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Controlling Photoexcited Carrier Relaxation Through Phonon Management in GaAs/AlAs Superlattices

2023· article· en· W4390189789 on OpenAlexaff
Muhammad Fainan Hanif, Miloš Dubajić, Stephen Bremner, Michael P. Nielsen, Santosh Shrestha, Gavin Conibeer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsKensington Health
Fundersnot available
KeywordsSuperlatticePhononPhotoluminescenceMaterials scienceCondensed matter physicsRelaxation (psychology)OptoelectronicsGallium arsenideBand gapCarrier lifetimePhysicsSilicon

Abstract

fetched live from OpenAlex

Superlattice structures have been subject of investigation to understand the hampered photoexcited carrier (PC) cooling since the first evidence of slow hot carrier cooling in AlGaAs /GaAs superlattices, compared to bulk GaAs superlattices by Rosenwack et al. It is believed that this slow carrier relaxation is somewhat caused by phonon-bottleneck effect and can be achieved by blocking one or more of energy relaxing mechanisms namely Klemens and/or Ridley. Both decays are highly material properties dependent and rely on the energy difference between lowest optical and highest acoustic phonon branches. Superlatticing provides a plausible way to tune phononic properties and provides a way to study phononic properties dependent carrier dynamics. Through spectral and time resolved photoluminescence measurements of phononically tailored GaAs/AlAs superlattices, we provide a novel way to achieve slower carrier cooling.

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 score0.005

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 head0.031
GPT teacher head0.268
Teacher spread0.237 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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