Rapid, Scalable Buchwald-Hartwig Amination by Resonant Acoustic Mixing (RAM): Establishing Parameters for RAM Reaction Design
Bibliographic record
Abstract
We outline the systematic development of Resonant Acoustic Mixing (RAM) for rapid, scalable Buchwald-Hartwig amination in the absence of bulk solvent. While RAM is rapidly emerging as a scalable methodology for media-free mechanochemical synthesis, the design parameters for reaction control, optimization, and scale-up remain poorly understood. This study establishes the filling ratio (φ), acceleration, and amount of liquid additive (η) as three critical parameters that can be used to design scalable Buchwald-Hartwig coupling reactions under RAM conditions. Systematic investigation of several model reactions reveals a relationship between reaction conversion and φ, providing a simple means to maximize conversion. The simultaneous real-time in situ monitoring of a model reaction through infrared thermography, fingerprint Raman, as well as low-frequency Raman (THz-Raman) spectroscopy enabled correlation of the reaction progress with the evolution of temperature during RAM, and established the RAM acceleration as a parameter that can be used to tune the reaction kinetic behavior. At high accelerations the reactions can proceed under sigmoidal kinetics, enabling multi-gram syntheses within 5 minutes, while lower accelerations can be used to switch the reactions to a more linear kinetic profile, associated with longer reaction times and milder temperature profiles. Following the reaction progress in the THz-Raman region is a reaction monitoring strategy that enables the detection of crystalline and non-crystalline phases in the reaction, permitting the observation of a non-crystalline intermediate whose evolution and replacement with the crystalline product could be tracked through non-negative least-squares fitting algorithm of the real-time spectroscopic data. This study establishes key parameters for manipulating the course and stoichiometric selectivity of a metal-catalyzed reaction in RAM and illustrates the scalability to at least 100 mmol without any protocol changes, except the volume of the vessel.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".