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Record W2021404456 · doi:10.1063/1.4893564

Benchtop fabrication of multi-scale micro-electromagnets for capturing magnetic particles

2014· article· en· W2021404456 on OpenAlexaff
Amin Hosseini, Leyla Soleymani

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFabricationElectromagnetMaterials sciencePermalloyElectrical conductorNanotechnologyMagnetMagnetic nanoparticlesElectrical engineeringMagnetic fieldMagnetizationEngineeringComposite materialNanoparticlePhysics

Abstract

fetched live from OpenAlex

Micro-electromagnets hold great promise for integration into portable and handheld lab-on-a- chip systems applicable to point-of-care disease management. Two major requirements must be satisfied in order for such devices to be applicable into practical, miniaturized, and portable biomedical instrumentation: low power operation and low-cost fabrication. In this paper, we use numerical modeling combined with a lithography-free fabrication process to create micro-electromagnets on a polymer substrate. Numerical modeling reveals that active-passive devices—ferromagnetic layers coupled with current-controlled planar coils—are essential for generating a sufficient magnetic force for magnetic particle manipulation at low currents (<50 mA). In addition, it is shown that current carrying conductors created from micro/nanotextured materials further enhance the generated magnetic force at a given current. To combine low-cost fabrication with low-current operation, we developed a benchtop fabrication method based on craft cutting, polymer induced thin film wrinkling, and electrodeposition to create a multilevel arrangement of multi-scale materials essential for low-current operation. We demonstrate that the fabricated active-passive devices featuring wrinkled copper active layers and permalloy passive layers capture 2.8 μm magnetic particles upon the application of a 35 mA current.

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 score0.289
Threshold uncertainty score0.639

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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