Bibliographic record
Abstract
Lithium-ion batteries (LIBs) have become the most widely used energy storage systems for portable electronic devices and electric vehicles. With the increasing requirements of high energy density, next-generation batteries, including Li-metal batteries, Na-metal batteries and solid-state batteries, have received huge attention in recent years. For most batteries, the interfacial issues between the electrolyte (both liquid and solid) and electrodes are critical factors affecting the performance of the batteries. Atomic and molecular layer deposition (ALD and MLD) are considered as ideal strategies for overcoming the interfacial issues for the batteries. In this talk, I will introduce our research about interface engineering and understanding for next-generation batteries. i) The interface is one of the key factors for the Li and Na deposition behaviors and battery performances. We developed ALD and MLD approaches to fabricate the artificial interface with significantly improved electrochemical performances and reduced dendrite formation for Li/Na metal anodes. ii) We further design different ALD/MLD thin films to stabilize the interfaces for solid-state Li batteries. iii) We have also developed ex-situ and in-situ synchrotron X-ray techniques for next-generation batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".