Enhancing Li Deposition Behavior through Valence Gradient-Assisted Iron Layer
Bibliographic record
Abstract
Uncontrolled lithium (Li) dendrite formation presents major safety risks and challenges in the Li host design. A novel approach is introduced, using a valence gradient in iron nanoparticles (Fe 0, Fe 2+, Fe 3+ ) to stabilize the anodes. An Fe 0 component, with fast Li diffusion, ensures a steady supply of Li to Fe 2+ and Fe 3+ components, which have slower Li diffusion. This coordinated interplay between fast and slow diffusion uniformizes Li deposition near the substrate, effectively reducing the rate of dendrite growth. The as-prepared framework demonstrates uniform Li plating with a minimal hysteresis voltage after extensive cycling for 1200 h in symmetric cells. Integrated into a full cell with LiFePO 4, it demonstrates outstanding cycling stability for almost 950 cycles with a capacity of 92.2 mA h g –1 at 1C with an ultralow N/P ratio of 1.19. This valence gradient design strategy broadens the design potential for transition-metal compounds in regulating Li deposition by mitigating interfacial Li + behavior.
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".