id,title,description,date_created,date_modified,date_published,original_publication_date,publication_doi,provider,is_published,reviews_state,version,is_latest_version,preprint_doi,license,tags_list,tags_data,contributors_list,contributors_data,first_author,subjects_list,subjects_data,download_url,has_coi,conflict_of_interest_statement,has_data_links,has_prereg_links,prereg_links,prereg_link_info,last_updated
ycmn4_v1,Age Ain’t Just a Number: Leveraging Recurrent Neural Networks to Predict Fuel Cell Degradation,"The long-term durability is beside costs currently the biggest
challenge for the commercialization of Proton Exchange Membrane Fuel Cells
(PEMFC). Precise monitoring mechanisms that can identify anomalies at an early
stage are essential to support the long-term operation. Cell voltage is a suitable
monitoring parameter, but its prediction is challenging due to its non-linear
dependence on numerous different operating conditions as well as on the history
of the fuel cell. An existing machine learning approach divides the prediction
task into two components: a stationary model and a conceptual degradation
model. While the stationary model has been already successfully developed, the
degradation model only has been proposed in theory. This work addresses this
gap by developing and implementing a degradation model that complements the
existing stationary model. A methodology is devised to calculate the targets for
the degradation model based on the stationary model. Subsequently, a foundation
model is trained using real data, testing different neural network structures.
To ensure practical applicability, transfer learning is employed to evaluate the
generalizability of the foundation model to three different data sets. The results
demonstrate that the newly developed degradation model successfully integrates
with the existing fuel cell monitoring approach, solving issues in prior work and
leading to significant improvements in long-term fuel cell monitoring.",2025-04-03T15:35:25.665904,2025-04-03T15:48:22.793738,2025-04-03T15:47:31.404737,,,ecsarxiv,1,accepted,1,1,https://doi.org/10.1149/osf.io/ycmn4_v1,CC-By Attribution-NonCommercial-NoDerivatives 4.0 International,Anomaly Detection; Digital Twin; Fuel Cells; Machine Learning; Monitoring,"[""Anomaly Detection"", ""Digital Twin"", ""Fuel Cells"", ""Machine Learning"", ""Monitoring""]",Laurin Holz; Lukas Klass; Alexander Kabza; Frank Sehnke; Markus Hölzle,"[{""id"": ""xrm83"", ""name"": ""Laurin Holz"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""f5sru"", ""name"": ""Lukas Klass"", ""index"": 1, ""orcid"": ""0000-0002-1379-3607"", ""bibliographic"": true}, {""id"": ""b5xru"", ""name"": ""Alexander Kabza"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""sghwf"", ""name"": ""Frank Sehnke"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""t6cga"", ""name"": ""Markus H\u00f6lzle"", ""index"": 4, ""orcid"": null, ""bibliographic"": true}]",Laurin Holz,Engineering; Physical Sciences and Mathematics; Computational Engineering; Energy; Computer Sciences; Fuel Cells; Artificial Intelligence and Robotics,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ae4667e6000f98dd9d"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""5ae728af4667e6000f98dda5"", ""text"": ""Computational Engineering""}, {""id"": ""5ae728b24667e6000f98dde3"", ""text"": ""Energy""}, {""id"": ""5ae728b44667e6000f98de41"", ""text"": ""Computer Sciences""}, {""id"": ""5ae728b54667e6000f98de47"", ""text"": ""Fuel Cells""}, {""id"": ""5ae728b74667e6000f98de87"", ""text"": ""Artificial Intelligence and Robotics""}]",https://osf.io/download/67eeaae83ee99dbee16dde9e,0,,not_applicable,not_applicable,[],,2025-04-09T21:06:18.944586
xhn73_v3,Open-circuit Voltage Anomalies in Yttria-stabilized Zirconia and Samaria-doped Ceria Bilayered Electrolytes (Considering Jarzynski’s Equality in Wagner’s Equation),"The OCV (open-circuit voltage) using SDC (samaria-doped ceria) electrolytes is explained by Wagner’s equation. According to this theory, even if there is no external current, there are two internal currents created by electrons and oxygen ions. Due to the ohmic loss caused by the internal ionic short-circuit current, the OCV is 0.80 V, which is lower than the theoretical voltage of 1.15 V. Coating a thin electron-blocking layer of YSZ (yttria-stabilized zirconia) onto the SDC electrolyte improves the OCV. However, while YSZ films deposited on the anode side are very effective, but YSZ films deposited on the cathode side are not nearly as effective as those deposited on the anode side. Thus, we proposed experiments to confirm whether electronic leakage currents can be blocked by YSZ films deposited on the cathode side. A polished YSZ electrolyte (500 m thickness) on the cathode side and a polished SDC electrolyte (970 m) on the anode side were physically contacted. By measuring the transient process, we showed that a high OCV (819 mV) was not due to stopping the electronic leakage current. Using Jarzynski’s equality, we explained the voltage loss (0.35 V) during ion hopping in SDC electrolytes.",2025-04-02T11:29:44.611181,2025-04-02T12:20:21.648407,2025-04-02T12:19:43.611601,2019-01-29T15:00:00,,ecsarxiv,1,accepted,3,1,https://doi.org/10.1149/osf.io/xhn73_v3,CC-By Attribution 4.0 International,SOFC; Wagner's equation; doped Ceria,"[""SOFC"", ""Wagner's equation"", ""doped Ceria""]",Tomofumi Miyahita,"[{""id"": ""ma3cy"", ""name"": ""Tomofumi Miyahita"", ""index"": 0, ""orcid"": ""0000-0001-5046-5875"", ""bibliographic"": true}]",Tomofumi Miyahita,Engineering; Materials Science and Engineering; Physical Sciences and Mathematics; Chemistry; Materials Chemistry; Solid Oxide Fuel Cells,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ae4667e6000f98dd9c"", ""text"": ""Materials Science and Engineering""}, {""id"": ""5ae728ae4667e6000f98dd9d"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""5ae728b24667e6000f98ddde"", ""text"": ""Chemistry""}, {""id"": ""5ae728b34667e6000f98de08"", ""text"": ""Materials Chemistry""}, {""id"": ""5ae728b34667e6000f98de19"", ""text"": ""Solid Oxide Fuel Cells""}]",https://osf.io/download/67ed1fc3cf9e3a761f8b805e,0,,available,available,"[""https://iopscience.iop.org/article/10.1149/MA2021-01412048mtgabs""]",,2025-04-09T21:06:17.633073
xhn73_v2,Open-circuit Voltage Anomalies in Yttria-stabilized Zirconia and Samaria-doped Ceria Bilayered Electrolytes (Considering Jarzynski’s Equality in Wagner’s Equation),"The OCV (open-circuit voltage) using SDC (samaria-doped ceria) electrolytes is explained by Wagner’s equation. According to this theory, even if there is no external current, there are two internal currents created by electrons and oxygen ions. Due to the ohmic loss caused by the internal ionic short-circuit current, the OCV is 0.80 V, which is lower than the theoretical voltage of 1.15 V. Coating a thin electron-blocking layer of YSZ (yttria-stabilized zirconia) onto the SDC electrolyte improves the OCV. However, while YSZ films deposited on the anode side are very effective, but YSZ films deposited on the cathode side are not nearly as effective as those deposited on the anode side. Thus, we proposed experiments to confirm whether electronic leakage currents can be blocked by YSZ films deposited on the cathode side. A polished YSZ electrolyte (500 m thickness) on the cathode side and a polished SDC electrolyte (970 m) on the anode side were physically contacted. By measuring the transient process, we showed that a high OCV (819 mV) was not due to stopping the electronic leakage current. Using Jarzynski’s equality, we explained the voltage loss (0.35 V) during ion hopping in SDC electrolytes.",2025-03-26T10:26:27.713924,2025-03-26T10:36:41.517622,2025-03-26T10:36:17.532156,2019-01-29T15:00:00,,ecsarxiv,1,accepted,2,1,https://doi.org/10.1149/osf.io/xhn73_v2,CC-By Attribution 4.0 International,SOFC; Wagner's equation; doped Ceria,"[""SOFC"", ""Wagner's equation"", ""doped Ceria""]",Tomofumi Miyahita,"[{""id"": ""ma3cy"", ""name"": ""Tomofumi Miyahita"", ""index"": 0, ""orcid"": ""0000-0001-5046-5875"", ""bibliographic"": true}]",Tomofumi Miyahita,Engineering; Materials Science and Engineering; Physical Sciences and Mathematics; Chemistry; Materials Chemistry; Solid Oxide Fuel Cells,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ae4667e6000f98dd9c"", ""text"": ""Materials Science and Engineering""}, {""id"": ""5ae728ae4667e6000f98dd9d"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""5ae728b24667e6000f98ddde"", ""text"": ""Chemistry""}, {""id"": ""5ae728b34667e6000f98de08"", ""text"": ""Materials Chemistry""}, {""id"": ""5ae728b34667e6000f98de19"", ""text"": ""Solid Oxide Fuel Cells""}]",https://osf.io/download/67e3d6c8ff2e801337ceab35,0,,available,available,"[""https://iopscience.iop.org/article/10.1149/MA2021-01412048mtgabs""]",,2025-04-09T21:06:17.217932
mgujh_v1,A User-Friendly Jupyter Notebook for Simplified Differential Pulse Voltammetry Analysis of Dihydroxy Phenols,"Differential pulse voltammetry (DPV) is a well-established electrochemical technique widely employed for quantitatively determining diverse analytes, particularly within complex matrices. However, the analysis of DPV data, especially in scenarios involving the simultaneous detection of multiple species, can be a complex and time-intensive undertaking.  This article introduces a Jupyter Notebook developed to facilitate the efficient and streamlined analysis of DPV data acquired from the simultaneous detection of dihydroxy phenols.  The notebook offers a user-friendly interface encompassing data import, pre-processing, peak identification, quantification, and visualization functionalities. By automating several key analysis steps, this tool reduces manual effort while simultaneously enhancing the accuracy and reproducibility of the process. Consequently, it offers particular value to researchers engaged in environmental monitoring, food analysis, and related disciplines where the detection of dihydroxy phenols is of critical importance. The complete source code for this tool is freely available and accessible via the following repository: https://github.com/anatarajank/Electrochemical-Sensor-Data-Analyzer",2025-02-05T17:49:27.292137,2025-02-05T18:20:00.214547,2025-02-05T18:19:22.221525,,,ecsarxiv,1,accepted,1,1,https://doi.org/10.1149/osf.io/mgujh_v1,CC-By Attribution 4.0 International,,[],Aravindan Natarajan; Preethi Sankaranarayanan,"[{""id"": ""25enh"", ""name"": ""Aravindan Natarajan"", ""index"": 0, ""orcid"": ""0000-0002-2190-9825"", ""bibliographic"": true}, {""id"": ""m8cbv"", ""name"": ""Preethi Sankaranarayanan"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}]",Aravindan Natarajan,Physical Sciences and Mathematics; Chemistry; Computer Sciences; Numerical Analysis and Scientific Computing; Electroanalytical; Software Engineering; Analytical Chemistry; Electrochemistry,"[{""id"": ""5ae728ae4667e6000f98dd9d"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""5ae728b24667e6000f98ddde"", ""text"": ""Chemistry""}, {""id"": ""5ae728b44667e6000f98de41"", ""text"": ""Computer Sciences""}, {""id"": ""5ae728b54667e6000f98de49"", ""text"": ""Numerical Analysis and Scientific Computing""}, {""id"": ""5ae728b54667e6000f98de52"", ""text"": ""Electroanalytical""}, {""id"": ""5ae728b74667e6000f98de88"", ""text"": ""Software Engineering""}, {""id"": ""5ae728b74667e6000f98de90"", ""text"": ""Analytical Chemistry""}, {""id"": ""5ae728b74667e6000f98de91"", ""text"": ""Electrochemistry""}]",https://osf.io/download/67a3a50dc671ff1901cc917d,0,,available,not_applicable,[],,2025-04-09T21:06:16.299171
bcvw4_v1,Rethinking electrolyzer design for optimal waste-heat utilization,"Green hydrogen and electric heating have received attention as separate solutions to make more sustainable material and energy supply networks. However, their combination has rarely been studied to supply both hydrogen and heat to the industry. The conventional design of electrolyzers overlooks the potential for waste-heat utilization. Moreover, the possibility of upgrading the waste heat using heat pumps has not been sufficiently explored. This work analyzes the benefit of designing electrolyzers for waste-heat utilization, evaluates the combination of low temperature electrolysis and heat pumps under different operating conditions, and compares its performance with other electric heating options. The results show that designing for waste-heat utilization leads to more compact electrolyzers and can reduce cost and emissions. Moreover, if there is a medium-temperature heat demand, waste heat upgrading via heat pumps is preferable to direct use for lower-temperature demand.",2024-11-15T16:55:54.623802,2024-11-15T17:11:03.157173,2024-11-15T17:10:36.154751,,,ecsarxiv,1,accepted,1,1,https://doi.org/10.1149/osf.io/bcvw4,CC-By Attribution 4.0 International,electric heating; heat pumps; hydrogen; low-temperature electrolysis; optimization; process systems engineering; synergy; techno-economic analysis; waste-heat upgrading; waste-heat utilization; water electrolysis,"[""electric heating"", ""heat pumps"", ""hydrogen"", ""low-temperature electrolysis"", ""optimization"", ""process systems engineering"", ""synergy"", ""techno-economic analysis"", ""waste-heat upgrading"", ""waste-heat utilization"", ""water electrolysis""]",Aldwin Lois Galvan Cara; Dominik Bongartz,"[{""id"": ""9r2qm"", ""name"": ""Aldwin Lois Galvan Cara"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""8w6r4"", ""name"": ""Dominik Bongartz"", ""index"": 1, ""orcid"": ""0000-0003-1790-0235"", ""bibliographic"": true}]",Aldwin Lois Galvan Cara,Engineering; Electrochemical Engineering; Energy; Chemical Engineering; Simulation; Mathematical Modeling; Electrolyzers; Other Chemical Engineering,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ad4667e6000f98dd98"", ""text"": ""Electrochemical Engineering""}, {""id"": ""5ae728b24667e6000f98dde3"", ""text"": ""Energy""}, {""id"": ""5ae728b34667e6000f98de09"", ""text"": ""Chemical Engineering""}, {""id"": ""5ae728b34667e6000f98de0c"", ""text"": ""Simulation""}, {""id"": ""5ae728b44667e6000f98de2b"", ""text"": ""Mathematical Modeling""}, {""id"": ""5ae728b64667e6000f98de6f"", ""text"": ""Electrolyzers""}, {""id"": ""5ae728b74667e6000f98de93"", ""text"": ""Other Chemical Engineering""}]",https://osf.io/download/67377d33bbc22d9ef79430fc,0,,not_applicable,not_applicable,[],,2025-04-09T21:06:12.090905
5qrn3_v1,Big Brother is Watching You: Leveraging Artificial Intelligence for Automated Fuel Cell Monitoring (Technical Report),"In this technical report, we present our second-generation digital
twin model for monitoring of fuel cells. This digital twin combines
two data-driven machine learning models: a stationary model for
stationary cell voltage prediction and a degradation model for
degradation correction. This combination of both models results in
a precise probabilistic prediction of the cell voltage over time.
Furthermore, we present a web-based framework for automated fuel
cell monitoring in real time. This framework allows training a digital
twin on existing data and subsequently applying the twin to
automatically check new data for anomalies.",2024-11-13T08:36:45.425136,2024-11-13T10:16:23.671100,2024-11-13T10:13:37.213268,2024-09-28T22:00:00,https://doi.org/10.1149/11405.0645ecst,ecsarxiv,0,withdrawn,1,1,https://doi.org/10.1149/osf.io/5qrn3,CC-By Attribution-NonCommercial-NoDerivatives 4.0 International,Anomaly Detection; Digital Twin; Fuel Cell; Machine Learning; Monitoring; PEMFC,"[""Anomaly Detection"", ""Digital Twin"", ""Fuel Cell"", ""Machine Learning"", ""Monitoring"", ""PEMFC""]",Lukas Klass; Laurin Holz; Lukas König; Alexander Kabza; Frank Sehnke; Katharina Strecker; Markus Hölzle,"[{""id"": ""f5sru"", ""name"": ""Lukas Klass"", ""index"": 0, ""orcid"": ""0000-0002-1379-3607"", ""bibliographic"": true}, {""id"": ""xrm83"", ""name"": ""Laurin Holz"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""h3ap6"", ""name"": ""Lukas K\u00f6nig"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""b5xru"", ""name"": ""Alexander Kabza"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""sghwf"", ""name"": ""Frank Sehnke"", ""index"": 4, ""orcid"": null, ""bibliographic"": true}, {""id"": ""c7ek2"", ""name"": ""Katharina Strecker"", ""index"": 5, ""orcid"": null, ""bibliographic"": true}, {""id"": ""t6cga"", ""name"": ""Markus H\u00f6lzle"", ""index"": 6, ""orcid"": null, ""bibliographic"": true}]",Lukas Klass,Engineering,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}]",,0,,not_applicable,not_applicable,[],,2025-04-09T21:06:12.552729
8ehbw_v1,Early-Stage Techno-Economic Evaluation of Electrochemical Nitrogen Reduction to Ammonia Based on Catalyst Performance,"The direct electrochemical reduction of nitrogen offers a promising alternative to produce ammonia, an important chemical and potential energy carrier. While current research focuses on developing and improving catalysts for this reaction, studies evaluating the process and establishing catalyst performance targets remain limited. We performed a techno-economic analysis to evaluate the process based on the performance of the nitrogen reduction reaction catalyst. As a result, we identify catalyst performance targets: minimal performance levels as a combination of cell potential, Faraday efficiency, and current density required to reach cost parity with benchmark prices. The minimal catalyst performance levels are illustrated via curves that relate the required current density to the Faraday efficiency. For a competitive process, current densities and Faraday efficiencies above 100mAcm−2 and 60 %, respectively, are required. Although some catalyst development studies report sufficiently high Faraday efficiencies, the current densities are well below the required. Contrary to the literature’s emphasis on maximizing the Faraday efficiency, our results underscore the need for higher current densities at sufficiently high Faraday efficiencies. Although parameters such as electricity or benchmark prices change the absolute values of the required catalyst performance, the primary conclusions remain unchanged. This analysis provides clear guidance for future catalyst development.",2024-11-11T08:36:59.470905,2025-01-17T08:30:13.296685,2024-11-11T11:33:45.916774,,,ecsarxiv,1,accepted,1,1,https://doi.org/10.1149/osf.io/8ehbw,CC-By Attribution-NonCommercial-NoDerivatives 4.0 International,Catalyst Evaluation; NRR; Nitrogen Reduction Reaction; Performance Requirements; Power-to-Ammonia; Techno-Economic Analysis,"[""Catalyst Evaluation"", ""NRR"", ""Nitrogen Reduction Reaction"", ""Performance Requirements"", ""Power-to-Ammonia"", ""Techno-Economic Analysis""]",Michael J. Rix; Alexander Mitsos,"[{""id"": ""j586k"", ""name"": ""Michael J. Rix"", ""index"": 0, ""orcid"": ""0009-0008-3915-5073"", ""bibliographic"": true}, {""id"": ""w5bse"", ""name"": ""Alexander Mitsos"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}]",Michael J. Rix,Engineering; Electrochemical Engineering; Physical Sciences and Mathematics; Systems Analysis; Chemistry; Chemical Engineering; Mathematical Modeling; Electrochemistry,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ad4667e6000f98dd98"", ""text"": ""Electrochemical Engineering""}, {""id"": ""5ae728ae4667e6000f98dd9d"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""5ae728b04667e6000f98ddb1"", ""text"": ""Systems Analysis""}, {""id"": ""5ae728b24667e6000f98ddde"", ""text"": ""Chemistry""}, {""id"": ""5ae728b34667e6000f98de09"", ""text"": ""Chemical Engineering""}, {""id"": ""5ae728b44667e6000f98de2b"", ""text"": ""Mathematical Modeling""}, {""id"": ""5ae728b74667e6000f98de91"", ""text"": ""Electrochemistry""}]",https://osf.io/download/6731c2772128ec2f214d6e15,0,,not_applicable,not_applicable,[],,2025-04-09T21:06:23.565578
3b5qw_v1,Seawater Electrolysis at Ultra-High Current Density: A Comparative Analysis of Cylindrical versus Conical Electrodes,"Seawater electrolysis preferentially leans towards Chlorine Evolution Reaction (CER) over Oxygen Evolution Reactions (OER) under conventional conditions, but OER becomes more dominant at sufficiently higher current densities. In this study, we evaluated the effector of cylindrical and conical electrode geometries on CER and hydrogen production at high current density (i.e., >1 A/cm2). We found the point of lowest CER within a voltage range of 40 V to 90 V. Conical electrodes, optimized to reduce CER, produced a magnitude less chloride (502 ppb)  than cylindrical electrodes (1485 ppb) at nearly double the current density (~12 and ~6 A/cm2 respectively). However, this reduction in CER with conical electrodes was accompanied by a 25% decrease in hydrogen production. In addition, both cylindrical and conical electrodes were able to heat 500 ml of seawater by approximately 6-7 degrees Celsius over a two-minute period with cylindrical electrodes heating slightly less than conical electrodes.",2024-11-03T23:00:41.164170,2024-11-04T12:16:03.707137,2024-11-04T12:15:51.174114,2024-11-03T07:00:00,,ecsarxiv,1,accepted,1,1,https://doi.org/10.1149/osf.io/3b5qw,CC-By Attribution-NonCommercial-NoDerivatives 4.0 International,CER; OER; chlorine; electrolysis; energy; graphite; high current density; hydrogen; instrumentation; oxygen; seawater,"[""CER"", ""OER"", ""chlorine"", ""electrolysis"", ""energy"", ""graphite"", ""high current density"", ""hydrogen"", ""instrumentation"", ""oxygen"", ""seawater""]",Søren Tornøe; John Koster; Andy V. Surin; Jacob H. Sands; Nobuhiko Paul Kobayashi,"[{""id"": ""w4ejp"", ""name"": ""S\u00f8ren Torn\u00f8e"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""r8tdy"", ""name"": ""John Koster"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""zf675"", ""name"": ""Andy V. Surin"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""xva89"", ""name"": ""Jacob H. Sands"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""rjxfa"", ""name"": ""Nobuhiko Paul Kobayashi"", ""index"": 4, ""orcid"": ""0000-0002-2721-1057"", ""bibliographic"": true}]",Søren Tornøe,Engineering; Electrochemical Engineering; Other Electrochemical Engineering; Energy; Electrolyzers,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ad4667e6000f98dd98"", ""text"": ""Electrochemical Engineering""}, {""id"": ""5ae728b04667e6000f98ddb2"", ""text"": ""Other Electrochemical Engineering""}, {""id"": ""5ae728b24667e6000f98dde3"", ""text"": ""Energy""}, {""id"": ""5ae728b64667e6000f98de6f"", ""text"": ""Electrolyzers""}]",https://osf.io/download/672800b0696c9d9b1b2e6f55,0,,no,not_applicable,[],,2025-04-09T21:06:20.893876
5u329_v1,Impact of pH on Ethanol Electro-oxidation in Seawater-like Electrolytes: Implications for Ocean-based Mitigation Strategies,"This study investigates the electro-oxidation of ethanol in seawater-like electrolytes with adjusted pH, exploring its potential for CO₂ mitigation strategies. Using a polycrystalline platinum bead as a model catalyst in a conventional three-electrode cell, we demonstrate that pH adjustment significantly influences electrochemical performance, with higher oxidation current densities observed at more alkaline pH values. At pH 12, usable current densities for ethanol oxidation were achieved, attributed to the decreased surface coverage of Cl⁻ ions and increased ethoxy ion concentrations, consistent with observations from similar systems in the literature. However, mass transport limitations emerged at higher potential scan rates, evident from the inversion in peak current densities between pH 13 and pH 14 compared to lower scan rates. Additionally, voltammetric profiles indicated a preference for certain platinum crystallographic faces due to variations in chloride and sulphate binding strength. Notably, potential oscillations, not previously reported under such elevated Cl⁻ concentrations, further support these findings. Tafel analysis in the high potential region (> 1.2 V) revealed that the platinum oxide surface does not become more sensitive to ethanol oxidation with increasing pH. These insights provide an initial understanding of the main opportunities and challenges in studying and applying such systems.",2024-10-13T23:12:25.475673,2024-10-18T16:17:22.122512,2024-10-18T16:17:22.098843,,,ecsarxiv,1,accepted,1,1,https://doi.org/10.1149/osf.io/5u329_v1,CC0 1.0 Universal,,[],Thiago Ferraz; Germano Tremiliosi-Filho; Hamilton Varela,"[{""id"": ""dxbmu"", ""name"": ""Thiago Ferraz"", ""index"": 0, ""orcid"": ""0000-0001-5245-6761"", ""bibliographic"": true}, {""id"": ""v8np9"", ""name"": ""Germano Tremiliosi-Filho"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""h27vk"", ""name"": ""Hamilton Varela"", ""index"": 2, ""orcid"": ""0000-0002-6237-6068"", ""bibliographic"": true}]",Thiago Ferraz,Engineering; Physical Sciences and Mathematics; Chemistry; Energy; Electrocatalysis; Fuel Cells; Electrolyzers; Electrochemistry,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ae4667e6000f98dd9d"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""5ae728b24667e6000f98ddde"", ""text"": ""Chemistry""}, {""id"": ""5ae728b24667e6000f98dde3"", ""text"": ""Energy""}, {""id"": ""5ae728b24667e6000f98dde9"", ""text"": ""Electrocatalysis""}, {""id"": ""5ae728b54667e6000f98de47"", ""text"": ""Fuel Cells""}, {""id"": ""5ae728b64667e6000f98de6f"", ""text"": ""Electrolyzers""}, {""id"": ""5ae728b74667e6000f98de91"", ""text"": ""Electrochemistry""}]",https://osf.io/download/670c54543fb82a20cea5e9d7,0,,not_applicable,not_applicable,[],,2025-04-09T21:06:16.297350
g9p7f_v1,A new Synthesis Route for Long Lasting and Rapid Charging Prussian Blue Analogue for Desalination and Aqueous Batteries,"This work reports a novel synthesis route for a Prussian blue analogue with high-rate capability and excellent cyclability in an aqueous electrolyte. The synthesis method is a hybrid of the traditional acid decomposition and precipitation synthesis methods. This new route allows the material to retain the high capacity of acid-decomposed analogues while achieving the cyclability of co-precipitated analogues. With an initial capacity of 37 mAh/g at 32C, the capacity retention of the material remained above 90% after 4,500 cycles and 70% after 15,000 cycles, offering great promise for its application in desalination batteries.",2024-10-07T01:29:02.447490,2024-10-18T16:15:00.740811,2024-10-18T16:14:34.103652,,,ecsarxiv,1,accepted,1,1,https://doi.org/10.1149/osf.io/g9p7f,GNU General Public License (GPL) 3.0,PBA Na-ion,"[""PBA Na-ion""]",Jacob Morton; Louis Hartmann; Matthieu Dubarry,"[{""id"": ""2vpqy"", ""name"": ""Jacob Morton"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""j2uvh"", ""name"": ""Louis Hartmann"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""ebtrx"", ""name"": ""Matthieu Dubarry"", ""index"": 2, ""orcid"": ""0000-0002-3228-1834"", ""bibliographic"": true}]",Jacob Morton,Engineering; Electrochemical Engineering,"[{""id"": ""5ae728ad4667e6000f98dd92"", ""text"": ""Engineering""}, {""id"": ""5ae728ad4667e6000f98dd98"", ""text"": ""Electrochemical Engineering""}]",https://osf.io/download/670339700ba129500032d96a,0,,not_applicable,not_applicable,[],,2025-04-09T21:06:20.848028