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Record W174766082 · doi:10.1007/0-387-25585-0_17

Electronic Biosensors Based on DNA Self-Assembled Monolayer on Gold Electrodes

2006· book-chapter· en· W174766082 on OpenAlexaff
Chen-Zhong Li, Yi‐Tao Long, Todd C. Sutherland, Jeremy S. Lee, Heinz‐Bernhard Kraatz

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2006
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMonolayerDielectric spectroscopyFerricyanideBiosensorCyclic voltammetrySelf-assembled monolayerElectrochemistryElectrodeDNAChemistryRutheniumNanotechnologyAnalytical Chemistry (journal)Materials scienceInorganic chemistryOrganic chemistryBiochemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Information concerning the immobilization and hybridization of DNA on a surface is paramount to the development of DNA-based electronic biosensors. This study looks at recent investigations of DNA immobilized on gold surfaces using standard electrochemical techniques such as cyclic voltammetry (CV), potential step chronocoulometry and electrochemical impedance electrochemical impedance spectroscopy (EIS). The thiol-gold linkage is exploited for the immobilization of single- and double-stranded DNA onto gold electrodes. Two redox markers of opposite charge, ferricyanide and ruthenium hexaammine, respectively, are used to probe the environment in the vicinity of thiol-derivatized DNA electrodes. M-DNA is a form of DNA which allows the specific incorporation of certain metal ions into its helical structure under stringent conditions (i.e. low ionic strength and pH of 8.5). Single-stranded DNA monolayer and double strands DNA monolayer resistances were evaluated using EIS, respectively, and CV response were compared each other. The addition of Zn 2+ , under M-DNA formation conditions, led to a dramatic enhancement of electrochemical response compared to B-DNA.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.002
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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations7
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueKluwer Academic Publishers eBooksSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207