Engineering strategies and active site identification of MXene-based catalysts for electrochemical conversion reactions
Bibliographic record
Abstract
MXenes have been extensively studied due to their high metallic conductivity, hydrophilic properties, tunable layer structure and attractive surface chemistry, making them highly desirable for energy-related applications. However, slow catalytic reaction kinetics and limited active sites have severely impeded their further practical applications. Surface engineering of MXenes has been rationally designed and investigated to regulate their electronic structure, increase the density of active sites, optimize the binding energy, and thus boost the electrocatalytic performance. In this review, we comprehensively summarized the surface engineering strategies for MXene nanostructures, including surface termination engineering, defect engineering, heteroatom doping engineering (metals or non-metals), secondary material engineering, and extension to MXene analogues. By identifying the roles of each component in the engineered MXenes at the atomic level, their intrinsic active sites have been discussed to establish the relationships between the atomic structures and catalytic activities. We highlighted the state-of-the-art progress of MXenes in electrochemical conversion reactions including hydrogen, oxygen, carbon dioxide, nitrogen and sulfur conversion reactions. The challenges and perspectives of MXene-based catalysts for electrochemical conversion reactions are presented to inspire more efforts toward the understanding and development of MXene-based materials to meet the ever-growing demand for a sustainable future.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".