1. INTRODUCTION
Gas sensing is essential for ensuring safety, particularly in environments where hazardous gases such as hydrogen or carbon monoxide may be released [1-7]. Early detection of gas emission and leaks can prevent accidents such as fires and minimize risks. It is also essential for environmental monitoring, as it helps in detecting pollutants and maintaining air quality. Accurate gas sensing plays a critical role in optimizing operations, particularly for renewable energy sources such as hydrogen. Through precise monitoring of hydrogen gas concentrations, safety, efficiency, and performance can be enhanced throughout the hydrogen production, storage, and distribution processes [8-16].
In hydrogen refueling stations exposed to high-pressure hydrogen, polymer materials fulfill essential functions [17-34]. For instance, polymers are widely used in liner materials, non-metallic pipelines, gaskets, and O-ring seals. Accordingly, they should be designed to withstand hydrogen environments as they are subjected to harsh conditions including wide temperature fluctuations (-70 °C to 90 °C) and pressure variations (1 MPa to 90 MPa). These conditions can lead to seal damage, insufficient contact with groove, and gas permeation through polymer seals, all of which can result in hydrogen leakage [35-39].
Gas permeation, including hydrogen in polymer materials, also occurs when gases pass through a polymer matrix due to diffusion. This process is influenced by factors such as pressure, temperature, and the chemical natures of both gases and polymers [40-44]. For instance, in the use of polymer membranes for gas separation, materials such as polyimide are employed to selectively permeate gases such as oxygen or carbon dioxide while blocking other gases. A common example is the use of polymeric membranes in CO2 capture systems, where the membrane's permeability to CO2 is crucial for effective separation from industrial emissions. However, the permeation rate can vary based on the polymer’s structure, with amorphous polymers typically having higher permeability compared to crystalline polymers. Gas permeation is a key consideration in applications such as gas storage and delivery, where polymers should have low gas diffusion/permeability to avoid leakage and maintain system integrity.
Conventional gas sensing technologies can selectively detect specific gases; however, they have limitations in quantitatively detecting hydrogen released into the air due to permeation or leakage through polymer materials. To address these limitations, advanced techniques utilizing gas chromatography (GC) [45-60] and mass spectrometry (MS) [61-73] have been developed to separate and accurately quantify hydrogen components within gas mixtures. However, these systems are costly and require regular maintenance and as such are unsuitable for real-time field monitoring. Consequently, the need for advanced gas sensors that are compact, exhibit rapid response times, offer high sensitivity, and maintain stable performance under diverse environmental conditions has become increasingly evident.
In this context, we have developed two types of gas sensors that meet these requirements. First, a sensor system utilizing a volumetric measurement method based on an image processing algorithm is developed [29,74]. This system is designed to provide real-time monitoring by connecting to a computer via a GPIB interface and using a diffusion analysis program to accurately measure gas concentration/diffusion in a polymer specimen with insensitivity to temperature/pressure changes. However, since this method utilizes water as a medium, it is not suitable for use in temperature conditions below 0 °C.
To overcome this limitation, a second system based on a manometric measurement approach was developed [75-77]. This compact gas detection system employs a commercial portable USB-type data logger to measure and record pressure and temperature in a sample container. This portable detection system can easily measure gas concentration and diffusion on-site in the polymer specimen in real-time without any chemical interactions with the gas.
It was demonstrated that the developed gas sensor systems can detect and characterize various gases such as H2, He, O2, N2, and Ar and effectively sense gas adsorption and diffusion in polymer materials in real-time. The sensors display reliable performance in terms of sensitivity, stability, and response time. The sensor technology presented in this study offers a compact and portable solution capable of replacing large-scale equipment. It has potential applications not only in hydrogen infrastructure safety management but also in the gas industry for environmental monitoring and other fields requiring real-time gas detection. We compare the principles, procedures, results, and characteristics of each method. Ultimately, the two developed gas sensing methods provide valuable insights into the transport properties, leakage, and sealing capabilities of O-rings under high-pressure conditions. They are applicable in hydrogen fueling stations and the gas industry and will enhance safety and enable real-time monitoring.
2. EXPERIMENTAL PROCEDURES
2.1. Sample preparation and high-pressure gas charging
The developed gas sensors are applied to measure the gas concentration and diffusion coefficient from polymer specimens during the desorption process of gas exposed to high pressure. The polymer materials used in this study are low-density polyethylene (LDPE) and ethylene propylene diene monomer (EPDM). LDPE is commonly employed in the production of containers, flexible packaging, and pipelines for gas transport and is known to offer gas-barrier properties when coated. EPDM is used as an O-ring sealing material in high-pressure gas chambers. The developed gas detection system allowed us to determine the permeation properties (solubility, diffusivity, and permeability) of polymer samples.
The LDPE specimen was manufactured by King Plastic Corporation and incorporates advanced antimicrobial technology. The composition and density of the polymer samples can be found in prior studies [1,76]. The EPDM was outgassed through thermal treatment at 60 °C for 48 hours to eliminate impurity gases incorporated during the manufacturing process. The polymer samples were prepared with the following shapes and dimensions:
- Cylinder-shaped LDPE : radius (R) 9.51 mm, thickness (T) 4.89 mm
- Cylinder-shaped EPDM : radius (R) 9.51 mm, thickness (T) 2.53 mm
For high-pressure gas exposure, we used a stainless steel (SUS 316) chamber with an internal radius of 35 mm and a height of 120 mm under a temperature of 293 K and specified pressure conditions, as shown in Figure 1(a). Gas exposure was conducted at a pressure of 5 MPa. The purity of the gases was as follows:
● H2, He, N2 and Ar: 99.999%, O2: 99.99 %
Before gas exposure, the gas atmosphere inside the stainless steel chamber was replaced by purging five times with the corresponding gas at 1 MPa. Samples were then exposed to the specific gas at the specified pressure for 48 hours. This duration of gas exposure was sufficient to reach equilibrium for gas absorption into the specimen. After the exposure, valve 2 in Fig 1(a) was opened to release the gas from the chamber. The elapsed time was recorded starting from the moment (t = 0) when the chamber pressure decreased to 0.1 MPa (atmospheric pressure).
2.2. Volumetric measurement of the released gas from charged polymers
A volumetric measurement technique to assess gas diffusion and permeation was developed. This method involves measuring the concentration of gas emitted from a specimen after charging to high-pressure gas (HG) followed by decompression. Figure 1 illustrates the volumetric analysis system used to quantify the gas emission at 293 K. The system consists of a high-pressure chamber for HG exposure and a cylinder submerged in a water container.
After exposure to HG and reaching atmospheric pressure, the specimen was placed in the upper air space of a graduated cylinder, as depicted in Fig 1. The gas emission from the specimen caused a gradual decrease in the water level within the cylinder. Consequently, the pressure (P) and volume (V) of the gas in the upper air space of the cylinder changed over time.
Gases within the cylinder adhere to the ideal gas law, expressed as PV = nRT, where n denotes the number of moles of gas released into the cylinder, R is the gas constant (8.20544 × 10-5 m3·atm/(mol·K)), and T represents the temperature inside the cylinder. The variations of pressure P(t) and volume V(t) of the gas emission in the cylinder can be described as follows[78]:
where Po represents the external pressure surrounding the graduated cylinder, ρ signifies the density of water, and g is the gravitation acceleration. h(t) describes the water level within the graduated cylinder over time, while Vo is the combined volume of gas and water inside the cylinder, measured relative to the water level in the container. Vh(t) indicates the volume of water in the cylinder as a function of time and Vs denote the volume occupied by the sample.
The amount of gas released by the polymer specimen was quantified by tracking the water level [Vh(t)] over time. Consequently, the total moles of gas emitted [n(t)] were calculated by measuring the total gas volume [V(t)] in the graduated cylinder, which corresponds to the decrease in the water level, as expressed by the following[78]:
(2)
Here, To and Po are the initial temperature and pressure of the gas in the cylinder, respectively. V(t) is the total volume, consisting of the fixed initial air volume (V(A)) and the gas volume [VH(t)] that increases over time, such that V(t)=VA+VH(t). nA denotes the mole number of the initial amount of air and nH(t) refers to the time-dependent mole number of gas corresponding to the increase in gas volume due to its release. Therefore, nH(t) was converted to the gas mass concentration, [C(t)], emitted per unit mass from the specimen as follows:
(3)
For instance, the molecular weight of hydrogen gas, mH2 [g/mol], is 2.016 g/mol. The molecular weight of nitrogen gas, mN2 [g/mol], is 28.013 g/mol. msample is the specimen mass. In Eqs. (2) and (3), the mole number of gas, nH(t), was converted to the gas mass concentration, [C(t)], by the factor k = m G a s m s a m p l e · n H ( t )
The release of gas was tracked by monitoring changes in the measured water level using image processing algorithm via a digital camera in front of the graduated cylinder. The procedure for obtaining the water level (emitted gas volume) from the water level measurement is described in previous works [3,74].
2.3. Manometric measurement of the released gas from exposed polymers
Figure 2 schematically illustrates the manometric measurement system used to determine the gas mass concentration and diffusivity of the emitted gas from the specimen at 293 K. The setup includes a high-pressure chamber for the HG exposure, as shown in Fig 2(a), a rectangular container equipped with a portable data logger (ELP sensor), and the polymer specimen, as seen in Fig 2(b). ELP sensors, which were used to measure pressure and temperature, are commercial data loggers capable of simultaneously recording both atmospheric pressure and temperature.
After exposure and decompression in the high-pressure chamber, the specimen was transferred into the rectangular specimen container illustrated in Fig 2(b). As the gas is emitted from the sample, the pressure inside the vessel increases over time. Consequently, both the pressure (P) and temperature (T) of the gas within the specimen container change as time progressed. The gas inside the container follows the ideal gas law: PV = nRT, where R is the gas constant with a value of 8.20544 × 10-5 m3·atm/(mol·K) and n denote the number of moles of the gas molecules released inside the specimen container.
The mole number of gas emitted from charged specimen is obtained by measuring the increase in pressure [P(t)] versus time by manometric measurement at constant volume of the container. Thus, the total mole number [n(t)] obtained by measuring the increased gas pressure [P(t)] due to emitted gas in the rectangular container is delineated as follows [1,76]:
(4)
where T0, V0, and P0 denote the initial temperature, constant volume, and initial pressure of the air at zero time inside the sample container, respectively. P(t) is the sum of the initial air pressure (P0) and emitted gas pressure over time [ΔP(t)], i.e., P(t)=P0+ΔP(t), n0 is the initial air mole, and Δn(t) is the time-dependent gas mole corresponding to the increase of gas pressure by the emitted gas. α(t) is the rate of change of temperature relative to T0. Thus, Δn(t) is transformed into the gas concentration [ΔC(t)] per mass for specimen as follows [76]:
(5)
where mg [g/mol] is the molecular weight of the gas used; for H2, mH2 [g/mol] = 2.016 g/mol and for N2, mN2 [g/mol] = 28.001 g/mol; and mspecimen is the sample mass. The first term, ΔP(t) in Eq. (5), represents the increase in pressure resulting from gas released by the sample. The second term, -α(t)P0, corresponds to the pressure variation caused by temperature change [α(t)]. The third term, -α(t)ΔP(t), accounts for the combined effects of temperature fluctuation [α(t)] and pressure increase [ΔP(t)].
According to Eqs. (4) and (5), the time-dependent gas mole, Δn(t), is transformed to gas mass concentration, [ΔC(t)], by scaling up using k: k = m g m s p e c i m e n
2.4. Diffusion analysis program for obtaining transport parameters
Assuming that the behavior of the gas emission follows Fickian diffusion behavior, the mass concentration of the emitted gas from the gas-exposed sample, CE(t), is calculated as follows [79,80]:
(6)
where βn represents the root of a zeroth-order Bessel function, J0(βn). Equation (6) gives the solution of Fick’s the second law of diffusion for a cylindrical shaped specimen. CE(t=∞)= C∞ is the saturated concentration of gas at infinite time, indicating total emission content (gas uptake). D is the diffusivity and l and ρ are the thickness and radius of the cylindrical sample, respectively.
For accurate calculation using Eq. (6), numerous terms from the product of two summations are considered. Thus, a specialized diffusion analysis program has been developed that precisely calculates C∞ and D. Using a diffusion analysis program based on a nonlinear optimization algorithm [72,81], we accurately calculate CE(t) and D from the experimental data by solving the complicated equation given in Eq. (6). Detailed descriptions of the diffusion analysis program and the operational algorithm can be found in previous studies [77].
3. Results and Discussion
3.1. Volumetric measurement
After decompressing a specimen enriched with nitrogen under high pressure, the released gas concentration and diffusivity were determined using the VM method with a graduated cylinder (Fig 1). The water level in the VM method is measured by employing an image processing algorithm and a digital camera [74]. Figure 3 displays the measured and fitted results for five gases (H2, He, N2, O2, and Ar) in the cylindrical LDPE determined from the water level measurement by VM. The left sides of Figs 3(a-e) show the corresponding emitted gas volume (blue filled circle) transferred from the water level measurement. The right sides of Figs 3(a-e) represent the corresponding gas emission data (black open square). The diffusion parameters D and C∞ are derived using a diffusion analysis program according to Equation (6). The blue line represents the fitted results obtained using Equation (6), with the diffusivity (D=5.57 × 10-11 m2/s) and the total gas uptake (C∞=295.4 wtppm) marked by a blue arrow. In Figs 3(a-e), single-mode gas emission behaviors for all gases in LDPE were observed under time-varying gas emission measurement. The single-mode gas emission in LDPE results from gas diffusion into the amorphous phase.
Similarly, to Fig 3, Figures 4(a-e) display the measured and fitted results for five gases in the cylindrical EPDM determined from VM measurement. The left and right sides of Figs 4(a-e) show corresponding gas volume and gas emission content, respectively. The right sides of Fig 4(a-e) represent the diffusion parameters D and C∞, derived using a diffusion analysis program according to Equation (6). The blue line represents the fitted data obtained using Equation (6), with diffusivity (D=5.57 × 10-11 m2/s) and the total gas uptake (C∞=295.4 wtppm) marked by a blue arrow. As shown in Figs 4(a-e), single-mode gas emission behaviors for all gases in EPDM were observed under time-varying gas emission measurement. The single-mode gas emission in EPDM results from gas diffusion into the amorphous phase.
3.2. Manometric measurement
After decompressing a specimen enriched with hydrogen under high pressure, the released gas concentration and diffusivity were determined using the MM method for the specimen loaded in the cylinder-shaped container (Fig 2). The water level in the MM method is measured by employing a commercial USB-type manometer. Figures 5(a-e) display the measured and fitted results for five gases (H2, He, N2, O2, and Ar) in the cylindrical LDPE determined from the water level measurement by MM. The left and right sides of Figs 5(a-e) show the gas volume transferred from the water level measurement and gas emission content, respectively. The right sides of Figs 5(a-e) represent the diffusion parameters D and C∞, derived using a diffusion analysis program according to Equation (6). The blue line represents the fitted data obtained using Equation (6), with gas diffusivity and the total gas uptake marked by a blue arrow. As shown in Fig 5, single-mode gas emission behaviors for all gases in LDPE were observed under time-varying gas emission measurement. The single-mode gas emission in LDPE results from gas diffusion into the amorphous phase.
Figures 6(a-e) display the measured and fitted results for five gases in the cylindrical EPDM determined from the water level measurement by MM. The left and right sides of Figs 6(a-e) show the gas volume and gas emission content, respectively. The right sides of Fig 6(a-e) represent the diffusion parameters D and C∞, derived using a diffusion analysis program according to Equation (6). The blue line represents the fitted data obtained using Equation (6), with gas diffusivity and the total gas uptake (C∞=295.4 wtppm) marked by a blue arrow.
Figure 7 show the corresponding gas uptake and diffusivity of LDPE as a function of exposed pressure for five distinct gases obtained by VM and MM. In Fig 7(a), the slope of gas uptake for the exposed pressure is represented by the slope value. In Fig 7(a), the black for VM and the blue line for MM linearly fitted to the gas uptake data have squared correlation coefficients of R2 > 0.97. This indicates that LDPE absorbs all the gas molecules in their molecular state without undergoing dissociation or chemical reactions, following Henry’s law. In Fig 7(b), the diffusivity does not exhibit substantial dependence on the exposed pressure. The diffusivity in Fig 7(b) is represented by the average value (Dave) of the three data points, as indicated by the black horizontal line for VM and the blue horizontal line for MM. The black error bars in Figs 7(a) and (b) denote the expanded measurement uncertainty of 10 %, as estimated in an earlier study. It is shown that the slope of gas uptake and the average diffusivity from the two methods for LDPE were identical within the expanded uncertainty.
Similarly, with Fig 7, Figure 8 depicts the gas uptake and diffusivity versus the exposed pressure obtained by VM and MM in EPDM for five gases. The slope in Fig 8(a) indicates the linear fit of gas uptake versus pressure, indicated by a black line for VM and a blue line for MM. The gas absorption behavior of EPDM in Fig 8(a) satisfactorily follows Henry’s law to a maximum of 10 MPa, as indicated by the black line, R2 > 0.97. As shown in Fig 8(b), the diffusivity is not pressure dependent. Hence, the average diffusivity is taken, as indicated by the black horizontal line for VM and blue horizontal line for MM. It is found that the slope of gas uptake and the average diffusivity from the two methods for EPDM were the same within the expanded uncertainty.
Moreover, gas solubility (S) is obtained from gas uptake versus pressure from Figs 3-6 as follows [75,82-84]:
3.3. Performance comparison between volumetric and manometric gas sensors
We have evaluated the performance of the two gas sensing systems, based on a volumetric image sensor and a manometric data logger sensor. The results of the performance tests are presented in Table 3 and encompass sensitivity, resolution, stability, measurable range, response time, and figure of merit (FOM). Sensitivity of the VM and MM sensors is defined as the change of gas uptake relative to the change in measured volume and measured pressure, respectively. The obtained sensitivities were 15.99 wt·ppm/mL for the volumetric sensor and 11.96 wt·ppm/hPa for the MM sensor. A sensor with higher sensitivity typically provides better resolution. For the VM sensor, the resolution is reflected by the minimum measurable pressure of 0.01 mL, corresponding to a mass concentration of 10.0 wt·ppm. For the MM sensor, the resolution is reflected by the minimum measurable pressure of 0.01 hPa, corresponding to a mass concentration of 0.12 wt·ppm. To improve the resolution further, it the inner volume of the graduated cylinder and the sample container could be reduced or the number of specimens could be increased. These adjustments would lead to better sensitivity and resolution.
The stability of the two sensor systems is quantified by the standard deviation of measurements taken over 36 hours after gas emission from the specimen ceased. This was found to be less than 0.2 % of the mass concentration for both sensors. The measurable range of the VM sensor is obtained from the maximum allowable concentration per mass of specimen within the graduated cylinder with inner volumes. The measurable range of the MM sensor is determined as the maximum allowable concentration per mass of specimen within a specimen container with inner volumes of 100 mL, 200 mL, and 300 mL. The measurable range was 1400 wt·ppm for the VM sensor and 1500 wt·ppm for the MM sensor, and these values can be adjusted by varying the sample mass, cylinder volume, and specimen container volume. The gas sensors’ volume and pressure response are almost instantaneous, occurring within ~1 second of gas emission. The figure of merit (FOM) is defined as the standard deviation between measured data and the theoretical value calculated using Equation (3). The FOM values below 0.4 % for the VM sensor and 0.7 % for the MM sensor indicate good agreement between the measured and theoretical values. Additionally, the measurable range, resolution, and sensitivity can be fine-tuned by adjusting the sample number and specimen container volume. Based on the performance testing, the specifications of the VM sensor and the MM sensor are similar.
4. CONCLUSIONS
Gas sensors are crucial for ensuring safety and protecting property in gas-related facilities. We have developed volumetric measurements with a graduated cylinder, coupled with an image processing algorithm and a digital camera. The variation in the water level, observed through pixel shifts in the graduated cylinder, is directly linked to changes in water volume caused by the released gas, allowing for precise measurement of gas concentration. By integrating a diffusion-permeation analysis program, this image-based volumetric gas sensor is capable of detecting not only gas concentrations but also solubility/diffusivity from gas-enriched specimens under high-pressure.
A straightforward manometric method was also developed for evaluating gas behavior. This method relies on pressure detections within a constant-volume sample container, utilizing a commercial/portable data logger. The system measures the pressure increase caused by the gas released from high-pressure polymer samples, enabling the determination of gas uptake and solubility/diffusivity by a diffusion analysis program.
The two portable gas sensor systems showcased several key performance metrics: a low detection limit for gas content, a measurable range of up to 1500 wt·ppm, stability of 0.11%, and a rapid response time within one second. Additionally, the sensors' insensitivity to temperature and pressure fluctuations made them highly reliable for gas detection. The high-performance systems, utilizing volumetric and manometric measurements, successfully demonstrated real-time monitoring and characterization of pure gases such as H2, He, N2, O2, and Ar. The sensors effectively measured gas uptake and diffusivity, considering influencing factors and calculating expanded uncertainty. In conclusion, the main features of the two developed gas sensing methods are summarized as follows:
- Cost-effective and simple techniques: The two methods provide affordable and straightforward solutions for measuring gas emission concentration and diffusivity in gas-exposed polymers under high-pressure environments.
- Stability against temperature and pressure variations: Both methods are stable under fluctuations in temperature and pressure. Both sensors are independent of specimen size and shape and gas type.
- Adjustable sensitivity and range: The volumetric and manometric methods offer flexible sensitivity, resolution, and measurement range, allowing for customization based on specific application requirements.
- No chemical interaction: Both sensing methods function without any interference between the gas molecules and the sensor, ensuring accurate measurements without altering the composition of the gas.
- Visible gas release monitoring: The volumetric method provides visual gas monitoring of gas release by changes in water level.
These two complementary sensing methods are highly effective for measuring gas transport properties in sealing elastomers and can be applied to assess the permeation characteristics of rubber materials and O-rings under high-pressure conditions. They are particularly well-suited for applications in hydrogen fueling stations. The consistent measurements of gas solubility and diffusivity obtained using both sensing methods, together with a comprehensive review of their characteristics and performance, demonstrate their reliability and applicability in real-world scenarios.
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