Adjusting Crystal Orientation to Promote Sodium‐Ion Transport in V<sub>5</sub>S<sub>8</sub>@Graphene Anode Materials for High‐Performance Sodium‐Ion Batteries
Bibliographic record
Abstract
Abstract Sodium‐ion batteries (SIBs) have inspired the potential for widespread use in energy storage owing to the advantages of abundant resources and low cost. Benefiting from the layered structure, 2D‐layered materials enable fast interlayer transport of sodium ions and thus are considered promising candidates as anodes for SIBs. Herein, a strategy of adjusting crystal orientation is proposed via a solvothermal method to improve sodium‐ion transport at the edge of the interlayers in 2D‐layered materials. By introducing surfactants and templates, the 2D‐layered V5S8 nanosheets are controlled to align the interlayer diffusion channels vertically to the surface, which promotes the fast transport of Na+ at the edge of the interlayers as revealed by experimental methods and ab initio calculations. Benefiting from the aligned crystal orientation and rGO coating, the vertical‐V5S8@rGO hybrid delivers a high initial discharge capacity of 350.6 mAh g−1 at a high current density of 15 A g−1. This work provides a strategy for the structural design of 2D‐layered anode materials by adjusting crystal orientation, which demonstrates the promise for applications in fast‐charging alkaline‐ion batteries.
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".