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Record W2765917361 · doi:10.1103/physrevb.97.045307

Valley-selective optical Stark effect probed by Kerr rotation

2018· article· en· W2765917361 on OpenAlexfundno aff
Trevor LaMountain, Hadallia Bergeron, Itamar Balla, Teodor K. Stanev, Mark C. Hersam, Nathaniel P. Stern

Bibliographic record

VenuePhysical review. B./Physical review. B · 2018
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsnot available
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaNational Institute of Standards and TechnologyNational Science Foundation
KeywordsStark effectExcitonPolarization (electrochemistry)DielectricSpectroscopyKerr effectAbsorption spectroscopyAtomic physicsSemiconductorQuantum-confined Stark effectChemistryPhysicsCondensed matter physicsOpticsElectric fieldOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

The authors show here that Kerr rotation is a sensitive probe of valley-dependent energy splitting induced by the optical Stark effect in two-dimensional semiconductors. Kerr rotation rejects the polarization-independent background and probes a complementary dielectric response from established absorption-based techniques, allowing detection of shifts as small as 4 \ensuremath{\mu}eV - the lowest reported value so far. The authors apply this improved valley Stark spectroscopy to a wider range of materials by observing Stark shifts of two energetically distinct exciton species in MoS${}_{2}$, representing the first valley- and energy-selective Stark effect in a single material.

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 score0.007

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.369
Teacher spread0.358 · 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

Citations41
Published2018
Admission routes1
Has abstractyes

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