Merging Single‐Atom‐Dispersed Iron and Graphitic Carbon Nitride to a Joint Electronic System for High‐Efficiency Photocatalytic Hydrogen Evolution
Bibliographic record
Abstract
Abstract Scalable and sustainable solar hydrogen production via photocatalytic water splitting requires extremely active and stable light‐harvesting semiconductors to fulfill the stringent requirements of suitable energy band position and rapid interfacial charge transfer process. Motivated by this point, increasing attention has been given to the development of photocatalysts comprising intimately interfaced photoabsorbers and cocatalysts. Herein, a simple one‐step approach is reported to fabricate a high‐efficiency photocatalytic system, in which single‐site dispersed iron atoms are rationally integrated on the intrinsic structure of the porous crimped graphitic carbon nitride (g‐C3N4) polymer. A detailed analysis of the formation process shows that a stable complex is generated by spontaneously coordinating dicyandiamidine nitrate with iron ions in isopropanol, thus leading to a relatively complicated polycondensation reaction upon thermal treatment. The correlation of experimental and computational results confirms that optimized electronic structures of Fe@g‐C3N4 with an appropriate d‐band position and negatively shifting Fermi level can be achieved, which effectively gains the reducibility of electrons and creates more active sites for the photocatalytic reactions. As a result, the Fe@g‐C3N4 exhibits a highlighted intramolecular synergistic effect, performing greatly enhanced solar‐photon‐driven activities, including excellent photocatalytic hydrogen evolution rate (3390 µmol h−1 g−1, λ > 420 nm) and a reliable apparent quantum efficiency value of 6.89% at 420 nm.
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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".