Scale‐up and optimization of an acoustic filter for 200 L/day perfusion of a CHO cell culture
Bibliographic record
Abstract
Acoustic cell retention devices have provided a practical alternative for up to 50 L/day perfusion cultures but further scale-up has been limited. A novel temperature-controlled and larger-scale acoustic separator was evaluated at up to 400 L/day for a 10(7) CHO cell/mL perfusion culture using a 100-L bioreactor that produced up to 34 g/day recombinant protein. The increased active volume of this scaled-up separator was divided into four parallel compartments for improved fluid dynamics. Operational settings of the acoustic separator were optimized and the limits of robust operations explored. The performance was not influenced over wide ranges of duty cycle stop and run times. The maximum performance of 96% separation efficiency at 200 L/day was obtained by setting the separator temperature to 35.1 degrees C, the recirculation rate to three times the harvest rate, and the power to 90 W. While there was no detectable effect on culture viability, viable cells were selectively retained, especially at 50 L/day, where there was a 5-fold higher nonviable washout efficiency. Overall, the new temperature-controlled and scaled-up separator design performed reliably in a way similar to smaller-scale acoustic separators. These results provide strong support for the feasibility of much greater scale-up of acoustic separations.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".