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Record W2089420744 · doi:10.1038/srep01782

Detection of gas atoms with carbon nanotubes

2013· article· en· W2089420744 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCarbon nanotubeImpulse (physics)Materials scienceResonatorMolecular dynamicsNanotechnologySensitivity (control systems)Continuum mechanicsInertiaChemical physicsOptoelectronicsMechanicsChemistryPhysicsComputational chemistryClassical mechanicsElectronic engineering

Abstract

fetched live from OpenAlex

Owning to their unparalleled sensitivity resolution, nanomechanical resonators have excellent capabilities in design of nano-sensors for gas detection. The current challenge is to develop new designs of the resonators for differentiating distinct gas atoms with a recognizably high sensitivity. In this work, the characteristics of impulse wave propagation in carbon nanotube-based sensors are investigated using molecular dynamics simulations to provide a new method for detection of noble gases. A sensitivity index based on wave velocity shifts in a single-walled carbon nanotube, induced by surrounding gas atoms, is defined to explore the efficiency of the nano-sensor. The simulation results indicate that the nano-sensor is able to differentiate distinct noble gases at the same environmental temperature and pressure. The inertia and the strengthening effects by the gases on wave characteristics of carbon nanotubes are particularly discussed and a continuum mechanics shell model is developed to interpret the effects.

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.

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 score0.180
Threshold uncertainty score0.501

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)0.0000.000

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 head0.006
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