Development of a quartz tuning-fork-based force sensor for measurements in the tens of nanoNewton force range during nanomanipulation experiments
Bibliographic record
Abstract
Understanding the mechanical properties of nanoscale systems requires new experimental and theoretical tools. In particular, force sensors compatible with nanomechanical testing experiments and with sensitivity in the nN range are required. Here, we report the development and testing of a tuning-fork-based force sensor for in situ nanomanipulation experiments inside a scanning electron microscope. The sensor uses a very simple design for the electronics and it allows the direct and quantitative force measurement in the 1-100 nN force range. The sensor response is initially calibrated against a nN range force standard, as, for example, a calibrated Atomic Force Microscopy cantilever; subsequently, applied force values can be directly derived using only the electric signals generated by the tuning fork. Using a homemade nanomanipulator, the quantitative force sensor has been used to analyze the mechanical deformation of multi-walled carbon nanotube bundles, where we analyzed forces in the 5-40 nN range, measured with an error bar of a few nN.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".