Design Considerations for Integration of Terahertz Time-Domain Spectroscopy in Microfluidic Platforms
Bibliographic record
Abstract
Microfluidic platforms have received much attention in recent years. In particular, there is interest in combining spectroscopy with microfluidic platforms. This work investigates the integration of microfluidic platforms and terahertz time-domain spectroscopy (THz-TDS) systems. A semiclassical computational model is used to simulate the emission of THz radiation from a GaAs photoconductive THz emitter. This model incorporates white noise with increasing noise amplitude (corresponding to decreasing dynamic range values). White noise is selected over other noise due to its contributions in THz-TDS systems. The results from this semiclassical computational model, in combination with defined sample thicknesses, can provide the maximum measurable absorption coefficient for a microfluidic-based THz-TDS system. The maximum measurable frequencies for such systems can be extracted through the relationship between the maximum measurable absorption coefficient and the absorption coefficient for representative biofluids. The sample thickness of the microfluidic platform and the dynamic range of the THz-TDS system play a role in defining the maximum measurable frequency for microfluidic-based THz-TDS systems. The results of this work serve as a design tool for the development of such systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".