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Record W2949774755 · doi:10.3390/coatings9060381

Relevance of the Preparation of the Target for PLD on the Magnetic Properties of Films of Iron-Doped Indium Oxide

2019· article· en· W2949774755 on OpenAlexfundno aff
Hasan B. Albargi, Marzook S. Alshammari, Kadi Y. Museery, Steve M. Heald, Feng-Xian Jiang, Ahmad M. Saeedi, A. M. Fox, G. A. Gehring

Bibliographic record

VenueCoatings · 2019
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
FundersArgonne National LaboratoryEngineering and Physical Sciences Research CouncilKing Abdulaziz City for Science and TechnologySaudi Arabia Cultural Bureau in LondonOffice of ScienceCanadian Light SourceU.S. Department of Energy
KeywordsIndiumMaterials scienceIron oxidePulsed laser depositionAnalytical Chemistry (journal)OxideThin filmMagnetizationDopingOxygenMetalAbsorption (acoustics)XANESHematiteInorganic chemistryChemistryNanotechnologyMetallurgyOptoelectronicsSpectroscopy

Abstract

fetched live from OpenAlex

This paper concerns the importance of the preparation of the targets that may be used for pulsed laser deposition of iron-doped indium oxide films. Targets with a fixed concentration of iron are fabricated from indium oxide and iron metal or one of the oxides of iron, FeO, Fe3O4 and Fe2O3. Films from each target were ablated onto sapphire substrates at the same temperature under different oxygen pressures such that the thickness of the films was kept approximately constant. The films were studied using X-ray diffraction, X-ray absorption (both XANES and EXAFS), optical absorption and magnetic circular dichroism. The magnetic properties were measured with a SQUID magnetometer. At the lowest oxygen pressure, there was evidence that some of the iron ions in the films were in the state Fe2+, rather than Fe3+, and there was also a little metallic iron; these properties were accompanied by a substantial magnetisation. As the amount of the oxygen was increased, the number of defect phases and the saturation magnetisation was reduced and the band gap increased. In each case, we found that the amount of the oxygen that had been included in the target from the precursor added to the effect of adding oxygen in the deposition chamber. It was concluded that the amount of oxygen in the target due to the precursor was an important consideration but not a defining factor in the quality of the films.

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 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 score0.002
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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