Partial Thermal Condensation Mediated Synthesis of High‐Density Nickel Single Atom Sites on Carbon Nitride for Selective Photooxidation of Methane into Methanol
Bibliographic record
Abstract
Abstract Direct selective transformation of greenhouse methane (CH4) to liquid oxygenates (methanol) can substitute energy‐intensive two‐step (reforming/Fischer–Tropsch) synthesis while creating environmental benefits. The development of inexpensive, selective, and robust catalysts that enable room temperature conversion will decide the future of this technology. Single‐atom catalysts (SACs) with isolated active centers embedded in support have displayed significant promises in catalysis to drive challenging reactions. Herein, high‐density Ni single atoms are developed and stabilized on carbon nitride (NiCN) via thermal condensation of preorganized Ni‐coordinated melem units. The physicochemical characterization of NiCN with various analytical techniques including HAADF‐STEM and X‐ray absorption fine structure (XAFS) validate the successful formation of Ni single atoms coordinated to the heptazine‐constituted CN network. The presence of uniform catalytic sites improved visible absorption and carrier separation in densely populated NiCN SAC resulting in 100% selective photoconversion of (CH4) to methanol using H2O2 as an oxidant. The superior catalytic activity can be attributed to the generation of high oxidation (NiIII═O) sites and selective C─H bond cleavage to generate •CH3 radicals on Ni centers, which can combine with •OH radicals to generate CH3OH.
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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".