Point-of-Need PFAS Detection: A Yes/No Biosensor Solution
Bibliographic record
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) pose one of the world's most prominent chemical health threats and are detected in virtually everything from the Antarctic environment to human blood. Due to the extreme half-lives and omnipresent distribution of the chemical class, the threat cannot be eliminated but rather must be continuously monitored and managed through widespread sample testing and targeted remediation long term. Unfortunately, the current standard detection method, liquid chromatography/tandem mass spectrometry (LC/MS/MS), is expensive, time-consuming, and limited to use by highly trained professionals in centralized laboratories. For this reason, there is an urgent need for a field-deployable, affordable, and easy-to-use device for PFAS detection. This paper addresses the issue with a protein-based electrochemical sensor for the point-of-need detection of perfluorooctanoic acid (PFOA), which is one commonly regulated PFAS compound of particular concern. Using two proteins, lubricin (LUB, proteoglycan 4) and human liver fatty acid binding protein engineered with a methylene blue redox tag (FABP1-MB), a response to PFOA is detected at 0.41 ng/L and 0.41 μg/L concentrations. Detection of PFOA is also demonstrated in real water and whole blood samples, demonstrating the sensors' possible future application for both environmental and biomedical monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".