Search Results (43)

This work presents a proof of concept including simulation and experimental validations of acoustic gas sensor prototypes for trace CO₂ detection up to 1 ppm. For the detection of lower gas concentrations especially, the dependency of acoustic resonances on the molecular weights and, consequently, the speed of sound of the gas mixture, is exploited. We explored two resonator types: a cylindrical acoustic resonator and a Helmholtz resonator intrinsic to the MEMS microphone’s geometry. Both systems utilized mass flow controllers (MFCs) for precise gas mixing and were also modeled in COMSOL Multiphysics 6.2 to simulate resonance shifts based on thermodynamic properties of binary gas mixtures, in this case, N₂-CO₂. We performed experimental tracking using Zurich Instruments MFIA, with high-resolution frequency shifts observed in µHz and mHz ranges in both setups. A compact and geometry-independent nature of MEMS-based Helmholtz tracking showed clear potential for scalable sensor designs. Multiple experimental trials confirmed the reproducibility and stability of both configurations, thus providing a robust basis for statistical validation and system reliability assessment. The good simulation experiment agreement, especially in frequency shift trends and gas density, supports the method’s viability for scalable environmental and industrial gas sensing applications. This resonance tracking system offers high sensitivity and flexibility, allowing selective detection of low CO₂ concentrations down to 1 ppm. By further exploiting both external and intrinsic acoustic resonances, the system enables highly sensitive, multi-modal sensing with minimal hardware modifications. At microscopic scales, gas detection is influenced by ambient factors like temperature and humidity, which are monitored here in a laboratory setting via NDIR sensors. A key challenge is that different gas mixtures with similar sound speeds can cause indistinguishable frequency shifts. To address this, machine learning-based multivariate gas analysis can be employed. This would, in addition to the acoustic properties of the gases as one of the variables, also consider other gas-specific variables such as absorption, molecular properties, and spectroscopic signatures, reducing cross-sensitivity and improving selectivity. This multivariate sensing approach holds potential for future application and validation with more critical gas species. Full article

The increasing atmospheric CO₂ concentration due to anthropogenic activities has led to the development of low-cost, portable, and user-friendly sensing technologies. Non-Dispersive Infrared (NDIR) sensors offer reliable CO₂ detection with high sensitivity, which makes them ideal for citizen scientists. In this context, we designed two low-cost CO₂ monitoring systems: an automatic opening chamber with a lid and a portable device using NDIR sensors. These monitoring systems were calibrated (R² = 0.99) with known CO₂ concentrations. Besides its reliability and accuracy, the Automated CO₂ Monitoring System costs approximately USD 220.77 and portable CO₂ device costs USD 151.43, which makes them suitable for citizen scientists. Due to CO₂ gas monitoring system’s simplicity, structure, and operation, non-expert users can use and actively participate in environmental monitoring data collection. This promotes public engagement in climate and air quality monitoring and enables citizen scientists to have reliable data for CO₂ monitoring and environmental awareness. Full article

The safety of power batteries in the automotive industry is of paramount importance and cannot be emphasized enough. As lithium-ion battery technology continues to evolve, the energy density of these batteries increases, thereby amplifying the potential risks linked to battery failures. This study explores pivotal safety challenges within the electric vehicle sector, with a particular focus on thermal runaway and gas emissions originating from lithium-ion batteries. We offer a non-dispersive infrared (NDIR) gas sensor designed to efficiently monitor battery emissions. Notably, carbon dioxide (CO₂) gas sensors are emphasized for their ability to enhance early-warning systems, facilitating the timely detection of potential issues and, in turn, improving the overall safety standards of electric vehicles. In this study, we introduce a novel CO₂ gas sensor based on the advanced pyroelectric single-crystal lead niobium magnesium titanate (PMNT), which exhibits exceptionally high pyroelectric properties compared to commercially available materials, such as lithium tantalate single crystals and lead zirconate titanate ceramics. The specific detection rate of PMNT single-crystal pyroelectric infrared detectors is more than four times higher than lithium tantalate single-crystal infrared detectors. The PMNT single-crystal NDIR gas detector is used to monitor thermal runaway in lithium-ion batteries, enabling the rapid and highly accurate detection of gases released by the battery. This research offers an in-depth exploration of real-time monitoring for power battery safety, utilizing the cutting-edge pyroelectric single-crystal gas sensor. Beyond providing valuable insights, the study also presents practical recommendations for mitigating the risks of thermal runaway in lithium-ion batteries, with a particular emphasis on the development of effective warning systems. Full article

A novel prototype based on the combination of a multi-junction, high-efficiency photovoltaic (PV) module and a supercapacitor (SC) able to self-power a wireless sensor node (WSN) for outdoor air quality monitoring has been developed and tested. A PV module with about an 8 cm² active area made of eight GaAs-based triple-junction solar cells with a nominal 29% efficiency was assembled and characterized under terrestrial clear-sky conditions. Energy is stored in a 4000 F/4.2 V supercapacitor with high energy capacity and a virtually infinite lifetime (10⁴ cycles). The node power consumption was tailored to the typical power consumption of miniaturized, low-consumption NDIR CO₂ sensors relying on an LED as the IR source. The charge/discharge cycles of the supercapacitor connected to the triple-junction PV module were measured under illumination with a Sun Simulator device at selected radiation intensities and different node duty cycles. Tests of the miniaturized prototype in different illumination conditions outdoors were carried out. A model was developed from the test outcomes to predict the maximum number of sensor samplings and data transmissions tolerated by the node, thus optimizing the WSN operating conditions to ensure its self-powering for years of outdoor deployment. The results show the self-powering ability of the WSN node over different insolation periods throughout the year, demonstrating its operation for a virtually unlimited lifetime without the need for battery substitution. Full article

A carbon dioxide (CO₂) gas sensor based on non-dispersive infrared (NDIR) technology has been developed and is suitable for use in portable devices for high-precision CO₂ detection. The NDIR gas sensor comprises a MEMS infrared emitter, a MEMS thermopile detector with an integrated optical filter, and a compact gas cell with high optical coupling efficiency. A dual-ellipsoid mirror optical system was designed, and based on optical simulation analysis, the structure of the dual-ellipsoid reflective gas chamber was designed and optimized, achieving a coupling efficiency of up to 54%. Optical and thermal simulations were conducted to design the sensor structure, considering thermal management and light analysis. By optimizing the gas cell structure and conditioning circuit, we effectively reduced the sensor’s baseline noise, enhancing the overall reliability and stability of the system. The sensor’s dimensions were 20 mm × 10 mm × 4 mm (L × W × H), only 15% of the size of traditional NDIR gas sensors with equivalent detection resolution. The developed sensor offers high sensitivity and low noise, with a sensitivity of 15 μV/ppm, a detection limit of 90 ppm, and a resolution of 30 ppm. The total power consumption of the whole sensor system is 6.5 mW, with a maximum power consumption of only 90 mW. Full article

This paper presents a novel technique for extracting the alveolar part of human breath. Gas exchange occurs between blood and inhaled air in the alveoli, which is helpful in medical diagnostics based on breath analysis. Consequently, the alveolar portion of the exhaled air contains specific concentrations of endogenous EVOC (exogenous volatile organic compound), which, among other factors, depend on the person’s health condition. As this part of the breath enables the screening for diseases, accurate sample collection for testing is crucial. Inaccurate sampling can significantly alter the composition of the specimen, alter the concentration of EVOC (biomarkers) and adversely affect the diagnosis. Furthermore, the volume of alveolar air is minimal (usually <350 mL), especially in the case of people affected by respiratory system problems. For these reasons, precise sampling is a key factor in the effectiveness of medical diagnostic systems. A new technique ensuring high accuracy and repeatability is presented in the article. It is based on analyzing the changes in carbon dioxide concentration in human breath using a fast and compensated non-dispersive infrared (NDIR) sensor and the simple moving adjacent average (SMAA) algorithm. Research has shown that this method accurately identifies exhalation phases with an uncertainty as low as 20 ms. This provides around 350 ms of breath duration for carrying out additional stages of the diagnostic process using various types of analyzers. Full article

Increasing concerns about air quality due to fossil fuel combustion, especially nitrogen oxides (NO_x) from marine and diesel engines, necessitate advanced monitoring systems due to the significant health and environmental impacts of nitrogen dioxide (NO₂). In this study, a gas detection system based on the principle of the non-dispersive infrared (NDIR) technique is proposed. Firstly, the pyroelectric detector was developed by employing an ultra-thin LiTaO₃ (LT) layer as the sensitive element, integrated with nanoscale carbon material prepared by wafer-level graphics technology as the infrared absorption layer. Then, the sensor was hermetically sealed using inert gas through energy storage welding technology, exhibiting a high detectivity (D*) value of 4.19 × 10⁸ cm·√Hz/W. Subsequently, a NO₂ gas sensor was engineered based on the NDIR principle employing a Micro Electro Mechanical System (MEMS) infrared (IR) emitter, featuring a light path chamber length of 1.5 m, along with integrated signal processing and software calibration algorithms. This gas sensor was capable of detecting NO₂ concentrations within the range of 0–500 ppm. Initial tests indicated that the gas sensor exhibited a full-scale relative error of less than 0.46%, a limit of 2.8 ppm, a linearity of −1.09%, a repeatability of 0.47% at a concentration of 500 ppm, and a stability of 2% at a concentration of 500 ppm. The developed gas sensor demonstrated significant potential for application in areas such as industrial monitoring and analytical instrumentation. Full article

Nowadays, indoor air pollution is a major problem that affects human health. For that reason, measuring indoor air quality has an increasing interest. Electronic noses are low-cost instruments (compared with reference methods) capable of measuring air components and pollutants at different concentrations. In this paper, an electro-optical nose (electronic nose that includes optical sensors) with non-dispersive infrared sensors and metal oxide semiconductor sensors is used to measure gases that affect indoor air quality. To validate the developed prototype, different gas mixtures (CH₄ and CO₂) with variable concentrations and humidity values are generated to confirm the discrimination capabilities of the device. Principal Component Analysis (PCA) was used for dimensionality reduction purposes to show the measurements in a plot. Partial Least Squares Regression (PLS) was also performed to calculate the predictive capabilities of the device. PCA results using all the measurements from all the sensors obtained PC1 = 47% and PC2 = 10%; results are improved using only the relevant information of the sensors obtaining PC1 = 79% and PC2 = 9%. PLS results with CH₄ using only MOX sensors received an RMSE = 118.8. When using NDIR and MOX sensors, RMSE is reduced to 19.868; this tendency is also observed in CO₂ (RMSE = 116.35 with MOX and RMSE = 20.548 with MOX and NDIR). The results confirm that the designed electro-optical nose can detect different gas concentrations and discriminate between different mixtures of gases; also, a better correlation and dispersion is achieved. The addition of NDIR sensors gives better results in measuring specific gases, discrimination, and concentration prediction capabilities in comparison to electronic noses with metal oxide gas sensors. Full article

The air quality in modern cities and urban areas is strongly affected by chemical pollutants such as toxic gases, volatile organic compounds, and particulate matter. They are monitored by governmental agencies using regulatory monitoring stations, which are highly accurate, but also very expensive, bulky, and maintenance demanding. There is a compulsory need to monitor air quality at high spatial–temporal resolution in smart cities for public health protection and environmental sustainability. Properly calibrated low-cost and low-accuracy sensors are usually deployed in stationary and mobile nodes for urban air quality monitoring. A simple indicator of the current status of urban air pollution is the Air Quality Index (AQI) used to communicate the pollution level under the time-changing trend of a specific pollutant. In this study, continuous measurements have been performed in the city of Bari (southern Italy) by electrochemical gas sensors (NO₂, O₃, CO), optical particle counters (OPC) for particulate matter (PM₁₀), and NDIR infrared sensors (CO₂), including microsensors for temperature and relative humidity. The sensors have been installed in stationary nodes located in urban sites and in a mobile node mounted on a public bus moving on urban routes. AQI data gathered by the low-cost sensors have been compared with reference instrumentations as a case study of citizen science. Full article

As the monitoring of carbon dioxide is an important proxy to estimate the air quality of indoor and outdoor environments, it is essential to obtain trustful data from CO₂ sensors. However, the use of widely available low-cost sensors may imply lower data quality, especially regarding accuracy. This paper proposes a new approach for enhancing the accuracy of low-cost CO₂ sensors using an extremely randomized trees algorithm. It also reports the results obtained from experimental data collected from sensors that were exposed to both indoor and outdoor environments. The indoor experimental set was composed of two metal oxide semiconductors (MOS) and two non-dispersive infrared (NDIR) sensors next to a reference sensor for carbon dioxide and independent sensors for air temperature and relative humidity. The outdoor experimental exposure analysis was performed using a third-party dataset which fit into our goals: the work consisted of fourteen stations using low-cost NDIR sensors geographically spread around reference stations. One calibration model was trained for each sensor unit separately, and, in the indoor experiment, it managed to reduce the mean absolute error (MAE) of NDIR sensors by up to 90%, reach very good linearity with MOS sensors in the indoor experiment (r² value of 0.994), and reduce the MAE by up to 98% in the outdoor dataset. We have found in the outdoor dataset analysis that the exposure time of the sensor itself may be considered by the algorithm to achieve better accuracy. We also observed that even a relatively small amount of data may provide enough information to perform a useful calibration if they contain enough data variety. We conclude that the proper use of machine learning algorithms on sensor readings can be very effective to obtain higher data quality from low-cost gas sensors either indoors or outdoors, regardless of the sensor technology. Full article

This paper proposes to apply a newly developed Non-Dispersive Infrared Spectroscopy (NDIR) gas sensing system composed of pyroelectric infrared detectors to monitor the thermal runaway (TR) process of lithium-ion batteries in real time and achieve an early warning system for the battery TR process. The new Electrical Vehicle Safety—Global Technical Regulation (EVS-GTR) requires that a warning be provided to passengers at least five minutes before a serious incident. The experimental results indicate that carbon dioxide and methane gas were detected during the overcharge test of the automotive battery, and the target gas was detected 25 s in advance before the battery TR when the battery vent was closed. In order to further explore the battery TR mechanism, an experiment was carried out using the battery sample with the battery vent opened. The target gas was detected about 580 s before the battery temperature reached the common alarm temperature (60 °C) of the battery management system (BMS). In this study, the beneficial effects of NDIR gas sensors in the field of thermal runaway warnings for automotive batteries were demonstrated and showed great application prospects and commercial value. Full article

Non-dispersive infra-red (NDIR) detectors have become the dominant method for measuring atmospheric CO₂, which is thought to be an important gas for honeybee colony health. In this work we describe a microcontroller-based system used to collect data from Senserion SCD41 NDIR sensors placed in the crown boards and queen excluders of honeybee colonies. The same sensors also provide relative humidity and temperature data. Several months of data have been recorded from four different hives. The mass change measurements, from hive scales, when foragers leave the hive were compared with the data from the gas sensors. Our data suggest that it is possible to estimate the colony size from the change in measured CO₂, however no such link with the humidity is observed. Data are presented showing the CO₂ decreasing over many weeks as a colony dies. Full article

Measurements of atmospheric gas concentrations using of NDIR gas sensors requires compensation of ambient pressure variations to achieve reliable result. The extensively used general correction method is based on collecting data for varying pressures for a single reference concentration. This one-dimensional compensation approach is valid for measurements carried out in gas concentrations close to reference concentration but will introduce significant errors for concentrations further away from the calibration point. For applications, requiring high accuracy, collecting, and storing calibration data at several reference concentrations can reduce the error. However, this method will cause higher demands on memory capacity and computational power, which is problematic for cost sensitive applications. We present here an advanced, but practical, algorithm for compensation of environmental pressure variations for relatively low-cost/high resolution NDIR systems. The algorithm consists of a two-dimensional compensation procedure, which widens the valid pressure and concentrations range but with a minimal need to store calibration data, compared to the general one-dimensional compensation method based on a single reference concentration. The implementation of the presented two-dimensional algorithm was verified at two independent concentrations. The results show a reduction in the compensation error from 5.1% and 7.3%, for the one-dimensional method, to −0.02% and 0.83% for the two-dimensional algorithm. In addition, the presented two-dimensional algorithm only requires calibration in four reference gases and the storing of four sets of polynomial coefficients used for calculations. Full article

Non-dispersive infrared (NDIR) absorption spectroscopy is a widespread approach to gas sensing due to its selectivity and conceptual simplicity. One of the main challenges towards the development of fully integrated NDIR sensors is the design and fabrication of microstructures, typically waveguides, that can combine high sensitivity with the ease of integrability of other sensor elements (sources, filters, detectors). Here, we investigate theoretically and experimentally a class of coupled strip-array (CSA) waveguides realized on a SiO₂/Si₃N₄ platform with mass semiconductor fabrication processes. We demonstrate that this class of waveguides shows comparable sensitivity for a wide range of presented geometries, making it a very promising platform for satisfying multiple sensor and fabrication requirements without loss of performance. Full article

This study presents a multi-gas analyzer based on tunable filter non-dispersive IR (TF-NDIR) sensors that operate with a wide dynamic range of wavelength and concentration. A pyroelectric sensor coupled with a microsized Fabry–Perot interferometer, namely a tunable filter, enables sensing within a narrowly selected wavelength band. Three detectors capable of tuning the bandpass wavelength with a range of 3.8–5.0 μm, 5.5–8.0 μm, and 8.0–10.5 μm are combined to encompass the entire mid-IR region. single-pass cell with an optical path length (OPL) of 5 cm and a multi-pass cell with an OPL of 10.5 m is selected to encompass a concentration range from ppmv to percent. The TF-NDIR sensors and gas cells can be reconfigured by manipulating the beam path. A homemade lock-in amplifier is used to enhance the signal-to-noise ratio 88 times greater than that of the bare signal. The performance of the gas analyzer is evaluated by measuring the SF₆ and Novec-4710/CO₂ mixture, which are the dielectric gas medium for a gas-insulated switch (GIS). The mixing ratio of the Novec-4710/CO₂ mixture is measured within a range of 3–7% using premixes. The measurement precision is 0.72% for 0.5 s. Trace level measurements of Novec-4710, CO₂, SF₆, which are measurands for detecting gas leakage from the GIS, CO, and SO₂ which are measurands for detecting product generated by the arc or thermal decomposition in the switching electrode, are conducted based on dynamic partial pressure adjustment using 1000 ppmv mother premixes in N₂. The limit of detection is 54.7 ppmv for Novec-4710, 112.8 ppmv for CO, 118.1 ppmv for CO₂, 69.5 ppmv for SO₂, and 33.5 ppmv for SF₆. Full article