Development of Low-Magnetic Susceptibility Microcoils via 5-Axis Machining for Analysis of Biological and Environmental Samples
Bibliographic record
Abstract
In environmental research, it is critical to understand how toxins impact invertebrate eggs and egg banks, which, due to their tiny size, are very challenging to study by conventional nuclear magnetic resonance (NMR) spectroscopy. Microcoil technology has been extensively utilized to enhance the mass-sensitivity of NMR. In a previous study, 5-axis computer numerical control (CNC) micromilling (shown to be a viable alternative to traditional microcoil production methods) was used to create a prototype copper slotted-tube resonator (STR). Despite the excellent limit of detection (LOD) of the resonator, the quality of the line shape was very poor due to the magnetic susceptibility of the copper resonator itself. This is best solved using magnetic susceptibility-matched materials. In this study, approaches are investigated that improve the susceptibility while retaining the versatility of coil milling. One method involves machining STRs from various copper/aluminum alloys, while the other involves machining ones from an aluminum 2011 alloy and electroplating them with copper. In all cases, combining copper and aluminum to produce resonators resulted in improved line shape and SNR compared to pure copper resonators due to their reduced magnetic susceptibility. However, the copper-plated aluminum resonators showed optimal performance from the devices tested. The enhanced LOD of these STRs allowed for the first 1 H– 13 C heteronuclear multiple quantum coherence (HMQC) of a single intact 13 C-labeled Daphnia magna egg (∼4 μg total biomass). This is a key step toward future screening programs that aim to elucidate the toxic processes in aquatic eggs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".