Using phase interference to characterize dynamic properties—a review of constant gradient, portable magnetic resonance methods
Bibliographic record
Abstract
Spatially resolved motion-sensitized magnetic resonance (MR) is a powerful tool for studying the dynamic properties of materials. Traditional methods involve using large, expensive equipment to create images of sample displacement by measuring the spatially resolved MR signal response to time-varying magnetic field gradients. In these systems, both the sample and the stress applicator are typically positioned inside a magnet bore. Portable MR instruments with constant gradients are more accessible, with fewer limitations on sample size, and they can be used in industrial settings to study samples under deformation or flow. We propose a view in which the well-controlled sensitive region of a magnet array acts as an integrator, with the velocity distribution leading to phase interference in the detected signal, which encodes information on the sample’s dynamic properties. For example, in laminar flows of Newtonian and non-Newtonian fluids, the velocity distribution can be determined analytically and used to extract the fluid’s dynamic properties from the MR signal magnitude and/or phase. This review covers general procedures, practical considerations, and examples of applications in dynamic mechanical analysis and fluid rheology (viscoelastic deformation, laminar pipe flows, and Couette flows). Given that these techniques are relatively uncommon in the broader magnetic resonance community, this review is intended for both advanced NMR users and a more general physics/engineering audience interested in rheological applications of NMR.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".