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Record W2567511353 · doi:10.1088/1361-6463/aa55b1

The tunneling magnetoresistance and spin-polarized optoelectronic properties of graphyne-based molecular magnetic tunnel junctions

2016· article· en· W2567511353 on OpenAlexaff
Zhi Yang, Bin Ouyang, Guoqing Lan, Li-Chun Xu, Ruiping Liu, Xuguang Liu

Bibliographic record

VenueJournal of Physics D Applied Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsQuantum tunnellingCondensed matter physicsGraphyneMagnetoresistanceTunnel magnetoresistanceSpin (aerodynamics)Materials sciencePhysicsFerromagnetismMagnetic fieldNanotechnologyQuantum mechanicsGraphene

Abstract

fetched live from OpenAlex

Abstract Using density functional theory and the non-equilibrium Green’s function method, we investigate the spin-dependent transport and optoelectronic properties of the graphyne-based molecular magnetic tunnel junctions (MMTJs). We find that these MMTJs exhibit an outstanding tunneling magnetoresistance (TMR) effect. The TMR value is as high as 10 6 %. When the magnetization directions of two electrodes are antiparallel under positive or negative bias voltages, two kinds of pure spin currents can be obtained in the systems. Furthermore, under the irradiation of infrared, visible or ultraviolet light, spin-polarized photocurrents can be generated in the MMTJs, but the corresponding microscopic mechanisms are different. More importantly, if the magnetization directions of two electrodes are antiparallel, the photocurrents with different spins are spatially separated, appearing at different electrodes. This phenomenon provides a new way to simultaneously generate two spin currents.

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 score0.154
Threshold uncertainty score0.717

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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