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Record W1969745059 · doi:10.1117/12.860894

Spectroscopic and magnetic properties of colloidal transition metal-doped transparent conducting oxide nanocrystals as building blocks for spintronic materials

2010· article· en· W1969745059 on OpenAlexaff
Shokouh S. Farvid, Ting Wang, Pavle V. Radovanovic

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Condensed Matter Physics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceNanocrystalline materialFerromagnetismDopantNanocrystalMagnetic momentDopingMagnetizationCrystallographySpintronicsAbsorption spectroscopyCrystal structureMagnetismCondensed matter physicsNanotechnologyMagnetic fieldOptoelectronicsOpticsChemistryPhysics

Abstract

fetched live from OpenAlex

The synthesis of colloidal Cr<sup>3+</sup>-doped In<sub>2</sub>O<sub>3</sub> NCs with the body-centered cubic bixbyte-type crystal structure, and Cr<sup>3+</sup>-doped SnO<sub>2</sub> NCs with the rutile crystal structure was described. Ligand-field electronic absorption spectroscopy suggests that Cr<sup>3+</sup> dopants have quasi-octahedral coordination in both In<sub>2</sub>O<sub>3</sub> and SnO<sub>2</sub> NC host lattices. Unlike free-standing nanocrystals, the nanocrystalline films fabricated from colloidal Cr<sup>3+</sup>-doped In<sub>2</sub>O<sub>3</sub> and SnO<sub>2</sub> nanocrystals exhibit room temperature ferromagnetism. Analogous magnetic behavior suggests the same origin of ferromagnetic ordering in both materials. The observed ferromagnetism has been related to the existence of extended structural defects, formed at the interfaces between nanocrystals in nanocrystalline films. These structural defects are likely responsible for the formation of charge carriers which mediate the dopant magnetic moment ordering.

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 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 score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Condensed Matter PhysicsFrench-language works237,207