Plasmonic Nanostructures Grown from Reacting Droplet‐In‐Microwell Array on Flexible Films for Quantitative Surface‐Enhanced Raman Spectroscopy in Plant Wearable In Situ Detection
Bibliographic record
Abstract
Abstract Plant wearable detection has garnered significant interest in advancing agricultural intelligence and promoting sustainable food production amidst the challenges of climate change. Accurately monitoring plant health and agrochemical residue levels necessitates qualities such as precision, affordability, simplicity, and noninvasiveness. Here, a novel attachable plasmonic film is introduced and designed for on‐site detection of agrochemical residues utilizing surface‐enhanced Raman spectroscopy (SERS). By functionalizing a thin polydimethylsiloxane film with silver nanoparticles via controlled droplet reactions in micro‐well arrays, a plasmonic film is achieved that not only maintains optical transparency for precise analyte localization but also conforms closely to the plant surface, facilitating highly sensitive SERS measurements. The reliability of this film enables accurate identification and quantification of individual compounds and their mixtures, boasting an ultra‐low detection limit ranging from 10−16 to 10−13 m, with mini mal relative standard deviation. To showcase its potential, on‐field detection of pesticide residues on fruit surfaces is conducted using a handheld Raman spectrometer. This advancement in fabricating plasmonic nanostructures on flexible films holds promise for expanding SERS applications beyond plant monitoring, including personalized health monitoring, point‐of‐care diagnosis, wearable devices for human–machine interface, and on‐site monitoring of environmental pollutants.
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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".