Advancements,strategies,and prospects of solid oxide electrolysis cells(SOECs):Towards enhanced performance and large-scale sustainable hydrogen production

Solid oxide electrolysis cells(SOECs)represent a crucial stride toward sustainable hydrogen generation,and this review explores their current scientific challenges,significant advancements,and potential for large-scale hydrogen production.In SOEC technology,the application of innovative fabrication tech-niques,doping strategies,and advanced materials has enhanced the performance and durability of these systems,although degradation challenges persist,implicating the prime focus for future advancements.Here we provide in-depth analysis of the recent developments in SOEC technology,including Oxygen-SOECs,Proton-SOECs,and Hybrid-SOECs.Specifically,Hybrid-SOECs,with their mixed ionic conducting electrolytes,demonstrate superior efficiency and the concurrent production of hydrogen and oxygen.Coupled with the capacity to harness waste heat,these advancements in SOEC technology present signif-icant promise for pilot-scale applications in industries.The review also highlights remarkable achieve-ments and potential reductions in capital expenditure for future SOEC systems,while elaborating on the micro and macro aspects of sOECs with an emphasis on ongoing research for optimization and scal-ability.It concludes with the potential of SOEC technology to meet various industrial energy needs and its significant contribution considering the key research priorities to tackle the global energy demands,ful-fillment,and decarbonization efforts.

High Purity Hydrogen Production by Metal Hydride System:A Parametric Study Based on the Lumped Parameter Model

The simulation of hydrogen purification in a mixture gas of hydrogen/carbon dioxide (H2/CO2) by metal hydride system was reported.The lumped parameter model was developed and validated.The validated model was implemented on the software Matlab/Simulink to simulate the present investigation.The simulation results demonstrate that the purification efficiency depends on the external pressure and the venting time.An increase in the external pressure and enough venting time makes it possible to effectively remove the impurities from the tank during the venting process and allows to desorb pure hydrogen.The impurities are partially removed from the tank for low external pressure and venting time during the venting process and the desorbed hydrogen is contaminated.Other parameters such as the overall heat transfer coefficient,solid material mass,supply pressure,and the ambient temperature influence the purification system in terms of the hydrogen recovery rate.An increase in the overall heat transfer coefficient,solid material mass,and supply pressure improves the hydrogen recovery rate while a decrease in the ambient temperature enhances the recovery rate.

AB047.Membrane binding of S100A10 protein and AHNAK peptide involved in cell membrane repair

Background:The S100A10 protein might be an early biomarker of diabetes development leading to diabetic retinopathy.The protein complex S100A10/annexin A2 allows the recruitment of the C-terminal of AHNAK protein(AHNAK C-ter peptide)to the membrane in presence of calcium,before forming a platform which can initiate membrane repair.However,no molecular data are currently available on membrane binding of the different proteins involved in this complex.We aim to study the membrane binding of S100A10,AHNAK C-ter peptide and their complex to better understand their roles in cell membrane repair process.Methods:Firstly,S100A10 will be overexpressed and purified by affinity chromatography and AHNAK C-ter peptide will be synthesized.Langmuir monolayers membrane model will then be used to characterize the interactions between these proteins and different phospholipids found in membranes.The secondary structure,orientation and membrane organization of these proteins will be studied by Polarization Modulation Infrared Reflection-Absorption Spectroscopy.Their lateral localization will be determined through the influence of these proteins on the physical state of lipids by fluorescence microscopy.Results:The optimization of the overexpression,purification and cleavage of the GST tag procedure to obtain pure S100A10 was completed.Protein identification by mass spectrometry and circular dichroism stability pre-studies were performed.In parallel,AHNAK C-ter peptide was studied by Langmuir monolayer model and the results indicate this peptide prefers lipids with negatively charged polar heads and unsaturated acyl chains.Preliminary solid-state NMR results confirm this phenomenon at 37℃.Conclusions:Our research will complete current knowledge on membrane binding of S100A10 and AHNAK C-ter peptide.We could also identify the conditions leading to modifications of these membrane bindings,and possibly to the loss of protein function.Thus,this project helps to better determine their roles in membrane repair,as well as in other physiological mechanisms in which these proteins are involved.

Marine proteins and peptides:Production,biological activities,and potential applications

Marine protein hydrolysates and peptides have grown in popularity due to their biological activities and robust properties.They are increasingly studied in the functional food,pharmaceutical,and cosmeceutical sectors.This article discusses the current knowledge about preparing protein hydrolysates and peptides from seaweed,seafood,and seafood processing byproducts.Gaps in knowledge and technical expertise required for their industrial integration have been identified.The desire for natural substances to use as functional food has gained prevalence as consumers have become more aware of the adverse side effects of synthetic drugs.Aging-related chronic diseases,including cancer,arteriosclerosis,and diabetes,can be prevented by actively introducing food-based functional ingredients.Marine-derived proteins and peptides still face several hurdles to commercialization,such as scaling up production and maintaining a sustainable supply of raw materials.Further understanding of the physiological functionalities,action mechanisms,and clinical efficacy of these peptides and proteins would facilitate their use in biomedical applications and as functional ingredients in food and cosmetics.

Artificial neural network based optimization of a six-step two-bed pressure swing adsorption system for hydrogen purification

The pressure swing adsorption(PSA)system is widely applied to separate and purify hydrogen from gaseous mixtures.The extended Langmuir equation fitted from the extended Langmuir-Freundlich isotherm has been used to predict the adsorption isothermal of hydrogen and methane on the zeolite 5A adsorbent bed.A six-step two-bed PSA model for hydrogen purification is developed and validated by comparing its simulation results with other works.The effects of the adsorption pressure,the P/F ratio,the adsorption step time and the pressure equalization time on the performance of the hydrogen purification system are studied.A four-step two-bed PSA model is taken into consideration,and the six-step PSA system shows higher about 13%hydrogen recovery than the four-step PSA system.The performance of the vacuum pressure swing adsorption(VPSA)system is compared with that of the PSA system,the VPSA system shows higher hydrogen purity than the PSA system.Based on the validated PSA model,a dataset has been produced to train the artificial neural network(ANN)model.The effects of the number of neurons in the hidden layer and the number of samples used for training ANN model on the predicted performance of ANN model are investigated.Then,the well-trained ANN model with 6 neurons in the hidden layer is applied to predict the performance of the PSA system for hydrogen purification.Multi-objective optimization of hydrogen purification system is performed based on the trained ANN model.The artificial neural network can be considered as a very effective method for predicting and optimizing the performance of the PSA system for hydrogen purification.

3D modeling of transformation of gaseous pollutants in the high-pressure turbine of an aircraft engine

Aircraft emissions contribute to global climate change and regional air pollution near airports.Understanding the formation and the transformation of emissions in the aircraft engine is essential to properly quantify the environmental impact and air pollution.However,precise investigation of chemical process in the turbine is challenging because of the complexity of the transformation process in the complex flow relating to the moving blade at high temperature and high pressure.We present here,the first published model study of 3D chemical formations inside a high-pressure turbine and first time to compare three numerical solutions(1D,2D and 3D calculations)of transformation of trace species inside an aircraft engine.The model has simulated the evolution of principal precursor pollutant gases(NOx and SOx)and other species(hydrogen,oxygen species and carbon oxides).Our results also indicated strong dissimilarities in chemical transformations of 3D calculations.In comparing the three solutions,the results obtained showed that the difference of mole fractions of species can be under predicted by 75%between 1D and 2D calculations and in the comparison of 2D and 3D calculation,the under predicted difference may be 90%.

Role of marine natural products in the development of antiviral agents against SARS‑CoV‑2:potential and prospects

A novel coronavirus,known as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has surfaced and caused global concern owing to its ferocity.SARS-CoV-2 is the causative agent of coronavirus disease 2019;however,it was only discovered at the end of the year and was considered a pandemic by the World Health Organization.Therefore,the develop-ment of novel potent inhibitors against SARS-CoV-2 and future outbreaks is urgently required.Numerous naturally occurring bioactive substances have been studied in the clinical setting for diverse disorders.The intricate infection and replication mechanism of SARS-CoV-2 offers diverse therapeutic drug targets for developing antiviral medicines by employing natural products that are safer than synthetic compounds.Marine natural products(MNPs)have received increased attention in the development of novel drugs owing to their high diversity and availability.Therefore,this review article investigates the infection and replication mechanisms,including the function of the SARS-CoV-2 genome and structure.Furthermore,we highlighted anti-SARS-CoV-2 therapeutic intervention efforts utilizing MNPs and predicted SARS-CoV-2 inhibitor design.

Impact of severe plastic deformation on kinetics and thermodynamics of hydrogen storage in magnesium and its alloys

Magnesium and its alloys are the most investigated materials for solid-state hydrogen storage in the form of metal hydrides,but there are still unresolved problems with the kinetics and thermodynamics of hydrogenation and dehydrogenation of this group of materials.Severe plastic deformation(SPD)methods,such as equal-channel angular pressing(ECAP),high-pressure torsion(HPT),intensive rolling,and fast forging,have been widely used to enhance the activation,air resistance,and hydrogenation/dehydrogenation kinetics of Mg-based hydrogen storage materials by introducing ultrafine/nanoscale grains and crystal lattice defects.These severely deformed materials,particularly in the presence of alloying additives or second-phase nanoparticles,can show not only fast hydrogen absorption/desorption kinetics but also good cycling stability.It was shown that some materials that are apparently inert to hydrogen can absorb hydrogen after SPD processing.Moreover,the SPD methods were effectively used for hydrogen binding-energy engineering and synthesizing new magnesium alloys with low thermodynamic stability for reversible low/room-temperature hydrogen storage,such as nanoglasses,high-entropy alloys,and metastable phases including the high-pressureγ-MgH2 polymorph.This work reviews recent advances in the development of Mg-based hydrogen storage materials by SPD processing and discusses their potential in future applications.

Predominance of non-adiabatic effects in zero-point renormalization of the electronic band gap

Electronic and optical properties of materials are affected by atomic motion through the electron–phonon interaction:not only band gaps change with temperature,but even at absolute zero temperature,zero-point motion causes band-gap renormalization.We present a large-scale first-principles evaluation of the zero-point renormalization of band edges beyond the adiabatic approximation.For materials with light elements,the band gap renormalization is often larger than 0.3 eV,and up to 0.7 eV.This effect cannot be ignored if accurate band gaps are sought.For infrared-active materials,global agreement with available experimental data is obtained only when non-adiabatic effects are taken into account.They even dominate zero-point renormalization for many materials,as shown by a generalized Fröhlich model that includes multiple phonon branches,anisotropic and degenerate electronic extrema,whose range of validity is established by comparison with first-principles results.

Remote sensing experiments for earth system science

The Earth system is an integrated system that can be divided into six main subsystems:geosphere,atmosphere,hydrosphere,cryosphere,biosphere,and anthrosphere.These subsystems are interconnected through the flows of global energy,water,and carbon,which are fundamental constituent cycles within the Earth system.To improve our predictive understanding of the subsystem on our changing planet,there is a need to better represent these subsystems through modelling and observations.The central role of these fundamental cycles has prompted scientific institutions or meteorological centers to develop digital platforms dedicated to the integration,modelling,and assimilation of Earth observation data,e.g.,Big Earth Data Science Engineering(Guo,Li,and Qiu 2020).

Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.

Modelling Anti-Corrosion Coating Performance of Metallic Bipolar Plates for PEM Fuel Cells:A Machine Learning Approach

Proton exchange membrane(PEM)fuel cells have significant potential for clean power generation,yet challenges remain in enhancing their performance,durability,and cost-effectiveness,particularly concerning metallic bipolar plates,which are pivotal for lightweight compact fuel cell stacks.Protective coatings are commonly employed to combat metallic bipolar plate corrosion and enhance water management within stacks.Conventional methods for predicting coating performance in terms of corrosion resistance involve complex physical-electrochemical modelling and extensive experimentation,with significant time and cost.In this study machine learning techniques are employed to model metallic bipolar plate coating performance,diamond-like-carbon coatings of varying thicknesses deposited on SS316L are considered,and coating performance is evaluated using potentiodynamic polarization and electrochemical impedance spectroscopy.The obtained experimental data is split into two datasets for machine learning modelling:one predicting corrosion current density and another predicting impedance parameters.Machine learning models,including extreme gradient boosting(XGB)and artificial neural networks(ANN),are developed,and optimized to predict coating performance attributes.Data preprocessing and hyperparameter tuning are carried out to enhance model accuracy.Results show that ANN outperforms XGB in predicting corrosion current density,achieving an R2>0.98,and accurately predicting impedance parameters with an R2>0.99,indicating that the models developed are very promising for accurate prediction of the corrosion performance of coated metallic bipolar plates for PEM fuel cells.

All inkjet-printed perovskite-based bolometers

We show flexible bolometer devices produced entirely using digital inkjet printing on polymer substrates.The bolometers consist of a silver interdigital electrode thermistor covered with a methylammonium lead trihalide perovskite absorber layer which shows good absorber characteristics at visible wavelengths.Both the standalone thermistor and the complete bolometer devices show polymer PTC thermistor-like behavior over a temperature range of 17 to 36℃,with a change in resistance up-to six orders of magnitude over this temperature range.The addition of the perovskite absorber to the thermistor structure provides the illumination-dependent behavior proper to bolometers.

A novel photographic approach for monitoring the structural heterogeneity and diversity of grassland ecosystems

Aims Studies that investigate the space-filling heterogeneity of biological struc-tures in plant communities remain scarce.The main objective of this study was to evaluate the relationship between newly developed photo-graphic measures of structural heterogeneity in digital images and plant species composition in the context of a long-term grassland experiment.Methods We tested a close-range photographic protocol using measures of structural heterogeneity in gray-tone images,namely mean infor-mation gain(MIG)and spatial anisotropy,to assess differences in the compositional(species richness)and functional characteristics(plant height and flowering)of 78 managed grassland communities.We also implemented a random placement model of community assembly to explore the links between our measures of structural complexity and the geometric pattern of plant communities.Important Findings MIG and spatial anisotropy correlated with the growth and species richness of grassland communities.Simulations showed that struc-tural heterogeneity in gray-tone digital images is a function of the size distribution and orientation pattern of plant modules.This easy,fast and non-destructive methodological approach could eventually serve to monitor the diversity and integrity of various ecosystems at different resolutions across space and time.