Exploration of Materials for Three-Dimensional NMR Microcoil Production via CNC Micromilling and Laser Etching
Bibliographic record
Abstract
The excellent versatility of 5-axis computer numerical control (CNC) micromilling has led to its application for prototyping NMR microcoils tailored to mass-limited samples (reducing development time and cost). However, vibrations during 5-axis milling can hinder the creation of complex 3D volume microcoils (i.e., solenoids and saddle coils). To address these limitations, a high-resolution NSCNC ELARA 4-axis milling machine was developed with the extra precision required for making complex 3D volume microcoils. Upon investigating the performance of resonators made with various copper-coated dielectrics, resonators with poly(methyl methacrylate) (PMMA) provided the best SNR/line shape. Thus, complex 1.7 mm microcoil designs were machined from Cu-coated PMMA. A milled 6.4 mm solenoid also provided 6.6× the total carbon signal for a 13 C-labeled broccoli seed compared to a commercial inverse 5 mm NMR probe (demonstrating potential for larger coil designs). However, the manufacture of coils <1.7 mm with copper-coated PMMA rods was challenging as ∼0.5 mm of remaining PMMA was needed to retain their structural integrity. To manufacture smaller microcoils, both a solenoid and saddle coil (both with 1 mm O.D., 0.1 mm thick walls) were etched from Cu-coated glass capillaries using a UV picosecond laser that was mounted onto an NSCNC 5-axis MiRA7L. Both resonators showed excellent signal and identified a wide range of metabolites in a 13 C-labeled algae extract, while the solenoid was further tested on two copepod egg sacs (∼4 μg of total sample). In summary, the flexibility to prototype complex microcoils in-house allows laboratories to tailor microcoils to specific mass-limited samples while avoiding the costs of cleanrooms.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".