Design of a Solid-State Electrochemical Methane Sensor Based on Laser-Induced Graphene for Deployment in the Natural Gas Distribution Network
Notice bibliographique
Résumé
Methane is the primary component of natural gas. As a result of mining and defects in the distribution network, methane emissions occur throughout the oil and gas industry. This corresponds to the largest anthropogenic source of the potent greenhouse gas and leads to a significant loss in revenue for the oil and gas industries: 60% of methane loss is due to fugitive release, out of which a major percentage is due to transmission lines (pipelines)[1]. However, these leaks are currently challenging to identify or detect due to the expansive nature of the distribution network using the currently available optical and combustion-based sensors. The development of inexpensive, low-power electrochemical sensors could provide a cost-effective means to carry out distributed sensing over the entire network. While high temperature, solid-state electrochemical methane sensors (> 500°C) have been developed using Yttria stabilized zirconia as a solid-state electrolyte,[2] the requirement of high temperature makes them impractical for use. More recently, non-volatile ionic liquids (ILs) have gained traction for use in gas sensor design; In particular, room temperature ILs (RTILs) have found increasing use since they circumvent the extreme process conditions required for certain oxidation pathways of the methane analyte.[3] Unlike many solid-state electrolyte systems, RTILs do not suffer from ionic contact loss with cycling, and do not require significant pre-processing to integrate into the sensor design. While promising results have been demonstrated using RTILs, previous designs have run into challenges with slow diffusion of gases through the fully dense liquid films formed or have been carried out by sparging the gases through the ionic liquid.[3] To address these challenges we explore the use of a porous, easy to process, pseudo-solid-state electrolyte comprised of a 1-ethyl-3-methylimidazolium tetrafluoroborate /polyvinylidene fluoride. The sensor electrodes are based on laser-induced graphene which can be rapidly patterned into flexible/conformable KaptonTM substrates using a commercially available CO2 laser technology.[4] Such lasers are inexpensive and commonly used in industry for high throughput cutting and etching. A method to decorate the high surface area interdigitated electrodes written into the KaptonTM with Pd nanoparticles was developed enabling minimal Pd content but high sensitivity. The porous nature of pseudo solid-state electrolyte in contact with the graphene-supported Pd nanoparticles enables fast gas diffusion and leads to a sensitivity to methane. As shown in Figure 1, on subjecting the sensor to dilute methane samples, the sensors can easily detect ppm and potentially ppb concentrations in air when a cell voltage of 0.6V is applied across the decorated electrodes. We analyze the performance as a function of potential using impedance spectroscopy and potentiostatic measurements under various conditions of temperature and interfering gases. The ease of processing, low cost and sensitivity make the system promising for future use in distributed sensing networks. References: [1] ICF International, “Economic Analysis of Methane Emission Reduction Opportunities in the Canadian Oil and Natural Gas Industries,” 2015. [2] P. K. Sekhar, J. Kysar, E. L. Brosha, and C. R. Kreller, “Development and testing of an electrochemical methane sensor,” Sensors Actuators, B Chem., vol. 228, pp. 162–167, 2016. [3] Xiaoyi Mu et al., “Fabrication of a miniaturized room temperature ionic liquid gas sensor for human health and safety monitoring,” IEEE Biomed. Circuits Syst. Conf., vol. 1, pp. 140–143, 2012. [4] J. Lin et al., “Laser-induced porous graphene films from commercial polymers,” Nat. Commun., 2015. 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 ».