Microchannels filled with diverse micro- and nanostructures fabricated by glancing angle deposition
Bibliographic record
Abstract
The integration of porous structures into microchannels is known to enable unique and useful separations both in electrophoresis and chromatography. Etched pillars and other nanostructures have received considerable interest in recent years as a platform for creating microchannels with pores tailored to specific applications. We present a versatile method for integration of three-dimensionally sculptured nano- and micro-structures into PDMS microchannels. Glancing angle deposition was used to fabricate nanostructures that were subsequently embedded in PDMS microchannels using a sacrificial resist process. With this technique, an assortment of structures made from a wide selection of materials can be integrated in PDMS microchannels; some examples of this versatility, including chiral and chevron nanostructures, are demonstrated. We also present a working device made using this process, separating 6/10/20 kbp and 10/48 kbp DNA mixtures in a DNA fractionator containing GLAD-deposited SiO(2) vertical posts as the separating medium. The separation mechanism was verified to resemble that found in prior fractionation devices, using total internal reflection fluorescence microscopy. GLAD fabrication enables insertion of three-dimensional structures into microchannels that cannot be fabricated with any existing techniques, and this versatility in structural design could facilitate new developments in on-chip separations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".