High-throughput, low-cost reaction screening using a modified 3D printer
Bibliographic record
Abstract
We describe a reaction screening system, based on a 96-well array, and scaled to suit use on the individual scientist's bench. The system was built by modifying a desktop 3D printer and fitting it with a glass syringe and microtiter plate. The effects of experimental variables were characterized, and the performance of the system was optimized. Precise volumes of reaction mixtures (<3% CV) were dispensed into the 96-well array in ca. 40 minutes. The system was used to screen reagents and solvents for the N-alkylation, Katritzky transamination, and Suzuki cross-coupling reactions. Product distributions derived from electrospray mass spectra and represented as heat maps facilitated recognition of optimum conditions. Screening of 96 reaction mixtures was completed in the modest time of approximately 105 minutes (∼65 seconds per reaction mixture). The system is constructed from open-source software and inexpensive 3D printer hardware.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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".