Slice through the water—Exploring the fundamental challenge of water suppression for benchtop NMR systems
Bibliographic record
Abstract
Abstract Benchtop NMR provides improved accessibility in terms of cost, space, and technical expertise. In turn, this encourages new users into the field of NMR spectroscopy. Unfortunately, many interesting samples in education and research, from beer to whole blood, contain significant amounts of water that require suppression in 1 H NMR in order to recover sample information. However, due to the significant reduction in chemical shift dispersion in benchtop NMR systems, the sample signals are much closer to the water resonance compared to those in a corresponding high‐field NMR spectrum. Therefore, simply translating solvent suppression experiments intended for high‐field NMR instruments to benchtop NMR systems without careful consideration can be problematic. In this study, the effectiveness of several popular water suppression schemes was evaluated for benchtop NMR applications. Emphasis is placed on pulse sequences with no, or few, adjustable parameters making them easy to implement. These fall into two main categories: (1) those based on Pre‐SAT including Pre‐SAT, PURGE, NOESY‐PR, and g‐NOESY‐PR and (2) those based on binomial inversion including JRS and W5‐WATERGATE. Among these schemes, solvent suppression sequences based on Pre‐SAT offer a general approach for easy solvent suppression for samples with higher analyte concentrations (sucrose standard and Redbull™). However, for human urine, binomial‐like sequences were required. In summary, it is demonstrated that highly efficient water suppression approaches can be implemented on benchtop NMR systems in a simple manner, despite the limited spectral dispersion, further illustrating the potential for widespread implementation of these approaches in education and research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".