Sponge-shaped Au nanoparticles: a stand-alone metallic photocatalyst for driving the light-induced CO<sub>2</sub> reduction reaction
Bibliographic record
Abstract
Abstract Coinage metal nanoparticles (NPs) enable plasmonic catalysis by generating hot carriers that drive chemical reactions. Making NPs porous enhances the adsorption of reactant molecules. We present a dewetting and dealloying strategy to fabricate porous gold nanoparticles (Au-Sponge) and compare their CO2 photoreduction activity with respect to the conventional gold nanoisland (Au-Island) morphology. Porous gold nanoparticles exhibit an unusually broad and red-shifted plasmon resonance which is in agreement with the results of finite difference time domain (FDTD) simulations. The key insight of this work is that the multi-step reduction of CO2 driven by short-lived hot carriers generated by the d → s interband transition proceeds extremely quickly as evidenced by the generation of methane. A 3.8-fold enhancement in the photocatalytic performance is observed for the Au-Sponge in comparison to the Au-Island. Electrochemical cyclic voltammetry measurements confirm the 2.5-fold increase in the surface area and roughness factor of the Au-Sponge sample due to its porous nature. Our results indicate that the product yield is limited by the amount of surface adsorbates i.e. reactant-limited. Isotope-labeled mass spectrometry using 13CO2 was used to confirm that the reaction product (13CH4) originated from CO2 photoreduction. We also present the plasmon-mediated photocatalytic transformation of 4-aminothiophenol (PATP) into p,p′-dimercaptoazobenzene (DMAB) using Au-Sponge and Au-Island samples.
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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".