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Record W2331941113 · doi:10.1021/ac103177w

Computer-Readable DNAzyme Assay on Disc for ppb-Level Lead Detection

2011· article· en· W2331941113 on OpenAlexafffund
Honglun Wang, Lily M. L. Ou, Yourui Suo, Hua‐Zhong Yu

Bibliographic record

VenueAnalytical Chemistry · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchU.S. Food and Drug AdministrationChinese Academy of Sciences
KeywordsChemistryDeoxyribozymeLead (geology)Environmental chemistryDetection limitChromatography

Abstract

fetched live from OpenAlex

A method for the convenient detection of lead at the parts-per-billion (ppb)-level has been developed; it uses a conventional compact disc (CD) as the platform for preparing DNAzyme assays and an unmodified optical drive of ordinary desktop/laptop computers as the readout device. In particular, by immobilization of Pb(2+)-specific DNAzyme sensing constructs on the "transparent side" of a conventional CD-R via mild surface reactions, the Pb(2+) concentration can be determined by a free diagnostic program that checks the error distribution on the CD (i.e., it extracts the number of errors in a prerecorded audio file). The reading errors increase monotonically over a wide range of Pb(2+) concentrations (from 10 nM to 1 mM), and the selectivity is confirmed by testing several other divalent cations (Zn(2+), Ba(2+), Mg(2+), Ca(2+), Cu(2+), and Hg(2+)).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.038
GPT teacher head0.282
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2011
Admission routes2
Has abstractyes

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