Design of a Solid-State Electrochemical Methane Sensor Based on Laser-Induced Graphene for Deployment in the Natural Gas Distribution Network
Bibliographic record
Abstract
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".