Fluid-Structure Coupling in Gas Damping Response of Nanowire Resonators
Bibliographic record
Abstract
Nanowires vibrating at resonance in a gas can serve as sensitive detectors of mass (< 10−18 grams), of use to molecular diagnosis of disease. They can also serve as sensitive detectors of damping force in an ambient gas environment. The Q-factor of resonance spectra, quantifies the sharpness of the peak and is a measure of the ratio of inertial to dissipative (damping) forces. Q-factor data enable quantification of the gas damping force in different regimes of rarefied gas dynamics. Measurements were made with silicon and rhodium nanowires of comparable size, in pure dry nitrogen, with pressure increasing from high vacuum (10−10 atm) to one atmospheric pressure. The data show that, for the silicon nanowires, the Q-factor begins to decrease from its high-vacuum value at a lower pressure and reaches a lower minimum value at one atmosphere, compared to the rhodium nanowires. We show that nanowire structural properties, namely the elastic modulus and intrinsic damping, are responsible for these differing sensitivities to a similar gas damping force range. The results show an important coupling of fluid and structural interaction for rarefied gas dynamics at nanoscale. For practical sensing applications in an ambient gas, this coupling indicates that silicon nanowires are better suited for gas damping force sensing, while rhodium nanowires would fare better as mass sensors for molecular diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".