Results 191 to 200 of about 17,780 (305)

Sulfide‐Based Electrolytes for All‐Solid‐State Sodium Batteries

Advanced Energy Materials, EarlyView.
This review covers the structural features and synthesis strategies of sulfide‐based solid electrolytes, as well as critical challenges related to conductivity, interfacial and moisture stability, and scaling‐up for practical application in Sodium‐based All Solid‐State Batteries.
Han Yang, Luca Weckelmann, Baolin Wu, Chih‐Long Tsai, Luc Raijmakers, Rüdiger‐A. Eichel, Anna Windmüller   +6 more
wiley   +1 more source

Optimization and analysis of battery thermal management system structure based on flat heat pipes and biomimetic fins. [PDF]

Heliyon
Liu Z, Xiong C, Du X.
europepmc   +1 more source

Upscaling Sodium‐Ion Battery Cells: From Aqueous Processing to Performance Assessment of Hard Carbon|Prussian White Pouch Cells

Advanced Energy Materials, EarlyView.
This study investigates the feasibility of scaling up Prussian White (PW)‐based cathode manufacturing at a pilot scale. Through careful PW dehydration combined with optimized aqueous processing, we report the stepwise development of industrially relevant 1 Ah pouch cells and evaluate their performance under various conditions.
Faduma M. Maddar, Katerina Gonos, Martand Singh, Jacob Compton, Daniel Atkinson, Matthew J. Capener, Mark Copley, Ivana Hasa   +7 more
wiley   +1 more source

Application of Refrigerant Cooling in a Battery Thermal Management System under High Temperature Conditions: A Review. [PDF]

ACS Omega
Kang Y, Hu Y, Zhang C, Yang K, Zhang Q.
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Recent advancements in battery thermal management system (BTMS): A review of performance enhancement techniques with an emphasis on nano-enhanced phase change materials. [PDF]

Heliyon
Rasool G, Xinhua W, Sun T, Hayat T, Sheremet M, Uddin A, Shahzad H, Abbas K, Razzaq I, Yuexin W.   +9 more
europepmc   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

A Quantitative Lithium Inventory Framework for Anode‐Free Lithium Metal Batteries

Advanced Energy Materials, EarlyView.
A component‐resolved lithium inventory framework quantitatively tracks Li redistribution across the cell in anode‐free NMC622||Cu pouch cells throughout cycling. Three sequential degradation stages are identified: formation‐driven cathode Li depletion, midlife inactive Li0 accumulation, and late‐stage runaway SEI thickening. The cathode, as the sole Li
Wurigumula Bao, Alex Liu, Thomas S. Marchese, Yuanlong Bill Zheng, Weikang Li, Dhevathi Rajan Rajagopalan Kannan, Vallabha Rao Rikka, Vinay Premnath, Judith Jeevarajan, Ying Shirley Meng   +9 more
wiley   +1 more source

Investigations of Li-Ion Battery Thermal Management Systems Based on Heat Pipes: A Review. [PDF]

ACS Omega
Wu H, Niu M, Shao Y, Wang M, Li M, Liu X, Li Z.   +6 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source