Composites of Ag-Doped ZnIn<sub>2</sub>S<sub>4</sub> Nanoplates with Graphitic Carbon Nitride and Reduced Graphene Oxide Nanosheets for Sunlight-Driven Hydrogen Production and Water Purification
Bibliographic record
Abstract
Photocatalysis is considered a promising technology to alleviate the energy crisis and environmental pollution; however, developing photocatalysts with improved light absorption efficiency is still a challenge. In this work, an effective strategy was proposed to synthesize a highly functional ternary nanocomposite (g-C 3 N 4 /RGO/AZIS) by coupling broader light-absorbing Ag-doped ZnIn 2 S 4 (AZIS) nanoplates with ultrathin g-C 3 N 4 and reduced graphene oxide (RGO) nanosheets. The 2D-on-2D stacking nanostructure of the composite provides a compact heterojunction, enlarged interfaces, and enriched active sites, resulting in the accelerated separation and relocation kinetics of charge carriers. Benefiting from these advantages, the g-C 3 N 4 /RGO/AZIS nanocomposite with systematically optimized contents of RGO and AZIS can serve as an efficient bifunctional photocatalyst for both H 2 production from water splitting and methyl orange (MO) photodegradation under the irradiation of visible light. The H 2 production rate of the ternary nanocomposite is 658.5 μmol h –1 g –1, which is 38 times higher than that of plain g-C 3 N 4 . The operation mechanism is proposed based on the results of scavenger tests and photoelectrochemical analysis. The formation of a type-II heterostructure between AZIS nanoplates and g-C 3 N 4 nanosheets along with RGO with lower potential can maximize the separation efficiency of photogenerated electron–hole pairs and decrease the charge recombination. This work provides a viable strategy to develop bifunctional photocatalysts with enhanced performance for both H 2 production and degradation of organic dyes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".