Results 21 to 30 of about 59,844 (265)

Carbon-Coatings Improve Performance of Li-Ion Battery

open access: yesNanomaterials, 2022
The development of lithium-ion batteries largely relies on the cathode and anode materials. In particular, the optimization of cathode materials plays an extremely important role in improving the performance of lithium-ion batteries, such as specific ...
Ziling Chen, Qian Zhang, Qijie Liang
doaj   +1 more source

Upcycling the cathodes of spent lithium ion batteries into high-performance cathodes

open access: yesNano Research Energy
Recycling spent lithium-ion batteries (LIBs) is crucial for environmental protection and sustainability. Currently, the main methods for recycling spent LIBs include element extraction and material regeneration.
Jiahui Xu   +9 more
doaj   +1 more source

Vanadium Oxide-Based Cathode Materials for Aqueous Zinc-Ion Batteries: Energy Storage Mechanism and Design Strategy

open access: yesInorganics, 2023
Aqueous zinc ion batteries (AZIBs) are an ideal choice for a new generation of large energy storage devices because of their high safety and low cost. Vanadium oxide-based materials have attracted great attention in the field of AZIB cathode materials ...
Yu Qiu   +9 more
doaj   +1 more source

High-throughput computational design of cathode coatings for Li-ion batteries

open access: yesNature Communications, 2016
Degradation of cathode materials is a key factor hindering the long-term stability of lithium ion batteries. Here, the authors develop a high-throughput computational approach to design effective cathode coating materials, proposing a selection of ...
Muratahan Aykol   +8 more
doaj   +1 more source

Electrochemical Behavior of Flame‐Sprayed Sc‐Doped AlCoCrFeMo High‐Entropy Alloy Coatings in 3.5% Sodium Chloride Solution

open access: yesAdvanced Engineering Materials, EarlyView.
Scandium (Sc)‐doped AlCoCrFeMo HEA coatings are fabricated via flame spraying with 0.1, 0.3, and 0.5 wt% Sc additions. Among these, the HEA‐Sc0.3 coating exhibits the highest corrosion resistance, indicated by a more positive corrosion potential and lower current density.
Pankaj Kumar   +7 more
wiley   +1 more source

Recent advances in cathode materials of rechargeable aqueous zinc-ion batteries

open access: yesMaterials Today Advances, 2020
Given good safety, low cost, and environmental friendliness, rechargeable aqueous zinc-ion batteries (ZIBs) are considered as a more feasible solution for grid-scale applications than Li-ion batteries.
L. Wang, J. Zheng
doaj   +1 more source

Cathode strategies to improve the performance of zinc‐ion batteries

open access: yesElectrochemical Science Advances, 2022
Zinc‐ion battery (ZIB) has been attracting extensive attention due to its high theoretical capacity, high safety, and low cost. However, the exploration of suitable cathode materials to host Zn ions remains a challenge, owing to the strong electrostatic ...
Pingge He, Shaowei Chen
doaj   +1 more source

Cathode Materials for Microbial Fuel Cells

open access: yesGazi Üniversitesi Fen Bilimleri Dergisi, 2023
The most important problems of today are meeting the increasing energy needs and avoiding environmental pollution caused by fossil resources usage for energy production.
Işılay BİLGİÇ
doaj   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

open access: yesAdvanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran   +6 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer   +4 more
wiley   +1 more source

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