Rational Interface Design for High-Performance All-Solid-State Lithium Batteries
Bibliographic record
Abstract
All-solid-state lithium batteries (ASSLBs) have gained substantial attention owing to their excellent safety and high energy density. However, the development of ASSLBs has been hindered by large interfacial resistance originating from the detrimental interfacial reactions, poor solid-solid contact, and lithium dendrite growth. The research in this thesis aims at achieving high-performance ASSLBs via rational interface design and understanding the interfacial reaction mechanisms.\nAt the cathode interface, an ideal dual core-shell nanostructure was first designed. Moreover, single-crystal LiNi0.5Mn0.3Co0.2O2 (SC-NMC532) cathode was compared with polycrystalline NMC532, the former exhibits much enhanced Li+ diffusion kinetics in ASSLIBs. Besides, it is found that the interfacial structural degradation significantly impedes interfacial Li+ transport in ASSLIBs. Fortunately, the interfacial coating is demonstrated to be effective in suppressing interfacial degradation.\nFurthermore, the ionic conductivity of interfacial layer LNTO was purposely tuned to investigate the effect of interfacial ionic conductivity on ASSLIBs, it is revealed that enhancing the interfacial ionic conductivity is very crucial for high-performance ASSLBs. The conclusion was confirmed by the in-situ growth of Li3InCl6. The high Li+-conductive Li3InCl6 coated LCO demonstrates an ultra-small interfacial resistance of 0.13 W.cm-2 and excellent electrochemical performance.\nAt the anode interface, an inorganic-organic hybrid interlayer and a solid-state plastic crystal electrolyte were successfully engineered to prevent the interfacial reactions and lithium dendrite formation. Last but not least, a solid-liquid hybrid electrolyte was developed as interfacial solid-liquid electrolyte interphase (SLEI) to achieve high-performance ASSLBs.\nIn summary, the discoveries in this thesis provide important guidance to achieve high-performance ASSLBs via rational interface design.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".