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Record W2316238282 · doi:10.7763/ijapm.2011.v1.31

Magnetotransport Properties of Co-Au Granular Alloys

2011· article· en· W2316238282 on OpenAlexaff
Conrad Rizal, Yuji Ueda, Ramesh K. Pokharel

Bibliographic record

VenueInternational Journal of Applied Physics and Mathematics · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceGrain sizeFerromagnetismMagnetoresistanceMagnetizationCondensed matter physicsAnnealing (glass)Current densityMagnetic momentGiant magnetoresistanceSaturation (graph theory)MetallurgyMagnetic fieldPhysics

Abstract

fetched live from OpenAlex

A series of Cox-Au1-x granular alloys was grown using pulsed-current electrodeposition on polyimide substrates. The relationship between the giant magnetoresistance effect, saturation magnetization, and grain size was examined as the deposition current density and annealing temperatures were changed. A maximum magnetoresistance (MR) ratio of 4.5 % was obtained. The magnetization and ferromagnetic grain size of the as-deposited and annealed alloy films were examined against the deposition current density. The saturation magnetization decreased as the composition of Au was increased for both the alloys deposited at the current densities of 1 and 5 mA/cm2. The total magnetic moment further decreased on annealing. The grain size is found to be highly influenced by both the deposition current density and temperature. A low temperature magnetization measurement suggested that the ferromagnetic grain size decreases with increase in current densities. For the annealed samples, the ferromagnetic grain sizes were found to be dispersed in the Au matrix with different diameters. The decrease in magnetic moments with annealing is correlated with the grain size of the Co and its distribution in the gold matrix. The MR ratio increased with the increase in the deposition current density as a result of the formation of smaller grain size at higher deposition current densities.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.390

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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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 designTheoretical or conceptual
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

Citations5
Published2011
Admission routes1
Has abstractyes

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