Advanced MoS2 nanocomposites for post-lithium-ion batteries
Bibliographic record
Abstract
The pursuit of advanced energy storage systems beyond lithium-ion batteries has intensified, driving the exploration of alternative electrode materials. Molybdenum disulfide (MoS 2 ) and its nanocomposites have emerged as promising active material candidates due to their unique structural characteristics and electrochemical properties . In this review, we provide a comprehensive overview of synthesis and design strategies of MoS 2 -based electrode materials tailored to address the challenges posed on rechargeable batteries by large Na + and K + ions, as well as the multivalent nature of Zn 2 + , Mg 2 + , and Al 3＋ ions. We review various structural design/modification strategies, such as interlayer engineering, defect engineering, crystal phase (1T/2H) engineering, heteroatom doping , hybridization, and morphology design. Through an examination of previous theoretical and experimental research, we summarize their impacts on enhancing the electrochemical performance of MoS 2 -based active materials, aiming to improve their capacity, kinetics, reversibility, and stability in post-lithium-ion storage. By compiling numerous research findings, this review offers insights into the rational design principles that guide the development of high-performance layered active materials for next-generation energy storage devices. With MoS 2 as a prototype transition metal dichalcogenide (TMD), these principles are applicable to other layered battery materials, contributing to the advancement of sustainable and efficient electrochemical energy storage technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".