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 × 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<sub>2</sub>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 ∼1 nM; carbaryl ∼10 nM; paraoxon ∼1 nM; malathion ∼10 nM) and rapid response times (∼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 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) | 0.002 | 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".