Iodide Functionalized Paper-Based SERS Sensors for Improved Detection of Narcotics
Bibliographic record
Abstract
An inkjet-printed paper-based Surface-enhanced Raman scattering (SERS) sensor is a robust and versatile device that provides trace sensing capabilities for the detection and analysis of narcotics and drugs. Such sensors generally work well for analytes with good binding affinity towards the Au or Ag plasmonic nanoparticles (NPs) resident in the sensors. In this report, we show that iodide functionalization of the printed sensors helps to remove adsorbed contaminants from AuNP surfaces enabling superior performance with improved detection of narcotics such as fentanyl, heroin and cocaine by SERS. SERS signals are easily doubled with the iodide-functionalized sensors which also showed orders of magnitude improvement in detection limit. In this report, we show that a short (90 s) iodide treatment of the sensors significantly improved the detection of heroin. We propose that iodide functionalization be integrated into field detection kits through the solvent that wets paper-based sensor prior to swabbing for narcotics. Alternatively, we have also demonstrated that iodide functionalized sensors can be stored in ambient for up to 1 week and retain the improved performance towards heroin detection. This report will help to significantly improve the performance of paper-based sensors for field detection of narcotic drugs.
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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) | 0.001 | 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 | 0.001 |
| 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".