Inkjet-printed paper-based surface enhanced Raman scattering (SERS) sensors for the detection of narcotics
Bibliographic record
Abstract
Abstract Recent advances in inkjet-printing of advanced materials have provided a versatile platform for the rapid development and prototyping of sensor devices. We have recently demonstrated inkjet-printed surface enhanced Raman scattering (SERS) sensors on flexible substrates for the detection of variety of small molecules [Tay et al. in Front Chem 9:680556 (2021); Tay et al. in J Raman Spectrosc 52:563 (2020)]. These flexible SERS sensors have many advantages for performing point-of-sampling testing, among them liquid or aerosol filtration and swabbing capabilities. These simple sampling and separation features make these inkjet-printed paper-based sensors ideal for field applications. SERS detection of molecules with poor binding affinity towards the plasmonic surfaces of the sensors tends to be inefficient. A surface functionalization approach has been applied to SERS sensors to improve the molecule affinity and hence their detection sensitivity. In this paper, we investigate the optimization of SERS sensor fabrication to achieve optimal performance. Three performance criteria: diffuse reflectance, SERS background intensity from the as-printed blank sensors and SERS performance of sensors exposed to the benzenethiol reporter molecule, are characterized carefully to derive the optimal inkjet-printing conditions for producing the best performing SERS sensors. Additionally, we demonstrate the use of a simple potassium iodide functionalization scheme to improve the detection sensitivity for narcotics such as fentanyl by two orders of magnitude. Graphical abstract
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".