A spectIR-fluidic reactor for monitoring fast chemical reaction kinetics with on-chip attenuated total reflection Fourier transform infrared spectroscopy
Bibliographic record
Abstract
Microfluidics has emerged as a powerful technology with diverse applications in microbiology, medicine, chemistry, and physics. While its potential for controlling and studying chemical reactions is well recognized, the extraction and utilization of the vast amounts of high-quality information generated within microfluidic devices remain challenging. This is mainly due to the limited tools available for in situ measurements of chemical reactions. In this study, we present a proof-of-concept spectIR-fluidic reactor design that combines microfluidics with Fourier transform infrared (FTIR) spectroscopy for in situ kinetic studies of fast reactions. By integrating a multi-ridge silicon attenuated total reflection (ATR) wafer into the microfluidic device, we enable multi-point measurements for precise reaction time monitoring. This work establishes a validated foundation for studying a wide range of chemical reactions using ATR-FTIR spectroscopy, which will enable simultaneous quantification of reagents, intermediates, and products. The spectIR-fluidic platform offers customizable designs, allowing for the investigation of reactions with various time scales, and has the potential to significantly advance reaction optimization and pathway exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".