Detection of Electrooxidation Products in Microfluidic Devices Using Raman Spectroscopy
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».