Design of an Arsine Gas Free Rapid Arsenic Detector Using a Novel Luminescent Metal-Organic Framework
Bibliographic record
Abstract
An estimated 140 million people have access to drinking water with an excessively high concentration of arsenic. Many of them are located in countries that lack the infrastructure and funding to support laboratories, thus requiring the use of cheaper rapid field tests to determine arsenic levels in water. These tests typically first reduce all the dissolved arsenic to arsine gas, then a paper strip laden with mercuric bromide is inserted into the reaction vessel. The arsine gas reacts with the mercuric bromide on the strip for a colorimetric change. Semi-quantitative results can be determined by comparing the color change to a provided color chart. These rapid tests are non-ideal since the user can be easily exposed to toxic arsine gas if the test enclosure is poorly sealed. Additionally, the colorimetric reaction between mercuric bromide and arsine gas does not work if the paper strip gets wet and likely has reduced performance in high humidity conditions. To improve the safety and ease of testing arsenic in water, we propose the use of a Zinc-Trimesic acid metal-organic framework (MOF) functionalized with 8-Hydroxyquiniline (ZnQ@Zn-BTC). When exposed to arsenate ions, the ZnQ@Zn-BTC MOF exhibits fluorescence quenching upon excitation with UV light. This luminescent reaction required no intermediate reactions, thereby simplifying the assay and eliminating the need for arsine gas. The MOF is available in a dehydrated solid form which can be re-suspended in solution to prepare for testing. The addition of microlitre-scale volumes of sample is enough for detection, indicating that the MOF may be amenable for low-volume detection applications. Preliminary testing in solution has been performed where an arsenic-laden water sample was first added to a ZnQ@Zn-BTC suspension in ethanol and allowed to rest for 10 minutes for binding events. The fluorescence of the sample was then measured using fluorescence spectroscopy, with excitation using 387 nm light, and peak emission observed at 510nm. The quenching was subsequently calculated as the simple difference between the fluorescence of the sample and the fluorescence of a control ZnQ@Zn-BTC solution. Current tests in solution reveal sensitivity for arsenic compounds down to a concentration of 100 ppb for sample volumes as small as 20 µL. There is maximum fluorescence quenching at 1.5 ppm of arsenic and samples with concentrations higher than this level would likely not demonstrate further quenching. Future development includes immobilization of the ZnQ@Zn-BTC on a paper substrate for an easy, rapid and quantitative arsenic testing method. By moving to a paper-based platform the assay can be simplified to a one-step process: simply introduce the sample and the capillary action of the paper will handle the rest. In addition, a reader for the strip can be developed for quantitative results with greater reliability than the color chart comparison that current rapid tests utilize. Paper-based microfluidics also has the advantages of being easily scalable and extremely stable, with little influence from environmental factors. These qualities make it suitable for deployment in less developed countries that may lack resources for more complex assays. Early identification of arsenic in water sources is a key step towards the reduction and prevention of arsenic poisoning. By introducing the proposed assay with a suitable education and awareness program, identification of arsenic in water can be performed quicker and its negative effects on the health of the community can be restrained faster.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".