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Spin Dynamics across Metallic Layers on the Few-Femtosecond Timescale

2024· article· en· W4402310255 on OpenAlexaff
Romain Géneaux, Hung-Tzu Chang, Alexander Guggenmos, Renaud Delaunay, François Légaré, Katherine Légaré, J. Lüning, T. Parpiiev, Ilana J. P. Molesky, Bethany R. de Roulet, Michael Zuerch, S. Sharma, Martin Schultze, Stephen R. Leone

Bibliographic record

VenuePhysical Review Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsInstitut National de la Recherche Scientifique
FundersHORIZON EUROPE European Research CouncilAir Force Office of Scientific ResearchDefense Advanced Research Projects AgencyDeutsche ForschungsgemeinschaftAlexander von Humboldt-StiftungW. M. Keck Foundation
KeywordsFemtosecondSpin (aerodynamics)Materials scienceDynamics (music)Condensed matter physicsMetalChemical physicsPhysicsLaserQuantum mechanicsThermodynamics

Abstract

fetched live from OpenAlex

We measure the light-driven response of a magnetic multilayer structure made of thin alternating layers of cobalt and platinum at the few-femtosecond timescale. Using attosecond magnetic circular dichroism, we observe how light rearranges the magnetic moment during and after excitation. The results reveal a sub-5 fs spike of magnetization in the platinum layer, which follows the shape of the driving pulse. With the help of time-dependent density functional theory, we interpret the observations as light-driven spin injection across the metallic layers of the structure. The light-triggered spin current is strikingly short, largely outpacing decoherence and dephasing. The findings suggest that the ability of shaping light fields in refined ways could be translated into shaping new forms of spin currents in materials.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.015
GPT teacher head0.290
Teacher spread0.275 · 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

Citations13
Published2024
Admission routes1
Has abstractyes

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Same venuePhysical Review LettersSame topicMagnetic properties of thin filmsFrench-language works237,207