The development of 2D materials for electrochemical energy applications: A mechanistic approach
Bibliographic record
Abstract
Energy production and storage is one of the foremost challenges of the 21st century. Rising energy demands coupled with increasing materials scarcity have motivated the search for new materials for energy technology development. Nanomaterials are an excellent class of materials to drive this innovation due to their emergent properties at the nanoscale. In recent years, two dimensional (2D) layered materials have shown promise in a variety of energy related applications due to van der Waals interlayer bonding, large surface area, and the ability to engineer material properties through heterostructure formation. Despite notable results, their development has largely followed a guess and check approach. To realize the full potential of 2D materials, more efforts must be made towards achieving a mechanistic understanding of the processes that make these 2D systems promising. In this perspective, we bring attention to a series of techniques used to probe fundamental energy related processes in 2D materials, focusing on electrochemical catalysis and energy storage. We highlight studies that have advanced development due to mechanistic insights they uncovered. In doing so, we hope to provide a pathway for advancing our mechanistic understanding of 2D energy materials for further research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".