Mo <sub>3</sub> S <sub>13</sub> Chalcogel: A High‐Capacity Electrode for Conversion‐Based Li‐Ion Batteries
Bibliographic record
Abstract
Abstract Despite large theoretical energy densities, metal‐sulfide electrodes for energy storage systems face several limitations that impact the practical realization. Here, we present the solution‐processable, room temperature (RT) synthesis, local structures, and application of a sulfur‐rich Mo 3 S 13 chalcogel as a conversion‐based electrode for lithium‐sulfide batteries (LiSBs). The structure of the amorphous Mo 3 S 13 chalcogel is derived through operando Raman spectroscopy, synchrotron X‐ray pair distribution function (PDF), X‐ray absorption near edge structure (XANES), and extended X‐ray absorption fine structure (EXAFS) analysis, along with ab initio molecular dynamics (AIMD) simulations. A key feature of the three‐dimensional (3D) network is the connection of Mo 3 S 13 units through S−S bonds. Li/Mo 3 S 13 half‐cells deliver initial capacity of 1013 mAh g −1 during the first discharge. After the activation cycles, the capacity stabilizes and maintains 312 mAh g −1 at a C/3 rate after 140 cycles, demonstrating sustained performance over subsequent cycling. Such high‐capacity and stability are attributed to the high density of (poly)sulfide bonds and the stable Mo−S coordination in Mo 3 S 13 chalcogel. These findings showcase the potential of Mo 3 S 13 chalcogels as metal‐sulfide electrode materials for LiSBs.
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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".