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Record W4310191023 · doi:10.3389/frobt.2022.1063987

A rolled-up-based fabrication method of 3D helical microrobots

2022· article· en· W4310191023 on OpenAlexaff
Zihan Wang, Xueliang Mu, Liyuan Tan, Yukun Zhong, U Kei Cheang

Bibliographic record

VenueFrontiers in Robotics and AI · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Calgary
FundersShenzhen Peacock PlanScience, Technology and Innovation Commission of Shenzhen MunicipalitySouthern University of Science and TechnologyDepartment of Education of Guangdong Province
KeywordsComputer scienceFabricationArtificial intelligence

Abstract

fetched live from OpenAlex

While the potential of using helical microrobots for biomedical applications, such as cargo transport, drug delivery, and micromanipulation, had been demonstrated, the viability to use them for practical applications is hindered by the cost, speed, and repeatability of current fabrication techniques. Hence, this paper introduces a simple, low-cost, high-throughput manufacturing process for single nickel layer helical microrobots with consistent dimensions. Photolithography and electron-beam (e-beam) evaporation were used to fabricate 2D parallelogram patterns that were sequentially rolled up into helical microstructures through the swelling effect of a photoresist sacrificial layer. Helical parameters were controlled by adjusting the geometric parameters of parallelogram patterns. To validate the fabrication process and characterize the microrobots' mobility, we characterized the structures and surface morphology of the microrobots using a scanning electron microscope and tested their steerability using feedback control, respectively. Finally, we conducted a benchmark comparison to demonstrate that the fabrication method can produce helical microrobots with swimming properties comparable to previously reported microrobots.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.664
Threshold uncertainty score0.575

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 head0.008
GPT teacher head0.254
Teacher spread0.245 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations8
Published2022
Admission routes1
Has abstractyes

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