A Unilateral Magnetic Resonance Moisture Sensor for Aerospace Composites
Bibliographic record
Abstract
Magnetic resonance (MR) is a proven, nondestructive analytical technique in chemistry and medicine. The sensitivity of a MR experiment is principally dependent on the strength and homogeneity of a polarizing static magnetic field, termed B0. This requirement has traditionally limited MR experiments to a 'closed' configuration of magnets or wires generating a highly uniform B0, but limiting the size of the sample that can be investigated. To remove this size requirement, 'open' or unilateral sensors have been developed, in which a planar arrangement of magnets generates a suitable B0at a location displaced from the sensor. This configuration increases the range of samples that can be examined, at the expense of B0homogeneity. This paper presents the design of a portable, unilateral MR sensor suitable for nondestructive moisture detection. The sensor comprises an array of permanent magnets, designed using an inversion approach in which a target magnetic field is specified in a volume over the array. Using an analytical optimization, the magnet sizes, strengths, and positions are then determined to give the target field. The result is a large region of field homogeneity, displaced ~4.5cm from the array, suitable for MR. The sensor is demonstrated for the in situ detection of moisture within aircraft composites. The paper discusses the design and construction of the magnet array, and presents results of successful moisture detection
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".