Evaluation of double‐tuned single‐sided planar microcoils for the analysis of small <sup>13</sup>C enriched biological samples using <sup>1</sup>H‐<sup>13</sup>C 2D heteronuclear correlation NMR spectroscopy
Bibliographic record
Abstract
Abstract Microcoils provide a cost‐effective approach to improve detection limits for mass‐limited samples. Single‐sided planar microcoils are advantageous in comparison to volume coils, in that the sample can simply be placed on top. However, the considerable drawback is that the RF field that is produced by the coil decreases with distance from the coil surface, which potentially limits more complex multi‐pulse NMR pulse sequences. Unfortunately, 1H NMR alone is not very informative for intact biological samples due to line broadening caused by magnetic susceptibility distortions, and 1H‐13C 2D NMR correlations are required to provide the additional spectral dispersion for metabolic assignments in vivo or in situ. To our knowledge, double‐tuned single‐sided microcoils have not been applied for the 2D 1H‐13C analysis of intact 13C enriched biological samples. Questions include the following: Can 1H‐13C 2D NMR be performed on single‐sided planar microcoils? If so, do they still hold sensitivity advantages over conventional 5 mm NMR technology for mass limited samples? Here, 2D 1H‐13C HSQC, HMQC, and HETCOR variants were compared and then applied to 13C enriched broccoli seeds and Daphnia magna (water fleas). Compared to 5 mm NMR probes, the microcoils showed a sixfold improvement in mass sensitivity (albeit only for a small localized region) and allowed for the identification of metabolites in a single intact D. magna for the first time. Single‐sided planar microcoils show practical benefit for 1H‐13C NMR of intact biological samples, if localized information within ~0.7 mm of the 1 mm I.D. planar microcoil surface is of specific interest.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".