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Record W2133674547 · doi:10.1109/icsens.2010.5690164

A MEMS tensile testing device for mechanical characterization of individual nanowires

2010· article· en· W2133674547 on OpenAlexaff
Yong Zhang, Changhai Ru, Xinyu Liu, Yu Lin Zhong, Xueliang Sun, David Hoyle, Ian Cotton, Yu Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsHitachi (Canada)Western UniversityUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceMicroelectromechanical systemsCapacitive sensingNanowireNanomaterialsTensile testingUltimate tensile strengthActuatorCharacterization (materials science)Deformation (meteorology)Scanning electron microscopeNanotechnologyComposite materialSubstrate (aquarium)Computer scienceElectrical engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a MEMS (microelectromechanical systems) device for tensile testing of individual one-dimensional nanomaterials. Consisting of capacitive force and displacement sensors and an electrostatic actuator, the device conducts tensile testing and electronically measures nanomaterial deformations and forces. Electronic measurement permits a higher data sampling rate than electron microscopy imaging, which can be useful for obtaining more complete stress-strain data during plastic deformation and failure of nanomaterials for understanding material behavior. A nanomanipulation procedure is developed to pick up a nanowire from its growth substrate and place it on the MEMS device inside a scanning electron microscope. The quasistatic tensile test of a tin oxide (SnO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) nanowire is demonstrated, revealing a Young's modulus of 116.0 GPa and failure strength of 2.31 GPa.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.441

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 head0.028
GPT teacher head0.262
Teacher spread0.234 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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