Degree of polarization of photoluminescence from facets of InP as a function of strain: some experimental evidence
Bibliographic record
Abstract
Previous work demonstrated a good fit to the degree of polarization (DOP) of luminescence measurements on {110} facets of InP using a simple dependence of DOP of luminescence on strain: ${-}{K_e} ({e_1} - {e_3})$, where ${K_e}$ is a positive calibration constant, and ${e_1}$ and ${e_3}$ are normal components of strain in the plane of the facet and along $\langle 1\bar 10\rangle$ and $\langle 001\rangle$ directions [Appl. Opt.43, 1811 (2004)APOPAI0003-693510.1364/AO.43.001811]. Recent analytic modeling, which by necessity to be analytic must make simplifying assumptions, has suggested that unless the measurements are along crystallographic axes, the dependence of the DOP of luminescence on strain is more complicated: ${-}{K_e} (1.315 {e_1} - 0.7987 {e_3})$ for measurements from an InP facet, with a similar "excess" ${e_1}$ for GaAs [Appl. Opt.59, 5506 (2020)APOPAI0003-693510.1364/AO.394624]. In this work, we fit finite element simulations (FEM) to DOP measurements of the photoluminescence from facets of InP bars with ${\{111\} _B}$ v-grooves that have been placed in a cylindrical bending moment. We find that the more complicated dependence of DOP on strain, as derived by the analytic model, fits the data better than the previously assumed simple dependence. This finding thus corroborates the analytical model and should have an impact on understanding the strain-dependent operation of optoelectronic devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".