Enrichment of magnetic particles using temperature and magnetic field gradients induced by benchtop fabricated micro-electromagnets
Bibliographic record
Abstract
The active transport of analytes inside biosensing systems is important for reducing the response time and enhancing the limit-of-detection of these systems. Due to the ease of functionalization with bio-recognition agents and manipulation with magnetic fields, magnetic particles are widely used for active and directed transport of biological analytes. On-chip active electromagnets are ideally suited for manipulating magnetic particles in an automated and miniaturized fashion inside biosensing systems. Unfortunately, the magnetic force exerted by these devices decays rapidly as we move away from the device edges, and increasing the generated force to the levels necessary for particle manipulation requires a parallel increase in the applied current and the resultant Joule heating. In this paper, we designed a study to understand the combined role of thermal and magnetic forces on the movement of magnetic particles in order to extend the interaction distance of on-chip magnetic devices beyond the device edges. For this purpose, we used a rapid prototyping method to create an active/passive on-chip electromagnet with a micro/nano-structured active layer and a patterned ferromagnetic passive layer. We demonstrated that the measured terminal velocities of particles positioned near the electromagnet edge (∼5.5 μm) closely reflect the values obtained by multi-physics modelling. Interestingly, we observed a two orders of magnitude deviation between the experimental and modelling results for the terminal velocities of particles far from the electromagnet edge (∼55.5 μm). Heat modelling of the system using experimentally-measured thermal gradients indicates that this discrepancy is related to the enhanced fluid movement caused by thermal forces. This study enables the rational design of thermo-magnetic systems for thermally driving and magnetically capturing particles that are positioned at distances tens to hundreds of microns away from the edges of on-chip magnetic devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".