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Record W4283456018 · doi:10.1002/chem.202200953

Development of a Bacterial Enzyme‐Based Biosensor for the Detection and Quantification of Selenate

2022· article· en· W4283456018 on OpenAlexafffund
Mozhgan Khorasani-Motlagh, Meissam Noroozifar, R. N. S. Sodhi, Heinz‐Bernhard Kraatz

Bibliographic record

VenueChemistry - A European Journal · 2022
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryBiosensorSelenateDifferential pulse voltammetryAnalyteX-ray photoelectron spectroscopyAnalytical Chemistry (journal)Calibration curveElectrodeSeleniumCyclic voltammetryInorganic chemistryDetection limitElectrochemistryChromatographyOrganic chemistryBiochemistryChemical engineering

Abstract

fetched live from OpenAlex

Abstract An enzymatic biosensor has been developed for the determination of selenate (SeO 4 2− ), in which selenate reductase (SeR) is chemically attached to a gold disk electrode by lipoic acid N ‐hydroxysuccinimide ester as linker, allowing the catalytic reduction of the SeO 4 2− to SeO 3 2− . Modification of the gold electrode was characterized by X‐ray photoelectron spectroscopy (XPS), time‐of‐flight secondary ion mass spectroscopy (ToF‐SIMS), and electrochemistry. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) measurements were performed in different buffers for selenate determination. Under optimum conditions, the calibration curve was linear over the range 7.0–3900.0 μg L −1 with limits of detection and quantification of 4.97 and 15.56 μg L −1 , respectively. The possible interference of the relevant oxyanions SO 4 2− , NO 3 − , NO 2 − , PO 4 3− and AsO 4 3− in the determination of SeO 4 2− was studied. Finally, the proposed biosensor was used to determine SeO 4 2− with recovery between 95.2 and 102.4 % in different real 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 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)0.001
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 score0.026
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.044
GPT teacher head0.252
Teacher spread0.208 · 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".

Quick stats

Citations5
Published2022
Admission routes2
Has abstractyes

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Same venueChemistry - A European JournalSame topicSelenium in Biological SystemsFrench-language works237,207