Low-cost production and sealing procedure of mechanical parts of a versatile 3D-printed perfusion chamber for digital holographic microscopy of primary neurons in culture
Bibliographic record
Abstract
We have developed a prototype of a low-cost and versatile 3D-printed perfusion chamber for digital holographic microscopy (DHM) of primary neurons in culture. The imaging chamber is 3D-printed in biocompatible plastic. It is easily convertible between a closed configuration, for refractive index - cellular thickness decoupling, and an open configuration, for electrophysiology. In the closed arrangement, the imaging volume is small, allowing a rapid laminar flow with a fast turnover for an optimal implementation of the decoupling procedure. This paper highlights especially the challenges faced while designing and prototyping the 3D-printed closed perfusion chamber with a small imaging volume for DHM. As all 3D-printed mechanical parts were initially leaking because of internal porosities, we developed a simple sealing protocol using acetone vapors to smooth surfaces. Using this protocol, almost all mechanical parts were successfully sealed. Therefore, the production process of the actual prototype, i.e. the 3D printing and the sealing method, is satisfactory for our target application in the field of microfluidics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".