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Recent advances in bimetallic-cobalt oxides and their composites as a potential candidate for supercapacitor electrode material 双金属钴氧化物及其复合材料作为超级电容器电极材料的研究进展

Next Energy

Pub Date : 2026-01-13 DOI: 10.1016/j.nxener.2025.100505

Tekalign Aregu Tikish , Yared Worku , Nithyadharseni Palaniyandy , Eno E. Ebenso

The growing demand for green energy has made energy storage crucial in energy generation systems. Supercapacitors (SCs) are gaining popularity in energy storage due to their high-power density and long cycle life. Bimetallic cobalt oxides (MCo₂O₄) are promising electrode materials due to their enhanced electrochemical performance and synergistic effects. This review provides a unique and exclusive focus on the recent 5-year progress (2020–2025) in MCo₂O₄ materials for SC applications. It provides a detailed analysis of various synthesis processes, the relationship between crystal structure (particularly the stable spinel structure) and electrochemical activity, the inherent battery-like charge storage mechanism of cobalt oxides, and a comparative performance evaluation. It also analyzes the electrolyte in Bimetallic Metal Oxides and their composites. The review highlights the strategic inclusion of a secondary metal (M = Ni, Cu, Fe, Mn, Zn) into cobalt oxide, which enhances key metrics, including specific capacitance, rate capability, and cyclic stability. Furthermore, this review demonstrated the strategies for improving overall SC performance through composite formation with conductive additives (carbon materials, metal oxides, conducting polymers, and MOFs). Lastly, the review concludes by summarizing the advanced and outlining crucial future research pathways to guide the development of superior bimetallic cobalt oxide-based SCs.

对绿色能源日益增长的需求使得储能在能源生产系统中变得至关重要。超级电容器因其高功率密度和长循环寿命在储能领域越来越受欢迎。双金属钴氧化物（MCo2O4）具有较强的电化学性能和协同效应，是一种很有前途的电极材料。本文综述了最近5年（2020-2025年）用于SC应用的MCo2O4材料的独特和独家的进展。详细分析了各种合成工艺，晶体结构（特别是稳定的尖晶石结构）与电化学活性的关系，钴氧化物固有的类似电池的电荷储存机制，并进行了性能比较评价。对双金属氧化物及其复合材料中的电解液进行了分析。该综述强调了将二次金属（M = Ni, Cu, Fe, Mn, Zn）战略性地包含在氧化钴中，从而提高了关键指标，包括比电容、倍率能力和循环稳定性。此外，本综述还展示了通过与导电添加剂（碳材料、金属氧化物、导电聚合物和mof）形成复合材料来提高整体SC性能的策略。最后，综述总结了先进的研究进展，并概述了未来重要的研究途径，以指导高性能双金属钴基纳米材料的发展。

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Predicting methane and nitrous oxide emissions from Indian cattle farming using advanced time series techniques 利用先进的时间序列技术预测印度畜牧业的甲烷和一氧化二氮排放

Next Energy

Pub Date : 2026-01-01 DOI: 10.1016/j.nxener.2025.100496

Binita Kumari , Dipanjali Ray , Ganeshkumar D. Rede , Soumik Ray , Shiwani Tiwari , Pradeep Mishra

This study aims to forecast methane (CH₄) and nitrous oxide (N₂O) emissions from cattle rearing in India, which contribute significantly to agricultural greenhouse gas (GHG) emissions. Data on these emissions was collected from the Food and Agricultural Organization for the years 1961–2022. Three time series models, namely, exponential smoothing (Holt-Winters), autoregressive integrated moving average (ARIMA), and trigonometric seasonality, Box-Cox transformation, ARMA errors, trend, and seasonal components (TBATS) were employed to predict future emissions. The dataset was partitioned into training (1961–2012) and testing (2013–2022) sets to evaluate model performance. Diagnostic metrics, including Akaike Information Criterion, root mean square error, mean absolute percentage error, and mean absolute scaled error, were used to assess accuracy. Results indicated that the ARIMA model outperformed the other 2 forecasting models by making over 90% accurate predictions. For N₂O, ARIMA (0,1,0) was identified as the optimal model, while ARIMA (2,1,2) was selected for CH₄. Thus, the study validates the use of ARIMA model in GHG forecasting. The study projects emissions up to 2030, providing critical insights for policymakers to design targeted mitigation strategies. The study also presses the need for implementing sustainable cattle management practices for cutting emissions in India.

本研究旨在预测印度养牛过程中甲烷（CH₄）和氧化亚氮（N₂O）的排放，这两种气体对农业温室气体（GHG）排放有很大贡献。这些排放的数据是从联合国粮农组织1961年至2022年收集的。采用指数平滑（Holt-Winters）、自回归综合移动平均（ARIMA）和三角季节性、Box-Cox变换、ARMA误差、趋势和季节分量（TBATS） 3种时间序列模型对未来排放进行预测。数据集被划分为训练集（1961-2012）和测试集（2013-2022），以评估模型的性能。诊断指标包括赤池信息标准、均方根误差、平均绝对百分比误差和平均绝对比例误差，用于评估准确性。结果表明，ARIMA模型的预测准确率在90%以上，优于其他2种预测模型。对于N₂O，最优模型为ARIMA(0,1,0)，而对于CH₄，最优模型为ARIMA（2,1,2）。因此，本研究验证了ARIMA模型在温室气体预测中的应用。该研究预测了到2030年的排放量，为政策制定者设计有针对性的减排战略提供了关键见解。该研究还强调了在印度实施可持续的养牛管理措施以减少排放的必要性。

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