Nanostructured Metal Borides for Energy‐Related Electrocatalysis: Recent Progress, Challenges, and Perspectives
Bibliographic record
Abstract
The discovery of durable, active, and affordable electrocatalysts for energy-related catalytic applications plays a crucial role in the advancement of energy conversion and storage technologies to achieve a sustainable energy future. Transition metal borides (TMBs), with variable compositions and structures, present a number of interesting features including coordinated electronic structures, high conductivity, abundant natural reserves, and configurable physicochemical properties. Therefore, TMBs provide a wide range of opportunities for the development of multifunctional catalysts with high performance and long durability. This review first summarizes the typical structural and electronic features of TMBs. Subsequently, the various synthetic methods used thus far to prepare nanostructured TMBs are listed. Furthermore, advances in emerging TMB-catalyzed reactions (both theoretical and experimental) are highlighted, including the hydrogen evolution reaction, the oxygen evolution reaction, the oxygen reduction reaction, the carbon dioxide reduction reaction, the nitrogen reduction reaction, the methanol oxidation reaction, and the formic acid oxidation reaction. Finally, challenges facing the development of TMB electrocatalysts are discussed, with focus on synthesis and energy-related catalytic applications, and some potential strategies/perspectives are suggested as well, which will profit the design of more efficient TMB materials for application in future energy conversion and storage devices.
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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 | 0.000 |
| 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 | 0.000 | 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".