Low‐field, not low quality: 1D simplification, selective detection, and heteronuclear 2D experiments for improving low‐field NMR spectroscopy of environmental and biological samples
Bibliographic record
Abstract
Abstract Understanding environmental change is challenging and requires molecular‐level tools to explain the physicochemical phenomena behind complex processes. Nuclear magnetic resonance (NMR) spectroscopy is a key tool that provides information on both molecular structures and interactions but is underutilized in environmental research because standard “high‐field” NMR is financially and physically inaccessible for many and can be overwhelming to those outside of disciplines that routinely use NMR. “Low‐field” NMR is an accessible alternative but has reduced sensitivity and increased spectral overlap, which is especially problematic for natural, heterogeneous samples. Therefore, the goal of this study is to investigate and apply innovative experiments that could minimize these challenges and improve low‐field NMR analysis of environmental and biological samples. Spectral simplification (JRES, PSYCHE, singlet‐only, multiple quantum filters), selective detection (GEMSTONE, DREAMTIME), and heteronuclear (reverse and CH3/CH2/CH‐only HSQCs) NMR experiments are tested on samples of increasing complexity (amino acids, spruce resin, and intact water fleas) at‐high field (500 MHz) and at low‐field (80 MHz). A novel experiment called Doubly Selective HSQC is also introduced, wherein 1H signals are selectively detected based on the 1H and 13C chemical shifts of 1H–13C J‐coupled pairs. The most promising approaches identified are the selective techniques (namely for monitoring), and the reverse and CH3‐only HSQCs. Findings ultimately demonstrate that low‐field NMR holds great potential for biological and environmental research. The multitude of NMR experiments available makes NMR tailorable to nearly any research need, and low‐field NMR is therefore anticipated to become a valuable and widely used analytical tool moving forward.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".