Development of All Solid State Sensors for Environmental Water Monitoring: Dissolved Oxygen and Free Chlorine
Bibliographic record
Abstract
Water contaminations in both natural water resource before collection and in treated drinking water during transportation jeopardise the supply of clean drinking water [1], [2]. Effective wastewater treatments, pollution controls in surface water and contamination free drinking water are three major goals to combat the water pollution sources on public and environmental health. Long term and continuous on-line sensing systems that can provide highly accurate data in real time can be the key component to benefit all these three aspects. However, the development of a comprehensive sensing system that can detect all parameters is labor and cost intensive. Thus, appropriate selection of targeted indicator is important in order to design a sufficient and practical sensing system. Since oxygen is an essential compound during the degradation of water containments, the dissolved oxygen (DO) level is always an important factor for water monitoring. Therefore, DO concentration is one of the primary factors for monitoring pollution in nature water. Additionally, DO is one of the major parameters that has to be precisely controlled during the wastewater treatment [3], [4]. Exceeding required DO concentration can cause waste of energy during aeration, whereas low DO concentration can cause failure treatment processes. Furthermore, free chlorine is one of the most common used disinfectants to ensure the drinking water quality during transportation and in wastewater treatment process for degradation of organic contaminants. The free chlorine concentration has to be strictly controlled to meet the requirement of different applications. For example, the residual chlorine in wastewater discharge is regulated as 0 ppm to prevent the damage in natural eco-system. Nevertheless, the residual chlorine in drinking water has to be maintained between 2 - 0.5 ppm to ensure drinking water safety. The DO and free chlorine is hence selected as the targeted indicators for the on-line sensing systems of water contamination prevention. Generally, the colorimetric methods are always the standard most widely used water monitoring techniques. For example, Winkler Method for DO sensing [5], and N,N-diethyl-p-phenylenediamine (DPD) method for free chlorine sensing [6]. These techniques are accurate and low interference; however, these techniques are expensive, tedious and labor intense which restricted their real-world application in on-line sensor developments. Among all the other sensing strategies, electrochemical methods are usually simple and low cost which are adequate for environmental sensing applications. However, there are still some challenges of the developments of on line DO and free chlorine sensors which needed to be overcome. Conventional DO sensors are expensive as they use platinum in their construction. Additionally, these sensors bio-foul when used in natural or waste water, which leads to reduced sensitivity and unstable performance. These problems are solved by i) replacing Pt with hemin as the low cost alternative for electrocatalysing the oxygen reduction reaction, and ii) using silicone rubber (PDMS) functionalized with polyethyleneglycol (PEG) as the anti-biofouling gas permeable membrane to provide selectivity with an increased device lifetime (a). This DO sensor has a sensitivity of 4.8 (µA/cm2)/(ppm) of dissolved oxygen in a concentration range of 0-20 ppm (b). [7] Additionally, a solid state palladium reference electrode composed by high surface area Pd nanostructure is also integrated with this DO sensor. The Pd/H electrode composed is able to maintain stable electrical potential for more than 5 hours after charging hydrogen. The all solid-state feature allows this sensing device to be applied in long-term on-line DO sensing. On the other hand, although several free chlorine sensors do exist, none of them is capable of long term and continuous on line sensing. We developed a resettable switching of doping states in carbon nanotube (CNT) films due to the chemical oxidation and reduction of phenyl-capped aniline tetramer (PCAT) adsorbed on it (c). The PCAT-CNT sensor obtains sufficient sensitivity (50 nA/ppm) and detection range (0.06-60 ppm) to ensure the safety of free chlorine level contained in drinking water (d). In addition, a cathodic polarization process after the sensing is found to electrochemically reset this sensor back to the original state so that the sensor can be used for subsequent sensing. In short, this sensor is reagent-free and resettable, suitable for long term and continuous monitoring the free chlorine level in drinking water [8]. Reference 1. Environ. Sci. Technol., v. 43, p. 240 2009. 2. Proc. Natl. Acad. Sci. , vol. 103 , pp. 7210 2006. 3. Environ. Int., vol. 30, p. 249 4. Chem. Eng. J., vol. 162, p. 1 5. Mar. Chem., vol. 122, p. 83 6. Guidelines for Canadian Drinking Water Quality - Chlorine, Ottawa 7. Sensors Journal, IEEE, vol. 14 p.3400 8. Appl. Phys. Lett., vol. 106, p. 063102 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".