Facile Scale-Up of the Flow Synthesis of Silver Nanostructures Based on Norrish Type I Photoinitiators
Bibliographic record
Abstract
Silver nanoparticles have become one of the most commercially and industrially relevant nanomaterials of the 21st century, owing to their potent antibacterial properties, as well as their useful catalytic and optical properties. Although many methods have been explored to produce AgNPs, we favor the photochemical approach using photoinitiators to produce AgNPs, owing to the high degree of control over reaction conditions, and the generation of so-called AgNP 'seeds' that can be used as-is, or as precursors for other silver nanostructures. In this work, we explore the scale-up of AgNP synthesis using flow chemistry and assess the usefulness of a range of industrial Norrish Type 1 photoinitiators in terms of flow compatibility and reaction time, as well as the resulting plasmonic absorption and morphologies. We establish that while all the photoinitiators used were able to generate AgNPs in a mixed aqueous/alcohol system, photoinitiators that generate ketyl radicals showed the greatest promise in terms of reaction times, while also showing greater flow compatibility compared to photoinitiators that generate 𝛼-aminoalkyl and α-hydroxybenzyl radicals. These findings help to establish a guideline for adapting photochemical AgNP syntheses to flow systems, helping to improve the scalability of the method in one of the largest industries in nanomaterial chemistry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".