Robust Chemiresistive Sensor for Continuous Monitoring of Free Chlorine Using Graphene-like Carbon
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Abstract Free chlorine is widely used in industry as a bleaching and oxidizing agent. Its concentration is tightly monitored to avoid environmental contamination and deleterious human health effects. Here, we demonstrate a solid state chemiresistive sensor using graphene like carbon (GLC) to detect free chlorine in water. A 15–20 nm thick GLC layer on a PET substrate was modified with a redox-active aniline oligomer (phenyl-capped aniline tetramer, PCAT) to increase sensitivity, improve selectivity, and impart fouling resistance. Both the bare GLC sensor and the PCAT-modified GLC sensor can detect free chlorine continuously and, unlike previous chemiresistive sensors, do not require a reset. The PCAT-modified sensor showed a linear response with a slope of 13.89 (mg/L)−1 to free chlorine concentrations between 0.2 and 0.8 mg/L which is relevant for free chlorine monitoring for drinking water and wastewater applications. The PCAT-modified GLC sensors were found to be selective and showed less than 0.5% change in current in response to species such as nitrates, phosphates and sulfates in water. They also were resistant to fouling from organic material and showed only a 2% loss in signal. Tap water samples from residential area were tested using this sensor which showed good agreement with standard colorimetric measurement methods. The GLC and PCAT-GLC sensors show high sensitivity and excellent selectivity to free chlorine and can be used for continuous automated monitoring of free chlorine.
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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".