Recent Advances in Engineered MoS<sub>2</sub>-Based Nanomaterials for CO<sub>2</sub> Electro-Reduction to CO and Beyond
Bibliographic record
Abstract
The conversion of carbon dioxide (CO 2 ) into value-added compounds is an emerging climate-change mitigation technique. Among various approaches, electrochemical CO 2 reduction (ECO 2 R) driven by renewable energy sources is considered one of the most viable methods for CO 2 reduction. Thus, developing efficient, cost-effective electrocatalysts that enhance reaction kinetics is vital for advancing ECO 2 R and enabling large-scale implementation. During the past few years, among the several transition metal dichalcogenides, molybdenum disulfide (MoS 2 ) has attracted much interest in the field of electrocatalysis owing to its two-dimensional (2D) structure and high density of active sites, which could lead to the development of several high-performance ECO 2 R catalysts. This review presents the development and design of MoS 2 -based nanomaterials tailored for electrochemical CO 2 reduction (ECO 2 R), exploring the relationship between engineering strategies, catalytic performance, CO 2 conversion efficiency, and reaction pathways, while also highlighting controlled synthesis methods, recent advances in catalyst design for active site stabilization, and the influence of electrolytes on ECO 2 R performance. It also underscores the significant challenges that need to be overcome for the real-world implementation of MoS 2 -based nanomaterials in ECO 2 R to produce value-added chemicals, emphasizing the need for further research and development in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".