Exploration of continuous‐flow benchtop NMR acquisition parameters and considerations for reaction monitoring
Bibliographic record
Abstract
This study focused on fundamental data acquisition parameter selection for a benchtop nuclear magnetic resonance (NMR) system with continuous flow, applicable for reaction monitoring. The effect of flow rate on the mixing behaviors within a flow cell was observed, along with an exponential decay relationship between flow rate and the apparent spin-lattice relaxation time (T1*) of benzaldehyde. We also monitored sensitivity (as determined by signal-to-noise ratios; SNRs) under various flow rates, analyte concentrations, and temperatures of the analyte flask. Results suggest that a maximum SNR can be achieved with low to medium flow rates and higher analyte concentrations. This was consistent with data collected with parameters that promote either slow or fast data acquisition. We further consider the effect of these conditions on the analyte's residence time, T1*, and magnetic field inhomogeneity that is a product of continuous flow. Altogether, our results demonstrate how fundamental acquisition parameters can be manipulated to achieve optimal data acquisition in continuous-flow NMR systems.
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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".