Editorial: Interfacial engineering of carbon-based materials for efficient energy conversion
Bibliographic record
Abstract
Over the past decade, carbon-based materials, such as simplex carbon materials, heteroatom-doped 14 carbon materials, carbon-transition metal composites, have received the increasing attentions for 15 energy conversion owning to their low cost, good electrical conductivity, and stable structure. Specially, 16 the diversity in their structure and composition could significantly enhance their applications in various 17 fields, including nanocatalysis, energy conversion, and energy storage. In general, catalytic reactions 18 usually occur on the surface or interface of electrodes. The interface structure is usually formed 19 between carbon-based materials and other components and can be theoretically used as a channel for 20 the transportation of electrons or intermediates. Therefore, interface engineering is one of the feasible 21 and effective strategies to enhance the performance of nanomaterials, which is the cornerstone for the 22 practical applications. We would like to thank all the authors for their meaningful work and all the reviewers for their valuable 71 contributions to this special issue. We expect that these endeavors will pave the way for further 72 advancements in the design and fabrication of carbon-based materials and their derivatives by using 73 interface engineering.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".