Chemically Bonded Plasmonic Triazole‐Functionalized Au/Zeolitic Imidazole Framework (ZIF‐67) for Enhanced CO<sub>2</sub> Photoreduction
Bibliographic record
Abstract
Abstract The design of functionalized metallic nanoparticles is considered an emerging technique to ensure the interaction between metal and semiconductor material. In the literature, this interface interaction is mainly governed by electrostatic or van der Waals forces, limiting the injection of electrons under light irradiation. To enhance the transfer of electrons between two compounds, close contact or chemical bonding at the interface is required. Herein, a new approach was reported for the synthesis of chemically bonded plasmonic Au NPs/ZIF‐67 nanocomposites. The structure of ZIF‐67 was grown on the surface of functionalized plasmonic Au NPs using 1H‐1,2,4‐triazole‐3‐thiol as the capping agent, which acted as both stabilizer of Au nanoparticles and a molecular linker for ZIF‐67 formation. As a result, the synthesized material exhibited outstanding photocatalytic CO2 reduction with a methanol production rate of 2.70 mmol h−1 g−1cat under sunlight irradiation. This work emphasizes that the diligent use of capping agents, with suitable functional groups, could facilitate the formation of intimate heterostructure for enhanced photocatalytic CO2 reduction.
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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".