Hot-Chemistry Structural Phase Transformation in Single-Crystal Chalcogenides for Long-Life Lithium Ion Batteries
Bibliographic record
Abstract
Tuned chalcogenide single crystals rooted in sulfur-doped graphene were prepared by high-temperature solution chemistry. We present a facile route to synthesize a rod-on-sheet-like nanohybrid as an active anode material and demonstrate its superior performance in lithium ion batteries (LIBs). This nanohybrid contains a nanoassembly of one-dimensional (1D) single-crystalline, orthorhombic SnS onto two-dimensional (2D) sulfur-doped graphene. The 1D nanoscaled SnS with the rodlike single-crystalline structure possesses improved transport properties compared to its 2D hexagonal platelike SnS 2 . Furthermore, we blend this hybrid chalcogenide with biodegradable polymer composite using water as a solvent. Upon drying, the electrodes were subjected to heating in vacuum at 150 °C to induce polymer condensation via formation of carboxylate groups to produce a mechanically robust anode. The LIB using the as-developed anode material can deliver a high volumetric capacity of ∼2350 mA h cm –3 and exhibit superior cycle stability over 1500 cycles as well as a high capacity retention of 85% at a 1 C rate. The excellent battery performance combined with the simplistic, scalable, and green chemistry approach renders this anode material as a very promising candidate for LIB applications.
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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".