Optimization of an Acoustic Cell Filter with a Novel Air‐Backflush System
Bibliographic record
Abstract
Increasing worldwide demand for mammalian cell production capacity will likely be partially satisfied by a greater use of higher volumetric productivity perfusion processes. An important additional component of any perfusion system is the cell retention device that can be based on filtration, sedimentation, and/or acoustic technologies. A common concern with these systems is that pumping and transient exposure to suboptimal medium conditions may damage the cells or influence the product quality. A novel air-backflush mode of operating an acoustic cell separator was developed in which an injection of bioreactor air downstream of the separator periodically returned the captured cells to the reactor, allowing separation to resume within 20 s. This mode of operation eliminated the need to pump the cells and allows the selection of a residence time in the separator depending on the sensitivity of the cell line. The air-backflush mode of operating a 10L acoustic separator was systematically tested at 10(7) cells/mL to define reliable ranges of operation. Consistent separation performance was obtained for wide ranges of cooling airflow rates from 0 to 15 L/min and for backflush frequencies between 10 and 40 h(-1). The separator performance was optimized at a perfusion rate of 10 L/day to obtain a maximum separation efficiency of 92 +/- 0.3%. This was achieved by increasing the power setting to 8 W and using duty cycle stop and run times of 4.5 and 45 s, respectively. Acoustic cell separation with air backflush was successfully applied over a 110 day CHO cell perfusion culture at 10(7) cells/mL and 95% viability.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".