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Record W2157258631 · doi:10.1116/1.4794182

Axial resistivity measurement of a nanopillar ensemble using a cross-bridge Kelvin architecture

2013· article· en· W2157258631 on OpenAlexafffund
Abeed Lalany, Ryan T. Tucker, Michael T. Taschuk, Michael D. Fleischauer, Michael J. Brett

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2013
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanopillarMaterials scienceElectrical resistivity and conductivityOptoelectronicsThin filmElectrodeResistorResistive touchscreenNanotechnologyTungsten trioxideIndium tin oxideTungstenElectrical engineeringNanostructureChemistry

Abstract

fetched live from OpenAlex

Achieving the full potential of nanopillar electrode based devices, such as next-generation solar cells, catalyst supports, and sensors, requires axial resistivity measurements to optimize electronic performance. Here, the authors demonstrate a technique for direct measurement of the ensemble electrical properties of nanopillar thin films along the structure's longitudinal axis. A cross-bridge Kelvin resistor architecture is adapted to accommodate an indium tin oxide (ITO) nanopillar thin film fabricated by glancing angle deposition (GLAD). As-deposited GLAD ITO nanopillars were found to have a measured resistivity of (1.1 ± 0.3) × 10−2 Ω cm using our technique. Planar ITO films deposited at near normal incidence were found to have a resistivity of (4.5 ± 0.5) × 10−3 Ω cm, determined by the standard four-point-probe technique. These measurements demonstrate the viability of this modified technique for nanopillar characterization, and identify experimental limitations related to device size and edge defects.

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)0.003
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 score0.032
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.274
Teacher spread0.243 · 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
Published2013
Admission routes2
Has abstractyes

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