Fast capture and stabilization of Li‐ions via physicochemical dual effects for an ultra‐stable self‐supporting Li metal anode
Bibliographic record
Abstract
Abstract Lithium (Li) metal is regarded as one of the most promising anode candidates for next‐generation batteries due to its extremely high specific capacity and low redox potential. However, its application is still hindered by the uncontrolled growth of dendritic Li and huge volume fluctuation during cycles. To address these issues, flexible and self‐supporting three‐dimensional (3D) interlaced N‐doped carbon nanofibers (NCNFs) coated with uniformly distributed 2D ultrathin NiCo2S4 nanosheets (denoted CNCS) were designed to eliminate the intrinsic hotspots for Li deposition. Physicochemical dual effects of CNCS arise from limited surface Li diffusivity with a higher Li affinity, leading to uniform Li nucleation and less random accumulation of Li, as confirmed by ab initio molecular dynamics simulations. Due to the unique structure, exchange current density is reduced significantly and metallic Li is further contained within the interspace between the NCNF and NiCo2S4 nanosheets, preventing the formation of dendritic Li. The symmetric cell with a Li/CNCS composite anode shows a long‐running lifespan for almost 1200 h, with an exceptionally low and stable overpotential under 1 mA cm−2/1 mAh cm−2. A full cell coupled with a LiFePO4 cathode at a low N/P ratio of 2.45 shows typical voltage profiles but more significantly enhanced performance than that of a LiFePO4 cathode coupled with a bare Li anode.
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".