Sensitivity of bulk electrical impedance spectroscopy (bio‐capacitance) probes to cell and culture properties: Study on <scp>CHO</scp> cell cultures
Bibliographic record
Abstract
Abstract Bulk electrical impedance spectroscopy (bio‐capacitance) probes, hold significant promise for real‐time cell monitoring in bioprocesses. Focusing on Chinese hamster ovary (CHO) cells, we present a sensitivity analysis framework to assess the impact of cell and culture properties on the complex permittivity spectrum, ε mix , and its associated parameters, permittivity increment, Δ ε , critical frequency, f c , and Cole‐Cole parameter, α, measured by bio‐capacitance probes. Our sensitivity analysis showed that Δε is highly sensitive to cell size and concentration, making it suitable for estimating biovolume during the exponential growth phase, whereas f c provides information about cumulative changes in cell size, membrane permittivity, and cytoplasm conductivity during the transition to death phase. The analysis indicated that specific information about cell membrane permittivity or internal conductivity cannot be extracted from ε mix spectrum. Based on the sensitivity analysis, we proposed two alternative parameters for monitoring cells in bioprocesses: Δ ε 1 MHz and Δ ε 1 MHz /Δ ε 0.3 MHz , using measurements at 300 kHz, 1 MHz, and 10 MHz. Δ ε 1 MHz is suitable for estimating viable cell density during the exponential growth phase due to its lower sensitivity to cell size. Δ ε 1 MHz /Δ ε 0.3 MHz can replace f c due to similar sensitivities to cell size and dielectric properties. These frequencies are within most bio‐capacitance probes' optimal operation range, eliminating the need for low‐frequency electrode polarization and high‐frequency stray capacitances corrections. Experimental measurements on CHO cells confirmed the results of sensitivity analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".