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Record W2038210431 · doi:10.1088/0953-8984/25/8/085501

A DFT +<i>U</i>study of (Rh, Nb)-codoped rutile TiO<sub>2</sub>

2013· article· en· W2038210431 on OpenAlexaff
Kulbir Kaur Ghuman, Chandra Veer Singh

Bibliographic record

VenueJournal of Physics Condensed Matter · 2013
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRutileBand gapMaterials scienceDopingRecombinationDensity functional theoryPhotocatalysisElectronic structureAbsorption edgeCharge (physics)Atomic physicsPhotochemistryChemistryComputational chemistryOptoelectronicsCatalysisPhysics

Abstract

fetched live from OpenAlex

A systematic study of electronic structure and band gap states is conducted to analyze the monodoping and charge compensated codoping of rutile TiO(2) with Rh and Nb, using the DFT + U approach. Doping of rutile TiO(2) with Rh atoms induces hybridized O 2p and Rh 4d band gap states leading to a red shift of the optical absorption edge, consistent with previous experimental studies. Since Rh monodoping may induce recombination centers, charge compensated codoping with Rh and Nb is also explored. This codoping induces an electron transfer from Nb induced states to Rh 4d states, which suppresses the formation of Rh(4+), thereby leading to a reduction in recombination centers and to the formation of more stable Rh(3+). A combination of band gap reduction by 0.5 eV and the elimination of band gap states that account for recombination centers makes (Rh, Nb)-codoped TiO(2) a more efficient and stable photocatalyst.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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

Citations36
Published2013
Admission routes1
Has abstractyes

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