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Record W7117109249 · doi:10.1016/j.isci.2025.114484

Light-induced selective speed alteration of magnetically rolled semiconductor particles

2025· article· en· W7117109249 on OpenAlexafffund
David P. Rivas, Zameer Hussain Shah, Henry Kai-Tat Shum, Sambeeta Das

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaAir Force Office of Scientific ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsSemiconductorHematiteDrug deliveryTargeted drug deliveryWork (physics)Magnetic nanoparticlesComponent (thermodynamics)Nanoparticle

Abstract

fetched live from OpenAlex

Microrobot teams or swarms are promising candidates for many applications, such as micromanipulation, microsurgery, or targeted drug delivery. However, attaining individual control of the microrobots, which is a critical component to many of their applications, remains a significant technical challenge. We introduce a method to control the magnetic rolling speed of hematite semiconductor particles using localized UV light, attributed to light-induced changes in particle-substrate friction. Simulations and theoretical models support our experimental observations, showing how particle-substrate separation influences speed. Additionally, we demonstrate fixed patterning of microparticles via selective UV illumination at lower pH, demonstrating selective immobilization of microrobots, a conceptual step toward applications such as targeted drug delivery or patterned cell stimulation in future studies. Therefore, this work provides a novel approach for independent control of microrobot systems by modulating particle-substrate interactions with light.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.229

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes2
Has abstractyes

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