Effect of concentration-driven magnetic phase changes on adsorption and diffusion in VSe2 monolayers: Implications for lithium-ion batteries
Bibliographic record
Abstract
A vanadium diselenide (VSe2) monolayer is a two-dimensional (2D) magnetic material that exhibits ferromagnetic ordering at room temperature and exceptional metal-ion storage capacity, making it useful in spintronics and energy storage applications. However, a robust correlation between the magnetic and electrochemical properties of VSe2 remains to be established. In this study, first-principles density functional theory calculations were performed to investigate the effect of increasing Li-ion concentrations on the magnetic properties, particularly the magnetic ground state of the VSe2 monolayer. The results indicate that, as the concentration of Li ions on the surface of VSe2 monolayer increases, magnetic phase transitions occur, leading to a shift from the intrinsic ferromagnetic (FM) state to antiferromagnetic (AFM) and non-magnetic ground states. Analyses of the diffusion properties of ferromagnetic and antiferromagnetic VSe2 monolayers revealed a considerable (∼71%) increase in the Li-ion diffusion energy barrier for the AFM state compared to the FM state. This implies that FM-VSe2 facilitates relatively faster diffusion of Li ions than AFM-VSe2. Therefore, the Li-ion concentration-induced phase change in the VSe2 monolayer leads to variable adsorption and diffusion characteristics, which will have significant implications for its use in Li-ion battery anodes.
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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) | 0.001 | 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".