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Record W4386857520 · doi:10.1002/admi.202300497

Steaming‐Assisted Conversion: A New Strategy for the Synthesis of Anatase TiO<sub>2</sub>, Nb, and W‐doped Anatase TiO<sub>2</sub> 2D Inverse Opal Films

2023· article· en· W4386857520 on OpenAlexafffund
Hua Li, Yufei Deng, Ralf Brüning, Yuwei Liu, Jacques Robichaud, Jian Liang, Weihui Jiang, Yahia Djaoued

Bibliographic record

VenueAdvanced Materials Interfaces · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMount Allison UniversityUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation FoundationCanada Foundation for Innovation
KeywordsAnataseMaterials scienceAmorphous solidDopingChemical engineeringMonolayerHeteroatomDopantElectrochromismNanotechnologyInorganic chemistryPhotocatalysisElectrodeCatalysisOrganic chemistryPhysical chemistryOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

Abstract Steaming‐assisted conversion route, a new strategy, is first adapted for the synthesis of highly crystallized anatase TiO 2 2D inverse opal (IO) monolayer films, and then to Nb‐doped TiO 2 and W‐doped TiO 2 2D IO monolayer films. Pure water, ammonia, or HCl solutions are used as a source of steaming vapor to convert dry films of amorphous TiO 2 IO, NbCl 5 /TiO 2 , and WCl 6 /TiO 2 composite IOs into anatase TiO 2 , Nb‐doped TiO 2 , and W‐doped TiO 2 IO films. This new strategy renders possible the doping of metal ions within the framework of the anatase TiO 2 IO films under low temperature and liquid‐free conditions. Further, the ordered array structure of the IO films is also effectively retained. The low steaming conversion temperature allows high dopant rates of homogeneously distributed heteroatoms, resulting in Nb doping as high as ≈34%. The thus prepared TiO 2 , Nb‐doped TiO 2 , and W‐doped TiO 2 anatase IO films are successfully used as active electrodes in the fabrication of electrochromic devices.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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