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Record W2947279597 · doi:10.1039/c9nr03212f

Remote control of charge transport and chiral induction along a DNA-metallohelicate

2019· article· en· W2947279597 on OpenAlexafffund
Mohammad S. Askari, Christophe Lachance‐Brais, Felix J. Rizzuto, Violeta Toader, Hanadi F. Sleiman

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCharge (physics)Duplex (building)DNARedoxMetalChemical physicsMaterials scienceCombinatorial chemistryNanotechnologyChemistryPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Herein we present a new strategy to achieve chiral induction and redox switching along the backbone of metallohelicate architectures, wherein a DNA duplex directs the handedness and charge transport properties of a metal-organic assembly more than 60 bonds away (a distance of >10 nm). The quantitative and site-specific binding of copper(i) ions to DNA-templated coordination sites imparts enhanced thermodynamic stability to the assembly, while the DNA duplex transfers its natural right-handed helicity to the proximal and distal metal centers of the helicates. When copper(ii) ions are employed instead of copper(i) ions, spontaneous DNA-mediated reduction occurs, which we propose is followed by a slower change in coordination environment (from pentacoordinate CuII to tetrahedral CuI) to generate copper(i) helicates. We demonstrate that the reduction of the adjacent and distal bis-phenanthroline sites is dependent on their proximity to DNA guanine bases (which act as the electron source). The kinetics of helical charge transport can thus be tuned based on guanine-CuII separation, resulting in a sequence- and distance-dependent redox switch that transfers electronic information from DNA to multiple linearly-arranged metal centers.

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 score0.008
Threshold uncertainty score0.577

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)0.0010.000

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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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
Published2019
Admission routes2
Has abstractyes

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