Enabling High Throughput Kinetic Experimentation by Using Flow as a Differential Kinetic Technique
Bibliographic record
Abstract
Kinetic data is most commonly collected through the generation of time-series data under either batch or flow conditions. Existing methods to generate kinetic data in flow collect integral data (concentration over time) only. Here, we report a method for the rapid and direct collection of differential kinetic data (direct measurement of rate) in flow by performing a series of instantaneous rate measurements on sequential small-scale reactions. This technique decouples the time required to generate a full kinetic profile from the time required for a reaction to reach completion, enabling high throughput kinetic experimentation. In addition, comparison of kinetic profiles constructed at different residence times allows the robustness, or stability, of homogeneously catalysed reactions to be interrogated. This approach makes use of a segmented flow platform which was shown to quantitatively reproduce batch kinetic data. The proline mediated aldol reaction was chosen as a model reaction to perform a high throughput kinetic screen of 216 kinetic profiles in 90 hours, one every 25 minutes, which would have taken an estimated continuous 3500 hours in batch, an almost 40-fold increase in experimental throughput matched by a corresponding reduction in material consumption.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".