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Article

γ-Fe2O3-Based MEMS Gas Sensor for Propane Detection

by
Xiang Gao
1,
Ying Chen
2,3,*,
Pengcheng Xu
2,3,
Dan Zheng
1,* and
Xinxin Li
2,3
1
School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai 201418, China
2
State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Electronics 2025, 14(5), 1050; https://doi.org/10.3390/electronics14051050
Submission received: 1 February 2025 / Revised: 27 February 2025 / Accepted: 5 March 2025 / Published: 6 March 2025
(This article belongs to the Special Issue High-Performance Gas Sensors: Materials, Technologies and Applications)
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Abstract

:
The selective detection of propane gas molecules using semiconductor gas sensors has always been a challenge within research. In this study, we successfully synthesized a γ-Fe2O3 nanomaterial with a selective catalytic effect on propane and loaded it onto a ZnO sensing material to construct a double-layer microsensor, which showed good sensing response characteristics in the detection of the refrigerant R290 (which is mainly propane). In addition, we also prepared a series of iron oxides, including nanomaterials such as α-Fe2O3, Fe3O4, and FeO, as well as γ-Fe2O3 materials with different specific surface areas obtained at various processing temperatures, and we carried out gas sensing research on R290. The results show that the γ-Fe2O3 material has a better sensitivity to R290, and the γ-Fe2O3 material calcined at 200 °C shows the best performance. Our results can provide a theoretical basis for the design and optimization of semiconductor gas sensors for alkane detection.

Graphical Abstract

1. Introduction

Freon is a widely used refrigerant and a typical greenhouse gas. The ozone layer is harmed and the greenhouse effect will worsen due to freon leaks [1,2,3]. Consequently, using environmentally friendly refrigerants is particularly important. Propane gas, the primary component of R290, a new type of refrigerant, is environmentally benign, energy-efficient, and does not destroy the ozone layer [4]. Nonetheless, the airborne propane gas explosion limit is 2.1% to 9.5% [5]. Therefore, in order to guarantee safety when utilizing R290 as a refrigerant, its concentration must be monitored in real-time [6,7,8,9].
At present, the detection methods used for propane gas include gas chromatography [10,11,12], magnetic resonance imaging (MRI) [13,14], infrared spectroscopy [15,16], etc. These techniques typically make use of big, expensive, and time-consuming apparatus, making it challenging to meet the demands of quick on-site detection [17]. Semiconductor gas sensors use metal oxide semiconductor materials as sensing materials. On the surface of the sensing material, the target gas molecules undergo a reaction with oxygen-containing substances like O−. The released/captured electrons formed by the reaction will cause the resistance of the sensing material (ΔR) to change, thereby achieving gas detection [18,19]. Semiconductor gas sensors have the characteristics of good sensitivity, good selectivity, and low cost, which are conducive to rapid on-site detection [20]. Semiconductor gas sensors are widely used in combustible gas leak detection devices in homes and factories and are suitable for the detection of methane, carbon monoxide, propane, etc., [21,22,23].
To improve the sensitivity of propane sensors with a minimum detection limit of 100 ppm, J. Aguilar-Leyva’s research group included silver as a catalyst in tin oxide thin films in 2007 [24]. In 2021, Emilio Huízar-Padilla’s group created a propane gas sensor by synthesizing ZnAl2O4 nanoparticles using the microwave-assisted colloid technique. The researchers studied the response of the sensors to propane gas at 50 ppm. The response time of the sensor at 200 °C was 206 s, and at 300 °C, the response time was 176 s [25]. In 2023, Asma Wasfi et al. designed a novel porous nitrogen-containing graphene (C2N) sensor to detect propane leaks in home environments. When using C2N sensors to detect propane, the current signal is high; however, when using C2N sensors to detect methane and ammonia, the current signal is low. The results of this research demonstrate the high selectivity of C2N sensors towards propane [26]. However, the above three sensors still have problems such as complex manufacturing processes, insufficient sensitivity, and high costs.
In this study, γ-Fe2O3 was used as the primary catalytic material to construct a MEMS chip-type propane gas sensor. We used ZnO nanoparticles loaded with iron oxides as sensing materials to study the sensitivity of these materials to propane. In this study, we first fabricated a micro-hotplate chip using MEMS technology [27,28,29,30,31,32] and then synthesized four different materials, γ-Fe2O3, α-Fe2O3, Fe3O4, and FeO, for use as propane gas sensing materials. Subsequently, we synthesized a type of γ-Fe2O3/ZnO double-layer composite sensing material. This material was loaded onto a micro-hotplate chip to fabricate the MEMS propane sensor [33,34,35]. In this research, the above-mentioned four kinds of catalysts were employed for the detection of propane gas, and their characteristics were investigated. To delve deeper into the sensitivity of γ-Fe2O3, we conducted a series of experiments. One of these experiments involved varying the specific surface area of γ-Fe2O3 to compare the performance of γ-Fe2O3 with different specific surface areas in the detection of R290 gas.

2. Materials and Methods

2.1. Chemicals

N, N-dimethylformamide (DMF), ferric nitrate hexahydrate, ethylenediamine (analytical pure), ferrous chloride tetrahydrate, ferric chloride monohydrate, ammonia, zinc oxide, and anhydrous ethanol were all purchased from Sigma Aldrich (Burlington, MA, USA), and the refrigerant R290 was purchased from Binglong Environmental Protection Technology Co., Ltd. (Shanghai, China). The SnO2 nanomaterial was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China).

2.2. Construction of ZnO Material on Micro-Hotplate

About 10 mg of ZnO nanomaterial was initially added to 1 mL of deionized water. Subsequently, the mixer was subjected to sonication for several minutes. After that, the suspension was dropped onto the surface of the micro-hotplate sensor with the aid of a commercial inkjet printer (GIX II Microplotter, SonoPlot Inc., Middleton, WI, USA). Once the process was completed, the micro-hotplate chip was placed in an oven at 60 °C for drying. After that, the micro-hotplate chip was fully prepared.

2.3. Synthesis of γ-Fe2O3

Firstly, a 0.6 mol/l solution was prepared by dissolving 3.636 g of ferric nitrate hexahydrate in 15 mL of DMF. Then, the solution was stirred at room temperature until it was completely dissolved, and then the mixture was dried. Finally, five kinds of γ-Fe2O3 samples were obtained by calcining at 100 °C, 150 °C, 200 °C, 250 °C, and 300 °C for 2 h [36].

2.4. Synthesis of α-Fe2O3

To prepare 90 mL of mixed solution (the volume ratio of methanol to water = 1:1), add 4 g of ferric nitrate hexahydrate to the solution, stir for 5 min, and then add 10 mL of ethylenediamine (analytically pure). At this time, the solution produces brownish-yellow precipitation, transfers the mixed solution after reaction to a 120 mL polytetrafluoroethylene reactor, and reacts in an oven for 4 h at a temperature of 180 °C. After the reaction, wait for the sample to cool naturally to room temperature, wash the sample repeatedly, and dry the filtered sample in an oven for 6 h at a temperature of 60 °C [37,38].

2.5. Synthesis of Fe3O4

Add 2.00 g of ferrous chloride tetrahydrate (FeCl2·4H2O) and 4.06 g of ferric chloride (FeCl3) into 100 mL of deionized water, stir to dissolve them, quickly add 20 mL of ammonia (28 wt%) to the above solution, set the speed at 600 rpm, heat it in a water bath at 75 °C, and when the mixture turns from orange-red to black, continue stirring for 15 min, and then carry out centrifugal washing and vacuum drying to obtain Fe3O4 [39].

2.6. Synthesis of FeO

Add 9.525 g of ferrous chloride sample into 50 mL of deionized water, then mix evenly, and continue at 700 rpm for one hour to obtain a uniform solution. Add ammonia to make the pH of the solution 8, and then keep the solution at 80 °C to turn the solution into a gel state, and then put the nanoparticles formed in it into a high-temperature oil bath at 100 °C to keep for 6 h, centrifugation, and vacuum drying [40].

2.7. Fabrication of R290 Gas Sensor

First, add about 20 mg of γ-Fe2O3 material to 2 mL of absolute ethanol. Then, treat the materials with ultrasonic for about 2 min to make the materials uniformly dispersed in ethanol. After that, use a commercial inkjet printer (GIX II microplotter, SonoPlot Inc., Middleton, WI, USA) to suck out part of the γ-Fe2O3 suspension, drop the suspension onto the zinc oxide substrate on the micro-hotplate, and then it is placed in an oven at 150 °C for 1 h. After drying, the micro-hotplate chip can be used to carry out the sensing experiment [41].

2.8. Gas Sensing Experiment

The sensor testing platform built in this experiment included five parts: an R290 gas sensor chip, a 10 L sealed test chamber, a gas generator, a power supply, a digital multimeter, and a data acquisition system, as described in our previous work [42]. A lab-made gas generator that mixes the standard R290 gas and the pure air was employed to supply gas with the desired concentration into the test chamber. All mass flow controllers (MFCs) were calibrated beforehand using an ADM flow meter (G6691A, Agilent, Santa Clara, CA, USA). R290 at specified concentrations was introduced into a 10 L test chamber for sensing evaluations. The carrier gas was pure air, which was dispersed using our lab air compressing system and the relative humidity range was controlled. The temperature of the gas remained stable and remained the same as the laboratory temperature. During the tests, we first connected the as-prepared R290 gas sensor to the DC power supply to apply a certain voltage to the micro-hotplate sensor for 48 h. Then, we used the digital multimeter (Agilent 34410A) to read the chemi-resistive sensing signal in real-time and recorded the resistance curve using a computer.

2.9. Characterization of Materials

In this work, the morphology of the four kinds of γ-Fe2O3 and three kinds of iron oxide (α-Fe2O3, Fe3O4, and FeO) were characterized using a scanning electron microscope (SEM, FE-SEM, Hitachi S4800, Hitachi High-Technologies Corporation, Tokyo, Japan). The FEI Tecnai G20 transmission electron microscope (TEM, 200 kV, Thermo Fisher Scientific, Hillsboro, OR, USA) produced by the FEI company was used to analyze the structural characteristics of the materials. Energy dispersion spectroscopy (EDS, Oxford X-MaxN, Oxford Instruments, Abingdon, UK) was used to identify the element distribution of the material. The focused ion beam microscope (FIB, FEI Quanta 3D FEG600, Thermo Fisher Scientific, Waltham, MA, USA) was used to determine the thickness of the sensitive layer.
In this study, the crystal structure characteristics of the materials were analyzed by X-ray diffraction (XRD, 40 kV) with the Model D8 advance produced by Bruker company in Bremen, Germany. The X-ray generator, operating at 40 kV and 30 mA with a wavelength of 0.154 nm, utilized Cu Kα radiation. The XRD data were gathered through sequential scans. The scanning range extended from 10° to 80°, and the sampling interval was 0.02°.
In this study, a high-performance adsorption analyzer (Micromeritics ASAP 2020 M, Morcoss, GA, USA) measured the N2 adsorption/desorption isotherms of γ-Fe2O3 at 77 K. By applying the Brunauer–Emmet–Teller (BET) equation, the specific surface area was calculated, and the Barrett–Joyner–Halenda (BJH) method was used to calculate the pore size distribution.

3. Results and Discussion

The phases of the prepared samples were identified by X-ray diffraction (XRD), and the five peaks shown in Figure 1a were attributed to the characteristic peaks of γ-Fe2O3 (210), (221), (311), (422), (440), and the JCPDS card number was 04-0755. The seven peaks shown in Figure 1b belong to the characteristic peaks of α-Fe2O3 (012), (104), (110), (113), (024), (116), (300). The JCPDS card number is 99-0060. The five characteristic peaks shown in Figure 1c belong to (220), (311), (400), (511), and (440) of Fe3O4, and the JCPDS card number is 99-0073. The five characteristic peaks shown in Figure 1d belong to (111), (200), (220), (311), and (222) of FeO, and the JCPDS card number is 06-0615.
The morphology of γ-Fe2O3 was characterized by SEM and TEM. Figure 2a is the SEM image of the prepared sample. The images indicate that the sample presents a large granular morphology. In order to better observe the details of the material, we took TEM images of the prepared sample. As shown in Figure 2b, the sample is granular and has a nearly spherical shape. γ-Fe2O3 particles are about tens of nanometers big, and the typical size is about 25 nm. The dispersion of the sample particles is low, which may be due to the agglomeration of nanoparticles.
Figure 3a shows the N2 adsorption/desorption isotherm obtained in this work, which is a typical type VI curve. It indicates that there is a relatively uniform pore size distribution in the material, and there may be a multi-layer adsorption phenomenon. The BET surface area of the synthetic material is measured as 36 m2/g. The size distribution of the composite sample is shown in Figure 3b. The sample has a size that varies between 5 and 25 nm. It is worth mentioning that the size distribution results are highly consistent with those obtained by TEM.
In this work, a micro-hotplate manufactured using MEMS technology was used to construct the gas sensor. The scheme of the chip is depicted in Figure 4a. The sensor has a suspended hotplate and the sensing electrodes are located in the center of the hotplate. The size of the micro-hotplate is 1 × 1 × 0.4 mm and the suspended micro-plate has a diameter of 300 μm. The detailed fabrication process of the micro-hotplate chip is referred to in our previous studies [42,43] and the fabrication flow schemes are shown in Figure 4b. On a 4-inch wafer, a thin film of silicon nitride, metal wires, and insulating layers were deposited, consequently creating a pattern. Then, the suspended micro-hotplate was released. Then, the substrate sensing material was constructed on the micro-hotplate, and finally, the catalyst was printed. An SEM image of the as-fabricated micro-hotplate chip is shown in Figure 4c. Figure 4d shows an SEM image of ZnO substrate constructed on the micro-hotplate. After the ZnO substrate assembly was finished, the γ-Fe2O3 sample was accurately loaded onto the ZnO material using a commercial inkjet printer (GIX II microplotter, sonoplot Inc.). The thickness of the sensitive layer is observed by a FIB microscope by cutting a hole through the sensitive layer. As shown in Figure 4f. The sensing layer is quite uniform and its thickness is about 4–5 μm. Figure 4g shows a high-resolution SEM image of a portion of Figure 4e, showing the local morphology of the double-layer structure sensing material. The sensing material has a clear double-layer structure and the γ-Fe2O3 sample can be seen in Figure 4e. The EDS in Figure 4h further indicates that the sensor is mainly composed of Zn, O, and Fe elements, and the other two elements, Si and Pt, originate from the micro-hotplate. The EDS results indicate that a double-layer material composed of zinc oxide and γ-Fe2O3 has been successfully assembled on the micro-hotplate.
To study the optimal operating temperature of the sensor, we used the same gas sensor to detect R290 at the same concentration at different operating temperatures. Figure 5a shows that the optimal operating temperature for the microsensor is 360 °C. When the sensing temperature rises to 360 °C, the microsensor consumes only 23 mW of power. Here, we define the sensor response S of the gas sensor as follows: S = ΔR/Ra = (Ra − Rg)/Ra. Where Ra is the resistance value of the sensor in air, and Rg is the resistance value of the gas sensor exposed to refrigerant R290 gas.
It is clearly observable that an increase in the concentration of R290 leads to a concomitant increase in the sensor’s response. As illustrated in Figure 5b, within the detection range, a good linear relationship exists between the R290 concentration and the sensor response S. Figure 5b shows the sensing curve of the gas sensor for real-time recording of R290 gas within the concentration range of 1–1000 ppm. It can be seen that, as the concentration of R290 increases, the response of the sensor also increases. As shown in Figure 5b, a good linear relationship can be obtained between the R290 concentration and the sensor response S within the detection concentration range. The sensing response S to 1 ppm R290 is about 7.5% and the noise value read from the curve is about 1.1%. Since the response value of the sensor to 1 ppm R290 is greater than three times the noise value, we consider the LOD of the double-layer R290 sensor to be lower than 1 ppm. This performance meets the requirements for the on-site detection of refrigerant R290 leakage.
Propane gas has broad application prospects in various fields. To evaluate the selectivity of this sensor for combustible gases, seven gases including carbon monoxide, acetylene, ethane, methane, propylene, ethylene, and hydrogen were selected as interference gases. As shown in Figure 5c, for interference gases with the same concentration of 100 ppm, all of the sensor responses S are less than 0.05, while for refrigerant R290 with the same concentration, the sensor response S is as high as 0.18. The results in Figure 5c confirm that the γ-Fe2O3 catalyst is of great significance in the specific detection of the refrigerant R290. The microsensor was exposed to 100 ppm of R290 refrigerant three times, as shown in Figure 5d, and the response S was 19.64%, 18.96%, and 19.25%, respectively. This proves that the prepared microsensor has good repeatability for R290 refrigerant. The effect of relative humidity on the response of the sensor was studied. With the relative humidity increase from 25% RH to 90% RH, the sensor response to 100 ppm R290 decreased slightly, as shown in Figure 5e. The response S of the same sensor was 20.1% at 25% RH, while it was 16.5% at 90% RH, indicating that the increasing humidity has no significant effect on the sensor response. We studied the sensing response of the same sensor to 100 ppm R290 gas for 6 months and recorded the response in each month. The results shown in Figure 5f demonstrate that the sensor has good long-term stability.
In order to compare the sensing effect of single-layer sensors and double-layer sensors, we used ZnO nanomaterials and γ-Fe2O3 to detect R290 refrigerant, respectively. Based on the test results presented in Figure 6a, the response of the original ZnO nanomaterial to 100 ppm of R290 is measured to be 0.33%. When the concentration of R290 gas is increased to 1000 ppm, the sensor response rises to 0.67%. As shown in Figure 6b, the single-layer sensor composed only of γ-Fe2O3 exhibits a response of 7.32% to 100 ppm of R290. When 1000 ppm of R290 gas is introduced, the sensor response reaches 20.49%. When compared with the double-layer sensor previously prepared in this study, it is clear that the double-layer sensor with ZnO as the substrate and γ-Fe2O3 as the catalyst demonstrates a superior sensing effect. This comparison experiment demonstrates the efficacy of using the double-layer sensor for R290 detection in this research work. The double-layer structure can provide a larger specific surface area, which helps to increase the adsorption capacity of the target gas and thus improve sensitivity. The synergistic sensitization effect of the double-layer structure can be explained as follows: γ-Fe2O3 is used to catalyze the inert R290 molecule. The electron signal generated during the catalytic process can be detected with semiconductor ZnO material with a high sensitivity and fast speed.
To verify the gas-sensing effect of ZnO as a substrate material, we used a SnO2 nanomaterial, which is a typical metal-oxide semiconductor sensing material, to carry out the control test. We prepared the R290 sensor with SnO2 as the substrate material using the same method described in Section 2.2 and Section 2.7 to fabricate double-layer sensors. The result of the control test is shown in Figure 7. When the SnO2 material was used as the sensing material, the response value (S) to 100 ppm R290 was 7.2%. When ZnO was used as the sensing material, S was 20.62%, which is more sensitive than the sensor that uses SnO2 as a substrate material. In addition, the response time of the sensor with SnO2 is several minutes, while that of the sensor with ZnO only takes a few seconds. This result indicates that the sensor with the ZnO substrate features a fast response speed. So, this control experiment demonstrates that ZnO is fit for use as the substrate material in this work.
Due to the good sensing performance of the prepared γ-Fe2O3 for R290 refrigerant, we continued to conduct a series of related experiments, synthesizing three other iron-containing oxides, and then preparing a double-layer gas sensor using the same method. At the same time, R290 at 100 ppm was tested, as shown in Figure 8. It was found that γ-Fe2O3 still had the best catalytic effect on R290, with a sensing response of 20.12%. Next was α-Fe2O3, with a response of 2.67%, Fe3O4 with a response of 1.33%, and FeO with almost no response. The data are shown in Table 1.
In order to further verify the catalytic effect of γ-Fe2O3 on R290, we changed the calcination temperature during the synthesis of γ-Fe2O3, prepared γ-Fe2O3 materials with different specific surface areas, and studied their sensing properties. As shown in Table 2 and Figure 9, with the increase in calcination temperature, the specific surface area of γ-Fe2O3 increases, and the sensitivity is also improved. Once the calcination temperature surpasses 200 °C, further increasing the temperature leads to a particular situation. Although the specific surface area keeps increasing, it does not enhance the sensor’s sensitivity. The sensor’s sensitivity shows a downward trend instead. We think the reason for this might be the change in the crystal structure. When the temperature rises beyond 250 °C, a change in the crystal structure occurs. Also, a portion of the generated γ-Fe2O3 is converted to α-Fe2O3.

4. Conclusions

This study focused on R290 and we conducted research on R290 sensing materials using semiconductor gas sensors. This work has found a simple and convenient method for synthesizing γ-Fe2O3, which can be calcined at 200 °C for 2 h to obtain the sample. Through extensive experimental comparisons, it has been found that γ-Fe2O3 calcined at 200 °C is the best catalyst for propane gas detection. It has been experimentally proven that the sensor prepared using ZnO as the substrate combined with γ-Fe2O3 as the catalyst has a better refrigerant R290 sensing performance than directly using γ-Fe2O3 as the sensitive material for the sensor.
We adopted a simple and novel method to calcine and synthesize γ-Fe2O3 nanoparticles in air, without complex procedures such as pH adjustment, gas atmosphere, additives, centrifugation, etc., during the preparation process. Three types of iron oxides, namely α-Fe2O3, Fe3O4, and FeO, were prepared to detect R290 refrigerant. The synthesized samples were characterized by TEM, SEM, XRD, and N2 adsorption experiments. In addition, to verify the catalytic effect of γ-Fe2O3, we synthesized γ-Fe2O3 with different specific surface areas. The experiments proved that the material synthesized at 200 °C has the best sensing effect. The sensing test results showed that the detection limit (LOD) of the gas sensor was below 1 ppm, and the gas sensor also had good selectivity and repeatability for R290 detection. Finally, it has been proven that the sensing performance of double-layer sensors is superior to that of single-layer sensors.
In summary, this study has developed a new portable gas sensor that not only improves the detection efficiency and sensitivity for propane gas but also provides technical support for the safe use of environmentally friendly refrigerants. Future research will continue to explore the impact of different materials and structures on sensing performance, in order to further improve the accuracy and reliability of detection.

Author Contributions

Conceptualization, P.X. and X.L.; Methodology, P.X. and Y.C; Investigation, X.G. and Y.C.; Writing, P.X., X.G. and Y.C.; Validation, D.Z.; funding acquisition, P.X. and X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the National Key Research and Development Program of China (2024YFF1502700), and the National Natural Science Foundation of China (62271473, U21A20500, 62227815, 61974155, 62104241).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. XRD characterization results for the prepared iron oxide samples. (a) γ-Fe2O3; (b) α-Fe2O3, (c) Fe3O4, (d) FeO.
Figure 1. XRD characterization results for the prepared iron oxide samples. (a) γ-Fe2O3; (b) α-Fe2O3, (c) Fe3O4, (d) FeO.
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Figure 2. (a) SEM image and (b) TEM image of the γ-Fe2O3 sample.
Figure 2. (a) SEM image and (b) TEM image of the γ-Fe2O3 sample.
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Figure 3. (a) N2 adsorption isotherm of γ-Fe2O3; (b) pore size distribution curve of γ-Fe2O3.
Figure 3. (a) N2 adsorption isotherm of γ-Fe2O3; (b) pore size distribution curve of γ-Fe2O3.
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Figure 4. (a) Scheme of the gas sensor; (b) schematic of the fabrication process of the sensor; (c) SEM image of micro-hotplate sensor; (d) low-resolution SEM image of gas sensor, the sensing material is loaded on the sensing area. Microheater located at the edge area of the micro-hotplate; (e) image of a microsensor loaded with sensing material, with the red frame area magnified for observation as shown in Figure 4g; (f) FIB image shows the cross-section of the sensing layer; (g,h) high-resolution SEM and EDS energy spectrum analysis of the red-framed area in Figure 4e both indicate that γ-Fe2O3 particles are loaded on the ZnO.
Figure 4. (a) Scheme of the gas sensor; (b) schematic of the fabrication process of the sensor; (c) SEM image of micro-hotplate sensor; (d) low-resolution SEM image of gas sensor, the sensing material is loaded on the sensing area. Microheater located at the edge area of the micro-hotplate; (e) image of a microsensor loaded with sensing material, with the red frame area magnified for observation as shown in Figure 4g; (f) FIB image shows the cross-section of the sensing layer; (g,h) high-resolution SEM and EDS energy spectrum analysis of the red-framed area in Figure 4e both indicate that γ-Fe2O3 particles are loaded on the ZnO.
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Figure 5. (a) The relationship between temperature and sensing response of 100 ppm refrigerant R290 gas; (b) the sensing curve measured in real-time by the R290 sensor. The concentration range of R290 is 1–1000 ppm. The inset exhibits the linear relationship between the concentration of R290 and S; (c) the selectivity of the sensor for 7 types of interfering gases, with the gas concentration of 100 ppm; (d) response of the sensor to three repeated detections of R290 with the same concentration. (e) Response of the sensor to 100 ppm R290 at different relative humidity. (f) Long-term stability test results of the sensor.
Figure 5. (a) The relationship between temperature and sensing response of 100 ppm refrigerant R290 gas; (b) the sensing curve measured in real-time by the R290 sensor. The concentration range of R290 is 1–1000 ppm. The inset exhibits the linear relationship between the concentration of R290 and S; (c) the selectivity of the sensor for 7 types of interfering gases, with the gas concentration of 100 ppm; (d) response of the sensor to three repeated detections of R290 with the same concentration. (e) Response of the sensor to 100 ppm R290 at different relative humidity. (f) Long-term stability test results of the sensor.
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Figure 6. (a) The response curve of R290 using ZnO material. (b) The response curve of R290 using γ-Fe2O3 material.
Figure 6. (a) The response curve of R290 using ZnO material. (b) The response curve of R290 using γ-Fe2O3 material.
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Figure 7. Response of two types of substrate-sensitive materials to 100 ppm R290.
Figure 7. Response of two types of substrate-sensitive materials to 100 ppm R290.
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Figure 8. The response of sensors prepared with four types of iron oxide materials to 100 ppm R290.
Figure 8. The response of sensors prepared with four types of iron oxide materials to 100 ppm R290.
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Figure 9. (a) Responses of five types of sensors prepared with different specific surface areas of γ-Fe2O3 to 100 ppm R290 gas; (b) the relationship between the specific surface area of γ-Fe2O3 and sensor responses.
Figure 9. (a) Responses of five types of sensors prepared with different specific surface areas of γ-Fe2O3 to 100 ppm R290 gas; (b) the relationship between the specific surface area of γ-Fe2O3 and sensor responses.
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Table 1. Test results for sensors made with four types of iron oxides for 100 ppm R290.
Table 1. Test results for sensors made with four types of iron oxides for 100 ppm R290.
Sensor No.CatalystSensor Response: S
Sensor1γ-Fe2O320.12%
Sensor 2α-Fe2O32.67%
Sensor 3Fe3O41.33%
Sensor 4FeO0.67%
Table 2. Test results for sensors prepared with different specific surface areas of γ-Fe2O3 for 100 ppm R290.
Table 2. Test results for sensors prepared with different specific surface areas of γ-Fe2O3 for 100 ppm R290.
Sensor No.Calcination Temperature
(°C)		Specific Surface Area
(m2/g)		Sensor Response
S
Sensor110012.712.56%
Sensor 215024.215.21%
Sensor 320036.118.03%
Sensor 425051.516.25%
Sensor 530086.213.74%
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Gao, X.; Chen, Y.; Xu, P.; Zheng, D.; Li, X. γ-Fe2O3-Based MEMS Gas Sensor for Propane Detection. Electronics 2025, 14, 1050. https://doi.org/10.3390/electronics14051050

AMA Style

Gao X, Chen Y, Xu P, Zheng D, Li X. γ-Fe2O3-Based MEMS Gas Sensor for Propane Detection. Electronics. 2025; 14(5):1050. https://doi.org/10.3390/electronics14051050

Chicago/Turabian Style

Gao, Xiang, Ying Chen, Pengcheng Xu, Dan Zheng, and Xinxin Li. 2025. "γ-Fe2O3-Based MEMS Gas Sensor for Propane Detection" Electronics 14, no. 5: 1050. https://doi.org/10.3390/electronics14051050

APA Style

Gao, X., Chen, Y., Xu, P., Zheng, D., & Li, X. (2025). γ-Fe2O3-Based MEMS Gas Sensor for Propane Detection. Electronics, 14(5), 1050. https://doi.org/10.3390/electronics14051050

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