MétaCan
Menu
Back to cohort

Influence of Nanoparticle Coating on the Differential Magnetometry and Wireless Actuation of Biohybrid Microrobots

2023· article· en· W4389666407 on OpenAlexaff
Veronika Magdanz, Jack R. Cumming, Sadaf Salamzadeh, Sven Tesselaar, Lejla Alić, Léon Abelmann, Islam S. M. Khalil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
FundersRadboud Universitair Medisch Centrum
KeywordsNanoparticleMagnetic nanoparticlesMaterials scienceMagnetometerNanobiotechnologyNanotechnologyMagnetic fieldNanosensorFaraday cageNuclear magnetic resonancePhysics

Abstract

fetched live from OpenAlex

Magnetic nanoparticles can be electrostatically assembled around sperm cells to form biohybrid micro robots. These biohybrid microrobots possess sufficient magnetic material to potentially allow for pulse-echo localization and wireless actuation. Alternatively, magnetic excitation of these nanoparticles can be used for localization based on Faraday's law of induction using a detection coil. Here, we investigate the influence of the electrostatic attraction between positively charged nanoparticles and negatively charged sperm cells on the activation of the nanoparticles during nonlinear differential magnetometry and wireless magnetic actuation. Activation of clusters of free nanoparticles and nanoparticles bound to the body of sperm cells is achieved by a combination of a high- frequency alternating field and a pulsating static field. The nonlinear response in both cases indicates that constraining the nanoparticles is likely to yield significant decreases in the magnetometry sensitivity. While the attachment of particles to the cells enables wireless actuation (rolling locomotion), the rate of change of the magnetization of the nanoparticles decreases one order of magnitude compared to free nanoparticles.

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 score0.050
Threshold uncertainty score0.160

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicMicro and Nano RoboticsFrench-language works237,207