Electrochemically deposited silver nanostructures for use as surface‐enhanced Raman scattering ( <scp>SERS</scp> ) substrates in point‐of‐need diagnostic devices
Bibliographic record
Abstract
Abstract In a world that increasingly demands answers in real‐time, there exists a distinct need for chemical sensors that can quickly and efficiently detect substances with high sensitivity and selectivity. To address this need, we use surface‐enhanced Raman scattering (SERS) as a powerful analytical technique that can provide ultrasensitive and versatile chemical detection on a mobile platform when implemented on a handheld Raman spectrometer. However, the large laser spot size of handheld Raman spectrometers requires SERS substrates of sufficient surface area. Here, we present a facile method for electrodepositing nanostructured silver (Ag) SERS substrates onto silicon microchips. In this method, silver ions are continuously reduced from a large volume of solution in an apparatus resembling a batch electrochemical reactor. The straightforward protocol is scalable, fast, and reproducible. Further, we investigate the influence of temperature and fluid agitation on the growth of Ag nanostructures with the intention of maximizing surface area coverage. We observe an increase in lateral nanostructure growth from heating due to an increase in the diffusion coefficient. However, no significant increase in lateral nanostructure growth is observed from stirring the reagent solution. Despite the absence of trends in lateral growth, we find that high agitation levels promote the growth of extraneous Ag structures on top of the nanostructured film, indicating the presence of a boundary layer at the silicon surface. Further, we find that increased diffusion rates at high temperatures shift the reaction towards the limits of the mass transfer‐controlled regime.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".