Highly sensitive and selective detection of selenate in water samples using an enzymatic gold nanodendrite biosensor
Bibliographic record
Abstract
Gold (Au) and glassy carbon electrodes have been decorated with gold nanodendrites (NDs) using galvanic replacement reactions. Modification of the ND surfaces by lipoic acid N-hydroxysuccinimide ester allows to immobilise selenate reductase to the electrode surface, rendering it selective for the catalytic reduction of selenate ([Formula: see text], Se(VI)) to selenite ([Formula: see text], Se(IV)). Electrode modifications have been characterized by electrochemical impedance spectroscopy and cyclic voltammetry (CV). For selenate detection, CV and differential pulse voltammetry measurements have been carried out in 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid buffer with pH 6.0. Under optimum conditions, the linear range for Au electrodes decorated with AuNDs was 0.3–203 µg/L Se with a limit of detection of 0.01 µg/L Se, which is a 274-fold improvement over using non-nanostructured surfaces for selenate detection. Other anions such as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] did not interfere with the detection of selenate. The electrochemical sensor was used for the detection of selenate in three different real samples with recovery between 98.5% and 102.0%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".