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Record W2115377037 · doi:10.1039/c5nr03665h

Electrostatics of DNA nucleotide–carbon nanotube hybrids evaluated from QM:MM simulations

2015· article· en· W2115377037 on OpenAlexafffund
Morteza Chehel Amirani, Tian Tang

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsWestern Canada Research GridCompute Canada
KeywordsCarbon nanotubeBiomoleculeElectrostaticsNucleotideStatic electricityDNANanoelectronicsNanotechnologyMaterials scienceMacromoleculeNucleobaseChemical physicsChemistryPhysicsPhysical chemistryGeneBiochemistry

Abstract

fetched live from OpenAlex

Biomolecule-functionalized carbon nanotubes (CNTs) have been studied vastly in recent years due to their potential applications for instance in cancer detection, purification and separation of CNTs, and nanoelectronics. Studying the electrostatic potential generated by a biomolecule-CNT hybrid is important in predicting its interactions with the surrounding environment such as charged particles and surfaces. In this paper, we performed atomistic simulations using a QM:MM approach to evaluate the electrostatic potential and charge transfer for a hybrid structure formed by a DNA nucleotide and a CNT in solution. Four types of DNA nucleotides and two CNTs with chiralities of (4,4) and (7,0) were considered. The types of nucleotides and CNTs were both found to play important roles in the electrostatic potential and charge transfer of the hybrid. At the same distance from the CNT axis, the electrostatic potential for the nucleotide-(4,4) CNT hybrids was found to be stronger compared with that for the nucleotide-(7,0) CNT hybrids. Higher electric charge was also shown to be transferred from the DNA nucleotides to the (7,0) CNT compared with the (4,4) CNT. These results correlate with the previous finding that the nucleotides bound more tightly to the (7,0) CNT compared with the (4,4) CNT.

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)0.001
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 score0.032
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.025
GPT teacher head0.281
Teacher spread0.255 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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