Reagentless Bidirectional Lateral Flow Bioactive Paper Sensors for Detection of Pesticides in Beverage and Food Samples
Bibliographic record
Abstract
A reagentless bioactive paper-based solid-phase biosensor was developed for detection of acetylcholinesterase (AChE) inhibitors, including organophosphate pesticides. The assay strip is composed of a paper support (1 x 10 cm), onto which AChE and a chromogenic substrate, indophenyl acetate (IPA), were entrapped using biocompatible sol-gel derived silica inks in two different zones (e.g., sensing and substrate zones). The assay protocol involves first introducing the sample to the sensing zone via lateral flow of a pesticide-containing solution. Following an incubation period, the opposite end of the paper support is placed into distilled deionized water (ddH(2)O) to allow lateral flow in the opposite direction to move paper-bound IPA to the sensing area to initiate enzyme catalyzed hydrolysis of the substrate, causing a yellow-to-blue color change. The modified sensor is able to detect pesticides without the use of any external reagents with excellent detection limits (bendiocarb approximately 1 nM; carbaryl approximately 10 nM; paraoxon approximately 1 nM; malathion approximately 10 nM) and rapid response times (approximately 5 min). The sensor strip showed negligible matrix effects in detection of pesticides in spiked milk and apple juice samples. Bioactive paper-based assays on pesticide residues collected from food samples showed good agreement with a conventional mass spectrometric assay method. The bioactive paper assay should, therefore, be suitable for rapid screening of trace levels of organophosphate and carbamate pesticides in environmental and food samples.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".