Inkjet-printed Surface-Enhanced Raman Scattering (SERS) sensors for field applications
Bibliographic record
Abstract
The development of rapid and sensitive detection technology for identifying of chemicals and biological agents such as contraband substances, narcotics and toxins is critical for decision-making among first responders and military personnel. Recent advances in nanofabrication, microelectronics and computational power have led to miniaturization of portable analytical instruments. Among these, handheld Raman analyzer coupled with Surface Enhanced Raman spectroscopy (SERS), have become increasingly common for field detection challenges due to the enormous sensitivity of SERS technique. In this paper, we demonstrate the fabrication and analysis of flexible and porous paper-based SERS sensors by inkjet printing of colloidal Au nanoparticles (AuNP) onto paper substrate. Our paper-based SERS sensors are cost-effective and robust, and they provide the added advantage of point-of-sampling capability that rigid SERS sensors lack. With their inherent filtration sampling capability, we coupled our paper-SERS sensors with air pump for active sampling and detection of chemical aerosols. Additionally, we printed the SERS sensors in test strip format to enable swab sampling of chemical contaminants on door handle as a simulated field-sampling and detection of chemical toxins. Our swab sampling successfully picked up enough benzenethiol, BPE and fentanyl molecules to trigger positive detection. The used swab can also be preserved for further confirmatory tests such as paper-spray mass spectrometry.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".