free case. The reinforcement mechanism of OV generation by the Ni/NiO heterostructure and the induced proton transfer were revealed through in situ spectroscopic measurement and theoretical calculations. The results verified that the OVs stimulate the production of surface hydroxyls and water-assisted proton hopping, providing an advantageous condition for carbamate decomposition

Oxygen Vacancy Enhanced Proton Transfer to Boost Carbamate Decomposition Kinetics with Tunable Heterostructure Ni/NiO

Catalytic carbamate decomposition is a feasible option for reducing the heat duty of amine solvent regeneration during the chemisorption of CO2 capture; advanced material with excellent proton transfer and exchange performance is crucial to boost the decomposition kinetics in an alkaline environment. Here, we prepared magnetic heterostructure Ni/NiO nanocatalysts with tunable Ni(0) nanoparticles and NiO support. The heterointerface of the proposed materials creates abundant surface oxygen vacancies (OVs) and offers abundant reactive active sites ascribed to the special electron transfer scheme of Ni0–NiO. The generated surface hydroxyls and unsaturated coordinated Ni, respectively, provide transferable protons and electrons, involved in the deprotonation of RNH3+ and C–N break of RNHCOO–. Thus, the obtained nanomaterials achieved considerably improved CO2 desorption of up to 3 mmol/min for a CO2-saturated monoethanolamine solvent, representing a substantial (approximately 50%) increase over the catalyst-free case. The reinforcement mechanism of OV generation by the Ni/NiO heterostructure and the induced proton transfer were revealed through in situ spectroscopic measurement and theoretical calculations. The results verified that the OVs stimulate the production of surface hydroxyls and water-assisted proton hopping, providing an advantageous condition for carbamate decomposition

data_sheet_4.XLSX

<p>Radial glial cells (RGCs) are the main macroglia in the teleost brain and have established roles in neurogenesis and neurosteroidogenesis. They are the only brain cell type expressing aromatase B (cyp19a1b), the enzyme that synthesizes estrogens from androgen precursors. There are few studies on the regulation of RGC functions, but our previous investigations demonstrated that dopamine stimulates cyp19a1b expression in goldfish RGCs, while secretoneurin A (SNa) inhibits the expression of this enzyme. Here, we determine the range of proteins and cellular processes responsive to SNa treatments in these steroidogenic cells. The focus here is on SNa, because this peptide is derived from selective processing of secretogranin II in magnocellular cells embedded within the RGC-rich preoptic nucleus. Primary cultures of RGCs were treated (24 h) with 10, 100, or 1,000 nM SNa. By using isobaric tagging for relative and absolute quantitation and a Hybrid Quadrupole Obritrap Mass Spectrometry system, a total of 1,363 unique proteins were identified in RGCs, and 609 proteins were significantly regulated by SNa at one or more concentrations. Proteins that showed differential expression with all three concentrations of SNa included H1 histone, glutamyl-prolyl-tRNA synthetase, Rho GDP dissociation inhibitor γ, vimentin A2, and small nuclear ribonucleoprotein-associated protein. At 10, 100, and 1,000 nM SNa, there were 5, 195, and 489 proteins that were downregulated, respectively, whereas the number of upregulated proteins were 72, 44, and 51, respectively. Subnetwork enrichment analysis of differentially regulated proteins revealed that processes such as actin organization, cytoskeleton organization and biogenesis, apoptosis, mRNA processing, RNA splicing, translation, cell growth, and proliferation are regulated by SNa based on the proteomic response. Moreover, we observed that, at the low concentration of SNa, there was an increase in the abundance of proteins involved in cell growth, proliferation, and migration, whereas higher concentration of SNa appeared to downregulate proteins involved in these processes, indicating a dose-dependent proteome response. At the highest concentration of SNa, proteins linked to the etiology of diseases of the central nervous system (brain injuries, Alzheimer disease, Parkinson’s disease, cerebral infraction, brain ischemia) were also differentially regulated. These data implicate SNa in the control of cell proliferation and neurogenesis.</p

data_sheet_3.XLSX

<p>Radial glial cells (RGCs) are the main macroglia in the teleost brain and have established roles in neurogenesis and neurosteroidogenesis. They are the only brain cell type expressing aromatase B (cyp19a1b), the enzyme that synthesizes estrogens from androgen precursors. There are few studies on the regulation of RGC functions, but our previous investigations demonstrated that dopamine stimulates cyp19a1b expression in goldfish RGCs, while secretoneurin A (SNa) inhibits the expression of this enzyme. Here, we determine the range of proteins and cellular processes responsive to SNa treatments in these steroidogenic cells. The focus here is on SNa, because this peptide is derived from selective processing of secretogranin II in magnocellular cells embedded within the RGC-rich preoptic nucleus. Primary cultures of RGCs were treated (24 h) with 10, 100, or 1,000 nM SNa. By using isobaric tagging for relative and absolute quantitation and a Hybrid Quadrupole Obritrap Mass Spectrometry system, a total of 1,363 unique proteins were identified in RGCs, and 609 proteins were significantly regulated by SNa at one or more concentrations. Proteins that showed differential expression with all three concentrations of SNa included H1 histone, glutamyl-prolyl-tRNA synthetase, Rho GDP dissociation inhibitor γ, vimentin A2, and small nuclear ribonucleoprotein-associated protein. At 10, 100, and 1,000 nM SNa, there were 5, 195, and 489 proteins that were downregulated, respectively, whereas the number of upregulated proteins were 72, 44, and 51, respectively. Subnetwork enrichment analysis of differentially regulated proteins revealed that processes such as actin organization, cytoskeleton organization and biogenesis, apoptosis, mRNA processing, RNA splicing, translation, cell growth, and proliferation are regulated by SNa based on the proteomic response. Moreover, we observed that, at the low concentration of SNa, there was an increase in the abundance of proteins involved in cell growth, proliferation, and migration, whereas higher concentration of SNa appeared to downregulate proteins involved in these processes, indicating a dose-dependent proteome response. At the highest concentration of SNa, proteins linked to the etiology of diseases of the central nervous system (brain injuries, Alzheimer disease, Parkinson’s disease, cerebral infraction, brain ischemia) were also differentially regulated. These data implicate SNa in the control of cell proliferation and neurogenesis.</p

data_sheet_1.XLSX

<p>Radial glial cells (RGCs) are the main macroglia in the teleost brain and have established roles in neurogenesis and neurosteroidogenesis. They are the only brain cell type expressing aromatase B (cyp19a1b), the enzyme that synthesizes estrogens from androgen precursors. There are few studies on the regulation of RGC functions, but our previous investigations demonstrated that dopamine stimulates cyp19a1b expression in goldfish RGCs, while secretoneurin A (SNa) inhibits the expression of this enzyme. Here, we determine the range of proteins and cellular processes responsive to SNa treatments in these steroidogenic cells. The focus here is on SNa, because this peptide is derived from selective processing of secretogranin II in magnocellular cells embedded within the RGC-rich preoptic nucleus. Primary cultures of RGCs were treated (24 h) with 10, 100, or 1,000 nM SNa. By using isobaric tagging for relative and absolute quantitation and a Hybrid Quadrupole Obritrap Mass Spectrometry system, a total of 1,363 unique proteins were identified in RGCs, and 609 proteins were significantly regulated by SNa at one or more concentrations. Proteins that showed differential expression with all three concentrations of SNa included H1 histone, glutamyl-prolyl-tRNA synthetase, Rho GDP dissociation inhibitor γ, vimentin A2, and small nuclear ribonucleoprotein-associated protein. At 10, 100, and 1,000 nM SNa, there were 5, 195, and 489 proteins that were downregulated, respectively, whereas the number of upregulated proteins were 72, 44, and 51, respectively. Subnetwork enrichment analysis of differentially regulated proteins revealed that processes such as actin organization, cytoskeleton organization and biogenesis, apoptosis, mRNA processing, RNA splicing, translation, cell growth, and proliferation are regulated by SNa based on the proteomic response. Moreover, we observed that, at the low concentration of SNa, there was an increase in the abundance of proteins involved in cell growth, proliferation, and migration, whereas higher concentration of SNa appeared to downregulate proteins involved in these processes, indicating a dose-dependent proteome response. At the highest concentration of SNa, proteins linked to the etiology of diseases of the central nervous system (brain injuries, Alzheimer disease, Parkinson’s disease, cerebral infraction, brain ischemia) were also differentially regulated. These data implicate SNa in the control of cell proliferation and neurogenesis.</p