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Record W4382918228 · doi:10.1039/d3an00622k

A reagent-free phosphate chemiresistive sensor using carbon nanotubes functionalized with crystal violet

2023· article· en· W4382918228 on OpenAlexafffund
Vinay Patel, Md Ali Akbar, Peter Kruse, P. Ravi Selvaganapathy

Bibliographic record

VenueThe Analyst · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
FundersGlobal Water FuturesNatural Sciences and Engineering Research Council of Canada
KeywordsReagentCrystal violetCarbon nanotubePhosphateChemistryCrystal (programming language)NanotechnologyMaterials scienceOrganic chemistryComputer scienceMicrobiology

Abstract

fetched live from OpenAlex

Phosphate is important for plant and animal growth. Therefore, it is commonly added as a fertilizer in agricultural fields. Phosphorus is typically measured using colorimetric or electrochemical sensors. Colorimetric sensors suffer from a limited measuring range and toxic waste generation while electrochemical sensors suffer from long-term drifts due to reference electrodes. Here, we propose a solid-state, reagent-free and reference electrode-free chemiresistive sensor for measuring phosphate using single-walled carbon nanotubes functionalized with crystal violet. The functionalized sensor exhibited a measuring range from 0.1 mM to 10 mM at pH 8. No significant interference was observed for common interfering anions like nitrates, sulphates, and chlorides. This study showed a proof-of-concept chemiresistive sensor, which can potentially be used to measure phosphate levels in hydroponics and aquaponics systems. The dynamic measuring range further needs to be extended for surface water samples.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.020
GPT teacher head0.233
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2023
Admission routes2
Has abstractyes

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