Effects of surfactant and gentle agitation on inkjet dispensing of living cells
Bibliographic record
Abstract
Inkjet dispensing is a promising method for patterning cells and biomaterials for tissue engineering applications. In a novel approach, this work uses a biocompatible surfactant to improve the reliability of droplet formation in piezoelectric drop-on-demand inkjet printing of Hep G2 hepatocytes onto hydrogels. During a long printing process, cell aggregation and sedimentation within the inkjet reservoir can lead to inconsistent printing results. In order to improve repeatability, the effects of gentle agitation on cell sedimentation and aggregation within the inkjet reservoir were also investigated. Cell viability and proliferation when printed onto prepared collagen substrates were assessed using live/dead staining and the Alamar Blue metabolic assay. The addition of 0.05% Pluronic as a surfactant did not reduce cell viability, which remained above 95% 2 days after printing. The surfactant improved the reliability of droplet formation. Although gentle stirring of the inkjet reservoir was sufficient to maintain a cell suspension and reduce sedimentation, aggregation within the suspension continued to affect printing performance over a 180 min printing period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".