Development and Field Validation of an End-User Photo-Thermal Device for Accurate Detection and Quantification of Analytes in Fluidic Samples
Bibliographic record
Abstract
While paper-based rapid tests are abundantly used in medicine, their performance is limited by the poor limit of detection and binary response of the test. We have previously shown that interpreting rapid tests based on laser-induced photothermal responses can offer over an order magnitude improvement in test performance. This work reports on miniaturization of our photothermal sensing paradigm in a low-cost handheld device and its field validations. The hand-held device excites assay gold nanoparticles with a modulated, low-power LED while recording their thermal wave responses with low-cost single-element sensors. An Arduino-based processor demodulates thermal wave responses while offering internet-of-things capability. Results from a human study on detection and quantification of Cannabis consumption will also be presented and discussed.
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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 | 0.000 | 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".