CO2 Sensor Research Articles

Adsorption performance and fitting of thermal runaway gases in power batteries on CuO-doped SnS monolayer: A DFT study

Thermal runaway failure of power batteries will release a large amount of thermal runaway gas (TRG), causing serious environmental pollution and safety accidents. Therefore, monitoring TRG is crucial to battery safety. In this paper, the adsorption capacity of TRG on SnS monolayer and SnS-CuO monolayer are compared based on DFT. SnS-CuO monolayer exhibits strong adsorption for C2H4 and CO. Compared with the SnS monolayer, the Eads are improved by 285.10 % and 527.24 %, respectively. Meanwhile, the electron affinity of TRG strongly correlates with adsorption properties (SSE < 0.06, R-square > 0.9, and RMSE < 0.2), indicating that it plays a significant role in explaining the adsorption characteristics of different TRG. Furthermore, the dielectric constant of the solvent exhibits a stronger correlation with the adsorptive properties of TRG (R-square > 0.96 and RMSE < 0.004), indicating the environmental tunability of TRG adsorption. In general, this research reveals the potential of SnS-CuO monolayer as a gas sensor for C2H4 and CO and provides theoretical guidance for exploring the impact of various TRG and solvent environments on the adsorption performance of SnS-CuO monolayer. It is expected to be widely used in TRG monitoring of power batteries in the future.

Long term monitoring of CO2 levels and ventilation rates in a naturally ventilated residential apartment

Indoor CO2 levels became particularly topical during the recent COVID-19 pandemic. In this study a long-term investigation of indoor CO2 levels in a 1970s built residential apartment in single occupancy is presented. Three NDIR CO2 sensors were used to measure CO2 levels over a one-year period. Mean CO2 levels over this period were 1278 ± 504 ppm, with elevated CO2 levels of greater than 2000 ppm not uncommon. Subsequent investigations used the single zone mass balance model and the decay of CO2 in the absence of occupants to estimate the ventilation rates in various configurations. A mean natural ventilation rate of 0.16 ACH was estimated with all windows closed. Opening fan light windows resulted in a mean ventilation rate of 2.86 ACH whereas opening all windows increased the mean ventilation rate to 19.1 ACH. Evidence was observed of the effect of both wind speed and indoor-outdoor temperature difference on the ventilation rates. It was concluded that with all windows closed the natural infiltration rate was insufficient to maintain optimal indoor air quality even in single occupancy. Opening the fan light windows was sufficient to achieve satisfactory indoor air quality but insufficient for the effective inhibition of airborne disease transmission.

Hetero-core fiber-optic surface plasmon resonance sensor with ionic liquid gel coating for CO2 sensing

Fiber-optic CO2 sensors are free of electromagnetic interference and highly resistant to corrosion but often require expensive measurement systems or sophisticated preparation procedures. To address this challenge, we developed a CO2 sensor comprising a hetero-core fiber-optic surface plasmon resonance sensor coated with an ionic liquid (IL) gel as a cost-effective CO2 sensing system. Among the various sensors tested, the sensor using ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) as the IL, prepared at a pull-up speed of 4.5 mm s−1, showed the best CO2 sensing performance. A hybrid membrane of [EMIM][BF4] and a CO2-absorbing polymer (monoethanolamine) demonstrated higher sensitivity for CO2 concentrations ranging from 0 to 100%. The highlight of the proposed system is its low-cost configuration comprising an 850 nm-wavelength LED and a photodiode as the light source and signal detection device, respectively. Along with facile preparation and low-cost system configuration, the proposed system exhibited significant potential for CO2 sensing in various fields.

Preliminary analysis of global column-averaged CO2 concentration data from the spaceborne aerosol and carbon dioxide detection lidar onboard AEMS

In contrast to the passive remote sensing of global CO2 column concentrations (XCO2), active remote sensing with a lidar enables continuous XCO2 measurements throughout the entire atmosphere in daytime and nighttime. The lidar could penetrate most cirrus and is almost unaffected by aerosols. Atmospheric environment monitoring satellite (AEMS, also named DQ-1) aerosol and carbon dioxide detection Lidar (ACDL) is a novel spaceborne lidar that implements a 1572 nm integrated path differential absorption (IPDA) method to measure the global XCO2 for the first time. In this study, special methods have been developed for ACDL data processing and XCO2 retrieval. The CO2 measurement data products of ACDL, including the differential absorption optical depth between the online and offline wavelengths, the integral weighting function, and XCO2, are presented. The results of XCO2 measurements over the period from 1st June 2022 to 30th June 2022 (first month data of ACDL) are analyzed to demonstrate the measurement capabilities of the spaceborne ACDL system.

Investigation of SnO2-based Thick Film Gas Sensors through Optimization of Artificial Neural Networks Parameters for Hazardous Gas Detection

In the present study, a systematic approach for optimizing artificial neural network parameters for the detection of a greenhouse gas carbon di-oxide (CO2) through SnO2-based thick film gas sensors for a wide range of concentrations is presented. The un-doped and Pd-doped SnO2 thick film is used as the sensor. The approach includes dataset preparation, input and output variable selection, artificial neural network architecture selection, training, validation, hyperparameter tuning, and testing. The input variables were selected based on their relevance to the problem at hand, such as the concentrations of the gas. The output variables were the changes in resistance of the sensor in response to the corresponding input variables. The artificial neural network architecture was carefully chosen, considering factors like the number of layers, neurons per layer, activation functions, and learning rate. Using the prepared dataset, the artificial neural network was trained by adjusting the connection weights between neurons to minimize the disparity between actual and predicted sensor responses. After training, the accuracy of the artificial neural network and generalization ability were assessed using a separate dataset for validation. The approach in the presented study can be used for different types of gases, and can be utilized in various applications

Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4 kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (−0.82 ppm and −0.44 ppm) and precision (0.12 ppm and −0.03 ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

Thermal decomposition and combustion of interior design materials

Research findings on the patterns of thermal decomposition and combustion are reported for widely used interior design materials. The experiments were conducted using a hardware and software system including a thermogravimetric analyzer (to record the characteristics of thermal decomposition), a gas analyzer (with H2, CH4, H2S, SO2, CO and CO2 sensors) and a high-speed camera (to record the characteristics of ignition and combustion). The temperature of the oxidizing medium ranged from 500 to 900°C to investigate the conditions of thermal decomposition initiation and sustained combustion. It was established that the highest concentrations of toxic emissions were typical of the combustion of polypropylene at a maximum temperature of the oxidizing medium (900°C). Wood showed the shortest ignition delay time and the longest duration of combustion. The experimental data were used for a physical problem statement and mathematical model of heat and mass transfer to explore the thermal decomposition and combustion of interior design materials in different rooms. A comparison of the experimental findings with the mathematical modeling results validates the developed model. The growth rates of carbon monoxide and carbon dioxide concentrations were determined for construction (wooden) and interior design (polymer) materials. The maximum concentrations of CO and CO2, and the minimum times taken to reach their threshold values corresponded to wood and polyvinyl chloride panels. This research provides a deeper insight into the thermal decomposition of a wide range of fuels and the formation of gaseous pyrolysis products of these fuels. The results obtained can be used to evaluate the toxicity of construction and interior design materials during compartment fires as well as to estimate the safe egress time and risks involved in the process. They can also serve as a database for the development and testing of mathematical models describing fire outbreaks and propagation in confined spaces.

Deep Weighted Fusion Learning (DWFL)-based multi-sensor fusion model for accurate building occupancy detection

With the advancement of artificial intelligence, the dominance of deep learning (DL) models over ordinary machine learning (ML) algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature pre-definition. With the growing demands for power savings, building energy loss reduction could benefit from DL techniques. For buildings/rooms with the varying number of occupants, heating, ventilation, and air conditioning (HVAC) systems are often found in operations without much necessity. To reduce the building’s energy loss, accurate occupancy detection/prediction (ODP) results could be used to control the proper operations of HVACs. However, ODP is a challenging issue due to multiple reasons, such as improper selection/deployment of sensors, inefficient learning algorithms for pattern recognition, varying room conditions, etc. To overcome the above challenges, we propose a DL-based framework, i.e., Deep Weighted Fusion Learning (DWFL), to detect and predict occupancy counts with optimal multi-sensor fusion structure. DWFL fuses the extracted features from multiple types of sensors with the priority/weight assignment to each sensor. Such weight assignment considers different room conditions and the pros/cons of each type of sensor. To evaluate DWFL model in terms of occupancy prediction accuracy, we have set up an experimental testbed with low-cost cameras, carbon dioxide (CO2), and passive infrared (PIR) sensors. Among the recently proposed occupancy detection models, DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88% accuracy in occupancy count estimation (Xue et al., 2019). Another deep learning-based model MI-PIR achieved 91% accuracy on raw analog data from PIR sensors (Andrews et al., 2020). Our research outcome is 94%. Therefore, the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

Perancangan Sistem Pendeteksi Polusi Udara Di Dalam Kamar Mesin Secara Wireless Menggunakan Sensor MQ135 Dan TGS2106 Berbasis Lora Ra-02

Air pollution is a problem that needs attention because it can threaten human life. Many human activities cause air pollution. Especially in closed rooms such as ship engine rooms. Therefore, an air pollution detection tool is needed to determine the air pollution index in that place in order to maintain pollution levels below the threshold value. Therefore, it is necessary to have a tool to detect air pollution using the MQ-135 gas sensor to detect carbon dioxide (CO2) gas and TGS2106 to detect nitrogen dioxide (NO2) gas. The type of research method used is the experimental research method. After designing the tool, testing the tool, the air pollution detection system in the engine room wirelessly using the MQ135 and TGS2106 sensors based on LoRa Ra-02 had an average error rate of 1.3% for the MQ135 sensor reading while the TGS2106 sensor reading had an average level. -average error of 6%. Therefore, the Air Pollution Detection System in the Engine Room Wirelessly Using the MQ135 and TGS2106 Sensors Based on LoRa Ra-02 can work well.

Developing a Chemiresistive Gas Sensor Array for Simultaneous Detection of Ammonia and Carbon Dioxide Gases.

Chemiresistive polymer-based sensors are promising platforms for monitoring various gases and volatile organic compounds. While they offer appealing attributes, such as ease of fabrication, flexibility, and cost-effectiveness, most of these sensors have a nearly identical response to cross-reactive gases, such as ammonia (NH3) and carbon dioxide (CO2). Aiming to address the shortcomings of chemiresistive polymer-based sensors in selectivity and simultaneous measurements of cross-reactive gases, a chemiresistive sensor array was developed consisting of components sensitive to carbon dioxide and ammonia as well as a control segment to provide the baseline. The designed system demonstrated a wide detection range for both ammonia (ranging from 0.05 to 1000 ppm) and carbon dioxide (ranging from 103 to 106 ppm) at both room and low temperatures (e.g., 4 °C). Our results also demonstrate the ability of this sensor array for the simultaneous detection of carbon dioxide and ammonia selectively in the presence of other gases and volatile organic compounds. Finally, the array was used to monitor CO2/NH3 in real food samples to demonstrate the potential for real-world applications.

Pd and Pd-B modified g-CN monolayer as innovative sensor and scavenger for CO, NO2, C2H2 and C2H4: A DFT study

The prompt detection of toxic gases like CO, NO2, C2H2, and C2H4 is crucial for both environmental preservation and industrial safety. In this work, we investigate the adsorption behavior and sensing performance of these four gases on both intrinsic g-CN and g-CN modified with single Pd atom and Pd-B pair, denoted as Pd-g-CN and Pd-B-g-CN, respectively, utilizing the first-principles calculations. The results indicate that the introduction of Pd atom and Pd-B pair significantly enhances the adsorption effect of g-CN monolayer toward these gases, with adsorption energies ranging from − 0.44 eV to − 1.43 eV. Furthermore, all adsorptions are identified as chemisorption, with the adsorption strength following the order of NO2 > CO > C2H4 > C2H2. Additionally, the adsorption of NO2, C2H2, and C2H4 induces notable changes in the work function of Pd-g-CN and Pd-B-g-CN, as well as approximately a 20-time increase in the bandgap of Pd-g-CN. Moreover, the Pd-g-CN and Pd-B-g-CN monolayers exhibit reasonable recovery times for C2H2 and C2H4, measured as 0.04 s and 6.40 μs at 298 K, respectively. Furthermore, NO2 can be rapidly desorbed from the Pd-B-g-CN surface at 398 K, with a short recovery time of 8.17 s. Therefore, the Pd-g-CN and Pd-B-g-CN monolayers can be regarded as potential gas-sensitive materials for the detection of C2H2 and C2H4 at room temperature, respectively. These research findings provide robust theoretical support for the design of novel gas sensors and efficient toxic gas adsorbents.

A Retrospective Analysis of Indoor CO2 Measurements Obtained with a Mobile Robot during the COVID-19 Pandemic.

This work presents a retrospective analysis of indoor CO2 measurements obtained with a mobile robot in an educational building after the COVID-19 lockdown (May 2021), at a time when public activities resumed with mandatory local pandemic restrictions. The robot-based CO2 measurement system was assessed as an alternative to the deployment of a net of sensors in a building in the pandemic period, in which there was a global stock outage of CO2 sensors. The analysis of the obtained measurements confirms that a mobile system can be used to obtain interpretable information on the CO2 levels inside the rooms of a building during a pandemic outbreak.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

A selective fluorescent probe derived from benzo[d]imidazole for carbon dioxide detection and bioimaging

In this work, a benzo[d]imidazole-derived fluorescent probe, DCBIF, was developed for the carbon dioxide (CO2) detection and imaging in living lung cells. On the basis of investigating previous reports and modifying the test method, the in-solution experiments showed that DCBIF exhibited the fluorescence response at 420 nm to CO2 under the excitation of 350 nm. The response was steady and rapid in physiological conditions. The linear range of the detecting system covered the practical necessity, and the limit of detection was 0.002 mL/min. There were few reports for detecting CO2 depicted the intracellular imaging while the cells were not pulmonary. Herein, in all the tested groups including oxygen-rich, hypoxia, and hypoxia recovered, DCBIF realized the imaging of the CO2 level in living A549 lung cells. This work was meaningful for developing fluorescent implements and imaging scenarios.

CO2 response screen in grass Brachypodium reveals the key role of a MAP kinase in CO2-triggered stomatal closure.

Plants respond to increased CO2 concentrations through stomatal closure, which can contribute to increased water use efficiency. Grasses display faster stomatal responses than eudicots due to dumbbell-shaped guard cells flanked by subsidiary cells working in opposition. However, forward genetic screening for stomatal CO2 signal transduction mutants in grasses has yet to be reported. The grass model Brachypodium distachyon is closely related to agronomically important cereal crops, sharing largely collinear genomes. To gain insights into CO2 control mechanisms of stomatal movements in grasses, we developed an unbiased forward genetic screen with an EMS-mutagenized B. distachyon M5 generation population using infrared imaging to identify plants with altered leaf temperatures at elevated CO2. Among isolated mutants, a "chill1" mutant exhibited cooler leaf temperatures than wild-type Bd21-3 parent control plants after exposure to increased CO2. chill1 plants showed strongly impaired high CO2-induced stomatal closure despite retaining a robust abscisic acid-induced stomatal closing response. Through bulked segregant whole-genome sequencing analyses followed by analyses of further backcrossed F4 generation plants and generation and characterization of sodium azide and CRISPR-cas9 mutants, chill1 was mapped to a protein kinase, Mitogen-Activated Protein Kinase 5 (BdMPK5). The chill1 mutation impaired BdMPK5 protein-mediated CO2/HCO3- sensing together with the High Temperature 1 (HT1) Raf-like kinase in vitro. Furthermore, AlphaFold2-directed structural modeling predicted that the identified BdMPK5-D90N chill1 mutant residue is located at the interface of BdMPK5 with the BdHT1 Raf-like kinase. BdMPK5 is a key signaling component that mediates CO2-induced stomatal movements and is proposed to function as a component of the primary CO2 sensor in grasses.

High-Sensitivity Differential Helmholtz Photoacoustic System Combined with the Herriott Multipass Cell and Its Application in Seed Respiration.

A highly sensitive photoacoustic detection system using a differential Helmholtz resonator (DHR) combined with a Herriott multipass cell is presented, and its implementation to sub-ppm level carbon dioxide (CO2) detection is demonstrated. Through the utilization of erbium-doped optical fiber amplifier (EDFA), the laser power was amplified to 150 mW. Within the multipass cell, a total of 22 reflections occurred, contributing to an impressive 33.6 times improvement in the system sensitivity. The normalized noise equivalent absorption coefficient (NNEA) was 8.64 × 10-11 cm-1·W·Hz-1/2 [signal-to-noise ratio, (SNR) = 1] and according to the Allan variance analysis, a minimum detection limit of 500 ppb could be achieved for CO2 at 1204 s, which demonstrates the long-term stability of the system. The system was applied to detect the respiration of rice and upland rice seeds. It is demonstrated that the system can monitor and distinguish the respiration intensity and respiration rate of different seeds in real time.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

First-principles exploration of N- and Pd-doped graphene as CO and NO2 gas sensor for operation status evaluation in AIS

To evaluate the operation status of air insulated switchgears (AIS), this work purposes N- and Pd- embedded graphene (N- and Pd-graphene) as potential gas sensors upon two typical faults gases (CO and NO2) from the first-principles simulations. It is found that the N and Pd atoms can be stably trapped on the C-vacancy of the C-defected graphene with the formation energy of −12.17 and −5.12 eV, respectively. N-graphene behaves physisorption towards CO and NO2 molecules while Pd-graphene behaves chemisorption instead. The resistance-type and work function (WF)-based sensing mechanisms of N- and Pd-graphene upon such two gas species are illustrated and uncovered by analyzing their deformations of electronic property and WF in the gas adsorption systems, which reveals the potential of Pd-graphene as a resistive CO and NO2 sensor, N-graphene as a resistive NO2 sensor, as well as the N- and Pd-graphene as WF-based gas sensor for NO2 detection. This work highlights the comparison of adsorption and sensing performances between N- and Pd-graphene upon two typical gas sensors in AIS, which would be meaningful to explore novel graphene-based sensing materials facilitating their investigations and applications in the power system.

Assessing the gas sensing capability of undoped and doped aluminum nitride nanotubes.

CO2 and CO gas sensors are very important to recognize the insulation situation of electrical tools. ToCO explore the application of noble metal doped of aluminum nitride nanotubes for gas sensors, DFT computations according to the first principal theory were applied to study sensitivity, adsorption attributes, and electronic manner. In this investigation, platinum-doped aluminum nitride nanotubes were offered for the first time to analyze the adsorption towards CO2 and CO gases. Firm construction of platinum-doped aluminum nitride nanotubes (Pt-AlNNT) was investigated in four feasible places, and the binding energy of firm construction is 1.314 eV. Respectively, the adsorption energy between the CO2 and Pt-AlNNT systems was - 2.107 eV, while for instance of CO, the adsorption energy was - 3.258 eV. The mentioned analysis and computations are considerable for studying Pt-AlNNT as a new CO2 and CO gas sensor for electrical tools insulation. The current study revealed that the Pt-AlNNT possesses high selectivity and sensitivity towards CO2 and CO. In this research, Pt-doped AlNNT (Pt-AlNNT) has been studied as sensing materials of CO and CO2 for the first time. The adsorption process of Pt-AlNNT has been computed and analyzed through the DFT approach. DFT computations by using B3LYP functional and 6-31 + G* basis sets have been applied in the GAMESS code for sensing attributes, which contribute to potential applications.

Mining Metal‐Biomolecular Framework/PANI Composite CO Sensor: Material Synthesis, Gas Sensing Performance and Mechanism

AbstractIn addressing the issue of poor gas sensitivity in semiconductor CO sensor materials used in mining, such as pure PANI. This study utilized an ice‐water bath in combination with an in‐situ polymerization method to synthesize a new composite material consisting of Cu(II)‐racemic glutamate nanofibers and PANI. The morphology, structure, and chemical bonds of the composite material were analyzed using scanning electron microscopy‐energy dispersive spectroscopy (SEM‐EDS) and spectral analysis. The material's ability to detect CO (100–500 ppm) at room temperature was investigated, revealing a response value of up to 6.8 % for 400 ppm CO detection when the composite material was deposited on a ceramic interdigital electrode. The material exhibited good selectivity and responsiveness to CO detection at room temperature, attributed to the heterojunction formed between Cu(II)‐racemic glutamate nanofibers and PANI, which enhanced carrier migration. Consequently, the Cu(II)‐racemic glutamate nanofiber/PANI composite(Cu(II)‐RGlu/PANI) material shows promise as a high‐performance sensing material for CO detection at room temperature and as a low‐power, integrable sensor material for underground coal mines. This research contributes to the development of a theoretical framework for such applications.