Millimeter-sized nanomanipulator with sub-nanometer positioning resolution and large force output
Bibliographic record
Abstract
Nanomanipulation in space-limited environments (e.g., inside a SEM (scanning electron microscope), and particularly in a TEM (transmission electron microscope)) requires small-sized nanomanipulators that are capable of producing sub-nanometer positioning resolutions and large output forces. This paper reports on a millimeter-sized MEMS (microelectromechanical systems) based nanomanipulator with a positioning resolution of 0.15 nm and a motion range of ± 2.55 µm. An amplification mechanism is employed to convert micrometer input displacements, generated by a conventional electrostatic comb-drive microactuator, into sub-nanometer output displacements. The device has a high load driving capability, driving a load as high as 98 µN without sacrificing positioning performance. Based on the pseudo-rigid-body approach, closed-form analytical models of the minification ratio and stiffness of the amplification mechanism are developed. Finite element simulation and testing results verify that the theoretical models are valid with an error smaller than 6.2% and that the mechanism has a high linearity (± 2.4%). The amplification mechanism and analytical models have general applicability to other MEMS transducer designs. A capacitive displacement sensor is integrated for detecting input displacements that are converted into output displacements via the minimization ratio, allowing closed-loop controlled nanomanipulation. The MEMS-based nanomanipulators are applicable to the characterization/manipulation of nanomaterials and construction of nanodevices. Corrections were made to this article on 29 August 2007 (in the Abstract and sections 3.2.3 and 5.2). The corrected electronic version is identical to the print version.
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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".