Wearable Fingernail-Based Microfluidic Paper Analytical Device for Naked-Eye Detection of γ-Hydroxybutyric Acid in Beverages
Bibliographic record
Abstract
Drug-facilitated sexual assault (DFSA) remains a critical public safety concern, with γ-hydroxybutyric acid (GHB) being one of the most frequently used substances due to its odorless, colorless, and tasteless properties, which make it nearly undetectable in beverages. As GHB can quickly induce sedation and is rapidly eliminated from the body, there is an urgent need for practical and preventive detection tools that empower potential victims. In this study, we present a wearable fingernail-based microfluidic paper analytical device (PAD) that allows users to rapidly and visually detect GHB in beverages prior to consumption. The device features a Fe 3+ -based colorimetric sensing reaction embedded in a microfluidic paper strip affixed to a fingernail, enabling effortless sampling by simple dipping. In the presence of GHB, Fe 3+ forms a stable complex, preventing its reduction by hydroxylamine and inhibiting the formation of the orange Fe 2+ -phenanthroline chromophore─resulting in a clear, observable color change from orange to colorless (within 15 min). The sensor provides a detection limit of 0.55 μg mL –1 (digital analysis) and a naked-eye cutoff at 10 mg mL –1 . This low-cost, instrument-free, and highly portable sensor successfully detects GHB in both alcoholic and nonalcoholic beverages, demonstrating its potential as a user-friendly screening tool for real-world application in social settings, promoting personal safety and DFSA prevention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".