Laminar Flow Infrared Spectroelectrochemistry
Bibliographic record
Abstract
In this work, we advance lab-on-chip electrochemistry and spectroscopy by combining these capabilities onto a single platform, thereby achieving mid-infrared spectroelectrochemistry (SEC) for the first time. The key feature of this technique is the use of deterministic laminar flow patterns to precisely transport a reacted solution from upstream electrodes to a downstream spectral detection region. Laminar flow spectroelectrochemistry (LF-SEC) is therefore a completely new approach, which derives its distinction and advantage over traditional SEC by physically separating electrode and attenuated total reflection (ATR) elements. As such, these functional elements retain optimal properties, such as inert, highly conductive electrodes and a bare ATR element for sensitive Fourier transform infrared (FTIR) spectroscopy. By combining ATR-FTIR with a scanning aperture system, LF-SEC provides the additional advantage of spectroscopically monitoring reactions at individual electrodes. The LF-SEC system design is first optimized through a series of targeted experiments using a ferricyanide/ferrocyanide redox pair to validate electrochemical functionality, undertake spectroscopic calibration, optimize experimental parameters, and finally validate the quantitative relationship between FTIR results and the reaction rate under galvanostatic control. After optimization, we demonstrate the technique by monitoring the oxidation of the therapeutic compound ascorbic acid (vitamin C) in the presence of biomolecular interference from a molecule with an overlapping oxidation potential. We find that molecular availability causes the reaction to switch between reaction pathways, which we could finely monitor using LF-SEC. This work opens the door to future developments that take advantage of the microfluidic reactor setup, with benefits ranging from portability to high-throughput studies under precise reaction conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".