Chemiresistive Detection of Silver Ions in Aqueous Media
Bibliographic record
Abstract
Silver is a precious metal that is commonly used in water filters to reduce growth of biofilm within the filter itself. Ionic silver is used as an effective disinfectant for potable water, giving a log10 reduction for L. pneumophilia, P. aeruginosa, and E. coli of 2.4, 4, and 7, respectively.1 In terms of human exposure, silver is not an essential metal therefore any exposure to silver is unwanted. When silver is ingested orally, the most common adverse effect is argyria, which is an extreme blue pigmentation of the skin and abdominal viscera.2 Occupational exposure to silver nitrate has been correlated to respiratory tract irritation.3 Currently there are no guidelines for silver ions in drinking water, and the World Health Organization (WHO) has set a health advisory (not a guideline value) of 100 ppb. The only country with a Maximum Allowable Content (MAC) is Germany, whose drinking water regulations (Trinkwasserverordnung) have a MAC of 80 ppb.4 Here we demonstrate a chemiresistive sensor for the in situ detection of silver (I) in aqueous media. Chemiresistive sensors function through the changes that occur in the electronic structure of the sensor itself. A graphite film attached to two copper contacts at either end is exposed to the silver ions such that only the graphite and not the contacts interact with the ions. To functionalize the graphite, silver (I)-specific ligands, such as bathocuproine, can be deposited onto the film to adsorb onto it, protecting it from interfering ions.5 Chemiresistive sensors have been demonstrated before for the detection of free chlorine in aqueous media. Rather than using graphite, carbon nanotubes (CNT) were utilized as the conductive film, with phenyl-capped aniline tetramer (PCAT) as the chlorine-specific ligand. As the PCAT doped CNT was exposed to chlorine, the PCAT oxidized, and the electronic changes were probed using a bias voltage. The linear range for this sensor was from 60 ppb to 60 ppm, providing sufficient sensitivity for household use.6 When testing other common ions, there were no significant interference's that compete with the response of silver (I) in solution, making this sensor quite selective to silver (I). pH tests show that there is no change in current induced by pH between the range of 6-10 pH. Below 6, the sensor functions as a "proton sensor" due to the protonation of the adsorbed bathocuproine. Above pH 10, AgOH may be formed, which will precipitate out of solution. All tests were performed at an analyte conductance of 31 μS/cm, which is typical for freshwater samples.7 When the fabricated sensor was exposed to silver (I) in aqueous solution, detection of the ions was observed through a step up in the current going through the sensor. Each step up in current was proportional to the concentration of silver (I) in solution. Based on this, a calibration curve was made using the Langmuir Isotherm model and a first-order exponential decay model. For the range of 3 ppb to 1 ppm, both the Langmuir Isotherm and the first-order exponential decay model gave R2 values of 0.9982 and 0.9939, respectively. The sensor could also be reliably reset to the same 0 ppm baseline after use by exposing it to a pH 3 solution. When exposed to silver (I) post-reset, current changes were also quite reproducible, with the values having a relative standard deviation no greater than 1.24%, highlighting the re-usability of this sensor. The limit of detection, calculated using a signal:noise ratio of 3, for this sensor would be 3 ppb. We have therefore demonstrated that chemiresistors based on functionalized nanocarbon films can be used as selective ion sensors in addition to their previously demonstrated application as redox sensors. The toolbox of organometallic chemistry can now be applied to extend this sensing platform to other cations for water quality sensing. References 1. Kim, J. S.; Kuk, E.; Yu, K. N.; Kim, J.-H.; Park, S. J.; Lee, H. J.; Kim, S. H.; Park, Y. K.; Park, Y. H.; Hwang, C.-Y.; et al. Antimicrobial Effects of Silver Nanoparticles. Nanomedicine: Nanotechnology, Biology and Medicine 2007, 3 (1), 95–101. 2. Marshall, J. P. Systemic Argyria Secondary to Topical Silver Nitrate. Archives of Dermatology 1977, 113 (8), 1077. 3. Toxicological Profile for Silver. Agency for Toxic Substances and Disease Registry 1990. 4. Silver as a Drinking-Water Disinfectant. World Health Organization 2018. 5. Saito, T. Transport of Silver(I) Ion through a Supported Liquid Membrane Using Bathocuproine as a Carrier. Separation Science and Technology 1998, 33 (6), 855–866. 6. Hsu, L. H. H.; Hoque, E.; Kruse, P.; Selvaganapathy, P. R. A Carbon Nanotube Based Resettable Sensor for Measuring Free Chlorine in Drinking Water. Applied Physics Letters 2015, 106 (6), 063102. 7. https://www.ontario.ca/data/provincial-stream-water-quality-monitoring-network (accessed on October 22, 2019) Figure 1
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".