Interface Welding via Thermal Pulse Sintering to Enable 4.6 V Solid‐State Batteries
Bibliographic record
Abstract
Abstract NASICON‐type Li 1.3 Al 0.3 Ti 1.7 (PO4) 3 (LATP) is one of the most promising solid‐state electrolytes (SSEs) to achieve high‐energy‐density solid‐state batteries (SSBs) due to its high ionic conductivity, high‐voltage stability, and low cost. However, its practical application is constrained by inadequate interfacial compatibility with cathode materials and significant incompatibility with lithium metal. In this work, a cost‐effective interface welding approach is reported, utilizing an innovative thermal pulse sintering (TPS) to fabricate LATP‐based solid‐state batteries. Initially, the rapid thermal pulses enhance the ionic conductivity of LATP SSE by inducing selective growth of LATP nanowires, effectively occupying interparticle voids. Additionally, this process results in the formation of a dense layer (GCM) comprising graphene oxide, carbon nanotubes, and MXene with a controlled Li + transport pathway, facilitating lithium stripping and plating processes. Moreover, these thermal pulses facilitate the interfacial fusion between LATP and cathode materials, while avoiding undesired phase diffusion. As a result, SSBs with a LiCoO 2 cathode deliver favorable cycle stability at 4.6 V, marking significant progress. This facile interface welding strategy represents a substantial step toward high‐energy‐density SSB development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".