Biosensor for Arsenite Using Arsenite Oxidase and Multiwalled Carbon Nanotube Modified Electrodes
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".