Chamberless NDIR CO2 Sensor Robust against Environmental Fluctuations

Environmental Effects on NDIR-Based CH4 Monitoring: Characterization and Correction

Nondispersive infrared (NDIR) sensors offer high sensitivity, selectivity, and low operational costs, making them particularly well-suited for environmental gas monitoring, where accurate detection of gases such as CH4 and CO2 is essential. However, these sensors are highly sensitive to environmental conditions, including temperature and humidity, which can significantly affect detection accuracy. This study characterizes the effects of these conditions and applies machine learning models to correct signal biases caused by multiple environmental factors. Experiments simulating natural environmental conditions for CH4 monitoring were conducted in the laboratory across a temperature range of 10-40 °C, relative humidity levels of 10-70%, and CO2 concentrations ranging from 0 to 1000 ppm, revealing significant signal variability under these conditions. The simulations and their results were comprehensively validated at the Ito Natural Analogue Site (INAS), a real-world field-testing location dedicated to investigating environmental impacts. Using machine learning regression algorithms for comprehensive compensation of environmental influences, we successfully mitigated signal biases caused by environmental factors. This offers a cost-effective solution for improving detection accuracy and reliability while reducing system complexity and operational costs.

Estimating Air Change Rate in Mechanically Ventilated Classrooms Using a Single CO2 Sensor and Automated Data Segmentation

With a growing emphasis on indoor air quality (IAQ) in educational environments, CO2 monitoring in classrooms has become commonplace. CO2 data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further linked to human health, performance, and building energy consumption. This study used a novel machine learning method to automatically segment CO2 concentration time series data into build-up, equilibrium, and decay periods, and then estimated classroom ACH using the corresponding CO2 mass balance equations. This method, applied to 40 classrooms in two mechanically ventilated K-6 schools, generated up to ten ACH estimates per day per classroom. A comparison with ACH calculated using the mechanical ventilation rates with 100% outdoor air reported by the building automation system during the study period reveals a slight underestimation by the decay and build-up methods, while the equilibrium method produced closer estimates. These differences may be attributed to uncertainties in occupancy, activity, CO2 emission rates, and air mixing. This research underscores the potential of leveraging CO2 data for more comprehensive IAQ assessments and highlights the challenges associated with accurately estimating ACH in real-world settings.

Low-Cost CO2 NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques

The study comprehensively evaluates low-cost CO2 sensors from different price tiers, assessing their performance against a reference-grade instrument and exploring the possibility of calibration using different machine learning techniques. Three sensors (Sunrise AB by Senseair, K30 CO2 by Senseair, and GMP 343 by Vaisala) were tested alongside a reference instrument (Los Gatos precision greenhouse gas analyzer). The results revealed differences in sensor performance, with the higher cost Vaisala sensors exhibiting superior accuracy. Despite its lower price, the Sunrise sensors still demonstrated reasonable accuracy. Meanwhile, the K30 sensor measurements displayed higher variability and noise. Machine learning models, including linear regression, gradient boosting regression, and random forest regression, were employed for sensor calibration. In general, linear regression models performed best for extrapolating data, whereas decision tree-based models were generally more useful in handling non-linear datasets. Notably, a stack ensemble model combining these techniques outperformed the individual models and significantly improved sensor accuracy by approximately 65%. Overall, this study contributes to filling the gap in intercomparing CO2 sensors across different price categories and underscores the potential of machine learning for enhancing sensor accuracy, particularly in low-cost sensor applications.

High selectivity and sensitivity through nanoparticle sensors for cleanroom CO2detection

Clean room facilities are becoming more popular in both academic and industry settings, including low-and middle-income countries. This has led to an increased demand for cost-effective gas sensors to monitor air quality. Here we have developed a gas sensor using CoNiO2nanoparticles through combustion method. The sensitivity and selectivity of the sensor towards CO2were influenced by the structure of the nanoparticles, which were affected by the reducing agent (biofuels) used during synthesis. Among all reducing agents, urea found to yield highly crystalline and uniformly distributed CoNiO2nanoparticles, which when developed into sensors showed high sensitivity and selectivity for the detection of CO2gas in the presence of common interfering volatile organic compounds observed in cleanroom facilities including ammonia, formaldehyde, acetone, toluene, ethanol, isopropanol and methanol. In addition, the urea-mediated nanoparticle-based sensors exhibited room temperature operation, high stability, prompt response and recovery rates, and excellent reproducibility. Consequently, the synthesis approach to nanoparticle-based, energy efficient and affordable sensors represent a benchmark for CO2sensing in cleanroom settings.

Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy

Greenhouse gases absorb the Earth's thermal radiation and partially return it to the Earth's surface. When accumulated in the atmosphere, greenhouse gases lead to an increase in the average global air temperature and, as a result, climate change. In this paper, an approach to measuring CO2 and CH4 concentrations using Fourier transform infrared spectroscopy (FTIR) is proposed. An FTIR spectrometer mockup, operating in the wavelength range from 1.0 to 1.7 μm with a spectral resolution of 10 cm-1, is described. The results of CO2 and CH4 observations throughout a day in urban conditions are presented. A low-resolution FTIR spectrometer for the 16U CubeSat spacecraft is described. The FTIR spectrometer has a 2.0-2.4 μm spectral range for CO2 and CH4 bands, a 0.75-0.80 μm range for reference O2 bands, an input field of view of 10-2 rad and a spectral resolution of 2 cm-1. The capabilities of the 16U CubeSat spacecraft for remote sensing of greenhouse gas emissions using a developed FTIR spectrometer are discussed. The design of a 16U CubeSat spacecraft equipped with a compact, low-resolution FTIR spectrometer is presented.

Development of a flat conical chamber-based non-dispersive infrared CO2 gas sensor with temperature compensation

In order to accurately monitor CO2 concentration based on the non-dispersive infrared technique, a novel flat conical chamber CO2 gas sensor is proposed and investigated by simulation analysis and experimental verification. First, the optical design software and computational fluid dynamics method are utilized to theoretically investigate the relationship between the energy distribution, absorption efficiency of infrared radiation, and chamber size. The simulation results show that the chamber length has an optimal value of 8 cm when the cone angle is 5° and the diameter of the detection surface is 1 cm, which makes infrared absorption efficiency optimal. Then, the flat conical chamber CO2 gas sensor system is developed, calibrated, and tested. The experimental results indicate that the sensor can accurately detect CO2 gas concentrations in the range of 0-2000 ppm at 25 °C. It is found that the absolute error of calibration is within 10 ppm, and the maximum repeatability and stability errors are 5.5 and 3.5%, respectively. Finally, the genetic neural network algorithm is presented to compensate for the output concentration of the sensor to solve the problem of temperature drift. Experimental results demonstrate that the relative error of the compensated CO2 concentration is varied from -0.85 to 2.32%, which is significantly reduced. The study has reference significance for the structural optimization of the infrared CO2 gas sensor and the improvement of the measurement accuracy.

Fast gas sensing scheme with multi-component gas measurement capacity based on non-dispersive frequency comb spectroscopy (ND-FCS)

A fast gas sensing scheme based on a non-dispersive frequency comb spectroscopy (ND-FCS) is proposed and experimentally demonstrated. Its capacity for multi-component gas measurement is experimentally investigated as well, by using the time-division-multiplexing (TDM) method to realize specific wavelength selection of the fiber laser optical frequency comb (OFC). A dual-channel optical fiber sensing scheme is established with a sensing path consisting of a multi-pass gas cell (MPGC), and a reference path with a calibrated signal to track the repetition frequency drift of the OFC for a real-time lock-in compensation and system stabilization. The long-term stability evaluation and the simultaneous dynamic monitoring are carried out, with the target gases of ammonia (NH₃), carbon monoxide (CO) and carbon dioxide (CO₂). The fast CO₂ detection in human breath is also conducted. The experimental results show that at an integration time of 10 ms, the detection limits of the three species are evaluated to be 0.0048%, 0.1869% and 0.0467%, respectively. A low minimum detectable absorbance (MDA) down to 2.8 × 10^-4 can be achieved and a dynamic response with millisecond time can be realized. Our proposed ND-FCS exhibits excellent gas sensing performance with merits of high sensitivity, fast response and long-term stability. It also shows great potential for multi-component gas monitoring in atmospheric monitoring applications.

A Review of Gas Measurement Practices and Sensors for Tunnels

The concentration of pollutant gases emitted by traffic in a tunnel affects the indoor air quality and contributes to structural deterioration. Demand control ventilation systems incur high operating costs, so reliable measurement of the gas concentration is essential. Numerous commercial sensor types are available with proven experience, such as optical and first-generation electrochemical sensors, or novel materials in detection methods. However, all of them are subjected to measurement deviations due to environmental conditions. This paper presents the main types of sensors and their application in tunnels. Solutions will also be discussed in order to obtain reliable measurements and improve the efficiency of the extraction systems.

Nitrous Oxide Is No Laughing Matter: A Historical Review of Nitrous Oxide Gas-Sensing Capabilities Highlighting the Need for Further Exploration

Nitrous oxide (N₂O), also known as laughing gas, is arguably one of the most detrimental greenhouse gases while concurrently being overlooked by the public. Specifically, N₂O is ∼300 times more damaging than its better-known counterpart carbon dioxide (CO₂) and has a longer-lived lifetime in the atmosphere than CO₂. There exist both natural and anthropogenic sources of N₂O, and thus, for a better understanding of sources, capture, and decomposition, it is pivotal to identify N₂O within the nitrogen biosphere. This review covers the past and current low-cost N₂O gas-sensing technologies, focusing specifically on low-cost metal oxide semiconductors (MOSs), chemiresistive and electrochemical sensors that can provide spatial and temporal monitoring of N₂O emissions from various sources. Additionally, compositional modifications to MOsS using metal-organic frameworks (MOFs) are discussed, potentially facilitating new awareness and efforts for increased sensing performance and functionality in N₂O detection.

Miniaturized CO2 Gas Sensor Using 20% ScAlN-Based Pyroelectric Detector

NDIR CO2 gas sensors using a 10-cm-long gas channel and CMOS-compatible 12% doped ScAlN pyroelectric detector have previously demonstrated detection limits down to 25 ppm and fast response time of ∼2 s. Here, we increase the doping concentration of Sc to 20% in our ScAlN-based pyroelectric detector and miniaturize the gas channel by ∼65× volume with length reduction from 10 to 4 cm and diameter reduction from 5 to 1 mm. The CMOS-compatible 20% ScAlN-based pyroelectric detectors are fabricated over 8-in. wafers, allowing cost reduction leveraging on semiconductor manufacturing. Cross-sectional TEM images show the presence of abnormally oriented grains in the 20% ScAlN sensing layer in the pyroelectric detector stack. Optically, the absorption spectrum of the pyroelectric detector stack across the mid-infrared wavelength region shows ∼50% absorption at the CO2 absorption wavelength of 4.26 μm. The pyroelectric coefficient of these 20% ScAlN with abnormally oriented grains shows, in general, a higher value compared to that for 12% ScAlN. While keeping the temperature variation constant at 2 °C, we note that the pyroelectric coefficient seems to increase with background temperature. CO2 gas responses are measured for 20% ScAlN-based pyroelectric detectors in both 10-cm-long and 4-cm-long gas channels, respectively. The results show that for the miniaturized CO2 gas sensor, we are able to measure the gas response from 5000 ppm down to 100 ppm of CO2 gas concentration with CO2 gas response time of ∼5 s, sufficient for practical applications as the average outdoor CO2 level is ∼400 ppm. The selectivity of this miniaturized CO2 gas sensor is also tested by mixing CO2 with nitrogen and 49% sulfur hexafluoride, respectively. The results show high selectivity to CO2 with nitrogen and 49% sulfur hexafluoride each causing a minimum ∼0.39% and ∼0.36% signal voltage change, respectively. These results bring promise to compact and miniature low cost CO2 gas sensors based on pyroelectric detectors, which could possibly be integrated with consumer electronics for real-time air quality monitoring.