MétaCan
Menu
Back to cohort
Record W4285399861 · doi:10.1149/ma2022-01522136mtgabs

Portable Rapid Paper-Based Device for Analysis of Carbamates and Organophosphates in Water Samples

2022· article· en· W4285399861 on OpenAlexaffabout
Fernanda Marques, Sushanta K. Mitra

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsContaminationTap waterPesticideAcetylcholinesteraseEnvironmental scienceChemistryComputer scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Traditionally, the analysis of contaminants in water samples is performed using sophisticated techniques, namely HPLC, GC, and MS, requiring qualified personnel and laboratory infrastructure. Such methods are not feasible for the detection of contaminants in the field. In contrast, paper-based portable devices have been demonstrated as versatile, sustainable, and cheap solutions for such purposes. Paper consists of a pressed sheet of entangled hydrophilic cellulose fibres with excellent porosity. Such features enable the capillarity motion of liquids and the immobilization of active substances on the surface. Pesticides are critical pollutants in regions where agricultural activity is intense and represent a serious health risk for rural workers and their communities. These chemicals have been applied in enormous quantities to increase crop production in many countries. Carbamates (CBs) and organophosphates (OPs) are employed as active ingredients in industrial formulations of insecticides. They are considered highly toxic due to their inhibition action in the cholinergic transmission found in the nervous system of living organisms. This work aims to present a low-cost and simple operational paper-based testing device for the fast detection of CBs and OPs in water samples based on Ellman’s method of acetylcholinesterase (AChE) inhibition. We have named the proposed device “dip-and-fold” due to its how-to-use procedure: first, the device’s bottom edge is dipped in the water sample. The liquid moves up by capillarity towards the detection zone, in which the analytes find the AChE previously immobilized on the paper. The top edge of the paper is covered with a thin parafilm layer to avoid leaking. After a 10-minute incubation, the AChE is inhibited in the presence of CBs and/or OPs. Then, the device is folded to release both enzymatic substrate acetylthiocholine iodide (ACThI) and indicator 5,5′-Dithiobis (2-nitrobenzoic acid) (DTNB) from a secondary paper to the detection zone for the colorimetric reaction. The detection zone area remains white in the presence of CBs and/or OPs (inhibited AChE); otherwise, it becomes yellow in the absence of these pesticides (active AChE). It is worthy to emphasize that the dip-and-fold has all required ingredients immobilized onto the paper substrates prior to tests, which excludes the necessity of extra reagents and materials. Besides, the sample can be transferred to the device by capillarity motion when dipping it directly into the contaminated water, eliminating any risk of having contact with the pesticides. Experiment The dip-and-fold device was fabricated using two analytical papers and a backing card as a foldable scaffold. Blotting paper (Whatman GB003) was used as the main device’s component for the sample loading by capillarity as well as for the immobilization of 3 µL AChE (100 U/mL), using chitosan 1% (w/v, in acetic acid 2%), as a crosslinker. The secondary component was the filter paper (Whatman grade 4) in which the 3µL of the substrate ACThI (25mM) and 3 µL of DTNB (10mM) were impregnated on the surface. The detection tests were carried out using standard solutions of carbofuran, propoxur (both carbamates) and chlorpyriphos (an organophosphate) at concentrations between 0.1 and 0.0001 mM. Control samples of DI water were also tested. A minimum volume of methanol was required to dissolve the pesticides in water. Yellow-coloured results of the tests were evaluated by taking digital photos of the devices using a smartphone. The RGB channels of images were analyzed using ImageJ software. Results and Conclusion The result interpretation was performed by comparing the plotted curves of the blue channel (B) values obtained from the images of each tested device for the different analytes and their respective concentrations. The evaluation of the plotted curves confirmed that the yellow colour's intensity is inversely proportional to the concentration of the analytes in the sample, which means, the more intense the yellow, the lower the concentration of pesticides in the sample. The concentrations applied in this study meet the parameters proposed by the Ontario Drinking Water Quality Standards (ODWQS) and may be compared to other paper-based devices described in the literature. Both propoxur and carbofuran presented consistent results that were observed by a steady decrease of the B values related to their concentration. In contrast, the chlorpyrifos presented a non-linear curve, which can be attributed to the higher amount of methanol used as a co-solvent to dissolve it in water due to its extremely poor solubility. The dip-and-fold device presented an exclusive, eco-friendly, and economical testing platform for pesticide detection in water, which offers an attractive solution for water assessment in vulnerable areas exposed to these toxic chemicals. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.215
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicBiosensors and Analytical DetectionFrench-language works237,207