REVOLVER: A low-cost automated protein purifier based on parallel preparative gravity column workflows
Bibliographic record
Abstract
Protein purification is a ubiquitous procedure in biochemistry and the life sciences, and represents a key step in the protein production pipeline. The need for scalable and parallel protein purification systems is driven by the demands for increasing the throughput of recombinant protein characterization. Therefore, automating the process to simultaneously handle multiple samples with minimal human intervention is highly desirable, yet there are only a handful of such systems that have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER takes a cell lysate sample and completes a full protein purification process with almost no human intervention and yields results indistinguishable from those obtained by an experienced biochemist when purifying a real-world protein sample. We further present and describe MULTI-VOLVER, a scalable version of the REVOLVER that allows for parallel purification of up to six samples and can be built for under $250 USD. Both systems can help accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".