Flow-based approach for scalable fabrication of Ag nanostructured substrate as a platform for surface-enhanced Raman scattering
Bibliographic record
Abstract
The droplet-based biphasic reaction is an efficient strategy for the fabrication of surface-bound nanostructures. Here, we developed a process of fabricating ordered micro-ring arrays of silver (Ag) nanostructures from surface nanodroplet reaction on a micro-patterned hydrophobic substrate for reproducible detection by surface-enhanced Raman spectroscopy (SERS). Our process consisted of the generation of surface nanodroplet arrays, followed by a biphasic chemical reaction between droplets and the continuous flow of silver nitrate (AgNO3) precursor solution. The parameters in the formation and reaction of the droplet array were well controlled to maintain the uniformity of Ag nanostructures throughout the substrate. By scaling up the process parameters and the size of the microchamber, we were able to produce a SERS substrate with a surface area of > 60 cm^2 in a single run. Such a large area could be sufficient for analyzing more than a thousand samples. We demonstrated the repeatability of SERS measurements using Ag nanostructures by analyzing three environmental (rhodamine 6G, chlorpyrifos, triclosan), a biological (indoxyl sulfate), and a psychoactive drug (tetrahydrocannabinol) compounds. 2D mapping of SERS intensities was also performed for both small and large-scale substrates by collecting data from more than 100 locations on the substrate. Our work demonstrated droplet-based biphasic reaction as a simple approach for the fabrication of SERS substrate with a large area. The technique may help to eliminate the requirement for sophisticated equipment for the fabrication of SERS active substrate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".