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Record W1976376412 · doi:10.1103/physrevb.75.233409

Exciton-state mixing effects in photoinduced intersubband transitions in quantum-well structures

2007· article· en· W1976376412 on OpenAlexaff
Y. Chen, Seyed M. Sadeghi, Wei‐Ping Huang

Bibliographic record

VenuePhysical Review B · 2007
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsExcitonPhysicsDipoleElectronCondensed matter physicsMixing (physics)Angular momentumQuantum wellAtomic physicsBiexcitonAtomic electron transitionValence (chemistry)Quantum mechanicsLaserSpectral line

Abstract

fetched live from OpenAlex

We study the effects of exciton-state mixing on photoinduced conduction intersubband transitions in undoped quantum wells. Valence-band mixing and exciton-state mixing of different orbital angular momenta ($s$, $p$, $d$, etc.) are fully accounted for in the analysis. We show that, when the exciton-state mixing is significant, an infrared laser near resonant with two conduction subbands in quantum wells can excite intersubband excitations with different orbital angular momentum attributions, imitating the electronic transitions in quantum dots. In other words, instead of pure electronic states, the initial and final states of such intersubband transitions become mixed excitonic states, making their dipole moments strongly dependent not only on the $s$ component but also on $p$ and $d$ components. Our results show that the inclusion of all the orbital angular momenta gives accurate evaluation of the dipole moment of the photoinduced intersubband transitions, which may have been drastically overestimated by electron to electron transition model or underestimated by the exciton transition model without exciton-state mixing.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.010
GPT teacher head0.317
Teacher spread0.307 · 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
Published2007
Admission routes1
Has abstractyes

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