High-Throughput Analysis in Catalysis Research Using Novel Approaches to Transmission Infrared Spectroscopy
Bibliographic record
Abstract
This study has demonstrated that high-throughput FTIR transmission measurements using a newly designed array-based support formed using silicon wells and a silicon wafer is a very useful and robust tool for the characterization of polymer composition for combinatorial materials research. The comonomer content in copolymers can be measured accurately with a fully automated throughput of >300 samples/day (8 h). The transmission measurement is more robust, reliable, and easier to automate than other spectroscopic methods. The support itself provides excellent resistance to aggressive organic solvents at elevated temperatures and allows the unattended deposition and preparation of polymer films for infrared analysis. Because of the excellent durability of the support with respect to the solvent, the support can be rinsed and reused many times. This high-throughput approach to infrared transmission spectroscopy can be used for measuring a wide array of polymer characteristics: vinyl content, geometrical isomers, crystallinity, and tacticity. As well, this IR approach can be used to predict the oxidative stability of the antioxidant packages. Because the support provides a means of containing hot polymer solutions while the solvent evaporates, the support is also suitable for high-throughput nanoindentation methods for the determination of modulus and other physical properties of the polymer.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".