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Record W2021067599 · doi:10.1063/1.4907631

A carbon nanotube based resettable sensor for measuring free chlorine in drinking water

2015· article· en· W2021067599 on OpenAlexaff
Huan‐Hsuan Hsu, Enamul Hoque, Peter Kruse, P. Ravi Selvaganapathy

Bibliographic record

VenueApplied Physics Letters · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChlorineReagentCarbon nanotubeInorganic chemistryMaterials scienceChemistryParts-per notationChemical engineeringNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Free chlorine from dissolved chlorine gas is widely used as a disinfectant for drinking water. The residual chlorine concentration has to be continuously monitored and accurately controlled in a certain range around 0.5–2 mg/l to ensure drinking water safety and quality. However, simple, reliable, and reagent free monitoring devices are currently not available. Here, we present a free chlorine sensor that uses oxidation of a phenyl-capped aniline tetramer (PCAT) to dope single wall carbon nanotubes (SWCNTs) and to change their resistance. The oxidation of PCAT by chlorine switches the PCAT-SWCNT system into a low resistance (p-doped) state which can be detected by probing it with a small voltage. The change in resistance is found to be proportional to the log-scale concentration of the free chlorine in the sample. The p-doping of the PCAT-SWCNT film then can be electrochemically reversed by polarizing it cathodically. This sensor not only shows good sensing response in the whole concentration range of free chlorine in drinking water but is also able to be electrochemically reset back many times without the use of any reagents. This simple sensor is ideally suited for measuring free chlorine in drinking water continuously.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.215
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2015
Admission routes1
Has abstractyes

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