Artificial intelligence applied to battery research: hype or reality?
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
Surface wettability for skin‐interfaced sensors and devices
The practical applications of skin‐interfaced sensors and devices in daily life hinge on the
rational design of surface wettability to maintain device integrity and achieve improved …
rational design of surface wettability to maintain device integrity and achieve improved …
Evaluating the effectiveness of in situ characterization techniques in overcoming mechanistic limitations in lithium–sulfur batteries
Advanced energy storage systems require high energy and power densities, abundant
availability of raw materials, low cost, reasonable safety, and environmental benignancy …
availability of raw materials, low cost, reasonable safety, and environmental benignancy …
Guiding the design of heterogeneous electrode microstructures for Li‐ion batteries: microscopic imaging, predictive modeling, and machine learning
Electrochemical and mechanical properties of lithium‐ion battery materials are heavily
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
[HTML][HTML] 2021 roadmap on lithium sulfur batteries
Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images
The segmentation of tomographic images of the battery electrode is a crucial processing
step, which will have an additional impact on the results of material characterization and …
step, which will have an additional impact on the results of material characterization and …