Search NASA⌕ Search

Engineering topics

Liang, Jie

Publications and source records attributed to Liang, Jie.

Challenges and Prospects of All-Solid-State Electrodes for Solid-State Lithium Batteries

In the development of all-solid-state lithium batteries (ASSLB), progress is made with solid-state electrolytes; however, challenges regarding compatibility and stability still exist with solid electrodes. These issues result in a low battery capacity and short cycle life, which limit the commercial application of ASSLBs. Here, this review summarizes the recent research progress on solid-state electrodes in ASSLBs including the solid–solid interface phenomena such as the interface between electrode materials and electrolytes. The mechanical stability problems in solid electrodes, including fracture, brittleness, and deformation of electrode materials, are also discussed, and corresponding methods to measure the solid electrode stress are provided. In addition, strategies for mitigating stress-related issues are examined. Finally, the fabrication process of solid electrodes is introduced and their future developments, including the exploration of new electrode materials and the design of more intelligent electrode structures, are proposed.

25 ENERGY STORAGE↗

Protein folds vs. protein folding: Differing questions, different challenges

We report protein fold prediction using deep-learning artificial intelligence (AI) has transformed the field of protein structure prediction. By combining physical and geometric constraints—and especially patterns extracted from the Protein Data Bank —these machine learning algorithms can predict protein structures at or near atomic resolution and do so in seconds. Today, these computational methods have now solved more than 200 million protein structures, which are accessible from the AlphaFold Protein Structure Database. This accomplishment seems all the more remarkable because few thought it possible or saw it coming. Deservedly, deep-learning AI was named Science magazine’s 2021 “breakthrough of the year”. Clearly, deep-learning AI represents a major advance in protein fold prediction.

54 ENVIRONMENTAL SCIENCES↗