Recent Advances in the Use of Surface-Enhanced Raman Scattering for Illicit Drug Detection
Bibliographic record
Abstract
The rapid increase in illicit drug use and its adverse health effects and socio-economic consequences have reached alarming proportions in recent years. Surface-enhanced Raman scattering (SERS) has emerged as a highly sensitive analytical tool for the detection of low dosages of drugs in liquid and solid samples. In the present article, we review the state-of-the-art use of SERS for chemical analysis of illicit drugs in aqueous and complex biological samples, including saliva, urine, and blood. We also include a review of the types of SERS substrates used for this purpose, pointing out recent advancements in substrate fabrication towards quantitative and qualitative detection of illicit drugs. Finally, we conclude by providing our perspective on the field of SERS-based drug detection, including presently faced challenges. Overall, our review provides evidence of the strong potential of SERS to establish itself as both a laboratory and in situ analytical method for fast and sensitive drug detection and identification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".