3D Printed Microfluidic Probes
Bibliographic record
Abstract
In this work, we fabricate microfluidic probes (MFPs) in a single step by stereolithographic 3D printing and benchmark their performance with standard MFPs fabricated via glass or silicon micromachining. Two research teams join forces to introduce two independent designs and fabrication protocols, using different equipment. Both strategies adopted are inexpensive and simple (they only require a stereolithography printer) and are highly customizable. Flow characterization is performed by reproducing previously published microfluidic dipolar and microfluidic quadrupolar reagent delivery profiles which are compared to the expected results from numerical simulations and scaling laws. Results show that, for most MFP applications, printer resolution artifacts have negligible impact on probe operation, reagent pattern formation, and cell staining results. Thus, any research group with a moderate resolution (≤100 µm) stereolithography printer will be able to fabricate the MFPs and use them for processing cells, or generating microfluidic concentration gradients. MFP fabrication involved glass and/or silicon micromachining, or polymer micromolding, in every previously published article on the topic. We therefore believe that 3D printed MFPs is poised to democratize this technology. We contribute to initiate this trend by making our CAD files available for the readers to test our "print & probe" approach using their own stereolithographic 3D printers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".