A reliable external calibration method for reaction monitoring with benchtop NMR
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical technique with the ability to acquire both quantitative and structurally insightful data for multiple components in a test sample. This makes NMR spectroscopy a desirable tool to understand, monitor, and optimize chemical transformations. While quantitative NMR (qNMR) approaches relying on internal standards are well-established, using an absolute external calibration scheme is beneficial for reaction monitoring as resonance overlap complications from an added reference material to the sample can be avoided. Particularly, this type of qNMR technique is of interest with benchtop NMR spectrometers as the likelihood of resonance overlap is only enhanced with the lower magnetic field strengths of the used permanent magnets. The included study describes a simple yet robust methodology to determine concentration conversion factors for NMR systems using single- and multi-analyte linear regression models. This approach is leveraged to investigate a pharmaceutically relevant amide coupling batch reaction. An on-line stopped-flow (i.e., interrupted-flow or paused-flow) benchtop NMR system was used to monitor both the 1,1'-carbonyldiimidazole (CDI) promoted acid activation and the amide coupling. The results highlight how quantitative measurements in benchtop NMR systems can provide valuable information and enable analysts to make decisions in real time.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".