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Record W3198350124 · doi:10.1039/d1na00563d

Tuning the band gap and carrier concentration of titania films grown by spatial atomic layer deposition: a precursor comparison

2021· article· en· W3198350124 on OpenAlexafffund
Claire Armstrong, Louis‐Vincent Delumeau, David Muñoz‐Rojas, A. Kuršumović, Judith L. MacManus‐Driscoll, Kevin P. Musselman

Bibliographic record

VenueNanoscale Advances · 2021
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersH2020 European Research CouncilEuropean Social FundDivision of Materials ResearchEngineering and Physical Sciences Research CouncilOntario Ministry of Economic Development, Job Creation and TradeMinistero dello Sviluppo EconomicoGirton College, University of CambridgeMinisterio de Ciencia e InnovaciónDepartament d'Innovació, Universitats i Empresa, Generalitat de CatalunyaGeneralitat de CatalunyaUniversity of CambridgeRoyal Academy of EngineeringMarie CurieEuropean Commission
KeywordsMaterials scienceAtomic layer depositionAmorphous solidAnataseDeposition (geology)Thin filmLayer (electronics)Chemical engineeringBand gapAtomic layer epitaxyBrookiteNanotechnologyOptoelectronicsPhotocatalysisChemistryCatalysisCrystallographyOrganic chemistry

Abstract

fetched live from OpenAlex

use is the HCl by-product, which was blamed for agglomeration in the films. Cl contamination was the likely cause of band gap narrowing and higher defect densities compared to TTIP-grown films. The carrier concentration of the nanofilms was found to increase with deposition temperature. The films were tested in hybrid bilayer solar cells to demonstrate their appropriateness for photovoltaic 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)0.000
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 score0.022
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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