Anion Vacancies Regulating Endows MoSSe with Fast and Stable Potassium Ion Storage
Bibliographic record
Abstract
Vacancy engineering is a promising approach for optimizing the energy storage performance of transition metal dichalcogenides (TMDs) due to the unique properties of vacancies in manipulating the electronic structure and active sites. Nevertheless, achieving effective introduction of anion vacancies with adjustable vacancy concentration on a large scale is still a big challenge. Herein, MoS 2(1– x ) Se 2 x alloys with anion vacancies introduced in situ have been achieved by a simple alloying reaction, and the vacancy concentration has been optimized through adjusting the chemical composition. Experimental and density functional theory calculation results suggest that the anion vacancies in MoS 2(1– x ) Se 2 x alloys could enhance the electronic conductivity, induce more active sites, and alleviate structural variation in the alloys during the potassium storage process. When applied as potassium ion battery anodes, the most optimized vacancy-rich MoSSe alloy delivered high reversible capacities of 517.4 and 362.4 mAh g –1 at 100 and 1000 mA g –1, respectively. Moreover, a reversible capacity of 220.5 mAh g –1 could be maintained at 2000 mA g –1 after 1000 cycles. This work demonstrates a practical approach to modifying the electronic and defect properties of TMDs, providing an effective strategy for constructing advanced electrode materials for battery systems.
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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".