Design, Fabrication and Testing of a Polymer Composite Based Hard-Magnetic Mirror for Biomedical Scanning Applications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".