Mechanical spectroscopy of nanocrystalline aluminum films: effects of frequency and grain size on internal friction
Bibliographic record
Abstract
Energy dissipation by internal friction is a property of fundamental interest for probing the effects of scale on mechanical behavior in nanocrystalline metallic films and for guiding the use of these materials in the design of high-Q micro/nanomechanical resonators. This paper describes an experimental study to measure the effects of frequency, annealing and grain size on internal friction at room temperature in sputter-deposited nanocrystalline aluminum films with thicknesses ranging from 60 to 120 nm. Internal friction was measured using a single-crystal silicon microcantilever platform that calibrates dissipation against the fundamental limits of thermoelastic damping. Internal friction was a weak function of frequency, reducing only by a factor of two over three decades of frequency (70 Hz to 44 kHz). Annealing led to significant grain growth and the average grain size of 100 nm thick films increased from 90 to 390 nm after annealing for 1 h at 450 (∘)C. This increase in grain size was accompanied by a decrease in internal friction from 0.05 to 0.02. Taken together, these results suggest that grain-boundary sliding, characterized by a spectrum of relaxation times, contributes to internal friction in these films.
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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".