Detection of Electrooxidation Products in Microfluidic Devices Using Raman Spectroscopy
Bibliographic record
Abstract
Microfluidics combined with Raman Spectroscopy has great promise for monitoring reaction processes with very little volume of liquid provided. This advanced method for in-situ and online detection allows for flexible manipulation of fluids, micro/nano-particles, and biological samples1. Lab-on-an-chip applications are often used for chemical analysis, but Raman microscopy has also been used to monitor reactions within microreactors2. In electrochemistry, the use of SERS to analyse surface species is common. However 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. The top figure shows the cell, which consists of a channel where the laser focuses at or near a Pt wire/mesh working electrode. The channel, which is built with 1 mm height Teflon spacers topped with cover glass, is connected to downstream cylindrical reservoirs with a reference electrode and counter electrode. A Teflon tube with a diameter of 1 mm connected to a syringe pump flows the liquid through the channel at a pre-set flow rate to oxidize alcohol for downstream or on-wire measurements. The width of the channel is fixed at 3 mm. Concentrated KOH (5 M) is used as the electrolyte, and in the proof-of-concept studies here 5 M methanol is used to ensure a high S/N ratio in the Raman spectra. The cell went through several design iterations, with detection above/beside the WE, WE above reflective roughened Au, or capture downstream above a roughened Au/Ag SERS substrate. A time sequence of spectra taken with the laser focused on the Pt WE shows production of formate (see bottom figure). Formate was the main product, and its dependence on time, potential, flow rate and laser position were investigated. The areas of the C-O stretching peaks for methanol and C=O stretching peaks for formate were calibrated with standard concentration solutions, to enable quantitative operation. A peak characteristic of carbonate was present but was too small to quantitate. Under the assumption that carbonate was the only other carbon-containing product, the calculated electron count from the products agreed with that from the charge passed electrochemically, indicating the feasibility of the method for qunatitative detection under flow conditions. One conclusion is that at higher potentials, the reaction tends to produce more formate than carbonate. Work is ongoing to optimize the conditions to improve the detection limit and to extend these studies to the oxidation of other alcohols. 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. [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] P.D. Fletcher, S.J. Haswell, X. Zhang, Monitoring of chemical reactions within microreactors using an inverted Raman microscopic spectrometer, Electrophoresis, 24, 3239 (2003). 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 | 0.000 |
| 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) | 0.001 | 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".