REVOLVER: a low-cost automated protein purifier based on parallel preparative gravity column workflows
Bibliographic record
Abstract
Abstract Protein purification is a ubiquitous operation in biochemistry and life sciences and represents a key step to producing purified proteins for research (understanding how proteins work) and various applications. The need for scalable and parallel protein purification systems keeps growing due to the increase in throughput in the production of recombinant proteins and in the ever-growing scale of biochemistry research. Therefore, automating the process to handle multiple samples in parallel with minimal human intervention is highly desirable; yet only a handful of such tools have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable and automatic protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER completes a full protein purification process with almost no human intervention and yields results equivalent to 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 will be useful to accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering. Specifications Table
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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 | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".