An Adaptive Auto‐Synthesizer using Online PAT Feedback to Flexibly Perform a Multistep Reaction
Bibliographic record
Abstract
Abstract Automated chemical synthesizers have become more common in recent years but struggle to apply rigid procedures to broad substrate scopes. We have developed an adaptive auto‐synthesizer that uses online HPLC and FTIR measurements to adapt to the changing reactivities of different substrates, allowing precise control of reaction conditions. To do so, we designed a flexibly‐timed procedure consisting of specific actions performed by our platform when specific reaction‐monitoring checkpoints are met. Online HPLC allowed our system to autonomously separate, label and quantify most reaction components, with orthogonal FTIR enabling non‐UV active species to be additionally tracked. We tested our platform with CDI‐mediated multistep amidation reactions using a variety of different acid and amine substrates. To demonstrate the high reproducibility and control afforded by our system, we determined the relative rates of both acid activation and subsequent amidation, providing insight into substrate reactivities and the reaction mechanism.
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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) | 0.001 | 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".