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An EIS study of the heterogeneity of redox labeled DNA SAMs on gold before and after hybridization

2025· article· en· W4406979134 on OpenAlexafffund
Tianxiao Ma, D. G. Baker, Gilberto Josué Martínez-Blanco, Dan Bizzotto

Bibliographic record

VenueElectrochimica Acta · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRedoxChemistryDNA–DNA hybridizationDNABiophysicsBiochemistryBiologyInorganic chemistry

Abstract

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Redox active labels on DNA SAMs are used for transducing hybridization via the change in the rate of contact-mediated redox with the electrode surface. This change in the fraction hybridized can be measured using CV or SWV. Here, we detail the use of EIS to quantify the change in the redox rate and thereby assess the hybridization characteristics for DNA SAMs. EIS of methylene blue (MB) labeled DNA SAMs on smooth single crystal bead electrodes were measured and the results fit to a series of equivalent circuits representing one redox rate, two redox rates or a distribution of redox rates. The rate of MB redox decreased with increasing complementary strand (cDNA) concentration, interpreted as an increase in the amount of hybridized dsDNA in the SAM. The EIS method was able to accurately measure the average rate of MB redox, but EIS was unable to independently measure the ssDNA and dsDNA populations as the redox rates were not sufficiently distinct. A majority of the redox active MB was one average time constant, but a consistent 10% of the MB had much faster redox rates which were interpreted as due to non-specifically adsorbed MB-labeled DNA. The EIS fits were not improved by using a CPE in place of C d l . However, the EIS fits were improved by replacing the RC circuit element with a distributed circuit element based on the Cole-Cole model, replacing a R with a CPE in one RC branch of the equivalent circuit. This distributed model better explained the EIS results giving the most probable redox rate and a distribution of rates. The EIS measurement approach was verified with a higher coverage DNA SAM which showed slower redox rates as expected. Interestingly, in all cases the amount of redox active MB ( Γ M B ) decreased with an increase in the [cDNA], suggesting that some MB was in an environment that prevented it from reaching the surface. This decrease in Γ M B with [cDNA] was reflected in the SWV results since it was sensitive to both the change in redox rate and the amount of redox active MB. In contrast, the EIS results were found to measure the k E T independent of the amount of redox active MB. Hybridization isotherms produced using either SWV or EIS results showed a significant difference between SWV (50 Hz) and k E T when fit to a Langmuir-Hill isotherm. These results suggested the SWV results must be interpreted with caution as the K A and Hill co-operativity coefficient ( n ) were different than the EIS measurements, but in a way that strongly depended on the coverage and the SWV frequency chosen. Overall, EIS of the redox labeled DNA SAM was successfully interpreted using a distributed redox rate expression based on the Cole-Cole model. EIS measured k E T provided a hybridization isotherm that was not convoluted with the amount of redox active MB, enabling an accurate assessment of the DNA SAM interface. • EIS was used to characterize redox(MB)-labelled DNA SAMs prepared on a gold electrode. • EIS results suggest the presence of at least two populations of redox active MB • The slow redox process represented contact-mediated electron transfer • The fast redox process may be due to non-specifically adsorbed MB-labelled DNA. • Fitting was improved with a Cole-Cole model using a distribution of redox rates. • Hybridization isotherms from SWV and the redox rate constant from EIS were compared

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.256
Teacher spread0.253 · 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".

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Citations2
Published2025
Admission routes2
Has abstractyes

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