From Batch to Flow Plasmon Catalysis: Revealing Mass Transport Limits inAu@Pd Nanocatalysts for Suzuki Coupling
Bibliographic record
Abstract
Plasmonic catalysis, as a powerful tool for synthetic transformations, has the potential to impact wide-scale applications by converting solar light into energy for chemical reactions. Current studies are limited to mL-scale batch reactors with mg-level nanocatalysts, lacking feasibility at common laboratory and industrially configurations. To overcome this limitation, transition of plasmonic chemistry from batch to flow mode is foreseen, however, there is a lack of understanding of how plasmon-driven processes couple with mass transport. To address this, we designed plasmonic catalysts for a flow system within tens of mL scale employing gram-scale Au@PdNPs-Al 2 O 3 nanostructures in a flow reactor. Using Suzuki cross-coupling as a model reaction, we showed that flow mode for Au@PdNPs-Al 2 O 3 increases reaction rate, time to full conversion and apparent quantum yield (AQY) ×3 times compared to batch mode and overperform previously reported ones. Fluid dynamic simulations showed critical effect of residence times of nanocatalyst–reactant complexes under illumination to product yield. This was consistent with photocurrent measurements, revealing electron transfer efficiency is enhanced under increased mass transport conditions. Unlike prior studies that primarily emphasized the carrier dynamic within metal–metal/semiconductor heterojunctions (e.g., Au/Pd) in batch mode, our flow system demonstrates that efficient carrier transfer to reactants is critical for achieving high TON and AQY. This work provides the first framework for translating plasmonic catalysis into flow, offering design principles for future light-driven chemical processes beyond conventional batch mode.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".