Water suppression 101 for benchtop NMR–An accessible guide and primer including fully interactive training videos
Bibliographic record
Abstract
Benchtop NMR is enjoying a renaissance with numerous manufacturers bringing products to the market over the last decade. The improved accessibility, lower cost of ownership and ease of use (vs high field NMR), is attracting new users into NMR spectroscopy, which is highly beneficial for the field in general. As benchtop NMR systems seldom require deuterated solvents, this allows samples to be analyzed “as is”, without extraction or alteration. However, many interesting samples, be it an organic reaction mixture, beer, or a biofluid, contain one or more solvent/water signals, which often require suppression. Due to the lower spectral dispersion of benchtop NMR's (vs high field) the frequency of solvent/water is much closer to the analytes of interest, making solvent suppression more challenging. As such, there is a conundrum, where novel users wish to analyze unaltered samples but are quickly faced with the challenge of water suppression, and the wealth of options in the high field literature can be overwhelming. It is important to note that all manufacturers offer some sort of automated water suppression that can be performed with a “single click” that are sufficient for “walk-up” applications or occasional users. This primer is aimed as an accessible guide to those wishing to take the next step and is suitable for users who; 1) would like to pick the optimal water suppression approach for their sample type and 2) wish to understand how water suppression works. The guide focuses on water suppression approaches that are easy to apply, namely presaturation based sequences, binomial sequences for aggressive suppression, and WET for multiple signal suppression, across a range of samples including sucrose standards, espresso, human blood serum and wine. The primer finishes with a flow chart that can be used to guide users in choosing the optimal water suppression approach for their specific sample type, with considerations, including exchangeable signals and the preservation of macromolecular signals, amongst others. In addition, the primer includes 3 fully interactive videos based on H5P technology, focusing on how to acquire data using the approaches described here. The videos include quizzes, with a first-person-perspective of the spectrometer software with interactive elements, as if the users were acquiring the data themselves. In summary, the primer is aimed at advanced undergraduates, graduate students, new users, or users wishing to expand their water/solvent suppression skills/knowledge using benchtop NMR.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".