Permanent magnet based magnetic resonance sensors
Bibliographic record
Abstract
In 2006 patent EP2069769A2 described using magnetic resonance (MR), instead of fluorescence, as the detection method for microarrays. This covered the concept of binding magnetic particles, such as Superparamagnetic Iron Oxide (SPIO), to a surface in order to change the MR signal that would normally be expected from the fluid covering the surface to which the particles were bound. In this thesis, measurement techniques are presented, utilising both pulsed and continuous wave nuclear magnetic resonance (CWNMR), where surface bound magnetic nanoparticles disrupt the MR signal that would normally be detected in the fluid covering the surface, using low magnetic field sensors constructed from permanent magnets. A pulsed technique is presented with a sensor constructed using permanent magnets in a Halbach arrangement. Using a technique called Magnetic Resonance Disruption (MaRDi), it is shown that the T2eff relaxation time of a test liquid, polydimethylsiloxane (PDMS), reduces as the proportion of the surface area covered with SPIO increases. In addition, a linear decrease in the signal amplitude from the PDMS as a function of SPIO coverage, which is observed both for an integral over 4096 NMR echoes and even just in the first echo. The latter result suggests the potential for a technique to be developed with simplified and low cost electronics. A CWNMR technique is also presented by revisiting the Look and Locker’s tone-burst experiments but modified to use a commercial marginal oscillator. Though observing the transient effect when a sweep coil is switched on, a parameter Tx can be determined that is related to relaxation time T1 that can subsequently be calculated with the aid of calibration samples. This Transient Effect Determination of Spin Lattice relaxation time (TEDSpiL) was automated using low cost microcontrollers. A potential industrial application of detecting moisture uptake through improperly stored dehydrated milk powder is also presented.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".