Toward “Receive-Only” Nuclear Magnetic Resonance Complementary Metal-Oxide-Semiconductor Microcoil Arrays for High-Throughput Analysis of Environmental Samples
Bibliographic record
Abstract
Daphnia magna neonates and eggs are critical life stages that show different susceptibilities to toxins and stressors compared to adults. Nuclear magnetic resonance (NMR) spectroscopy has unique potential to uncover the underlying biochemical causes, but such very small, mass-limited samples are challenging to study. While the enhanced mass sensitivity of microcoils leads to markedly improved NMR analysis, experiments are often still long, leading to low throughput. Microcoil arrays can improve throughput by allowing concurrent analysis of multiple samples, but practical and economical challenges, including expensive receivers and lack of physical space, are prohibitive. Here, approaches to overcome these challenges were examined. First, “receive-only” planar complementary metal-oxide-semiconductor (CMOS) microcoils were explored wherein the microcoil was used for detection, but an external volume coil was used for excitation. This resulted in considerably improved nutation and provided a 70% boost to the signal-to-noise ratio (SNR) compared to excitation on the planar CMOS coil. Steady-state free precession experiments further improved the SNR by a factor of at least 2.5, reducing acquisition time and improving throughput. To complete a three-coil “receive-only” array, an inexpensive ($315) software-defined radio board was adapted to act as an NMR receiver with similar performance to commercial receivers. The three-coil array was used to study three D. magna eggs concurrently, potentially opening the door for high-throughput analysis and toxicological studies, where the control and exposed cohorts can be studied concurrently, reducing day-to-day variability. Overall, microcoil arrays show considerable promise for the study of D. magna eggs and other mass-limited samples.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".