Simultaneous measurements of photoemission and morphology of various Al alloys during mechanical deformation
Bibliographic record
Abstract
We report simultaneous measurements of strain and photoelectron emission from high purity Al (1350), Al–Mg (5052), Al–Mn (3003), Al–Cu (2024), and Al–Mg–Si (6061) alloys under uniaxial tension due to pulsed excimer laser radiation (248nm). The emission of low-energy photoelectrons is sensitive to deformation-induced changes in surface morphology, including the formation of slip lines and slip bands. Alloy composition and surface treatment significantly influence the photoemission during deformation. Surface oxide enhances the signal-to-noise level during photoemission measurement. In the early stage of deformation (strain ⩽0.04), photoemission intensity increases gradually in a nonlinear fashion. While subsequent photoemission increases almost linearly with strain until failure in samples with thin oxide layer (∼31Å), there are two linear segments of photoemission for the samples with oxide of 45Å. The onset of strain localization corresponds to the intersection point of two linear segments, usually at a strain of 0.08–0.20. A constitutive model incorporating microstructure evolution and work hardening during tensile deformation is proposed to qualitatively interpret the growth of the photoemission as a function of strain. Photoemissions from various alloys are interpreted in the light of surface treatment, work function, composition, and microstructural development during deformation.
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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".