High-precision micromilling for low-cost fabrication of metal mold masters
Bibliographic record
Abstract
High-precision micromilling was employed as a cost-efficient method preparation of metal masters useful in fabrication of polymer microfluidic devices through replication techniques. In first application, a brass mold master was used for hot embossing of microchip electrophoresis devices in poly(methyl methacrylate) (PMMA). The sidewalls of the milled microstructures were characterized by a maximum average roughness (Ra) of 110 nm and mean peak height (Rpm) of 320 nm. SEM imaging showed a transfer of the sidewall roughness from the molding tool to the polymer microdevice. The electroosmotic flow (EOF) values for micromilled-based microchannels were comparable to ones in the LiGA-prepared devices (sidewall Ra = 20 nm) with values of ca. 3.7 x 10-4 cm2V-1s-1 (20 mM TBE buffer, pH 8.2), indicating insignificant effects of wall roughness on the bulk EOF. Numerical simulations showed that the additional volumes present in an injection cross due to curvature of the corners produced by micromilling lead to elongated sample plugs. PMMA microchip electrophoresis devices were used for a separation of pUC19 Sau3AI double-stranded DNA. The plate numbers achieved exceeded 1 million m-1 and were comparable to the plate numbers for the LiGA-based devices of similar geometry. In second application brass master was used as tool for preparation of poly(dimethylsiloxane) PDMS stencils for patterning of DNA microarrays onto a PMMA substrate. Four zip code probes immobilized onto the PMMA surface directed allele-specic ligation products containing mutations in the KRAS2 gene (12.2D, 12.2A, 12.2V, and 13.4D) to the appropriate address of a universal array with minimal amounts of crosshybridization or misligation.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".