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Record W1967287346 · doi:10.1088/1757-899x/8/1/012031

Charge transfer and re-distribution in the TiO<sub>2</sub>-SrTiO<sub>3</sub>hetero-structure

2010· article· en· W1967287346 on OpenAlexaff
H.L. Kwok

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2010
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCharge densityCharge (physics)Materials scienceSubstrate (aquarium)Seebeck coefficientThermoelectric effectWork (physics)ConductanceCondensed matter physicsAnalytical Chemistry (journal)ChemistryThermodynamicsPhysicsThermal conductivityComposite material

Abstract

fetched live from OpenAlex

This work examined the charge density distribution in the TiO2-SrTiO3 hetero-structure using thermoelectric data reported in the literature. The first part of our work was to identify the equation necessary to extract charge densities. Our findings suggested that the observed thermoelectric power was best described by an equation applicable to a metal. Together with a distributed model that accounted for the layered structure, we computed the charge densities in the different layers. As observed, the charge density in the SrTiO3 substrate was high (> 1024 m−3) nonetheless in agreement with the value extracted from the depth profile measurement. Proportionally, the interface charge density was also very high (~ 1028 m−3). Using these values, we computed the "effective" charge spread from the sheet conductance data, which was of the order of hundreds of microns. We had found no evidence that such charge spread could have originated from the interface and tended to believe that they were linked to oxygen vacancies embedded in the substrate. This was confirmed by a reduced charge spread when the top TiO2 layer had been removed. In general, we showed that the thermoelectric power and the power factor were dominated by the properties of the substrate.

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)0.001
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 score0.007
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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