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Record W2004160370 · doi:10.1139/cjp-2014-0381

Application of the GGA-1/2 excited-state correction method to p-electron defective states: the special case of nitrogen-doped TiO<sub>2</sub>

2014· article· en· W2004160370 on OpenAlexvenueno aff
Mauro C. C. Ribeiro

Bibliographic record

VenueCanadian Journal of Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPhysicsExcited stateAtomic physicsQuasiparticleDopingCutoffElectronExcitationValence (chemistry)Vacancy defectImpurityCondensed matter physicsQuantum mechanics

Abstract

fetched live from OpenAlex

One of the issues in applying the generalized gradient approximation–1/2 (GGA-1/2) quasiparticle approximation to correct the excited-state properties in defective semiconductors is determining the cutoff to the self-energy function at the point defect. Pure and large supercells of N-doped rutile TiO2 were studied using this method to correct excited states and find the correct position of the defect states. A relation between the Bader charge at the defect and the self-energy cutoff parameter of the GGA-1/2 method is shown, with the cutoff value in its p-orbital, which optimizes the position of the defect levels. It was found that, upon nitrogen substitution and associated oxygen vacancy formation, an impurity level at 0.40–0.52 eV above the valence band maximum appears. The same result was obtained upon nitrogen substitution and associated background charge. Finally, the method was also applied to other p-orbital defect systems like Si:X (X = N, P, B) to validate the method.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations9
Published2014
Admission routes1
Has abstractyes

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