Selective laser etching fabrication of stacked microporous membranes for multisize particle separation in 3D microfluidics
Bibliographic record
Abstract
Glass substrates are widely utilized in microfluidic applications due to their exceptional properties, including optical transparency, biocompatibility, chemical and thermal stability, and compatibility with standard microfabrication techniques. These characteristics enable high-resolution micro- and nanopatterning through methods such as wet and dry etching, laser ablation, and photolithography, facilitating the fabrication of complex and reproducible microfluidic components-such as microchannels, microchambers, micropumps, mixers, sensors, and membranes. In this study we leverage femtosecond technology to fabricate a novel multilayer microfluidic system that integrates two porous membranes, with precisely engineered pore geometries. Fabrication of the multilayer device was based on a selective laser etching process (SLE) using a glass 3D printer (LightFab GmbH, Germany), to obtain microchannels and membranes with pore sizes of 5 [Formula: see text]m and 25 [Formula: see text]m. The SLE sequence was optimized to minimize thermal ablation, preserving pore integrity and achieving high fidelity in pore size and distribution. Potassium hydroxide (KOH) was used for wet etching, leveraging the selectivity of fused silica to further refine pore geometry. A microwelding technique was optimized to achieve a consistent interlayer gap, essential for structural integrity and effective filtration. Results of filtration tests demonstrated that 30 [Formula: see text]m particles were selectively trapped in 25 [Formula: see text]m-pore membranes and 8 [Formula: see text]m particles were selectively trapped in 5 [Formula: see text]m filters, while both membranes allow the passage of 2 [Formula: see text]m particles. These results validate the ability of the system to perform size-based separation in microfluidic environments, highlighting the potential of femtosecond laser-based fabrication to produce robust, scalable, multilayer filtration devices for high throughput applications. This approach opens new avenues for developing integrated microfluidic systems capable of simultaneous filtration, separation, and analysis, paving the way for automated lab-on-a-chip applications.
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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 | 0.001 |
| 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 | 0.001 | 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".