Design and fabrication of a continuous flow mixer for investigating protein folding kinetics using focal plane array Fourier transform infrared spectroscopy
Bibliographic record
Abstract
This thesis presents the design, fabrication, and testing of a micromixing device intended for use in investigating protein dynamics on a microsecond timescale by Fourier transform infrared (FTIR) spectroscopy. Numerical modeling of flow was implemented to predict the influence of flow rates and geometric variations on mixing performance in three passive mixers. The simulation models were validated by experimental measurements using optical and infrared detection. The optimum level of mixing was observed in a multi-lamination mixer that combined thin filaments of differing fluids in an alternating manner. The multi-laminates were transferred onto polished calcium fluoride infrared-transparent optical windows by lithographic processing of an Epon-based polymer, SU-8. A rigid seal between two microchannels was accomplished through thermal bonding of an unexposed resist layer, which acted as a thermal epoxy under the influence of temperature. The multi-lamination mixer was used to study the changes in the secondary structure of beta-Lactoglobulin in deuterated phosphate buffer under varying physicochemical conditions by time-resolved FTIR spectroscopy using focal plane array detection. Upon a pH jump from pH 2 to neutral pH, a gradual loss of alpha-helical content, accompanied by an increase in random coils and turns was observed within 2 ms of mixing. In a second kinetic experiment, mixing of a neutral-pH solution of beta-Lactoglobulin with a 60% trifluoroethanol solution resulted in the formation of an alpha-helical intermediate with an accompanying increase in intramolecular beta-sheet structure within 500 mus of mixing. These results indicate that the multi-lamination mixer designed and fabricated in this study is well suited for investigations of protein dynamics on the micro- to millisecond timescale by time-resolved FTIR spectroscopy.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".