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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".