Nano-liter perfusion microfluidic device made entirely by two-photon polymerization for dynamic cell culture with easy cell recovery
Bibliographic record
Abstract
Polydimethylsiloxane (PDMS) has been the material of choice for microfluidic applications in cell biology for many years, with recent advances encompassing nano-scaffolds and surface modifications to enhance cell-surface interactions at nano-scale. However, PDMS has not previously been amenable to applications which require complex geometries in three dimensions for cell culture device fabrication in the absence of additional components. Further, PDMS microfluidic devices have limited capacity for cell retrieval following culture without severely compromising cell health. This study presents a designed and entirely 3D-printed microfluidic chip (8.8 mm × 8.2 mm × 3.6 mm) using two-photon polymerization (2PP). The 'nest' chip is composed of ten channels that deliver sub-microliter volume flowrates (to ~ 600 nL/min per channel) to 10 individual retrievable cell sample 'cradles' that interlock with the nest to create the microfluidic device. Computational fluid dynamics modelling predicted medium flow in the device, which was accurately validated by real-time microbead tracking. Functional capability of the device was assessed, and demonstrated the capability to deliver culture medium, dyes, and biological molecules to support cell growth, staining and cell phenotype changes, respectively. Therefore, 2PP 3D-printing provides the precision needed for nanoliter fluidic devices constructed from multiple interlocking parts for cell culture application.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".