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Record W3186536481 · doi:10.1149/ma2021-01571554mtgabs

Modifying Nanocarbon Films with Switchable Dopant Molecules for the Detection of Aqueous Permanganate

2021· article· en· W3186536481 on OpenAlexaff
Md Ali Akbar, Omar Sharif, P. Ravi Selvaganapathy, Peter Kruse

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPermanganatePotassium permanganateReagentAqueous solutionChemistryMaterials scienceNanotechnologyInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Monitoring disinfectants is essential to maintain water free from pathogens. Permanganate (MnO 4 - ) is one of the commonly used disinfectants and is applied as potassium permanganate (KMnO 4 ) solution in drinking water and wastewater treatment plants. It is used as a pre-oxidant at the beginning of the treatment process for the control of reduced iron (II) and manganese (II) concentration. 1,2 Though dilute KMnO 4 solutions are sometimes used as topical antiseptics and astringents, higher concentration (over 200 mg/L) can cause gastrointestinal distress. 3 A Do Not Consume concentration of 7 mg/L KMnO 4 is recommended based on clinical experience. 3 Therefore, to ensure the proper level of permanganate during and after the water treatment process, it is necessary to monitor the concentration of permanganate. Current methods for permanganate detection focus on spectrophotometric measurements (direct and indirect). The direct method involves no additional chemical, however, lacks the suitability for the samples with less than 0.75 mg/L of MnO 4 - . 4 Indirect methods provide better sensitivity but require the need for reagents like NaI and ABTS (unstable). 4 A sensitive single-particle-detection (SPD) method has been reported recently which uses dark-field optical microscopy on graphene nanoplatelets silver (GNP@Ag) core-shell nanoparticles, but, this method is quite complex and requires sample preparation before analysis. 1 Moreover, these methods are unsuitable for the implementation in a treatment plant for the online monitoring of disinfectant. To tackle this problem, we introduced a chemiresistive sensing platform capable of quantifying permanganate in aqueous media. Chemiresistive sensors are becoming popular in sensing due to their low cost, easy fabrication technique, and the ability to detect different analyte using appropriate ligand. 5 Chemiresisive sensors have been reported before for the continuous measurement of free chlorine using carbon nanotube (CNT) substrate doped with redox-active aniline oligomer named phenyl capped aniline tetramer (PCAT). 6 The sensor consists of two parallel electrodes connected by a layer of carbon nanotubes. Oligoanilines are immobilized on the substrate through noncovalent interactions. Oligoanilines are known to dope CNTs differently depending on which one of the three oxidation states they are in (fully reduced, half oxidized, and fully oxidized). 7 , 8 Oxidation of this oligoaniline (PCAT) attached to the surface of CNT by permanganate molecule leads to a change in the oxidation state of the oligoaniline; this change in oxidation state in the molecule changes the doping characteristics of CNT leading to a change in resistance of the CNT film. This resistance change can be used to quantify the concentration of permanganate. 9 This sensor can be reset with fresh water and reused for further analysis. Here we present a chemiresistive sensing array that can detect permanganate in the aqueous environment. We have functionalized the nanocarbon substrate with five different redox-active molecules. Four of them are pH-responsive and one is not responsive to the pH range used for the analysis. The sensors were tested for the range of 0.17 mg/L to 1.33 mg/L of MnO 4 - at a pH range of 6.5 to 9.5. These redox-active molecules create active sites on the substrate, and when in contact with the analyte, dopant molecules attached on the surface react with analytes and give responses. These responses vary in magnitude depending on pH and the redox-active molecule. The sensor responses at different pHs were then analyzed using principal component analysis (PCA) to identify the pH of the permanganate solution. PCA analysis shows a clear separation of different pH solutions. We have therefore demonstrated a nanocarbon based sensing array that is not only capable of continuously measuring the low concentration of permanganate, but also the pH of the aqueous media. References: Z. Ye et al., Anal. Chem. , 90 , 13044–13050 (2018). J. L. Cleasby, J. Am. Water Works Assoc. , 67 , 147–149 (1975). C. C. Willhite, V. S. Bhat, G. L. Ball, and C. J. McLellan, Hum. Exp. Toxicol. , 32 , 275–298 (2013). S. T. McBeath, D. P. Wilkinson, and N. J. D. Graham, Chemosphere , 251 , 126626 (2020). J. Dalmieda, A. Zubiarrain-Laserna, D. Ganepola, P. R. Selvaganapathy, and P. Kruse, Sensors Actuators B Chem. , 328 , 129023 (2020). L. H. H. Hsu, E. Hoque, P. Kruse, and P. Ravi Selvaganapathy, Appl. Phys. Lett. , 106 , 063102 (2015). A. Mohtasebi, A. D. Broomfield, T. Chowdhury, P. R. Selvaganapathy, and P. Kruse, ACS Appl. Mater. Interfaces , 9 , 20748–20761 (2017). E. Hoque, T. Chowdhury, and P. Kruse, Surf. Sci. , 676 , 61–70 (2018). E. Hoque, L. H. H. Hsu, A. Aryasomayajula, P. R. Selvaganapathy, and P. Kruse, IEEE Sensors Lett. , 1 , 4500504 (2017). Figure 1

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 score0.175
Threshold uncertainty score0.260

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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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