Interfacial Amide Bonding in Single-Atom TCPP(Pt)@Zn<sub><i>x</i></sub>Cd<sub>1–<i>x</i></sub>S-Cysteine Photocatalyst for Enhanced Sunlight-Driven Hydrogen Production
Bibliographic record
Abstract
In this study, a novel approach for synthesizing TCPP(Pt)@Zn x Cd 1– x S-Cys (TCPP(Pt)@ZCS x -Cys) nanocomposites was developed by coupling cysteine-functionalized Zn x Cd 1– x S (ZCS x -Cys) with single platinum atom-integrated TCPP through amide bond formation. This covalent linkage markedly enhances interfacial charge transfer, thereby promoting efficient electron utilization. In addition, the incorporation of TCPP(Pt) provides exposed Pt single-atom active sites, contributing to improved photocatalytic reduction performance. Among the series of composites with different TCPP(Pt) concentrations (1, 2, and 4 wt %), the 2% TCPP(Pt)@ZCS 0.25-Cys sample exhibited the highest H 2 production rate, achieving 33.96 mmol·g –1 ·h –1 after the first hour of light irradiation, which was 16.3-fold higher than that of ZCS 0.25-Cys. Impressively, the apparent quantum yield (AQY) of this composite reached approximately 60.4% at λ = 420 nm, 53.8% at 460 nm, and 40.1% at 500 nm, respectively, ranking among the highest reported for photocatalytic hydrogen generation. The physical, optical, and electronic properties confirmed the formation of a type-II heterojunction, which facilitates spatial charge separation and directional charge transfer, thereby enhancing photocatalytic efficiency under solar irradiation. This work not only offers a promising strategy for the development of TCPP(Pt)@ZCS x -Cys photocatalysts for solar-driven hydrogen production but also addresses critical limitations associated with charge recombination and catalytic site accessibility in photocatalytic systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".