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Long-Distance Ultrafast Spin Transfer over a Zigzag Carbon Chain Structure

2021· article· en· W3121177309 on OpenAlexaff
Jing Liu, Chun Li, Wei Jin, Georgios Lefkidis, Wolfgang Hübner

Bibliographic record

VenuePhysical Review Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Manitoba
FundersNational Science Foundation
KeywordsZigzagSpin (aerodynamics)Atom (system on chip)PhysicsSymmetry (geometry)Carbon fibersEmbeddingCoupling (piping)Condensed matter physicsTopology (electrical circuits)Molecular physicsMaterials scienceComputer science

Abstract

fetched live from OpenAlex

Using high-level ab initio quantum theory we suggest an optically induced subpicosecond spin-transfer scenario over 4.428 nm, a distance which is directly comparable to the actual CMOS scale. The spin-density transfer takes place between two Ni atoms and over a 40-atom-long zigzag carbon chain. The suitable combination of the local symmetries of the participating carbon atoms and the global symmetry of the whole molecule gives rise to what we term the dynamical Goodenough-Kanamori rules, allowing the long-range coupling of the two Ni atoms. We also present local spin-flip scenarios, and compare spin flip and spin transfer with respect to their sensitivity against an external static magnetic gradient. Finally, we use two identical laser pulses, rather than a single one, which allows us to accurately control local (intrasite) vs global (intersite) processes, and we thus solve the problem of embedding individually addressable molecular nanologic elements in an integrated nanospintronic circuit. Our results underline the great potential of carbon chain systems as building and supporting blocks for designing future all-optical magnetic processing units.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2021
Admission routes1
Has abstractyes

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