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Record W2109034940 · doi:10.1039/c3nr06442e

Inkjet printed superparamagnetic polymer composite hemispheres with programmed magnetic anisotropy

2014· article· en· W2109034940 on OpenAlexaff
Olgaç Ergeneman, Christian Peters, Maurizio R. Gullo, Loïc Jacot‐Descombes, Simone Gervasoni, Berna Özkale, Philipe Fatio, Víctor J. Cadarso, Massimo Mastrangeli, Salvador Pané, Juergen Brügger, Christofer Hierold, Bradley J. Nelson

Bibliographic record

VenueNanoscale · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsCMC Microsystems (Canada)
FundersEidgenössische Technische Hochschule ZürichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsSuperparamagnetismAnisotropyMaterials scienceComposite numberPolymerMagnetic anisotropyNanotechnologyComposite materialMagnetic fieldPhysicsOpticsMagnetization

Abstract

fetched live from OpenAlex

We present the fabrication and characterization of large arrays of inkjet-printed superparamagnetic polymer composite (SPMPC) hemispherical microstructures. SPMPCs are appealing for applications in microsystems and nanorobotics due to the added functionality of polymers and the significant magnetic attributes of embedded nanostructures. SPMPC-based microarchitectures can be used to perform different functions wirelessly in various media (e.g. water, solvents) using external magnetic fields: handling and assembling small objects, delivering drugs or biomass, or sensing specific physical or chemical changes. In this work superparamagnetic magnetite nanoparticles are dispersed in SU-8 to form magnetic hemispheres. Magnetically anisotropic hemispheres as well as standard SPMPC hemispheres are fabricated. Magnetic anisotropy is programmed by applying a magnetic field during curing. The distribution of nanoparticles inside the polymer matrix and magnetic characteristics of the SPMPC are investigated. Magnetic manipulation of hemispheres is demonstrated at liquid-liquid interfaces. Different assembly strategies to form lines or geometric shapes from hemispheres as well as their independent dynamic control are demonstrated. Finally, a two-interface assembly strategy is demonstrated to assemble hemispheres into complete spheres for advanced self-assembly tasks.

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 categoriesInsufficient payload (model declined to judge)
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 score0.414
Threshold uncertainty score1.000

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)0.0010.000

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 head0.203
Teacher spread0.198 · 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.

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

Citations20
Published2014
Admission routes1
Has abstractyes

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