[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126156-en":3,"doc-seo-126156-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126156,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Hydrogen Evolution Reaction of Electrodeposited Ni-W Films in Acidic Medium and Performance Optimization Using Machine Learning","Ni–W alloy films were electrodeposited from a gluconate aqueous bath (pH 5.0) while systematically varying current density and temperature. Although compositions remain similar (~12 at.% W), hydrogen evolution reaction performance in 0.5 M H2SO4 depends strongly on surface morphology. Kinetic analysis from linear sweep voltammetry cycles identifies optimal deposition conditions at 4.8 mA/cm2 and 50 °C. Tafel slopes and overpotentials (η10) after 200 cycles feed a machine-learning model to minimize activity loss over time, enabling superior films with b = 33–45 mV/dec and η10 = 0.09–0.10 V after 200 cycles.","ChemSusChem  \nResearch Article  \n[doi.org/10.1002/cssc.202400444](doi.org/10.1002/cssc.202400444)  \n[www.chemsuschem.org](www.chemsuschem.org)  \nHydrogen Evolution Reaction of Electrodeposited Ni-W Films in Acidic Medium and Performance Optimization Using Machine Learning  \nRoger de Paz-Castany,[a] Konrad Eiler,[a] Aliona Nicolenco,[b] Maria Lekka,[b] Eva García-Lecina,[b] Guillaume Brunin,[c] Gian-Marco Rignanese,[c] David Waroquiers,[c] Thomas Collet,[d] Annick Hubin,[d] and Eva Pellicer*[a]  \nNi􀀀 W alloy films were electrodeposited from a gluconate aqueous bath at pH = 5.0, at varying current densities and temperatures. While there is little to no difference in composition, i. e., all films possess ~12 at.% W, their activity at hydrogen evolution reaction (HER) in acidic medium is greatly influenced by differences in surface morphology. The kinetics of HER in 0.5 M H2SO4 indicates that the best performing film was obtained at a current density of 􀀀 4.8 mA/cm2 and 50 °C. The Tafel slopes (b) and the overpotentials at a geometric current  \ndensity of 􀀀 10 mA/cm2 (η10) obtained for 200 cycles of linear sweep voltammetry (LSV) from a set of films deposited using different parameters were fed into a machine learning algorithm to predict optimum deposition conditions to minimize b, η10, and the degradation of samples over time. The optimum deposition conditions predicted by the machine learning model led to the electrodeposition of Ni􀀀 W films with superior performance, exhibiting b of 33–45 mV/dec and an η10 of 0.09–0.10 V after 200 LSVs.  \nIntroduction  \nThe need to reduce our dependence on fossil fuels with the help of green energy and the development of efficient solutions for energy storage are among the most urgent challenges of current times. In this context, hydrogen technology emerges asa key factor, offering a clean fuel with a high energy density that can be produced through water electrolysis. However, the widespread usage of hydrogen as an energy vector is hindered by a significant bottleneck, namely, most electrodes contain platinum group metals (PGM) due to their high catalytic activity and stability. The presence of PGMs increases the cost of the catalyst due to their scarcity which is expected to increase in the following years.[1] This impending cost escalation makes hydrogen and fuel cell technology too costly to be implemented on a larger scale at the current state of the art. Hence, there is a need to develop alternative catalysts not reliant on PGMs. For hydrogen production in an electrolyzer, the main  \n[a] Physics Department, Universitat Autònoma de Barcelona, Bellaterra, Cerdanyola del Vallès, Spain  \n[b] CIDETEC, Basque Research and Technology Alliance (BRTA), San Sebastián, Spain  \n[c] Matgenix, A6K Advanced Engineering Centre, Charleroi, Belgium  \n[d] SURF Department, Vrije Universiteit Brussel, Brussels, Belgium Correspondence: Eva Pellicer, Physics Department, Universitat Autònomade Barcelona, Campus de la UAB, 08193 Bellaterra, Cerdanyola del Vallès, Spain.  \n[Email:](Email: Eva.Pellicer@uab.cat)[ Eva.Pellicer@uab.cat](Email: Eva.Pellicer@uab.cat)  \n Supporting Information for this article is available on the WWW under [https://doi.org/10.1002/cssc.202400444](https://doi.org/10.1002/cssc.202400444)  \n © 2024 The Authors. ChemSusChem published by Wiley-VCH GmbH. This isan open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nreaction is the hydrogen evolution reaction (HER) . Depending on the electrolyzer, this reaction can take place in either acidic or alkaline media, though the acidic process is usually more efficient.  \nNickel is a very good candidate for HER due to its catalytic activity, price, and abundance. [2–5] When evaluating the hydrogen-metal bond energies of various common metals, it becomes evident that Ni possesses a nearly ideal value, presenting ","cbCaij9ogCFPIumQ","https://ap.wps.com/l/cbCaij9ogCFPIumQ","pdf",5940801,5,1,13,"English","en",105,"# Introduction\n## Motivation for non-PGM HER catalysts\n## Ni–W alloy rationale and deposition background\n# Methods and Performance Optimization\n## Electrodeposition conditions and film characteristics\n## HER kinetics in acidic medium\n## Machine-learning-based optimization","[{\"question\":\"How were Ni–W films prepared for HER testing?\",\"answer\":\"Ni–W alloy films were electrodeposited from a gluconate aqueous bath at pH 5.0, with varying current densities and temperatures.\"},{\"question\":\"What determines hydrogen evolution reaction activity in acidic medium?\",\"answer\":\"Even when composition is similar, HER activity in 0.5 M H2SO4 is strongly influenced by differences in surface morphology.\"},{\"question\":\"How does machine learning improve Ni–W film performance?\",\"answer\":\"Tafel slopes and η10 values after 200 LSV cycles are used as inputs to a machine-learning algorithm to predict deposition conditions that minimize b, η10, and degradation over time.\"}]","Hydrogen Evolution Reaction of Electrodeposited Ni-W Films in Acidic Medium and Performance Optimization Using Machine Learning | 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were Ni–W films prepared for HER testing?","Question",{"text":77,"@type":78},"Ni–W alloy films were electrodeposited from a gluconate aqueous bath at pH 5.0, with varying current densities and temperatures.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What determines hydrogen evolution reaction activity in acidic medium?",{"text":82,"@type":78},"Even when composition is similar, HER activity in 0.5 M H2SO4 is strongly influenced by differences in surface morphology.",{"name":84,"@type":75,"acceptedAnswer":85},"How does machine learning improve Ni–W film performance?",{"text":86,"@type":78},"Tafel slopes and η10 values after 200 LSV cycles are used as inputs to a machine-learning algorithm to predict deposition conditions that minimize b, η10, and degradation over 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