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Record W3102106976

Sensitive Magnetic Force Detection with a Carbon Nanotube Resonator

2014· article· en· W3102106976 on OpenAlexaff
Jonathan Baugh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResonatorCarbon nanotubeMagnetic fieldMaterials scienceMagnetBohr magnetonBohr modelCondensed matter physicsMagnetic momentNuclear magnetic resonanceNanotechnologyOptoelectronicsPhysicsQuantum mechanics
DOInot available

Abstract

fetched live from OpenAlex

We propose a technique for sensitive magnetic point force detection using a suspended carbon nanotube (CNT) mechanical resonator combined with a magnetic field gradient generated by a ferromagnetic gate electrode. Numerical calculations of the mechanical resonance frequency show that single Bohr magneton changes in the magnetic state of an individual magnetic molecule grafted to the CNT can translate to detectable frequency shifts, on the order of a few kHz. The dependences of the resonator response to device parameters such as length, tension, CNT diameter, and gate voltage are explored and optimal operating conditions are identified. A signal-to-noise analysis shows that, in principle, magnetic switching at the level of a single Bohr magneton can be read out in a single shot on timescales as short as 10 μs. This force sensor should enable new studies of spin dynamics in isolated single molecule magnets, free from the crystalline or ensemble settings typically studied.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.370

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 head0.003
GPT teacher head0.184
Teacher spread0.181 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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