Renewable Energy最新文献_第10页

One-pot depolymerization of forest residues to potential aviation fuel over hybrid zeolite – N-doped activated carbon supported NiMo catalyst

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-14 DOI: 10.1016/j.renene.2025.122835

Quoc Khanh Tran , Muhammad Abdus Salam , Phuoc Hoang Ho , Huy Xuan Le , Christian Kugge , Derek Creaser , Louise Olsson

{"title":"One-pot depolymerization of forest residues to potential aviation fuel over hybrid zeolite – N-doped activated carbon supported NiMo catalyst","authors":"Quoc Khanh Tran , Muhammad Abdus Salam , Phuoc Hoang Ho , Huy Xuan Le , Christian Kugge , Derek Creaser , Louise Olsson","doi":"10.1016/j.renene.2025.122835","DOIUrl":"10.1016/j.renene.2025.122835","url":null,"abstract":"<div><div>In this work, sawdust and bark are depolymerized by catalytic reductive liquefaction using a bimetallic NiMo catalyst, with the aim to generate bio-fuel components in a single reaction step, that potentially could be used to produce sustainable aviation fuel (SAF). The hybrid support Zeolite Y combined with N-doped on activated carbon (YNAC) was synthesized from zeolite Y (silica/alumina ratio, SAR = 80) and N-doped activated carbon (NAC). The effect of temperature, pressure, and catalyst loading were systematically investigated to obtain conditions favorable for the yield and quality of the liquid product. The result at 400 °C, 20 bar H<sub>2</sub> (at room temperature), 4 h residence time with 30 wt% catalyst loading of NiMo@YNAC (75:25) showed the lowest solid yields, which was 3.9 wt% when using sawdust. The solid yield increased to 18.2 wt% when using bark and was intermediate (8.4 wt%) when using a sawdust/bark blend with 8/2 wt ratio. Sawdust was mainly converted into a liquid product consisting of cycloalkanes (C<sub>4</sub>-C<sub>7</sub>) (48.1 wt%), aromatics (2.1 wt%), phenolic compounds (15.8 wt%), and a heavy oil fraction (9.2 wt%). Meanwhile, bark was converted into similar compounds, however, with higher yields of mainly naphthenic and biphenyl components. The catalytic activity of NiMo on other supports such as ɤ-Al<sub>2</sub>O<sub>3</sub>, ZrO<sub>2</sub>, TiO<sub>2</sub>, and CeO<sub>2</sub> were also examined at the same conditions as NiMo@YNAC (75:25). Moreover, acidic washing of the bark was very beneficial resulting in that the solid yield significantly decreased, from 18 % to 6 %, while the bio-oil yield was improved (from 78 % to 91 %). The results showed that the NiMo@YNAC (75:25) catalyst with high deoxygenation and hydrogenation effects is a promising candidate for depolymerization of biomass into biofuels.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122835"},"PeriodicalIF":9.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient calculation of distributed photovoltaic power generation power prediction via deep learning

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-14 DOI: 10.1016/j.renene.2025.122901

Jiaqian Li , Congjun Rao , Mingyun Gao , Xinping Xiao , Mark Goh

{"title":"Efficient calculation of distributed photovoltaic power generation power prediction via deep learning","authors":"Jiaqian Li , Congjun Rao , Mingyun Gao , Xinping Xiao , Mark Goh","doi":"10.1016/j.renene.2025.122901","DOIUrl":"10.1016/j.renene.2025.122901","url":null,"abstract":"<div><div>Distributed photovoltaic (PV) power generation has gained significant support from national policies and has seen rapid development due to its ability to adapt to local conditions, its cleanliness and efficiency, as well as its notable environmental and economic benefits. However, PV power generation is highly susceptible to fluctuations and unpredictability caused by varying weather conditions. Accurate prediction of PV power generation is essential for maintaining grid stability and efficient operation. To improve prediction accuracy, we propose a novel model, PerfCNN-LSTM, which combines a convolutional neural network (CNN) and a long short-term memory (LSTM) network with the Performer self-attention mechanism. This model aims to enhance PV power generation forecasting. By extracting local features from the data, the model further captures global features through the integration of the Performer self-attention mechanism layer. This layer introduces linear random feature mapping, transforming the originally nonlinear attention weight calculation into linear attention, which simplifies the attention process and reduces the model's computational complexity. The output from the Performer layer is directly fed into the LSTM model to generate the final PV power generation prediction. We evaluated the performance of the model across three different datasets using key metrics such as MAE, RMSE, MSE, and <em>R</em><sup>2</sup>. When compared with six other deep learning models, the PerfCNN-LSTM demonstrates superior prediction accuracy.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122901"},"PeriodicalIF":9.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seasonal distribution analysis and short-term PV power prediction method based on decomposition optimization Deep-Autoformer

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-14 DOI: 10.1016/j.renene.2025.122903

Jing Ouyang , Zongxu Zuo , Qin Wang , Qiaoning Duan , Xuanmian Zhu , Yang Zhang

{"title":"Seasonal distribution analysis and short-term PV power prediction method based on decomposition optimization Deep-Autoformer","authors":"Jing Ouyang , Zongxu Zuo , Qin Wang , Qiaoning Duan , Xuanmian Zhu , Yang Zhang","doi":"10.1016/j.renene.2025.122903","DOIUrl":"10.1016/j.renene.2025.122903","url":null,"abstract":"<div><div>In complex weather and seasonal scenarios, single-model photovoltaic (PV) power prediction often lacks stability due to its limited capacity to capture temporal dynamics. To address this, we propose a multi-step prediction framework that incorporates seasonal decomposition to enhance accuracy. Our approach begins with analyzing annual temperature and solar irradiance characteristics using Piecewise Aggregate Approximation for initial seasonal segmentation. This segmentation is refined through optimized Seasonal Clustering to minimize transition intervals and improve seasonal boundaries. A similarity coefficient between transition and adjacent intervals is calculated to further enhance prediction accuracy. Central to our framework is the Savitzky-Golay decomposition block, which is designed for hierarchical decomposition and feature extraction. This forms the core of the SG-Deep-Autoformer model, which effectively separates trend and weather components, capturing complex weather dynamics across multiple temporal scales, and thus improving the forecasting accuracy significantly. Evaluations show that our method outperforms traditional models, including Long Short-Term Memory and Gated Recurrent Unit networks, with an 11.21 % reduction in mean absolute percentage error and notable improvements in mean absolute error and mean squared error. The method's effectiveness is validated across diverse datasets, demonstrating robust performance under varying weather and seasonal conditions. By integrating seasonal decomposition with advanced feature extraction, the SG-Deep-Autoformer provides a reliable tool for PV power forecasting, thereby enhancing energy management and grid operations.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122903"},"PeriodicalIF":9.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling fractured-vuggy reservoirs for large-scale gas storage: Green hydrogen, natural gas, and carbon dioxide

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-14 DOI: 10.1016/j.renene.2025.122906

Peng Deng , Zhangxin Chen , Xiaolong Peng , Xiaobo Li , Chaojie Di , Suyang Zhu , Chaowen Wang , Yilei Song , Kanyuan Shi

{"title":"Enabling fractured-vuggy reservoirs for large-scale gas storage: Green hydrogen, natural gas, and carbon dioxide","authors":"Peng Deng , Zhangxin Chen , Xiaolong Peng , Xiaobo Li , Chaojie Di , Suyang Zhu , Chaowen Wang , Yilei Song , Kanyuan Shi","doi":"10.1016/j.renene.2025.122906","DOIUrl":"10.1016/j.renene.2025.122906","url":null,"abstract":"<div><div>Fractured-vuggy reservoirs typically exhibit flow capacities up to ten times higher than conventional sandstone reservoirs, providing a significant advantage for Underground Gas Storage (UGS). However, their complex connectivity introduces uncertainty in gas flow pathways, leaving these potential benefits largely unexplored. To reuse this type of reservoir for UGS, we developed a flow velocity model that effectively captures the influence of multi-scale pore and fracture networks on gas flow behavior. The classified flow pathways were subsequently employed to evaluate the injection–production behavior of hydrogen, natural gas, and carbon dioxide within UGS. The results indicate that fractured-vuggy reservoirs can deliver effective peak-shaving capacity and are well-suited for UGS. Notably, hydrogen achieved a recovery factor of up to 88.5 %, and the economic analysis demonstrates that profitable storage is achievable at the current cost level of natural gas storage. The recovery factor of natural gas exceeded 92 %, yielding a net present value of $20.7 M, whereas carbon dioxide performance was highly dependent on tailored injection strategies and capture costs. This discovery suggests the potential of fractured-vuggy reservoirs for UGS and provides technical guidance for future site selection.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122906"},"PeriodicalIF":9.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contribution of green bonds and green growth in clean energy capacity under the moderating role of political stability

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-14 DOI: 10.1016/j.renene.2025.122888

Syed Sumair Shah , Gulnora Murodova , Anwar Khan

{"title":"Contribution of green bonds and green growth in clean energy capacity under the moderating role of political stability","authors":"Syed Sumair Shah , Gulnora Murodova , Anwar Khan","doi":"10.1016/j.renene.2025.122888","DOIUrl":"10.1016/j.renene.2025.122888","url":null,"abstract":"<div><div>The current study proposes links between green bonds, green growth, and clean energy capacity under the moderating effect of political stability for the top 25 green bond-issuing countries. The data between 2014 and 2021 is analyzed using instrumental variable GMM (IV-GMM) and Driscoll and Kraay (D&K) approaches. The results from IV-GMM indicated that a one per cent rise in green bonds drives clean energy capacity by 0.350 % [0.029 % with D&K]. Likewise, the outcomes show an incremental response of 0.398 % [0.008 % with D&K] on clean energy capacity with a one per cent increase in green growth for chosen countries. Regarding the moderating relationship, the outcomes proved that political stability is conducive to enhancing the relationship between green bonds-clean energy capacity [0.116 %] and green growth-clean energy capacity [0.452 %] for the selected countries. The results from changing the green finance variable and estimations with different approaches [Fully modified-OLS and Quantile regression] further authenticated the results. Based on these connections, we propose policymakers design integrated policies on green finance and growth to boost renewable energy infrastructure, ensuring a stable political system to attract investors for enduring green energy progress.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122888"},"PeriodicalIF":9.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cobalt-manganese-boron/nickel foam for hydrogen generation from the hydrolysis of sodium borohydride solution

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-13 DOI: 10.1016/j.renene.2025.122899

Yutong Han , Xinpeng Yang , Fengyan Xu , Yuntong Wang , Wenjing Liu , Jiaxin Ma , Yan Wang , Ke Zhang , Zhongqiu Cao , Guode Li , Shiwei Wu

{"title":"Cobalt-manganese-boron/nickel foam for hydrogen generation from the hydrolysis of sodium borohydride solution","authors":"Yutong Han , Xinpeng Yang , Fengyan Xu , Yuntong Wang , Wenjing Liu , Jiaxin Ma , Yan Wang , Ke Zhang , Zhongqiu Cao , Guode Li , Shiwei Wu","doi":"10.1016/j.renene.2025.122899","DOIUrl":"10.1016/j.renene.2025.122899","url":null,"abstract":"<div><div>Sodium borohydride (NaBH<sub>4</sub>) is widely favored for its own high hydrogen storage capacity. However, the H<sub>2</sub> release rate of NaBH<sub>4</sub> hydrolysis is very slow in the absence of catalysts, and so it is important to add catalysts with high performance for efficient hydrolysis. In this work, Co-Mn-B/Ni foam materials were synthesized via chemical deposition way in a mild environment and employed to catalyze NaBH<sub>4</sub> hydrolysis. By changing the concentration of reducing agent, the optimal Co-Mn-B/Ni foam with high catalytic performance was obtained, providing the highest H<sub>2</sub> generation rate of 8710 mL min<sup>−1</sup>·g<sup>−1</sup> and low apparent activation energy of 34.6 kJ mol<sup>−1</sup>. The catalytic performance was obviously better than that of binary Co-B/Ni foam catalyst. The improved activity of the catalyst could be attributed to the special fluffy spherical morphology of the surface, which supplied high specific surface area to efficiently transport of H<sub>2</sub>, as well as the synergistic effect of the multiple components. In addition, the hydrogen production rate was maintained about 55.5 % of the first value after 5 cycles, showing the superior stability of Co-Mn-B/Ni foam during the hydrolysis.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122899"},"PeriodicalIF":9.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Photovoltaic power prediction based on multi-scale photovoltaic power fluctuation characteristics and multi-channel LSTM prediction models

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-13 DOI: 10.1016/j.renene.2025.122866

Fengpeng Sun, Longhao Li, Dunxin Bian, Wenlin Bian, Qinghong Wang, Shuang Wang

{"title":"Photovoltaic power prediction based on multi-scale photovoltaic power fluctuation characteristics and multi-channel LSTM prediction models","authors":"Fengpeng Sun, Longhao Li, Dunxin Bian, Wenlin Bian, Qinghong Wang, Shuang Wang","doi":"10.1016/j.renene.2025.122866","DOIUrl":"10.1016/j.renene.2025.122866","url":null,"abstract":"<div><div>The global shortage of non-renewable energy sources has catalyzed the vigorous development of photovoltaic (PV) energy. Accurate prediction of PV power output is essential for ensuring the safety and stability of integrating small-scale PV systems into the power grid. Therefore, this paper proposes a hybrid multi-station parallel PV power prediction method (MCFC-MAOA-MCLSTM-Attention) based on multi-scale historical PV power fluctuation feature extraction. First, for the problem of variable weather types due to the existence of strong fluctuations in meteorological factors, a weather classification algorithm based on multi-scale fluctuation characteristics (MCFC) is proposed, and combined with the similar day algorithm to select the classified meteorological data secondly and improve the correlation between the data. Subsequently, this paper proposes a multi-channel structured long and short-term neural network modeling method (MCLSTM) to further extract the spatio-temporal correlation of different PV sites in the region and realize the integrated prediction based on geographic location and time series. To address the challenges associated with calibrating model parameters, which significantly impact the prediction accuracy, the modified Archimedean optimization approach (MAOA) was employed to optimize these parameters. The experimental results demonstrate that the model is both highly reliable and generalizable for predicting photovoltaic power data.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122866"},"PeriodicalIF":9.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Internal mixing mechanism and mixed layer development characteristics of hydrogen recirculation ejector

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-13 DOI: 10.1016/j.renene.2025.122896

Jiang Bian , Gaoya Ding , Yue Zhang , Xuewen Cao , Bo Yu

{"title":"Internal mixing mechanism and mixed layer development characteristics of hydrogen recirculation ejector","authors":"Jiang Bian , Gaoya Ding , Yue Zhang , Xuewen Cao , Bo Yu","doi":"10.1016/j.renene.2025.122896","DOIUrl":"10.1016/j.renene.2025.122896","url":null,"abstract":"<div><div>Clarifying the internal flow characteristics and mixing mechanism of ejectors is highly important for improving ejector efficiency and further promoting the wide application of hydrogen fuel cells in various areas, such as ships, distributed power generation and energy storage. The employing of an ejector to recycle excess unreacted hydrogen in PEMFC can significantly improve its efficiency, but the mixed mechanism between the fluids in the ejector is unclear. In this study, a CFD numerical model of an ejector was established, the transport properties between the fluids were investigated, and the mixed mechanism of different fluids was revealed. The growth characteristics and mixed properties of the restricted compressible mixed layer were also elucidated. The results show that the mixing between fluids in the ejector satisfies the mixed mechanism of energy exchange with vortices as carriers. In addition, an adverse pressure gradient can effectively enhance mixing while accelerating vortex dissipation. For the restricted compressible mixed layer, the development can be divided into three stages by the influence of the velocity ratio and <em>M</em>c. The mixing between the mainstream and the entrainment stream occurs in phases, with local development dominating at first and overall development dominating later.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122896"},"PeriodicalIF":9.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Calibration methodology of static, dynamic and ageing parameters of an electrochemical model for a Li-ion cell based on an experimental approach

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-13 DOI: 10.1016/j.renene.2025.122793

Francesco Mazzeo , Eduardo Graziano , Silvia Bodoardo , Davide Papurello

{"title":"Calibration methodology of static, dynamic and ageing parameters of an electrochemical model for a Li-ion cell based on an experimental approach","authors":"Francesco Mazzeo , Eduardo Graziano , Silvia Bodoardo , Davide Papurello","doi":"10.1016/j.renene.2025.122793","DOIUrl":"10.1016/j.renene.2025.122793","url":null,"abstract":"<div><div>This study presents a novel methodology for developing a digital twin of a lithium-ion coin cell battery (Graphite-NMC622), accurately replicating the average discharge behaviour of various laboratory-tested batteries and characterizing degradation phenomena through cyclic ageing experiments. Given the anticipated rise in electric vehicle adoption, this work is particularly relevant for addressing the growing demand for lithium-ion batteries. The experimental characterization identified the minimum requirements for battery modelling, with tests conducted up to a C/5 current. Degradation behaviours were analysed through cycle ageing tests at two State-Of-Charge (SOC) ranges (100 %–0 % and 90 %–10 %), establishing a robust foundation for modelling degradation trends. While further calendar ageing tests could enhance the degradation modelling, they would require extensive data and time. Despite these constraints, the virtual coin cell model developed using GT-AutoLion, an industry-standard CAE software, demonstrated excellent accuracy, achieving an RRMSE of less than 2.0 % and R<sup>2</sup> greater than 0.95. This work is significant as it provides a reliable framework for battery modelling that can assist companies in optimizing battery design and performance.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122793"},"PeriodicalIF":9.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting PEM fuel cell performance with a designed mesoporous carbon-supported catalytic layer

IF 9 1区工程技术

Renewable Energy Pub Date : 2025-03-13 DOI: 10.1016/j.renene.2025.122885

Yulin Wang , Shiwei Qin , Fei Ma , Cheng Wang , Wei He , Benxi Zhang , Hua Li

{"title":"Boosting PEM fuel cell performance with a designed mesoporous carbon-supported catalytic layer","authors":"Yulin Wang , Shiwei Qin , Fei Ma , Cheng Wang , Wei He , Benxi Zhang , Hua Li","doi":"10.1016/j.renene.2025.122885","DOIUrl":"10.1016/j.renene.2025.122885","url":null,"abstract":"<div><div>The mesoporous carbon-supported (MCS) catalytic layer (CL) has a great specific surface area, thereby benefiting the formation of Pt active sites within the CLs of polymer electrolyte membrane (PEM) fuel cells. The primary pores of mesoporous carbons (MCs) significantly impact electrochemical reactions and mass transport inside the MCS CL, consequently affecting fuel cell performance. A stochastic algorithm is employed to recreate the microstructure of the MCS CL. The effects of primary pore size, depth and number on oxygen reduction reaction (ORR) within the MCS CL, considering the Pt poisoning effects resulting from the intruded ionomer, are evaluated via the lattice Boltzmann (LB) method. The simulation findings show that a larger primary pore size promotes mass transfer inside the MCS CL; nevertheless, the ionomer tends to invade the pores, causing Pt poisoning, especially under a high ionomer intrusion ratios. Furthermore, increasing the pore depth and number initially increases and consequently decreases the ORR rate. The result indicates that the optimal primary pore size, depth, and number for MCS CL are 6 nm, 15 nm, and 6, respectively. Ultimately, the ORR rate is increased from 2.51e-15 mol s<sup>−1</sup> for the conventional SC CL to 6.73e-15 mol s<sup>−1</sup> for the optimal MCS CL.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"246 ","pages":"Article 122885"},"PeriodicalIF":9.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0