Variation Of Gas Concentration Research Articles

The effect of synthesis techniques on the gas sensing properties of TiO2/SiC/CoFe2O4 nanocomposites as gas sensor

This study investigates the impact of two distinct methodologies on the structural, morphological, and gas sensing properties of TiO2/SiC/CoFe2O4 (TSC) nanocomposites determined using x-ray diffraction (XRD), scanning electron microscopy (SEM), LCR meter, and gas sensing unit respectively. The TiO2/SiC/CoFe2O4 nanocomposites were synthesized using chemical co-precipitation method (C-TSC) and the solid state method (G-TSC). The Scherrer formula was used to calculate the average grain size of C-TSC and G-TSC, which was estimated to be 8 ± 2 nm and 10 ± 2 nm, respectively. The formation of TSC nanocomposites was confirmed by XRD, SEM, and EDX analysis. The response (%) toward ethanol and NH3 gas was tested as a function of flow rate (ppm) and temperature from room temperature (28 °C) to 300 °C. The response (%) was observed to be increasing with increasing temperature and three intermediate temperatures were found. The response and recovery time were also measured with varying gas concentrations. The long-term stability of devices was tested up to 30 days and less variation in result was found, which confirms stability of sensor. The material synthesized using chemical co-precipitation method (C-TSC) shows better properties than G-TSC.

Design & development of adulteration detection system by fumigation method & machine learning techniques

A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food.

The role of gas emissions (He, Rn, and CO2) from fault zones in understanding fault and seismic activity

Active fault zones are critical pathways for the migration of deep fluids to the Earth’s surface, carrying gases such as He, Rn, and CO2 that provide evidence for the physical and chemical dynamics of the Earth’s interior. This review examines the geochemical characteristics of fault zone gases and their implications for understanding fault activity and seismic events. Fault zones with high activity levels exhibit significant gas release, and variations in soil and hot spring gas concentrations can serve as indicators of seismic activity. Changes in gas concentrations and isotopic ratios, particularly before and after earthquakes, reflect the dynamic interplay between deep-sourced and shallow-sourced fluids. Seismic-induced stress alterations enhance gas release along fault zones, leading to observable anomalies that can aid in earthquake monitoring and prediction. The study underscores the importance of isotope tracing in deciphering fluid sources, migration pathways, and the evolution of fault zones, providing valuable information for assessing tectonic activity and mitigating seismic risks.

Greenhouse Gas Emissions Characteristics and Driving Factors Analysis in the Eutrophic Saline Lake Daihai Lake

To investigate the greenhouse gas emission characteristics and driving factors of eutrophic saline lakes in northern China, considering Daihai Lake in Inner Mongolia as an example, 10 monitoring sites were selected based on hydrological distribution characteristics in April, July, and October 2023. Using headspace gas chromatography and modeling methods, dissolved concentrations and exchange fluxes of carbon dioxide （CO2）, methane （CH4）, and nitrous oxide （N2O） were determined in the nearshore zone, open lake area, and lake center surface water. During the study period, Daihai Lake exhibited significant seasonal variations in greenhouse gas concentration and flux. The average concentrations of CO2, CH4, and N2O in surface water were （26.52 ± 17.58） μmol·L-1, （282.30 ± 172.30） nmol·L-1, and （9.09 ± 1.64） nmol·L-1, respectively. The average fluxes were （5.29 ± 11.98） mmol·（m2·d）-1, （178.24 ± 63.34） μmol·（m2·d）-1, and （-0.74 ± 1.28） μmol·（m2·d）-1, with cumulative emissions of 50 770.77, 543.52, -4.21 kg·km-2 and a global warming potential （expressed in CO2-equivalent） of 50 770.77, 15 218.49, -1 254.48 kg·km-2. Daihai Lake acted as a source of atmospheric CO2 and CH4 but a sink for N2O during the study period. Correlation and stepwise regression analyses revealed that pH and total dissolved solids （TDS） influenced CO2 concentration and flux, while the factors affecting CH4 were water temperature （WT）, water depth （WD）, wind speed （WS）, oxidation-reduction potential （ORP）, and total nitrogen （TN）. For N2O, the influencing factors were WT, WS, and TN. Additionally, Daihai Lake's eutrophication and salinity characteristics influenced the generation and emission of greenhouse gases. This study provides insights into the greenhouse gas dynamics and environmental factors in eutrophic saline lakes like Daihai Lake.

High Sensitivity CH4 and CO2 Gas Sensor Using Fiber Bragg Grating Coated with Single Layer Graphene

This article outlines the development of a Fiber Bragg Grating (FBG) intended for use as a sensor for CH4 and CO2 gases. Following fabrication, the FBG was effectively treated with a layer of Graphene Material through a modified RF Sputtering process. This coating procedure involved introducing argon gas into the chamber and subjecting the FBG, securely held by two vacuum stages, to a temperature range of 27°C to 600°C by adjusting the power supplied to the cathode and anode, ranging from 0 to 125 Watts. Subsequently, the FBG was employed as a key sensing element within an experimental setup aimed at measuring gas concentrations within a confined space. The assessment involved analyzing the reflected signal of the FBG using an Optical Interrogator System, which demonstrated a shift in the Bragg wavelength of the reflected signal corresponding to varying gas concentrations. This study indicates promising outcomes for the Graphene-coated FBG as a gas sensor. The sensor’s sensitivity was evaluated based on the Bragg wavelength shift resulting from gas presence within the chamber. The Graphene-coated FBG exhibited sensitivities of 3.3 ppm for CH4 and 3.7 ppm for CO2, surpassing those reported in prior research efforts.

Revolutionizing Automated Agriculture: A Brief Overview of an Advanced Smart Agriculture System Using Arduino UNO

This project presents a Smart Agriculture System that uses sensor technologies to improve crop monitoring and automate irrigation procedures. A variety of sensors are integrated into the system, such as a temperature sensor, a gas sensor, a photodiode for ambient light sensing, and a soil moisture sensor. When taken as a whole, these sensors offer real-time information on the concentrations of gases, ambient light levels, temperature fluctuations, and soil conditions in agricultural environments. The system’s primary function is to continuously monitor soil moisture levels in order to determine when irrigation is necessary. A water pump is activated when a predetermined moisture threshold is reached, guaranteeing timely and precise irrigation for the best possible crop growth. By monitoring the climate, the temperature sensor helps determine the environmental factors that affect crop health. Farmers can monitor important parameters with ease thanks to the convenient and user-friendly interface that the real-time display of sensor data on an LCD screen provides. Furthermore, by measuring the amount of natural light present, the ambient light sensor helps the system to optimize planting schedules and comprehend the impact of sunlight on crop development. The addition of a gas sensor expands the capabilities of the system by identifying possible environmental risks or variations in gas concentrations that could have an impact on crop quality. In addition to providing farmers with useful information about the health of their crops, this integrated smart agriculture system makes irrigation automation easier, saving water and increasing overall agricultural productivity. Due to its flexibility and scalability, the system is an invaluable resource for contemporary precision agriculture, contributing to sustainable farming practices and increased crop yields.

Leakage Diffusion Modeling of Key Nodes of Gas Pipeline Network Based on Leakage Concentration

In order to achieve the prediction and early warning of city gas pipe network leakage accidents, as well as to provide rapid and precise support for emergency response to such accidents, this study focuses on a Gaussian diffusion model applied to a large urban gas pipeline network. Specifically, it investigates the gas gate wells, which are key nodes in the pipeline network, to develop a leakage model. The objective is to analyze the variation in internal gas concentration in the gate wells and determine the range of danger posed by external gas diffusion from the gate wells. In addition, Fluent simulation is utilized to compare the accuracy of the model’s calculations. The findings of this study indicate that the gas concentration inside the gate well, as predicted by the model fitting results and Fluent simulation, exhibit a high level of agreement, with coefficient of determination (R2) values exceeding 0.99. Moreover, when predicting the hazardous distance of gas leakage outside the gate well, the model’s results show an average relative error of 0.15 compared to the Fluent simulation results. This demonstrates that the model is highly accurate and meets the practical application requirements.

A new forecasting behavior of fractional model of atmospheric dynamics of carbon dioxide gas

This paper gives insight on the nonlinear mathematical model of fractional order, which defines the dynamic variation of carbon dioxide gas (CO2) concentration in the atmosphere X(t) and examines its solution by applying an efficient technique, namely the q-homotopy analysis generalized transform method (q-HAGTM). This study also highlights the effects of variations in human population N(t) and forest biomass F(t) on the dynamics of CO2 gas concentration in the atmosphere. This technique combines the q-homotopy analysis method (q-HAM) and the generalization of the Laplace transform (GLT) to provide an accurate approximation result. The results prove that the applied technique is suitable for high approximation of the atmospheric carbon dioxide gas concentration fractional model solution and simultaneously proves the efficiency of the applied method. The fluctuating behavior of carbon dioxide gas, forest biomass, and human population concentration is illustrated by the graphical representation of the fractional derivative with variation in time. The convergence and uniqueness of the obtained solutions are evaluated for the studied fractional model. The objective of this work is to assess the pattern of CO2 and its effects on the human population and ecosystem, such as global warming and biomass variation, which in turn affect the patterns of drought, floods, storms, and the spread of climatic and vector-borne diseases.

FTM-GCN: A novel technique for gas concentration predicting in space with sensor nodes

Accurate and real-time prediction of gas concentrations is a critical component of intelligent prediction systems and holds significant importance for production safety and quality of life. However, due to the constraints of the network topology in gas sensor networks and the temporal nature of gas concentration variations, concentration prediction has always been considered a significant challenge. To simultaneously capture the spatiotemporal correlations in gas concentration data with spatiotemporal characteristics, this study proposes a fully-connected temporal multilayer graph convolutional network (FTM-GCN). This method combines multiple graph convolutional layers (MGC), fully connected layers, and gated recurrent units (GRU). FTM-GCN exhibits three prominent features: (1) It leverages MGC to learn the topology of gas sensor networks, enhancing the model's ability to graph data and capture spatial characteristics. MGC is incorporated to capture spatial features in the data. (2) GRU is employed to capture the dynamic changes in sensor network data and the temporal characteristics of the data. (3) Continuous fully-connected layers are introduced to enhance the model's performance. Experimental results demonstrate that FTM-GCN effectively captures the spatiotemporal correlations in spatiotemporal data across various prediction perspectives and outperforms GCN, GRU, T-GCN and STGCN.

Annual fluctuation of gas concentration within the landfill surface layer in a tropical climate: A case study of a municipal wasteyard

AbstractThis study looks at the annual variations in landfill gas concentrations within the surface layer of the dump as well as the effect of climatic factors on these variations under the tropical climate. The experiment was carried out at part of the Karadiyana landfill, Sri Lanka, containing old mixed municipal solid waste covered by vegetation. The gas concentrations of the landfill surface layer were measured from three gas wells established on the dump surface layer, which contains 2.5 m depth, throughout the year with the measuring of climatic factors. The results show the annual fluctuation of temperature, cumulative rainfall, relative humidity, and atmospheric pressure were 27–42°C, 66.7–357.7 mm, 24.7%–78.2%, and 999.5–1014 mbar around the studied landfill area, and the average concentration value of the CH4, NH3, H2S, and VOCs gases were 18.94 ± 11.49%, 15.48 ± 15.17 mg m−3, 15.06 ± 19.02 mg m−3, and 0.83 ± 0.99 mg m−3, respectively. The rainfall and atmospheric temperature were significant metrological factors influencing the surface layer gas concentrations and showed a significant positive correlation with cumulative rainfall and a negative correlation with atmospheric temperature. The relative humidity shows a moderate positive correlation, and there was no evidence of a considerable impact of atmospheric pressure on the surface layer concentrations. Annual fluctuation is a critical factor for landfill gas emission quantification, and surface layer concentration variation can be used to study the emission fluctuation by considering their relationship.

Numerical evaluation of passive autocatalytic recombiner performance under post-accident conditions

In the accident conditions, the operational behavior of passive autocatalytic recombiners (PARs) has garnered significant attention, particularly with regards to the complicated atmosphere in the containment. This paper aims to systematically study and analyze the PAR performance under post-accident conditions. CFD simulation resolving individual catalytic channels of PARs provide valuable insights into hydrogen consumption performance and temperature distribution. A simplified model of PARs, consisting of a gas channel and two catalyst plates, was developed using the CFD software STAR-CCM+. One-step reaction mechanism was used in this model to define the hydrogen consumption rate on the catalytic surface. To study the variation in gas concentration and temperature distribution, physical values of interest were extracted from designated locations. An analysis of catalyst section was conducted to provide a comprehensive understanding of the reaction process occurring within PARs. Subsequent tests were conducted using the developed simplified model to examine the effect of inlet gas components variation and operating conditions on PAR performance under post-accident conditions. Our findings show that the variation of steam concentration primarily alters the heat absorption capacity of the gas mixture, rather than the catalytic reaction rate. Furthermore, inlet velocity and operating pressure play a role in controlling the inlet mass flow rate, thereby influencing the reaction process of the gas channel by introducing varying amounts of hydrogen.

Evaluation and Optimization of Multi-Parameter Prediction Index for Coal Spontaneous Combustion Combined with Temperature Programmed Experiment

Coal spontaneous combustion (CSC) is a serious threat to the safe mining of coal resources, and the selection of suitable gas indicators to predict the CSC state is crucial for the prevention and control of coal mine fires. In this paper, the temperature-programmed experiment of CSC was first carried out to analyze the gas components and compositions in the oxidative pyrolysis process of three coal samples (lignite, long-flame coal, and lean coal) with different coalification degrees. Subsequently, the spontaneous combustion tendency of these three coal samples was evaluated. Finally, through the variation of gas concentration, gas concentration ratio, and fire coefficient with coal temperature, the indicators suitable for predicting the spontaneous combustion of coal were preferred, and a multi-parameter indicator system was established to make a comprehensive judgment on the spontaneous combustion status of coal. The results show that coal rank is negatively correlated with oxygen consumption rate. The higher the coalification degree of coal, the slower the oxidation reaction and the later the characteristic temperature point appears. The lignite selected in this experiment is a type of coal that is more prone to spontaneous combustion than long-flame coal and poor coal, and the CO concentration, C2H6/CH4, and second fire coefficient R2 can be used as the main indicators for predicting CSC, while the other gases, olefin-alkane ratio and fire coefficient can be used as auxiliary indicators. To some extent, the research content can effectively and accurately determine the stage and degree of coal spontaneous combustion, which has a certain guiding role in predicting CSC.

Within-city spatial variations in long-term average outdoor oxidant gas concentrations and cardiovascular mortality: Effect modification by oxidative potential in the Canadian Census Health and Environment Cohort.

We conducted a retrospective cohort study of participants in the Canadian Census Health and Environment Cohort who lived in Toronto or Montreal, Canada, from 2002 to 2015. Cox proportional hazards models were used to estimate associations between outdoor concentrations of oxidant gases (Ox, a redox-weighted average of nitrogen dioxide and ozone) and cardiovascular deaths. Analyses were performed across strata of two measures of PM2.5 oxidative potential and reactive oxygen species concentrations (ROS) adjusting for relevant confounding factors. PM2.5 mass concentration showed little within-city variability, but PM2.5 oxidative potential and ROS were more variable. Spatial variations in outdoor Ox were associated with an increased risk of cardiovascular mortality [HR per 5 ppb = 1.028, 95% confidence interval (CI): 1.001, 1.055]. The effect of Ox on cardiovascular mortality was stronger above the median of each measure of PM2.5 oxidative potential and ROS (e.g., above the median of glutathione-based oxidative potential: HR = 1.045, 95% CI: 1.009, 1.081; below median: HR = 1.000, 95% CI: 0.960, 1.043). Within-city spatial variations in PM2.5 oxidative potential may modify long-term cardiovascular health impacts of Ox. Regions with elevated Ox and PM2.5 oxidative potential may be priority areas for interventions to decrease the population health impacts of outdoor air pollution.

Experimental study on gas explosions of methane-air mixtures in a full-scale residence building

The field tests were carried out in a full-scale residence building with volume of 105 m3 to reveal the hazards and mechanism of methane-air mixture explosions. The real layout and variations of gas concentration, initial state and ignition position are considered in the explosion tests. The pressure–time histories and flame development of each test were recorded and analyzed. The concentration distribution was also measured by using the fixed concentration sensors and mobile robots. It is found that when the methane diffuses freely in the resident buildings, concentration stratification can be observed in the vertical direction and the concentration of region with height of 0.64 ∼ 2.38 m is within the explosive limit in the 9.5 vol% case. In the real scenario, the maximum overpressure of gas explosions in residence buildings is less than 10 kPa due to the effective venting of doors and windows. The concentration of 9.5 vol%, uniform gas distribution and ignition position in room 2 can bring about more intensive gas explosions and in turn result in higher overpressure loads and stronger external flame propagation. What is more, the acoustic oscillation is significantly restrained by the disturbance on the acoustic field caused by the effective venting, gas stratification and obstacle distribution in residence buildings.

Development of room temperature ammonia sensor based on CNF/ Nano-ZSM-5 composites

This study deals with ammonia sensing performance of nanocomposites. NanoZSM-5 zeolite is synthesized by microwave assisted hydrothermal process and is modified to have modified forms of ZSM-5 namely D-ZSM-5, H-ZSM-5, Fe-ZSM-5 and Cu-ZSM-5 via either dealumination or ion exchange process. The composites are developed by blending modified zeolites with cellulose nanofibers (CNF) as a dispersing medium. The composites are prepared with fixed proportions; 80 %CNF and 20% modified zeolites by wt%. Composite films, prepared by solution casting method, are used for ammonia detection. The films are characterised by XRD and FTIR to identify the phases and the presence of functional groups. The thermal behaviour of these composites are studied by TGA. Sensing parameters such as operating temperature, response and recovery time, gas concentration variation and gas uptake capacity are determined. The operating temperature for ZSM-5 is found to be 30°c with highest sensitivity. These sensors can be used as potential ammonia sensors.

Gas Concentration Monitoring Prewarning Based on Adaptive Prediction and Feature Extraction

This study presents a reliable method for predicting gas concentration and implementing prewarning analysis. Gas monitoring data are decomposed into intrinsic mode functions (IMFs) with different time scales by using empirical mode decomposition (EMD), which represents the intrinsic features of gas concentration on different time scales. The prediction accuracy is evaluated by the prediction effectiveness, and the IMF phase space parameters and the Gaussian process regression (GPR) hyperparameters are dynamically adjusted to achieve adaptive prediction. Combined with singular value decomposition (SVD) to extract the intrinsic features of gas monitoring data, a prediction and prewarning model is established. The case study shows that the prediction accuracy of the adaptive model is significantly higher than that of direct GPR prediction and that it solves the problem of low prediction accuracy at mutational points in gas concentration time series. The degree of influence of the production process on the variation in gas concentration is quantitatively determined to improve the reliability of prewarning applications.

Structure Effect on the Response of ZnGa2O4 Gas Sensor for Nitric Oxide Applications.

We fabricated a gas sensor with a wide-bandgap ZnGa2O4 (ZGO) epilayer grown on a sapphire substrate by metalorganic chemical vapor deposition. The ZGO presented (111), (222) and (333) phases demonstrated by an X-ray diffraction system. The related material characteristics were also measured by scanning electron microscopy, transmission electron microscopy and X-ray photoelectron spectroscopy. This ZGO gas sensor was used to detect nitric oxide (NO) in the parts-per-billion range. In this study, the structure effect on the response of the NO gas sensor was studied by altering the sensor dimensions. Two approaches were adopted to prove the dimension effect on the sensing mechanism. In the first approach, the sensing area of the sensors was kept constant while both channel length (L) and width (W) were varied with designed dimensions (L × W) of 60 × 200, 80 × 150, and 120 ×100 μm2. In the second, the dimensions of the sensing area were altered (60, 40, and 20 μm) with W kept constant. The performance of the sensors was studied with varying gas concentrations in the range of 500 ppb~10 ppm. The sensor with dimensions of 20 × 200 μm2 exhibited a high response of 11.647 in 10 ppm, and 1.05 in 10 ppb for NO gas. The sensor with a longer width and shorter channel length exhibited the best response. The sensing mechanism was provided to explain the above phenomena. Furthermore, the reaction between NO and the sensor surface was simulated by O exposure of the ZGO surface in air and calculated by first principles.

Characteristics and source analysis of greenhouse gas concentration changes at Akedala Station in Central Asia

Mole fractions of atmospheric carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and sulfur hexafluoride (SF6) have been continuously measured since September 2009 at the Akedala Station (47°06′N, 87°58′E, 563.3 masl) in China. The station is located in the Central Asia and northwest of China, and it is the only station in that region with background conditions for long-term greenhouse gas observations. Characteristics of the mole fractions, growth rates, and influence of long-distance transport were studied considering data from September 2009 to December 2019. The greenhouse gases concentrations at Akedala Station show a trend of year-on-year growth, with CO2 concentrations ranging from 389.80 × 10−6 to 408.79 × 10−6 (molar fraction of substances, same below), CH4 concentrations ranging from 1890.07 × 10−9 to 1976.32 × 10−9, N2O concentrations ranging from 321.26 × 10−9 to 332.03 × 10−9, and SF6 concentrations ranging from 7.04 × 10−12 to 10.07 × 10−12, the growth rate of which is similar to the decadal average growth rate in the Northern Hemisphere. There exist obvious seasonal variations, with CO2 concentrations showing high in winter and low in summer and CH4 showing a distinct “W”-shaped trend while N2O and SF6 showing little difference between the four seasons. A relatively strong correlation and homology exist among the four greenhouse gases except in summer, and the analysis based on backward trajectories model shows that the Akedala Station is influenced by the airflow from northwest or southwest throughout the year. The Akedala Station is an important atmospheric background station in Central Asia, and its greenhouse gas concentration levels and variation characteristics are significantly different from those of the background stations in the monsoon region. Its degree changes are closely related to local source emissions, monsoon transport, and atmospheric photochemical processes.

Highly sensitive fiber Bragg grating based gas sensor integrating polyaniline nanofiber for remote monitoring

Optical fiber-based ammonia (NH3) sensors, such as fiber Bragg gratings (FBGs), have attracted significant attention owing to their involvement in different areas. Etched–tapered FBGs were fabricated and studied to detect NH3 leaks at room temperature for real-time remote monitoring over 3 km by using an erbium-doped fiber amplifier (EDFA). First, the two FBGs were chemically etched to obtain a thickness of 50 µm. Etched areas were then tapered to waist diameters of 15 and 30 µm by using the Vytran glass processing system workstation (GPX3000, USA). The use of etched–tapered FBG sensors for the real-time monitoring of NH3 is yet to be considered. A thin layer of polyaniline nanofibers (PANI) was deposited on FBGs to enhance their sensing characteristics when exposed to different concentrations of NH3 by using the drop-cast method. The sensing process was based on a shift in the Bragg wavelength due to NH3 exposure because of the PANI refractive index modification upon exposure to NH3. The developed FBGs were stable, sensitive, and repeatable and exhibited a linear response. The sensor with the smallest waist diameter (i.e., 15 µm) exhibited superior reaction with the highest sensitivity towards NH3. The developed setup was easily incorporated with well-established optical fiber networks, such as Fiber To The Home (FTTH). The setup can be utilized to fill double roles, i.e., transmission of data and sensing the variations in gas concentrations.

Exchange current density of reversible solid oxide cell electrodes

Reversible solid oxide cells (r-SOCs) can be operated in either solid oxide fuel cell or solid oxide electrolysis cell mode. They are expected to become important in the support of renewable energy due to their high efficiency for both power generation and hydrogen generation. The exchange current density is one of the most important parameters in the quantification of electrode performance in solid oxide cells. In this study, four different fuel electrodes and two different air electrodes are fabricated using different materials and the microstructures are compared. The temperature, fuel humidification, and oxygen concentration at the air electrode are varied to obtain the apparent exchange current density for the different electrode materials. In contrast to ruthenium-and-gadolinia-doped ceria (Rh-GDC) as well as nickel-and-gadolinia-doped ceria (Ni-GDC) electrodes, significant differences in the apparent exchange current density were observed between electrolysis and fuel cell modes for the nickel-scandia-stabilized zirconia (Ni-ScSZ) cermet. Variation of gas concentration revealed that surface adsorption sites were almost completely vacant for all these electrodes. The apparent exchange current densities obtained in this study are useful as a parameter for simulation of the internal properties of r-SOCs.