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Record W2031081703 · doi:10.1063/1.2840119

Dynamics of charge currents ballistically injected in GaAs by quantum interference

2008· article· en· W2031081703 on OpenAlexaff
Hui Zhao, Eric J. Loren, Arthur L. Smirl, H. M. van Driel

Bibliographic record

VenueJournal of Applied Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectronPhysicsAtomic physicsAmbipolar diffusionSpace chargePhotonCharge carrierCondensed matter physicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The dynamics of charge currents ballistically injected in GaAs bulk and quantum wells are spatially and temporally resolved. The electrons and holes are injected with oppositely directed velocities without the use of accelerating fields by quantum interference between two photon absorption of a 200fs, 1430nm fundamental pulse and one photon absorption of the corresponding second harmonic pulse. The subsequent charge motion is followed with ∼200fs temporal and ∼1nm spatial resolution by using tightly focused optical differential transmission techniques that are dependent on the relative phase of the incident pump pulses. Initially, the electrons and holes ballistically separate by up to ∼20nm, and a space charge field forms, which decelerates the carriers. Within this ∼1ps regime, the momentum relaxes by electron-hole and phonon scatterings, and the space charge field restores the electrons and holes to a common position; on time scales long compared to 1ps, ambipolar diffusion and recombination complete the return of the system to equilibrium. A rigid shift (damped simple harmonic oscillator) model for the electron motion reproduces the key features in the data, and the procedure for extracting the spatiotemporal dynamics of the electrons is shown to be immune to energy relaxation effects and forgiving of nonlinear saturation.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.666

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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 designTheoretical or conceptual
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

Citations34
Published2008
Admission routes1
Has abstractyes

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