Infrared Chemical Imaging of Custom-Made Microfluidic Devices
Bibliographic record
Abstract
Microfluidic devices are designed to streamline and improve the operation of chemical processes in a wide variety of industries. Miniaturization has a vast number of advantages; most notably, the large surface area to volume ratio which allow for enhanced control over the physical properties within devices. Such precision leads to more efficient reactions and higher quality products making on-chip chemical synthesis desirable. Optimization of device performance requires an in-depth analysis of the flow profiles, requiring in-situ characterization techniques. Infrared (IR) imaging integrated within microfluidic devices is a label-free, non-invasive detection strategy which provides an in-situ probe to visualize flow patterns and can be utilized to identify and quantify molecules on-chip. IR light is quickly attenuated by optically dense matter such as solvents and device materials when samples are probed in transmission or reflectance modes. In attenuated total reflection (ATR) mode, IR light is directed through a high refractive index material such as a Si internal reflective element (IRE) interrogating the sample-IRE interface with an evanescent wave, limited to within one micron of the surface in the wavelengths of interest. Such a localized probe depth allows for design freedom in both the channel depth and materials. Within a microfluidic device, this imaging technique probes the solution near the no-slip boundary; fluid near channel extremes is flowing at a much slower rate than the bulk due to resistance of the solution with the walls of the channels. As a comparison of the experimental results with the physical phenomena within the devices is crucial to justify the technique, flow profiles must be modelled with both commercial software and mathematical predictions. \n\nThis thesis aims to develop and demonstrate the focal plane array (FPA) imaging capabilities of the horizontal ATR microscope at the Mid-IR beamline of the Canadian Light Source by imaging fluid flow in custom-made microfluidic devices. Resulting images are compared to expected flow profiles generated by simulations. This work is highly motivated by a desire to implement synchrotron IR imaging using this endstation and offers a prerequisite study for larger field-of-view optimization with a globar source before the extension to synchrotron light, with lower noise and smaller spatial resolution, may be realized.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".