Laser‐Irradiated Holey Graphene‐Supported Single‐Atom Catalyst towards Hydrogen Evolution and Oxygen Reduction
Bibliographic record
Abstract
Abstract Single‐atom catalysts (SAC) can boost the intrinsic catalytic activity of hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR). However, the challenge remains due to the complex synthesis process and insufficient stability. A sustainable approach is applied to synthesizing SACs through laser irradiation and gaining mesoporous graphene oxide (MGO). The surface dangling bonds of nitrogen‐doped MGO (NMGO) extract metal atoms species from Co or Fe metal foams and convert them to SAC via an appropriate synthesis approach. Notably, the Co‐NMGO electrocatalyst requires low potentials of 146 mV to convey a current density of 10 mA cm−2 towards HER. Similarly, the Fe‐NMGO electrocatalyst offers an onset of 0.79 V towards ORR in acidic solution. The individual metal atoms are confirmed via aberration‐corrected scanning transmission electron microscopy, and X‐ray absorption near‐edge structure and extended X‐ray absorption fine structure. Density functional theory calculations by applying the grand canonical potential kinetics model revealed that Co‐NMGO shows the optimum free reaction energy of −0.17 eV at −0.1 V for HER, and Fe‐NMGO has less limiting potential than that of Co‐NMGO for ORR case. This work opens a new approach towards the synthesis of SAC and its mechanistic understandings.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".