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Wiring efficiency of a metallizable DNA linker for site-addressable nanobioelectronic assembly

2007· article· en· W2132553341 on OpenAlexfundno aff
Gary Withey, Jin Ho Kim, Jimmy Xu

Bibliographic record

VenueNanotechnology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersMcMaster University
KeywordsLinkerMaterials scienceOligonucleotideBiosensorElectron transferDNANanotechnologyRedoxCarbon nanotubeCombinatorial chemistryEnzymeElectrical contactsDivalentChemistryComputer scienceBiochemistryOptoelectronicsPhotochemistry

Abstract

fetched live from OpenAlex

We report the first demonstration of DNA oligonucleotide tags used to address the site-specific assembly of multiple redox enzymes onto spatially distinct regions of a nanoelectronic platform, establishing a direct electrical contact. The resulting system constitutes a multiplexed carbon nanotube-redox protein biosensor capable of detecting varying concentrations of several different substances in real time. The efficiency and robustness of the enzyme linking scheme is explored in detail, showing a high degree of preservation of enzymatic activity and an efficient electrical contact at the enzyme-nanoelectrode interface. While five proteins have been used as a demonstration in this study, there is virtually no limit to the number of enzymes that could be bound in parallel using this linking strategy, which is universally applicable to all proteins due to the simple conjugation chemistry involved. We further demonstrate metallization of the linker in the presence of a divalent metal cation, inducing elevated electron transfer efficiency relative to the native DNA link.

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 score0.122
Threshold uncertainty score0.706

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 integrity0.0010.000
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 head0.009
GPT teacher head0.280
Teacher spread0.270 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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