Characterization of Low-Temperature Cofired Ceramic Tiles as Platforms for Gas Chromatographic Separations
Bibliographic record
Abstract
A gas chromatography (GC) column is fabricated within a low-temperature cofired ceramic (LTCC) tile, and its analytical properties are characterized. By using a dual-spiral design, a 100 μm wide square channel up to 15 m in length is produced within an 11 cm × 5.5 cm LTCC tile. The channel is dynamically coated with an OV-101 stationary phase that is cross-linked with dicumyl peroxide. While the uncoated LTCC tiles were able to separate a mixture of n-alkanes, the peak shapes were broad (base width of ~2 min) and tailing. In contrast to this, the coated LTCC tiles produced sharp (base width of ~8-10 s), symmetrical, well-resolved peaks for the same analytes. By using a 7.5 m long channel, about 15,000 plates were obtained for a dodecane test analyte. Further, the coated LTCC tiles were found to produce plate heights that were about 3-fold smaller than those obtained from a conventional capillary GC column of similar length, dimension, and coating operated under the same conditions. As a result, test analyte separations were slightly improved in the LTCC tiles, and their overall performance fared well. In terms of temperature programming, it was found that a series of n-alkanes separated on the LTCC tile provided a cumulative peak capacity of around 54 peaks when using C₈ to C₁₃ as analyte markers. Results indicate that LTCC tiles provide a viable and useful alternative platform for performing good quality GC 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".