Unraveling the Origin of Lithiophilicity toward a Molten Li Metal: Zn Metal as Trojan Horse
Bibliographic record
Abstract
In this work, we investigated the origin of lithiophilicity of a Cu foil substrate modified by a sputtered Zn thin film (Cu@Zn) in contact with a molten Li metal to understand the reaction mechanism between Li and Cu@Zn. We studied the reaction between the molten Li metal and the Cu surface during Li solidification via in situ scanning electron microscopy (SEM), subsequently performed post-mortem energy dispersive spectroscopy (EDS) and Grazing Incidence X-ray Diffraction (GIXRD) on the coatings to analyze the chemistry of the reaction products, and compared the results for different thicknesses of nanometric Zn films (5–50 nm). For the first time in the literature, we report the existence of a metastable ternary Li-Cu-Zn alloy at 300 °C after the reaction of Cu@Zn with the molten Li metal. We also report the segregation of Cu and Zn by formation of Cu-Zn intermetallic compounds during the cooling down step. The results of our in situ study are pivotal to clarify the interfacial reactions occurring between a lithiophilic current collector and a molten Li metal and have utmost importance for designing advanced anode materials for future solid-state battery applications.
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 | 0.001 |
| 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".