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Record W1981257969 · doi:10.1063/1.4906034

Piezoresistive characterization of bottom-up, n-type silicon microwires undergoing bend deformation

2015· article· en· W1981257969 on OpenAlexafffund
Megan M. McClarty, Jared P. Bruce, Michael S. Freund, Derek R. Oliver

Bibliographic record

VenueApplied Physics Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of Manitoba
KeywordsPiezoresistive effectMaterials scienceSiliconDopingOhmic contactChemical vapor depositionDeformation (meteorology)TungstenOptoelectronicsElectrical conductorComposite materialNanotechnologyLayer (electronics)Metallurgy

Abstract

fetched live from OpenAlex

The piezoresistance of silicon has been studied over the past few decades in order to characterize the material's unique electromechanical properties and investigate their wider applicability. While bulk and top-down (etched) micro- and nano-wires have been studied extensively, less work exists regarding bottom-up grown microwires. A facile method is presented for characterizing the piezoresistance of released, phosphorus-doped silicon microwires that have been grown, bottom-up, via a chemical vapour deposition, vapour-liquid-solid process. The method uses conductive tungsten probes to simultaneously make electrical measurements via direct ohmic contact and apply mechanical strain via bend deformation. These microwires display piezoresistive coefficients within an order of magnitude of those expected for bulk n-type silicon; however, they show an anomalous response at degenerate doping concentrations (∼1020 cm−3) when compared to lower doping concentrations (∼1017 cm−3), with a stronger piezoresistive coefficient exhibited for the more highly doped wires. This response is postulated to be due to the different growth mechanism of bottom-up microwires as compared to top-down.

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 score0.027
Threshold uncertainty score0.569

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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

Citations11
Published2015
Admission routes2
Has abstractyes

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