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Record W1997389583 · doi:10.1142/s0129156408005849

BIOMOLECULAR SENSING USING CARBON NANOTUBES: A SIMULATION STUDY

2008· article· en· W1997389583 on OpenAlexaff
G. B. Abadir, Konrad Walus, Robin F. B. Turner, D.L. Pulfrey

Bibliographic record

VenueInternational Journal of High Speed Electronics and Systems · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsCarbon nanotubeMoleculeMolecular dynamicsMaterials scienceConductanceNanotechnologyLocal density of statesDensity functional theoryElectroluminescenceCarbon fibersChemical physicsFunction (biology)BiosensorComputational chemistryChemistryPhysicsCondensed matter physicsOrganic chemistry

Abstract

fetched live from OpenAlex

A simulation study using molecular dynamics and the density-functional-theory/non-equilibrium-Green's-function approach has been carried out to investigate the potential of carbon nanotubes (CNT) as molecular-scale biosensors. Single molecules of each of two amino acids (isoleucine and asparagine) were used as the target molecules in two separate simulations. The results show a significant suppression of the local density of states (LDOS) in both cases, with a distinct response for each molecule. This is promising for the prospect of CNT-based single-molecule sensors that might depend on the LDOS, e.g., devices that respond to changes in either conductance or electroluminescence.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations7
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of High Speed Electronics and SystemsSame topicCarbon Nanotubes in CompositesFrench-language works237,207