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Record W2897182992 · doi:10.1103/physrevb.98.155115

Using coherent phonons for ultrafast control of the Dirac node of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>SrMnSb</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>

2018· article· lv· W2897182992 on OpenAlexaff
C. Weber, Madison G. Masten, Thomas C. Ogloza, Bryan S. Berggren, Michael K. L. Man, Keshav M. Dani, Jinyu Liu, Zhiqiang Mao, D. D. Klug, Adebayo A. Adeleke, Yansun Yao

Bibliographic record

VenuePhysical review. B./Physical review. B · 2018
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversity of SaskatchewanNational Research Council Canada
FundersLouisiana Board of RegentsOffice of Experimental Program to Stimulate Competitive ResearchU.S. Department of EnergyNational Science Foundation
KeywordsDirac (video compression format)PhysicsPhononDispersion (optics)GrapheneSemimetalUltrashort pulseFermionBand gapCondensed matter physicsQuantum mechanicsLaser

Abstract

fetched live from OpenAlex

${\mathrm{SrMnSb}}_{2}$ is a candidate Dirac semimetal whose electrons near the $Y$ point have the linear dispersion and low mass of a Dirac cone. Here we demonstrate that ultrafast, 800-nm optical pulses can launch coherent phonon oscillations in ${\mathrm{Sr}}_{0.94}{\mathrm{Mn}}_{0.92}{\mathrm{Sb}}_{2}$, particularly an ${A}_{g}$ mode at 4.4 THz. Through first-principles calculations of the electronic and phononic structure of ${\mathrm{SrMnSb}}_{2}$, we show that high-amplitude oscillations of this mode would displace the atoms in a way that transiently opens and closes a gap at the node of the Dirac cone. The ability to control the nodal gap on a subpicosecond timescale could create opportunities for the design and manipulation of Dirac fermions.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.028
GPT teacher head0.303
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

Citations22
Published2018
Admission routes1
Has abstractyes

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