An Accessible Platform to Quantify Oxygen Diffusion in Cell‐Laden Hydrogels and Its Application to Alginate‐Immobilized Pancreatic Beta Cells
Bibliographic record
Abstract
Hydrogels are commonly used to immobilize mammalian cells, offering mechanical support in 3D cultures and acting as barriers for immunoprotection in transplantation, such as islet encapsulation for diabetes therapy. Cell immobilization restricts bulk fluid motion, resulting in diffusion-limited molecular transport and nutrient concentration gradients, particularly for oxygen consumed by immobilized cells. Oxygen mass transport models are essential for designing immobilization strategies but often rely on assumed diffusion coefficients due to a lack of experimental data. We propose a cost-effective, accessible system for experimentally measuring oxygen diffusion coefficients in cell-laden hydrogels, tested on alginate-immobilized pancreatic beta cells (MIN6). Compared to water, oxygen diffusivity was significantly lower in alginate gels and inversely correlated with the dynamic loss modulus. Diffusivity also decreased with increasing alginate concentration from 2% to 5%. Cell viability depended heavily on gel concentration and cell density, as predicted by Thiele modulus and effectiveness factor values calculated from the measured diffusion coefficients. This platform, combining a simple experimental setup with dimensionless numbers, offers a practical way to predict maximal diffusion distances in cell immobilization strategies. The proposed approach can support rational design of cell encapsulation, immobilized cell culture, and tissue engineering strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".