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Batteries, Volume 12, Issue 6 (June 2026) – 2 articles

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43 pages, 3760 KB  
Review
Metal–Air Batteries as a Platform for the Chiral-Induced Spin Selectivity (CISS) Effect: A Review
by Alberta Carella, Francesco Rossella and Claudio Fontanesi
Batteries 2026, 12(6), 186; https://doi.org/10.3390/batteries12060186 - 22 May 2026
Abstract
The chiral-induced spin selectivity (CISS) effect enables the spin-selective transport of electrons through chiral systems, linking handedness with spin polarization. This review provides a comprehensive examination of the emerging field of chiral electrocatalysis, detailing also the extensive experimental and theoretical endeavor conducted to [...] Read more.
The chiral-induced spin selectivity (CISS) effect enables the spin-selective transport of electrons through chiral systems, linking handedness with spin polarization. This review provides a comprehensive examination of the emerging field of chiral electrocatalysis, detailing also the extensive experimental and theoretical endeavor conducted to gain a deeper understanding of the fundamental physical principles and mechanistic characteristics of this phenomenon. In particular, the CISS effect has garnered significant attention within the scientific community due to its potential for broad applicability across several fields, ranging from spintronics to biology. Among them, the prospective harnessing of the CISS effect into electrocatalytic processes offers an innovative strategy to improve the performance of energy conversion and storage technologies. This review deeply examines the practical applications of the CISS effect across different electrocatalytic reactions, with particular emphasis on its influence on the oxygen reduction reaction (ORR) and its critical role in energy conversion systems where the ORR reaction is a key process, such as in metal–air batteries, whose safety and performance can be enhanced through spin-selective electron transport. Full article
20 pages, 1970 KB  
Article
Toward Generalizable State-of-Charge Prediction of Lithium-Ion Batteries Using Deep Learning and Real-World Data
by Montaha Khedhiri, Rim Slama, Eduardo Redondo-Iglesias and Rochdi Trigui
Batteries 2026, 12(6), 185; https://doi.org/10.3390/batteries12060185 - 22 May 2026
Abstract
Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider [...] Read more.
Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider real-world battery operating conditions. In practice, batteries operate under highly diverse usage patterns, environmental conditions, and user profiles, which can significantly affect SoC estimation accuracy. In this paper, we address this limitation by leveraging real-world data, which contains measurements from vehicle batteries under heterogeneous user behaviors and operating scenarios. The proposed methodology includes a data cleaning and filtering preprocessing stage, followed by an original DL framework designed to evaluate SoC estimation under different learning conditions. The framework is data driven and built upon a TimerV2-based architecture capable of capturing long-term temporal dependencies and nonlinear relationships in battery signals. Furthermore, transfer learning strategies are explored to enhance adaptability across different battery configurations and datasets for efficient knowledge transfer. Extensive experiments show that the proposed approach achieves high estimation accuracy and strong generalization performance, demonstrating its suitability for reliable real-time SoC estimation in practical battery management systems. Full article
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