Enhancement of Scattering and Near Field of TiO<sub>2</sub>–Au Nanohybrids Using a Silver Resonator for Efficient Plasmonic Photocatalysis
Bibliographic record
Abstract
Recently, localized surface plasmon resonances (SPRs) of metallic nanoparticles (NPs) have been widely used to construct plasmonic nanohybrids for heterogeneous photocatalysis. For example, the combination of plasmonic Au NPs and TiO 2 provides pure TiO 2 visible-light activity. The SPR effect induces an electric field and consequently enhances light scattering and absorption, favoring the transfer of photon energy to hot carriers for catalytic reactions. Numerous approaches have been dedicated to the improvement of SPR absorption in photocatalysts. Here, we have designed a core@shell–satellite nanohybrid catalyst whereby an Ag NP core, as a plasmonic resonator featuring unique dual functions of strong scattering and near-field enhancement, is encapsulated by SiO 2 and TiO 2 layers in sequence, with Au NPs on the outer surface, Ag@SiO 2 @TiO 2 –Au, for efficient plasmonic photocatalysis. By varying the size and number of Ag NP cores, the Au SPR can be tailored over the visible and near-infrared spectral region to reabsorb the scattered photons. In the presence of the Ag core, the incident light is efficiently confined in the reaction suspension by undergoing multiple scattering, thus leading to an increase of the optical path to the photocatalysis. Moreover, using numerical analysis and experimental verifications, we demonstrate that the Ag core also induces a strong near-field enhancement at the Au–TiO 2 interface via SPR coupling with Au. Consequently, the activity of the TiO 2 –Au plasmonic photocatalyst is significantly enhanced, resulting in a high H 2 production rate under visible light. Thus, the design of a single structural unit with strong scattering and field enhancement, induced by a plasmonic resonator, is a highly effective strategy to boost photocatalytic activity.
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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".