Silver nanoparticle enhanced metal-organic matrix with interface-engineering for efficient photocatalytic hydrogen evolution
Bibliographic record
Abstract
Abstract Integrating plasmonic nanoparticles into the photoactive metal-organic matrix is highly desirable due to the plasmonic near field enhancement, complementary light absorption, and accelerated separation of photogenerated charge carriers at the junction interface. The construction of a well-defined, intimate interface is vital for efficient charge carrier separation, however, it remains a challenge in synthesis. Here we synthesize a junction bearing intimate interface, composed of plasmonic Ag nanoparticles and matrix with silver node via a facile one-step approach. The plasmonic effect of Ag nanoparticles on the matrix is visualized through electron energy loss mapping. Moreover, charge carrier transfer from the plasmonic nanoparticles to the matrix is verified through ultrafast transient absorption spectroscopy and in-situ photoelectron spectroscopy. The system delivers highly efficient visible-light photocatalytic H 2 generation, surpassing most reported metal-organic framework-based photocatalytic systems. This work sheds light on effective electronic and energy bridging between plasmonic nanoparticles and organic semiconductors.
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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".