Detection of Electrooxidation Products in Microfluidic Devices Using Raman Spectroscopy
Bibliographic record
Abstract
Microfluidic devices combined with Raman Spectroscopy have great promise for in-situ and online detection of reaction processes with only a small volume of liquid required. This advanced method allows for flexible manipulation of fluids, micro/nano-particles, and biological samples1. Lab-on-a-chip applications are often used for chemical analysis, but Raman microscopy has also been used to monitor reactions within microreactors. The combination of the controlled mass transport in microfluidics with detection of soluble intermediate and product species by Raman offers the possibility of mechanistic kinetic studies of electrocatalytic reactions. We here show the feasibility of this method for studying electrocatalysis of oxidation of alcohols. Two types of microfluidic flow devices were fabricated, which used glass slides and printed circuit boards (PCBs) as substrates, respectively. For glass-slide-based microfluidic devices, Raman spectra were used to monitor methanol oxidation catalyzed by a Pt mesh electrode, and calibration with known solutions enabled quantification of methanol and formate concentrations. A comparative study of catalytic oxidations of glycerol, ethanol, ethylene glycol and 1-propanol using electrodeposited Ni on carbon paper showed that under strong alkaline conditions, glycerol and ethylene glycol principally gave formate and carbonate products, while ethanol and 1-propanol did not give formate. Acetate was found to be the major oxidation product of ethanol, with only a small amount of carbonate. For PCB-based microfluidic substrates, electroplated Ni on Cu pads gave planar working electrodes, which were used to catalyze the oxidation of glycerol. On-board PdH reference electrodes were incorporated2. 1. A.F. Chrimes, K. Khoshmanesh, P.R. Stoddart, A. Mitchell, K. Kalantar-zadeh, Microfluidics and Raman microscopy: current applications and future challenges, Chem. Soc. Rev., 42, 5880 (2013). 2. E.V. Fanavoll, D.A. Harrington, S. Sunde, G. Singh and F. Seland, A Microfluidic Electrochemical Cell with Integrated PdH Reference Electrode for High Current Experiments, Electrochim. Acta., 225 , 69 (2017). Acknowledgement: This research was conducted as part of the Engineered Nickel Catalysts for Electrochemical Clean Energy project administered from Queen’s University and supported by Grant No. RGPNM 477963-2015 under the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Frontiers Program. Figure 1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".