Functionalization of metal Nanoparticles for SERS-based Detections of illicit drugs in biological samples
Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS) has been widely used for the detection of illicit drugs due to its excellent chemical fingerprint information, high sensitivity by plasmon-enhanced excitation and scattering, and independence of aqueous solution impact. Even though it has been more than one decade since the first SERS was synthesized, extensive effort has recently been undertaken to improve hand-held Raman analyzer and make SERS a practical point-of-care (POC) device for the detection of illicit drugs in real samples. Lately, there has been a fast growth in improving methods for precise control over metal nanoparticle size and modification of their detection performance with functionalizing agents, which will dramatically enhance their application in various biomedical applications.In this study, a general view on the background of the SERS and several basic concepts and focuses are discussed. Then we put forward a summary of the progress in trace determination of illicit drugs using various functionalization methods for enhancing characteristics of the substrate toward a more selective detection of the analytes.
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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 | 0.000 | 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".