Design and optimization of arrays of neodymium iron boron-based magnets for magnetic tweezers applications
Bibliographic record
Abstract
We present the design methodology for arrays of neodymium iron boron (NdFeB)-based magnets for use in magnetic tweezers devices. Using finite element analysis (FEA), we optimized the geometry of the NdFeB magnet as well as the geometry of iron yokes designed to focus the magnetic fields toward the sample plane. Together, the magnets and yokes form a magnetic array which is the basis of the magnetic tweezers device. By systematically varying 15 distinct shape parameters, we determined those features that maximize the magnitude of the magnetic field gradient as well as the length scale over which the magnetic force operates. Additionally, we demonstrated that magnetic saturation of the yoke material leads to intrinsic limitations in any geometric design. Using this approach, we generated a compact and light-weight magnetic tweezers device that produces a high field gradient at the image plane in order to apply large forces to magnetic beads. We then fabricated the optimized yoke and validated the FEA by experimentally mapping the magnetic field of the device. The optimization data and iterative FEA approach outlined here will enable the streamlined design and construction of specialized instrumentation for force-sensitive microscopy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".