Chemically Bonded Ni Cocatalyst onto the S Doped g-C<sub>3</sub>N<sub>4</sub> Nanosheets and Their Synergistic Enhancement in H<sub>2</sub> Production under Sunlight Irradiation
Bibliographic record
Abstract
Nickel deposited S-doped carbon nitride (Ni–S:g-C 3 N 4 /Ni-SCN) nanosheets have been synthesized using calcination followed by a sulfidation process. X-ray photoelectron spectra revealed that the doped S atoms are successfully introduced into the 301 lattices of the host g-C 3 N 4 . XPS spectra indicated that the deposited Ni species are chemically bonded onto the host SCN nanosheets through sulfur bonds. The sunlight-driven photocatalytic hydrogen production efficiency of the synthesized Ni-SCN nanosheets is found to be 3628 μmol g –1 h –1, which is around 1.5 folds higher than that of Pt-SCN that synthesized in the present study. The observed efficiency is attributed to the chemical bonding of Ni through S that largely favored the photocatalytic process in terms of charge-separation as well as self-catalytic reactions. The apparent quantum efficiency of the photocatalyst at 420 nm is estimated to be 17.2%, which is relatively one of the higher values reported in the literature. The photocatalytic recyclability results showed consistent hydrogen evolution efficiency over 4 cycles (8 h) that revealed the excellent stability of the photocatalyst. This work has demonstrated that the chemical bonding of cocatalyst onto the host photocatalyst is relatively an effective strategy as compared to the conventional deposition of cocatalyst by means of electrostatic or van der Waals forces.
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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".