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Record W2910228216 · doi:10.1002/chem.201805423

Oxygen Vacancy Induced Structural Distortions in Black Titania: A Unique Approach using Soft X‐ray EXAFS at the O–K Edge

2019· article· en· W2910228216 on OpenAlexafffund
Brett Leedahl, Tristan de Boer, Xiaotao Yuan, A. Moewes

Bibliographic record

VenueChemistry - A European Journal · 2019
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsExtended X-ray absorption fine structureDistortion (music)Materials scienceAbsorption edgeAbsorption (acoustics)X-ray absorption fine structureVacancy defectX-ray absorption spectroscopySiliconLattice (music)OxygenBand gapChemical physicsAbsorption spectroscopyOpticsCondensed matter physicsPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

result in the formation of black titania, which has garnered significant interest as a photocatalytic material due to the accompanying electronic changes. Herein, the nature of the lattice distortion caused by an oxygen vacancy was determined that in turn results in the formation of mid-band-gap states found in previous studies of black titania. An innovative technique is introduced using a state-of-the-art silicon drift detector, which can be used in conjunction with extended X-ray absorption fine structure (EXAFS) to measure bulk interatomic distances. Also discussed is how the energy dispersive nature of such a detector can allow for an unimpeded signal, indefinitely in energy space, thereby sidestepping the hurdles of more conventional EXAFS, which is often impeded by other absorption edges.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.229
Teacher spread0.206 · 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 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
Published2019
Admission routes2
Has abstractyes

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Same venueChemistry - A European JournalSame topicElectronic and Structural Properties of OxidesFrench-language works237,207