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Record W3093533957 · doi:10.1063/5.0014391

A piezoelectric stick–slip drive nanopositioner with large velocity under high load

2020· article· en· W3093533957 on OpenAlexaff
Sen Gu, Peng Pan, Junhui Zhu, Yong Wang, Feiyu Yang

Bibliographic record

VenueAIP Advances · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsPiezoelectricityStiffnessMATLABSlip (aerodynamics)Finite element methodMaterials scienceControl theory (sociology)AcousticsStructural engineeringEngineeringPhysicsComposite materialComputer scienceThermodynamics

Abstract

fetched live from OpenAlex

Piezoelectric stick–slip drive nanopositioners are of central importance in in situ SEM nanorobotic systems due to their high precise positioning, large stroke, high speed, and compact structure. However, the output velocity under high load will be seriously influenced. In this paper, a new piezoelectric stick–slip drive nanopositioner with large velocity under high load by introducing the adjust bolts to decouple the driving unit and moving unit is presented. A MATLAB simulation model has been created to optimize the nanopositioner for a certain velocity, and a FEM is used to confirm that the leaf hinge has sufficient stiffness. The size of the prototype is 30 × 32 × 25 mm3. Testing results indicate that the nanopositioner achieves a maximum velocity of 3.467 mm/s and a minimum resolution of 6 nm. When the load increases from 0.4 kg to 2 kg, the maximum velocities only decrease from 3.457 mm/s to 3.143 mm/s. The proposed piezoelectric stick–slip nanopositioner shows large velocity under high load.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.240
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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