Photo-catalytic hydrogen production over Au/g-C<sub>3</sub>N<sub>4</sub>: effect of gold particle dispersion and morphology
Bibliographic record
Abstract
Metal/semiconductor interactions affect electron transfer rates and this is central to photocatalytic hydrogen ion reduction. While this interaction has been studied in great detail on metal oxide semiconductors, not much is known of Au particles on top of polymeric semiconductors. The effects of gold nanoparticle size and dispersion on top of g-C3N4 were studied by core and valence level spectroscopy and transmission electron microscopy in addition to catalytic tests. The as-prepared, non-calcined catalysts displayed Au particles with uniform dimension (mean particle size = 1.8 nm) and multiple electronic states: XPS Au 4f7/2 lines at 84.9 and 87.1 eV (each with a spin-orbit splitting of 3.6-3.7 eV). These particles, which did not show localized surface plasmon resonance (LSPR), before the reaction, doubled in size after the reaction giving a pronounced LSPR at about 550 nm. The effect of the heating environment on these particles (in air or in H2) was further investigated. While heating in H2 gave Au nanoparticles of different shapes, heating under O2 gave exclusively spherical particles. Similar activity towards photocatalytic hydrogen ion reduction under UV excitation was seen in both cases, however. XPS Au 4f analyses indicated that an increase in deposition time, during catalyst preparation, resulted in an increase in the initial fraction of oxidized gold particles, which were easily reduced under hydrogen. The valence band region for Au/gC3N4 was further studied in an effort to compare it to what is already known for Au/metal oxide semiconductors. A shift of over 2 eV for the Au 5d doublets was noticed between reduced and oxidized gold particles with mean particle sizes between 2 and 6 nm, which is consistent with the final state effect. A narrow range of gold loading for optimal catalytic performance was seen, where it seems that a density of one Au particle per 10 × 10 nm2 is the most suitable. Particle size and shape had a minor effect on performance, which may indicate the absence of a plasmonic effect on the reaction rate.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".