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Inter-base Electronic Coupling for transport through DNA

2005· preprint· en· W2952633115 on OpenAlexfundno aff
H. Mehrez M. P. Anantram

Bibliographic record

VenueArXiv.org · 2005
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsTight bindingHamiltonian (control theory)ThymineValence (chemistry)Electron transport chaink-nearest neighbors algorithmPhysicsChemistryMolecular physicsCondensed matter physicsDNAElectronic structureQuantum mechanics

Abstract

fetched live from OpenAlex

We develop a new approach to derive single state tight binding (SSTB) model for electron transport in the vicinity of valence-conduction bands of poly(G)-poly(C) and poly(A)-poly(T) DNA. The SSTB parameters are derived from {\it first principles} and are used to model charge transport through finite length DNA. We investigate the rigor of reducing the full DNA Hamiltonian to SSTB model to represent charge transport in the vicinity of valence-conduction band. While the transmission coefficient spectrum is preserved, its position shifts in energy. Thymine is poorly represented and its peak height is substantially reduced. This is attributed to the abstraction of the HOMO-LUMO coupling to other eigen-states in the nearest neighbor DNA bases, and can be corrected within $2^{nd}$ order time independent perturbation theory. Inter-strand charge transport has also been analyzed and it is found that hopping to the nearest neighbor in the complementary strand is the most important process except in the valence band of poly(G)-poly(C), where hopping to the second nearest neighbor between $3'-$ends is the most dominant process. As a result, transport between $3'-$ends and $5'-$ends in the vicinity of valence band of poly{G}-poly{C} is asymmetric.

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 categoriesMeta-epidemiology (narrow)
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 score0.107
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.023
GPT teacher head0.273
Teacher spread0.250 · 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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

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