Future Batteries最新文献

The effects of substitutional tin (Sn) doping on the layered LiMnO2 cathode material for lithium-ion batteries 替代锡（Sn）掺杂对锂离子电池层状LiMnO2正极材料的影响

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.fub.2026.100140

R.B. Mokgabudi, K.T. Malatji, N.N. Ngoepe, P.E. Ngoepe

{"title":"The effects of substitutional tin (Sn) doping on the layered LiMnO2 cathode material for lithium-ion batteries","authors":"R.B. Mokgabudi, K.T. Malatji, N.N. Ngoepe, P.E. Ngoepe","doi":"10.1016/j.fub.2026.100140","DOIUrl":"10.1016/j.fub.2026.100140","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) have emerged as a dominant force in global energy storage, powering electric vehicles (EVs), grid-scale storage systems, and consumer electronics. Their dominance stems for unparalleled advantages, including high operating voltage, high energy density, long cycle life, high power, efficiency, and eco-friendliness. As a result, LIBs have become the dominant power source in the rechargeable battery market. However, their performance is often hindered by Jahn-Teller distortion, which causes structural degradation. To address this, substitutional cation doping has been explored as a strategy to enhance structural stability, mechanical performance, and conductivity. In this study, first-principles calculations combined with cluster expansion techniques were employed to investigate Sn-substituted layered LiMnO<sub>2</sub> (R-3m), generating 29 distinct phases. Among these, three stable configurations Li<sub>4</sub>MnSn<sub>3</sub>O<sub>8</sub>, Li<sub>4</sub>Mn<sub>2</sub>Sn<sub>2</sub>O<sub>8</sub>, and Li<sub>4</sub>Mn<sub>3</sub>SnO<sub>8</sub> were identified, with Li<sub>4</sub>Mn<sub>3</sub>SnO<sub>8</sub> exhibiting the highest thermodynamic stability, metallic behaviour, indicating superior electron conductivity and improved mechanical stability. Structural analysis revealed good agreement with previous studies. The substitution of Sn for Mn was found to be doubly beneficial: it enlarges lithium diffusion channels due to its larger ionic radius and critically modulates voltage characteristics, with excessive Sn leading to a sharp voltage increase. providing valuable insights for designing advanced, stable Li-rich Mn-based cathodes with optimized Sn substituting for next-generation batteries.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reduced-order thermal model of battery thermal management system for online applications based on proper orthogonal decomposition and Galerkin projection method 基于适当正交分解和伽辽金投影法的在线电池热管理系统降阶热模型

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-01-17 DOI: 10.1016/j.fub.2026.100149

Yankong Song , Lili Li , Chao Lyu , Xiao Liang , Wei Li

{"title":"A reduced-order thermal model of battery thermal management system for online applications based on proper orthogonal decomposition and Galerkin projection method","authors":"Yankong Song , Lili Li , Chao Lyu , Xiao Liang , Wei Li","doi":"10.1016/j.fub.2026.100149","DOIUrl":"10.1016/j.fub.2026.100149","url":null,"abstract":"<div><div>The temperature of lithium-ion batteries (LIBs) manifests significant hysteresis effects that substantially impede precise temperature regulation within battery thermal management systems (BTMS). The application of model predictive control (MPC) has been identified as a potentially effective strategy for mitigating thermal hysteresis. However, the existing thermal models for LIBs lack the requisite accuracy and computational efficiency for effective implementation in online MPC frameworks. In this paper, a reduced-order thermal model (ROTM) of BTMS is established based on the proper orthogonal decomposition (POD) and Galerkin projection. Firstly, a finite element model (FEM) of three parallel and eight series air-cooling battery module is constructed in aim of generating original data. The basis vectors of the flow and temperature fields of the battery module are subsequently extracted from the original data by the POD method. Finally, the Navier-Stokes equation and the Fourier's law of heat conduction are projected on the basis vectors previously described. The ROTM can thus be obtained. In comparison with the FEM, the ROTM exhibits a significantly reduced computational time and maintains adequate accuracy. The computational time for ROTM is merely one ten-thousandth of that required by FEM, whilst under 1.25 C constant-current conditions the maximum error between the two methods is less than 0.2°C.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100149"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Equity-centred social life cycle assessment of lithium-ion battery recycling 以公平为中心的锂离子电池回收社会生命周期评估

Future Batteries Pub Date : 2026-02-01 Epub Date: 2025-12-29 DOI: 10.1016/j.fub.2025.100138

Esther O. Oluwabiyi , Adeola Ajoke Oni , Somke Pamela Madueke , Francis T. Omigbodun , Amirlahi Ademola Fajingbesi , Funso P. Adeyekun

{"title":"Equity-centred social life cycle assessment of lithium-ion battery recycling","authors":"Esther O. Oluwabiyi , Adeola Ajoke Oni , Somke Pamela Madueke , Francis T. Omigbodun , Amirlahi Ademola Fajingbesi , Funso P. Adeyekun","doi":"10.1016/j.fub.2025.100138","DOIUrl":"10.1016/j.fub.2025.100138","url":null,"abstract":"<div><div>The rapid growth of electric vehicles is accelerating demand for lithium-ion battery recycling, yet most assessments overlook the social equity implications of facility siting and technology choice. This study developed a social life cycle assessment (s-LCA) framework integrating a Community Health Burden Index (CHBI) with multi-criteria decision analysis (MCDA) to evaluate end-of-life recycling options. The system boundary encompassed collection, transportation, mechanical pretreatment, and recycling via pyrometallurgical and hydrometallurgical processes. Emissions were modelled using AERMOD, incorporating census-tract demographic and health data, while CHBI considered pollutant toxicity, exposure intensity, and socioeconomic vulnerability. Results show that hydrometallurgy with the best available emission controls reduced CHBI by 42 % compared to baseline pyrometallurgy, while equity-weighted MCDA scenarios lowered burdens by up to 55 %, with cost increases of less than 7 %. Sensitivity analysis demonstrated consistent rankings, with uncertainty margins below ±10 % for emissions factors and ±8 % for vulnerability weights. These findings suggest that incorporating equity metrics into recycling planning facilitates “no-regrets” siting decisions, thereby advancing both environmental justice and circular-economy objectives.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate state of health estimation of lithium-ion batteries based on deep learning and polarization features 基于深度学习和极化特征的锂离子电池健康状态准确估计

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-02-16 DOI: 10.1016/j.fub.2026.100160

Zhichao Li , Zhiguo Qu , Baobao Hu

{"title":"Accurate state of health estimation of lithium-ion batteries based on deep learning and polarization features","authors":"Zhichao Li , Zhiguo Qu , Baobao Hu","doi":"10.1016/j.fub.2026.100160","DOIUrl":"10.1016/j.fub.2026.100160","url":null,"abstract":"<div><div>Accurate state of health (SOH) estimation of lithium-ion batteries is crucial for ensuring the safety of electric vehicles. However, factors such as operating conditions and C-rates can influence battery degradation, which is typically accompanied by voltage fluctuations and increased polarization. Existing studies predominantly rely on direct utilization of voltage and current data for SOH estimation, neglecting the polarization features, thereby compromising estimation accuracy. To address this, this study proposes a deep learning model (CBS-Net) based on polarization features for SOH estimation. This model integrates convolutional neural networks (CNN), bidirectional gated recurrent units (BiGRU), squeeze-and-excitation network, and residual connections. It achieves SOH estimation by inputting only three features: initial charging voltage, initial discharging voltage, and the average voltage change rate. Publicly available lithium-ion battery datasets are employed to train and validate the accuracy and generalization ability of the CBS-Net. Results demonstrate that the coefficient of determination (<em>R</em><sup>2</sup>) > 0.99, with both root mean square error (RMSE) and mean absolute error (MAE) < 1 %, indicating excellent estimation accuracy. Moreover, during generalization validation, the model maintains an <em>R</em><sup>2</sup> > 0.98 with RMSE and MAE < 3 %, confirming its strong generalization ability. In comparisons with single models such as CNN and BiGRU, CBS-Net exhibits the best estimation accuracy. Taking CNN as an example, the RMSE and MAE of the CBS-Net model are reduced by 75 % and 76 %, respectively. This study provides a novel model framework and an effective health features selection method for battery SOH estimation.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100160"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safer, longer-lasting batteries using a smart gel electrolyte mix 使用智能凝胶电解质混合物的更安全，更持久的电池

Future Batteries Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.fub.2025.100132

Amirlahi Ademola Fajingbesi , Adeola Ajoke Oni , Peter T. Oluwasola , Owoade O. Odesanya , Elizabeth A. Adeola , Adeyinka G. Ologun , Funso P. Adeyekun , Francis T. Omigbodun

{"title":"Safer, longer-lasting batteries using a smart gel electrolyte mix","authors":"Amirlahi Ademola Fajingbesi , Adeola Ajoke Oni , Peter T. Oluwasola , Owoade O. Odesanya , Elizabeth A. Adeola , Adeyinka G. Ologun , Funso P. Adeyekun , Francis T. Omigbodun","doi":"10.1016/j.fub.2025.100132","DOIUrl":"10.1016/j.fub.2025.100132","url":null,"abstract":"<div><div>This study investigates a dual-additive gel polymer electrolyte (GPE) system designed to enhance the electrochemical performance of sodium metal batteries through interphase engineering and improved ion transport. The objective was to evaluate, through simulation modelling, the effect of combining potassium tetrafluoroborate (KBF₄) and lithium difluoro(oxalato)borate (LiDFOB) within a fluorostyrene–PEGDA polymer matrix on ionic conductivity, electrochemical stability, and cycling durability. A coupled transport–kinetics framework incorporating Nernst-Planck, Butler-Volmer, and SEI-growth models was used to predict conductivity, voltage response, and interface behaviour. Results indicate a room-temperature conductivity of approximately 4.1 × 10⁻³ S cm⁻¹ , representing an improvement of > 20 % over typical sodium GPE values. The electrolyte also demonstrated a stability window near 5.0 V vs Na⁺/Na and sustained symmetric-cell stability of> 1200 h without dendrite-induced failure. Full-cell simulation with a Na₃V₂(PO₄)₃ cathode yielded ∼93 % capacity retention over 7000 cycles at 10 C, with predicted error margins within ±3 % for conductivity and ±2.5 mV in voltage deviation. These findings highlight the potential of dual-additive polymer electrolytes for high-rate, long-life sodium-based energy storage systems.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-based battery life prediction and smart charging optimization for renewable energy storage systems 基于机器学习的可再生能源存储系统电池寿命预测和智能充电优化

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-01-13 DOI: 10.1016/j.fub.2026.100147

Subhash Kumar Mandal, Pankaj Pateriya, Yogendra Singh Dohare

{"title":"Machine learning-based battery life prediction and smart charging optimization for renewable energy storage systems","authors":"Subhash Kumar Mandal, Pankaj Pateriya, Yogendra Singh Dohare","doi":"10.1016/j.fub.2026.100147","DOIUrl":"10.1016/j.fub.2026.100147","url":null,"abstract":"<div><div>The fast development of renewable energy integration has amplified the demand for reliable, efficient, and intelligent energy storage solutions. Lithium-ion batteries take over current applications, however achieving accurate prediction of the state of charge (SoC), state of health (SoH), and optimizing charging strategies keep on challenging due to their nonlinear and aging-dependent behavior. This study suggests a hybrid machine learning framework that integrates long short-term memory (LSTM) networks for battery state forecast with a reinforcement learning (RL)-based smart charging controller<strong>.</strong> The novelty lies in the joint predictive–control design, where analytical visions from the LSTM model dynamically guide the RL agent for safe and enhanced charging. Relative analysis using benchmark datasets shows that the proposed model achieves an R² of 0.97 and RMSE of 0.067 for SoC prediction, overtaking traditional models. The RL-based charging policy achieves an 18 % reduction in charging time, a 2.7 % increase in charging efficiency, and a projected 15 % improvement in cycle life compared to conventional CCCV charging. These findings point to the fact that, in addition to boosting battery reliability and lifetime, the combination of ML and RL can be used to facilitate the cost-effective storage of renewable energy, because it lowers the leveled cost of storage (LCOS) and optimizes the overall system sustainability.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of ionomer chemical degradation on low-Pt proton exchange membrane fuel cells 离子单体化学降解对低铂质子交换膜燃料电池的影响

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.fub.2026.100143

Xiaohui Yan , Shiqing Liu , Yongjian Su , Jiabin You , Huiyuan Li , Xiaojing Cheng , Congfan Zhao , Yong Feng , Miaomiao He , Guoqiang Zhang , Junliang Zhang

{"title":"Effects of ionomer chemical degradation on low-Pt proton exchange membrane fuel cells","authors":"Xiaohui Yan , Shiqing Liu , Yongjian Su , Jiabin You , Huiyuan Li , Xiaojing Cheng , Congfan Zhao , Yong Feng , Miaomiao He , Guoqiang Zhang , Junliang Zhang","doi":"10.1016/j.fub.2026.100143","DOIUrl":"10.1016/j.fub.2026.100143","url":null,"abstract":"<div><div>Free radicals are a class of reactive substances produced during the operation of proton exchange membrane fuel cells (PEMFCs), which have a great impact on the durability of PEMFCs. Previous research on the fuel cell degradation mechanism mainly focused on the degradation of the membrane electrode assembly (MEA) in high Pt loading PEMFCs, especially the chemical degradation of proton exchange membrane (PEM). However, there are significant differences in the characteristics and performance of PEMFCs with low and high Pt loading especially under the high current density, which is mainly due to the oxygen transport process in cathode catalyst layers (CCLs). Currently, few relevant research has explored the impact of chemical degradation on oxygen transport in the cathode of low-Pt PEMFCs. Therefore, this work investigates the effects of free radical attack on the structure of ionomer films, the local oxygen transport process and the evolution of the ionomer coated Pt/C structure in CCLs through physicochemical characterizations, electrochemical measurements and molecular dynamic simulations. Our research found that free radical attacks decreased the electrochemical active area of CCLs, but it also temporarily improved the cell performance at high current densities. Furthermore, molecular dynamics simulations demonstrated that the ionomer exhibited higher oxygen self-diffusion and a more relaxed structure after degradation.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing battery technology for wearable and implantable devices, the current challenges and future directions - A short review 推进可穿戴和植入式设备电池技术，当前的挑战和未来的方向-简短回顾

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-01-20 DOI: 10.1016/j.fub.2026.100148

Darren John Haines , Mian Hammad Nazir

引用次数: 0

Smart magnesium batteries: Using AI to power greener and more reliable desalination systems 智能镁电池：利用人工智能为更环保、更可靠的海水淡化系统供电

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-01-22 DOI: 10.1016/j.fub.2026.100151

Oluwafemi Babatunde Olasilola , Adeola Ajoke Oni , Rukayat Abisola Olawale , Adeyinka G. Ologun , Amirlahi Ademola Fajingbesi , Kemi K. Oladapo , Francis T. Omigbodun

{"title":"Smart magnesium batteries: Using AI to power greener and more reliable desalination systems","authors":"Oluwafemi Babatunde Olasilola , Adeola Ajoke Oni , Rukayat Abisola Olawale , Adeyinka G. Ologun , Amirlahi Ademola Fajingbesi , Kemi K. Oladapo , Francis T. Omigbodun","doi":"10.1016/j.fub.2026.100151","DOIUrl":"10.1016/j.fub.2026.100151","url":null,"abstract":"<div><div>This study develops a focused AI-based optimisation framework to improve the performance of magnesium alloy batteries for renewable-powered desalination systems. The objective is to enhance voltage stability, reduce internal resistance, and extend cycle life through coordinated optimisation of material and operating parameters. An analytical–simulation methodology is adopted, combining electrochemical degradation models with machine learning prediction and genetic algorithm optimisation. Key variables include alloy composition, electrolyte type, operating temperature, and current density. Neural networks were trained using a literature-anchored dataset and validated through cross-validation, while genetic algorithms were used to identify optimal multi-objective operating conditions. The optimised Mg–Al configurations demonstrated a 25 % reduction in voltage degradation, a 50 % decrease in internal resistance<strong>,</strong> and a 20 % increase in cycle life compared with baseline (non-optimised) conditions, achieving up to 220 stable cycles. The predictive models attained a 94.5 % accuracy with <strong>a</strong> root mean square error of 0.015 V<strong>,</strong> indicating low prediction uncertainty and robust generalisation within the studied domain. These quantified improvements translate into higher energy efficiency and reduced maintenance demand for desalination applications. Overall, the results confirm that AI-assisted optimisation provides a reliable, resource-efficient pathway for designing sustainable magnesium-based energy storage systems aligned with circular economy objectives.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating carbochlorination for processing natural flake graphite into Li-ion battery-grade graphite 评价碳氯化法将天然鳞片石墨加工成锂离子电池级石墨

Future Batteries Pub Date : 2026-02-01 Epub Date: 2026-02-05 DOI: 10.1016/j.fub.2026.100153

Samuel Quéméré , Alceni Yasser Barry , Mathieu Toupin , Ali Crawford , Nicole A. Poirier , Jean-Yves Huot , Kevin Watson , Lionel Roué

{"title":"Evaluating carbochlorination for processing natural flake graphite into Li-ion battery-grade graphite","authors":"Samuel Quéméré , Alceni Yasser Barry , Mathieu Toupin , Ali Crawford , Nicole A. Poirier , Jean-Yves Huot , Kevin Watson , Lionel Roué","doi":"10.1016/j.fub.2026.100153","DOIUrl":"10.1016/j.fub.2026.100153","url":null,"abstract":"<div><div>Chlorination was evaluated as a HF-free purification step within the conversion process of natural flake graphite (NFG) into coated spherical purified graphite (CSPG) for Li‑ion battery anodes. Two industrially realistic sequences were compared: chlorination before micronization–spheroidization (MS) and chlorination after MS. Processing used caustic leaching followed by carbochlorination, jet milling micronization and autogenous impact spheroidization, and petroleum‑pitch coating/carbonization. Detailed characterization was performed at each step of the processing. Carbochlorination raised purity to 99.98 wt% in both routes while preserving the graphitic structure as confirmed by X-ray diffraction and Raman spectroscopy analyses. MS produced spherical particles (median size ⁓17 µm, median circularity ⁓0.90). Pitch coating/carbonization lowered the surface area to ∼2 m<sup>2</sup> g<sup>‑1</sup> and led to a tap density of ∼1.1 g cm<sup>‑3</sup>. Half‑cell testing showed similar electrochemical performance for both routes with an initial coulombic efficiency of ⁓93 % and a reversible capacity of ⁓360 mAh g<sup>‑1</sup> after 90 cycles, closely matching a commercial CSPG. Differences between the sequences were thus minor, indicating that carbochlorination can be flexibly integrated into industrial CSPG flowsheets. The findings support chlorination as a scalable, HF‑free purification alternative to produce battery-grade natural graphite.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0