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Record W2020141562 · doi:10.1149/2.001402jes

Design, Fabrication and Testing of a Polymer Composite Based Hard-Magnetic Mirror for Biomedical Scanning Applications

2013· article· en· W2020141562 on OpenAlexaff
Manu Pallapa, John T. W. Yeow

Bibliographic record

VenueJournal of The Electrochemical Society · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceScannerRadius of curvatureFabricationOpticsSurface roughnessOptoelectronicsCurvatureComposite materialPhysics

Abstract

fetched live from OpenAlex

In this work, we combine the advantages of hard magnets, polydimethylsiloxane (PDMS) micro-molding and printed circuit board planar coils to propose an electromagnetically actuated bidirectional scanner with potential applications in bio-medical scanning. The proposed bidirectional scanner (4 mm × 4 mm × 250 μm) is fabricated by micromolding an isotropic Nd-Fe-B micropowder (MQFP-12-5) doped in a PDMS matrix at 80% weight percentage and magnetized using a standard dipole electromagnet. A reflective gold layer of 100 nm is evaporated onto the polymer composite structure. A maximum magnetic field of 20 mT is measured for the polymer magnetic mirror. Rectangular planar coils (trace width and spacing: 254 μm; 10 turns) are employed to actuate the bidirectional scanner electromagnetically by Lorentz force. Actuation in both static and dynamic excitation modes is tested. Power consumption at a maximum rotational angle (15 degrees – optical) is one watt at 40 Hz resonant frequency. Initial surface roughness of the mirror (165 nm) and radius of curvature (75 mm) has been addressed by depositing a thin layer of PDMS, resulting in 66.24 nm surface roughness and 321.37 mm overall radius of curvature after integration. Major advantages of the proposed bidirectional scanner include low fabrication cost, low input voltage and large actuator displacement compared to existing electrostatic scanners.

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: Methods · Consensus signal: none
Teacher disagreement score0.318
Threshold uncertainty score0.204

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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
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

Citations29
Published2013
Admission routes1
Has abstractyes

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