Visual Nanosensor for the Detection of Heavy Metals in Water
Bibliographic record
Abstract
Introduction It has been known for decades that metals with a thin oxide coating can exhibit interference colours by a Fabry-Perot interference phenomenon.1 The colour produced by constructive interference of light reflecting from the air/oxide and oxide/metal interfaces is directly related to the oxide film thickness, and this property has been used as a convenient way to determine oxide film thickness by the unaided eye. Recently, interference colours of nanostructured metal oxides formed at Al, Ti, and Si have been reported. The observed colour of these nanostructured oxide films is dependent on film thickness, surface modification, and porosity of the nanostructures. These parameters have been investigated for potential applications in sensing and displays.2,3 In this study, the interference colours of Ta oxide nanotubular films have been used to create a sensor that allows for the visual detection of heavy metals in water (ppm to ppb levels). Methods and Results We have previously reported that thin films of highly ordered Ta2O5 nanotubes (NTs) can be formed by the anodization of Ta foil.4 The Ta2O5 NTs were prepared by anodization of Ta in a solution containing HF, H2SO4 and H2O in a two-electrode cell configuration. Ta2O5 NT diameter was controlled by the applied voltage, and NT length was varied by controlling the anodization time and HF concentration,5 as shown in Figure 1 (a-d). The colour of the NT film changes with NT length, progressing through brown, deep purple, deep blue, yellow, orange, purple, and blue as NT length increases from 20-500 nm. Beyond a length of ~500 nm, teal and pink colours dominate the visible appearance of the oxide films. The peaks and valleys present in the diffuse reflectance spectrum vary with oxide film thickness, and with the refractive index of the nanotubular film. This was seen to change upon NT infiltration with different analytes (as shown in Figure 1e), allowing for visual sensing applications. Due to the conductive Ta metal NT support, heavy metals, such as Cu, Cd, and Pb, can be electrodeposited inside the NT film, resulting in refractive index change, and thus, a visible colour change. This results in a rapid visual sensor that does not require costly or bulky instrumentation, making it ideal for detecting heavy metals in water in remote locations. Acknowledgements We gratefully acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC), and scholarship support from NSERC and Alberta Innovates Technology Futures for CH. We also thank Drs. Dimitre Karpuzov and Shihong Xu (Alberta Centre for Surface Engineering and Science, Edmonton, Alberta) for assistance with the FE-SEM imaging. References (1) Charlesby, A.; Polling, J. J. Proc. R. Soc. A Math. Phys. Eng. Sci. 1955, 227, 434–447. (2) Zhao, B. X.; Meng, G.; Xu, Q.; Han, F. Adv. Mater. 2010, 22, 2637–2641. (3) Lezec, H. J.; McMahon, J. J.; Nalamasu, O.; Ajayan, P. M. Nano Lett. 2007, 7, 329–333. (4) El-Sayed, H.; Singh, S.; Greiner, M. T.; Kruse, P. Nano Lett. 2006, 6, 2995–2999. (5) Horwood, C.; El-Sayed, H.; Birss, V. Electrochim. Acta 2014, 132, 91–97. 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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".