Development of a Bioactive Paper Sensor for Detection of Neurotoxins Using Piezoelectric Inkjet Printing of Sol−Gel-Derived Bioinks
Bibliographic record
Abstract
There is an increasing interest in new strategies to rapidly detect analytes of clinical and environmental interest without the need for sophisticated instrumentation. As an example, the detection of acetylcholinesterase (AChE) inhibitors such as neurotoxins and organophosphates has implications for neuroscience, drug assessment, pharmaceutical development, and environmental monitoring. Functionalization of surfaces with multiple reagents, including enzymes and chromogenic reagents, is a critical component for the effective development of "dipstick" or lateral flow biosensors. Herein, we describe a novel paper-based solid-phase biosensor that utilizes piezoelectric inkjet printing of biocompatible, enzyme-doped, sol-gel-based inks to create colorimetric sensor strips. For this purpose, polyvinylamine (PVAm, which captures anionic agents) was first printed and then AChE was overprinted by sandwiching the enzyme within two layers of biocompatible sol-gel-derived silica on paper. AChE inhibitors, including paraoxon and aflatoxin B1, were detected successfully using this sensor by measuring the residual activity of AChE on paper, using Ellman's colorimetric assay, with capture of the 5-thio-2-nitrobenzoate (TNB(-)) product on the PVAm layer. The assay provided good detection limits (paraoxon, approximately 100 nM; aflatoxin B1, approximately 30 nM) and rapid response times (<5 min). Detection could be achieved either by eye or using a digital camera and image analysis software, avoiding the need for expensive and sophisticated instrumentation. We demonstrate that the bioactive paper strip can be used either as a dipstick or a lateral flow-based biosensor. The use of sol-gel-based entrapment produced a sensor that retained enzyme activity and gave reproducible results after storage at 4 degrees C for at least 60 days, making the system suitable for storage and use in the field.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".