Detection of free chlorine in water using graphene-like carbon based chemiresistive sensors
Bibliographic record
Abstract
Free chlorine is the most commonly used water disinfectant. Measuring its concentration during and after water treatment is crucial to ensure its effectiveness. However, many of the existing methods do not allow for continuous on-line monitoring. Here we demonstrate a solid state chemiresistive sensor using graphene-like carbon (GLC) that overcomes that issue. GLC films that were either bare or non-covalently functionalized with the redox-active phenyl-capped aniline tetramer (PCAT) were successfully employed to quantify aqueous free chlorine, although functionalized devices showed better performance. The response of the sensors to increasing concentrations of free chlorine followed a Langmuir adsorption isotherm in the two tested ranges: 0.01-0.2 ppm and 0.2-1.4 ppm. The limit of detection was estimated to be 1 ppb, permitting the detection of breaches in chlorine filters. The devices respond to decreasing levels of free chlorine without the need for a reset, allowing for the continuous monitoring of fluctuations in the concentration. The maximum sensor response and saturation concentration were found to depend on the thickness of the GLC film. Hence, the sensitivity and dynamic range of the sensors can be tailored to different applications by adjusting the thickness of the films. Tap water samples from a residential area were tested using these sensors, which showed good agreement with standard colorimetric measurement methods. The devices did not suffer from interferences in the presence of ions commonly found in drinking water. Overall, these sensors are a cost-effective option for the continuous automated monitoring of free chlorine in drinking water.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".