Screening Tablets for DOB Using Surface‐Enhanced Raman Spectroscopy*
Bibliographic record
Abstract
2,5,-Dimethoxy-4-bromoamphetamine (DOB) is of particular interest among the various "ecstasy" variants because there is an unusually long delay between consumption and effect, which dramatically increases the danger of accidental overdose in users. Screening for DOB in tablets is problematic because it is pharmacologically active at 0.2-3 mg, which is c. 50 times less than 3,4-methylenedioxy-N-methylamphetamine (MDMA) and makes it more difficult to detect in seized tablets using conventional spot tests. The normal Raman spectra of seized DOB tablets are dominated by the bands of the excipient with no evidence of the drug component. Here we report the first use of on-tablet surface-enhanced Raman spectroscopy (SERS) to enhance the signal from a low concentration drug. Raman studies (785-nm excitation) were carried on series of model DOB/lactose tablets (total mass c. 400 mg) containing between 1 mg and 15 microg of DOB and on seized DOB tablets. To generate surface-enhanced spectra, 5 microL of centrifuged silver colloid was dispensed onto the upper surface of the tablets, followed by 5 microL of 1.0 mol/dm(3) NaCl. The probe laser was directed onto the treated area and spectra accumulated for c. 20 sec (10 sec x 2). It was found that the enhancement of the DOB component in the model tablets containing 1 mg DOB/tablet and in the seized tablets tested was so large that their spectra were completely dominated by the vibrational bands of DOB with little or no contribution from the unenhanced lactose excipient. Indeed, the most intense DOB band was visible even in tablets containing just 15 microg of the drug. On-tablet surface-enhanced Raman spectroscopy is a simple method to distinguish between low dose DOB tablets and those with no active constituent. The fact that unique spectra are obtained allows identification of the drug while the lack of sample preparation and short signal accumulation times mean that the entire test can be carried out in <1 min.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".