From Nanoalloy to Nano‐Laminated Interfaces for Highly Stable Alkali‐Metal Anodes
Bibliographic record
Abstract
Abstract Metal anodes are considered the holy grail for next‐generation batteries because of their high gravimetric/volumetric specific capacity and low electrochemical potential. However, several unsolved challenges have impeded their practical applications, such as dendrite growth, interfacial side reactions, dead layer formation, and volume change. An electrochemically, chemically, and mechanically stable artificial solid electrolyte interphase is key to addressing the aforementioned issue with metal anodes. This study demonstrates a new concept of organic and inorganic hybrid interfaces for both Li‐ and Na‐metal anodes. Through tailoring the compositions of the hybrid interfaces, a nanoalloy structure to nano‐laminated structure is realized. As a result, the nanoalloy interface (1Al 2 O 3 –1alucone or 2Al 2 O 3 –2alucone) presents the most stable electrochemical performances for both Li‐and Na‐metal anodes. The optimized thicknesses required for the nanoalloy interfaces for Li‐ and Na‐metal anodes are different. A cohesive zone model is applied to interpret the underlying mechanism. Furthermore, the influence of the mechanical stabilities of the different interfaces on the electrochemical performances is investigated experimentally and theoretically. This approach provides a fundamental understanding and establishes the bridge between mechanical properties and electrochemical performance for alkali‐metal anodes.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".