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Record W2157877712 · doi:10.1063/1.4926793

Electro-physical characterization of individual and arrays of ZnO nanowires

2015· article· en· W2157877712 on OpenAlexaff
Bhargav Mallampati, Abhay Pratap Singh, Alex Shik, Harry E. Ruda, Usha Philipose

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanowireCapacitanceMaterials scienceElectrodeOptoelectronicsConductivityCondensed matter physicsDielectricTransistorVoltageNanotechnologyChemistryElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Capacitance measurements were made on an array of parallel ZnO nanowires embedded in a polymer matrix and provided with two electrodes perpendicular to the nanowires. The capacitance monotonically increased, and saturated at large negative (depleting) and large positive (accumulating) voltages. A qualitative explanation for this behavior is presented, taking into account specific features of quasi-one-dimensional screening. The increasing or decreasing character of the capacitance-voltage characteristics were determined by the conductivity type of the nanowires, which in our case was n-type. A dispersion of the experimental capacitance was observed over the entire frequency range of 1 kHz to 5 MHz. This phenomenon is explained by the slow discharge of the nanowires through the thin dielectric layer that separates them from the top electrode. Separate measurements on individual identical nanowires in a field effect transistor configuration yielded an electron concentration and mobility of approximately 1017 cm−3 and 150 cm2/Vs, respectively, at room temperature.

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 score0.003
Threshold uncertainty score0.227

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 head0.027
GPT teacher head0.240
Teacher spread0.213 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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