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Record W2678389253 · doi:10.1139/cjp-2017-0092

Strain and different edge terminations modulated electronic and magnetic properties of armchair AlN/SiC nanoribbons: first-principles study

2017· article· en· W2678389253 on OpenAlexvenueno aff
Xiujuan Du, Zhengwei Zhang, Yu-Ling Song

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
FundersXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of SciencesNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsMagnetismCondensed matter physicsDangling bondEnhanced Data Rates for GSM EvolutionDensity functional theoryMaterials scienceElectronic structurePhysicsSiliconOptoelectronics

Abstract

fetched live from OpenAlex

Using first-principle calculations based on density functional theory, we investigate the strain and different edge terminations modulated electronic and magnetic properties of armchair AlN/SiC nanoribbons. The results show that the edge terminations Fe, Co, Cl can decrease or even eliminate the edge deformation of AlN/SiC nanoribbon. The magnetism of the nanoribbons is greatly adjusted by magnetic atoms Fe and Co, but not by Cl atoms. Apart from the nanoribbon with Cl terminations, the magnetism of the residual nanoribbons can be adjusted by increasing the compressed or stretched strain. The magnetic semiconductor nanoribbon with Co terminations becomes a magnetic half-metal system and then becomes a magnetic metal system, with the increase of the compressed strain. The magnetism of the nanoribbon with dangling bonds is attributed to the SiC edge and its nearest-neighbour C atoms, whereas the magnetism of the nanoribbon with Fe (or Co) terminations is mainly contributed by Fe (or Co) terminations.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.845

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 head0.037
GPT teacher head0.228
Teacher spread0.190 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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