Editors’ Choice—Review—Conductive Forms of MoS<sub>2</sub> and Their Applications in Energy Storage and Conversion
Bibliographic record
Abstract
Conductive forms of MoS2 are important emerging 2D materials due to their unique combination of properties such as high electrical conductivity, availability of active sites in edge and basal planes for catalytic activity and expanded interlayer distances. Consequently, there has been a drive to find synthetic routes toward conductive forms of MoS2. Naturally occurring or synthetically grown semiconducting 2H-MoS2 can either be converted into metallic 1T-MoS2, or various dopants may be introduced to modulate the electronic band gap of the 2H-MoS2 phase and increase its conductivity. Chemical and electrochemical intercalation methods, hydrothermal and solvothermal processes, and chemical vapor deposition have all been developed to synthesize conductive MoS2. Conductive MoS2 finds applications in energy storage devices, electrocatalytic reactions, and sensors. Here, we summarize a detailed understanding of the atomic structure and electronic properties of conductive MoS2 which is crucial for its applications. We also discuss various fabrication methods that have been previously reported along with their advantages and disadvantages. Finally, we will give an overview of current trends in different applications in energy storage and electrocatalytic reactions in order to help researchers to further explore the applications of conductive MoS2.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".