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Record W2064783207 · doi:10.1021/ac070766i

Biosensor for Arsenite Using Arsenite Oxidase and Multiwalled Carbon Nanotube Modified Electrodes

2007· article· en· W2064783207 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnalytical Chemistry · 2007
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsNational Research Council CanadaBiotechnology Research Institute
Fundersnot available
KeywordsArseniteChemistryBiosensorDetection limitArsenateTap waterArsenicChromatographyNuclear chemistryInorganic chemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A biosensor for arsenite has been developed using molybdenum-containing arsenite oxidase, prepared from the chemolithoautotroph NT-26 that oxidizes arsenite to arsenate. The enzyme was galvanostatically deposited for 10 min at 10 microA onto the active surface of a multiwalled carbon nanotube modified glassy carbon electrode. The resulting biosensor enabled direct electron transfer, i.e., effecting reduction and then reoxidization of the enzyme without an artificial electron-transfer mediator. Arsenite was detected within 10 s at an applied potential of 0.3 V with linearity up to 500 ppb and a detection limit of 1 ppb. The biosensor exhibited excellent reproducibility, 2% at 95% confidence interval for 12 repeated analyses of 25 ppb arsenite. Copper, a severe interfering species commonly found in groundwater, did not interfere, and the biosensor was applicable for repeated analysis of spiked arsenite in tap water, river water, and a commercial mineral water.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.050
Threshold uncertainty score1.000

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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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