Exploring the Potential of Broadband Complementary Metal Oxide Semiconductor Micro-Coil Nuclear Magnetic Resonance for Environmental Research
Bibliographic record
Abstract
With sensitivity being the Achilles’ heel of nuclear magnetic resonance (NMR), the superior mass sensitivity offered by micro-coils can be an excellent choice for tiny, mass limited samples such as eggs and small organisms. Recently, complementary metal oxide semiconductor (CMOS)-based micro-coil transceivers have been reported and demonstrate excellent mass sensitivity. However, the ability of broadband CMOS micro-coils to study heteronuclei has yet to be investigated, and here their potential is explored within the lens of environmental research. Eleven nuclei including 7Li, 19F, 31P and, 205Tl were studied and detection limits in the low to mid picomole range were found for an extended experiment. Further, two environmentally relevant samples (a sprouting broccoli seed and a D. magna egg) were successfully studied using the CMOS micro-coil system. 13C NMR was used to help resolve broad signals in the 1H spectrum of the 13C enriched broccoli seed, and steady state free precession was used to improve the signal-to-noise ratio by a factor of six. 19F NMR was used to track fluorinated contaminants in a single D. magna egg, showing potential for studying egg–pollutant interactions. Overall, CMOS micro-coil NMR demonstrates significant promise in environmental research, especially when the future potential to scale to multiple coil arrays (greatly improving throughput) is considered.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".