Ultrafast surface strain dynamics in MnAs thin films observed with second harmonic generation
Bibliographic record
Abstract
Optical second harmonic generation (SHG) is used to probe surface strain in 150 and 190-nm thin films of MnAs grown epitaxially on GaAs(001). The $p$-polarized SHG signal produced by $p$-polarized 775-nm, 200-fs pulses is theoretically and experimentally shown to be sensitive to the normal component of surface strain from $\ensuremath{-}20$ to 70 ${}^{\ensuremath{\circ}}$C, which includes the ferromagnetic/paramagnetic striped coexistence phase region that exists from $\ensuremath{\sim}$10 to 40 ${}^{\ensuremath{\circ}}$C. We use this dependence to time-resolve the surface strain dynamics in MnAs following pumping with 200-fs pulses of 1.0 or 2.0 mJ cm${}^{\ensuremath{-}2}$ that raise the surface temperature by tens of degrees. For a film at $\ensuremath{-}20{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}$C the strain reaches a minimum value in $\ensuremath{\sim}$10 ps, indicative of electron-lattice thermalization, before recovering on a 500-ps time scale consistent with a one-dimensional heat diffusion model. For a film at 20 ${}^{\ensuremath{\circ}}$C the minimum strain is reached only after $\ensuremath{\sim}$200 ps and attains a value higher than predicted by the heat diffusion model; recovery, however, still occurs in $\ensuremath{\sim}$500 ps. The long strain fall time possibly reflects the influence of latent heat and stripe dynamics in the coexistence phase. The larger calculated drop in surface strain may be due to deficiencies in the one-dimensional heat diffusion model. The nonequilibrium surface strain also may not be determined by the local temperature alone but by the constraints throughout the film/substrate system, which are certainly known to govern the strain and stripe characteristics under equilibrium conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".