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Record W2151948160 · doi:10.1039/c1an15117g

Detection of mercury and phenylmercury ions using DNA-based fluorescent probe

2011· article· en· W2151948160 on OpenAlexfundno aff
Yang‐Wei Lin, Huan‐Tsung Chang

Bibliographic record

VenueThe Analyst · 2011
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsnot available
FundersNational Research Council CanadaNational Science Council
KeywordsMercury (programming language)FluorescenceIonDNAChemistryAnalytical Chemistry (journal)ChromatographyPhysicsOpticsBiochemistryComputer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

A DNA probe labeled with a 4-([4-(dimethylamino)phenyl]azo)benzoic acid (DABCYL) quencher and a carboxyfluorescein (FAM) donor at its 5'- and 3'-termini can be used for the detection of Hg(2+) ions and phenylmercury ions (PhHg(+)). This DNA probe possesses a random coil structure that changes into a hairpin-like structure upon binding Hg(2+) and PhHg(+) ions. As a result, the fluorescence of the FAM unit decreased through quenching between the donor and the quencher. In the presence of ethylenediaminetetraacetic acid (EDTA), the DNA probe allowed the selective detection of PhHg(+) ions at concentrations as low as 70.0 nM, mainly as a result of T-Hg(2+)-T coordination and π-π stacking between the Ph unit and DNA bases. A linear correlation existed between the fluorescence intensity and the concentration of PhHg(+) ions over the range from 0.10 to 1.0 μM (R(2) = 0.99). After acid hydrolysis and neutralization of the samples, all of the mercury species are converted to Hg(2+) ions, allowing us to use the DNA-based probe to determine the concentrations of total mercury species at the nM level. The practicality of this probe has been validated by the analyses of pond water and fish samples, showing its advantages of sensitivity, selectivity, and simplicity.

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)0.000
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 score0.015
Threshold uncertainty score0.268

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 head0.030
GPT teacher head0.227
Teacher spread0.197 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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