Design, implementation, and application of a microresonator platform for measuring energy dissipation by internal friction in nanowires
Bibliographic record
Abstract
Accurate measurements of internal friction in nanowires are required for the rational design of high-Q resonators used in nanoelectromechanical systems and for fundamental studies of nanomechanical behavior. However, measuring internal friction is challenging because of the difficulties associated with identifying the contributions of material dissipation to structural damping. Here, we present an approach for overcoming these difficulties by using a composite microresonator platform that is calibrated against the ultimate limits of thermoelastic damping. The platform consists of an array of nanowires patterned at the root of a low-loss single-crystal silicon microcantilever. The structure is processed using a lift-off technique, implemented using electron-beam lithography, to achieve excellent control over the size, alignment, dispersion and location of the nanowire array. As the first application of this platform, we measured internal friction at room temperature in aluminum nanowires that ranged from 50 to 100 nm in thickness and 100 to 400 nm in width. Internal friction is ~0.03 at frequencies of 6.5-21 kHz. Transmission electron microscopy of the nanocrystalline grain structure, and comparison with previously measured values of internal friction in continuous thin films of aluminum, suggest that grain-boundary sliding is a major source of internal friction in these nanowires.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".