A <sup>1</sup>H Background-Free 3D Printing Digital Light Processing Resin for Applications in NMR Spectroscopy
Bibliographic record
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique with a wide range of applications. To support the analysis of diverse and complex samples, various NMR tools and accessories have been created. Three-dimensional (3D) printing is an underutilized production method for NMR hardware, mainly due to the lack of 1 H NMR background-free resins. Here, a photobleached perfluorinated resin was developed for 3D printing of 1 H NMR invisible components. 1 H NMR showed the resin produced no spectral background while solvent exposure tests demonstrated compatibility with all common NMR solvents. To demonstrate NMR applications of the resin, first commercially available accessories were 3D printed, including magnetic susceptibility plugs and coaxial inserts. Finally, novel accessories that open up new biological and environmental applications were demonstrated. This included a multicompartment holder that, when combined with NMR slice selection techniques, allows the simultaneous study of various samples. Furthermore, salt-tolerant inserts were developed that allowed for NMR to be collected even under the most challenging marine conditions, thus expanding NMR’s potential for oceanic research. The effectiveness of the salt-tolerant inserts was demonstrated with the marine organism, Tigriopus californicus. Even in 5 M salt, the full performance of a cryoprobe could be retained using the inserts, whereas using a standard NMR tube, radiofrequency performance was beyond the power handling limits of the probe and 3-fold loss in SNR was seen. In summary, the development and use of “NMR invisible” perfluorinated resins presents an economical, accessible, rapid, and versatile approach for building NMR components allowing new applications and prototyping.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".