Interfacial Engineering of a Carbon Nitride–Graphene Oxide–Molecular Ni Catalyst Hybrid for Enhanced Photocatalytic Activity
Bibliographic record
Abstract
Carbon nitrides (CN x ) are a promising class of photocatalyst for fuel and chemical synthesis as they are nontoxic and readily synthesized at a low cost. This study reports the enhanced photocatalytic activity for simultaneous alcohol oxidation and proton reduction when graphene oxide (GO) or reduced graphene oxide (RGO) is employed as an interlayer between a cyanamide-functionalized melon-type carbon nitride ( NCN CN x ) and a phosphonated Ni-bis(diphosphine) H 2 -evolution catalyst ( NiP ). Introduction of the GO/RGO enhanced the activity three times, reaching a specific activity of 4655 ± 448 μmol H 2 (g NCN CN x ) −1 h –1 with a NiP -based turnover frequency of 116 ± 3 h –1 . Mechanistic studies into this closed photoredox system revealed that the rate of electron extraction from NCN CN x is rate limiting. GO/RGO is commonly employed to improve the electron transfer dynamics on nanosecond time scales, but time-resolved photoluminescence and transient absorption spectroscopy reveal that these properties are not significantly affected in our NCN CN x -GO hybrid on fast time scales (<0.1 s). However, long-lived “trapped-electrons” generated upon photoexcitation of NCN CN x in the presence of organic substrates are shown by photoinduced absorption spectroscopy to be quenched faster with GO/RGO, supporting that GO/RGO improves electron transfer from NCN CN x to NiP on time scales >0.1 s. The absorption profile of NiP in the presence of different GO loadings reveals that GO acts as a conductive interfacial “binder” between NiP and NCN CN x . The enhancement in activity therefore does not primarily arise from changes in the photophysics of the NCN CN x, but rather from GO/RGO enabling better electronic communication between NCN CN x and NiP .
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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".