Low-cost Gas Sensors Research Articles

Exploration of Volatileomics and Optical Properties of Fusarium graminearum-Contaminated Maize: An Application Basis for Low-Cost and Non-Destructive Detection.

Fusarium graminearum (F. graminearum) in maize poses a threat to grain security. Current non-destructive detection methods face limited practical applications in grain quality detection. This study aims to understand the optical properties and volatileomics of F. graminearum-contaminated maize. Specifically, the transmission and reflection spectra (wavelength range of 200-1100 nm) were used to explore the optical properties of F. graminearum-contaminated maize. Volatile organic compounds (VOCs) of F. graminearum-contaminated maize were determined by headspace solid phase micro-extraction with gas chromatography-tandem mass spectrometry. The VOCs of normal maize were mainly alcohols and ketones, while the VOCs of severely contaminated maize became organic acids and alcohols. The ultraviolet excitation spectrum of maize showed a peak redshift as fungi grew, and the intensity decreased in the 400-600 nm band. Peak redshift and intensity changes were observed in the visible/near-infrared reflectance and transmission spectra of F. graminearum-contaminated maize. Remarkably, optical imaging platforms based on optical properties were developed to ensure high-throughput detection for single-kernel maize. The developed imaging platform could achieve more than 80% classification accuracy, whereas asymmetric polarization imaging achieved more than 93% prediction accuracy. Overall, these results can provide theoretical support for the cost-effective preparation of low-cost gas sensors and high-prediction sorting equipment for maize quality detection.

Air Quality Analysis through IoT Device and Risk Prediction of Asthma Attack using ML Techniques

Background: The air quality of any area depends upon the various PMs (particulate matter) and hazardous gases present in the air. Low-cost PM sensors and gas sensors are present in different target places to monitor the air quality, read the environmental data, and transmit it to local servers through the IoT device. The low-cost sensor is not reliable due to its low sensing capacity; therefore, the read data is calibrated with the meteorological data presented by the nearby meteorological Centre of that particular area. The calibrated reading data sent to the server could be analyzed through some Machine Learning [ML] models. The ML models help to predict the risk of asthma in a particular area. The risk of asthma is directly related to the air quality of the surroundings. It is observed that the air quality of the industrial area is much worse than the non-industrial belt. Air quality monitoring of industrial areas is always a challenging task due to the ununiformed pollution in some segregated places around the industry, emitting pollutants mostly from chimneys. The air quality of any area depends upon the PM (PM), i.e., PM2.5 and PM10.0, as well as the gasses like NO2(Nitrogen Dioxide), NH3 (Ammonia), SO2(Sulfur dioxide), CO(Carbon monoxide), O3(Ozone) and Benzene. These are the most hazardous gases generally emitted by common heavy industries like iron and steel. In this article, the researchers considered the industrial belt of the Asansol- Durgapur region of West Bengal, India, and predicted the risk of asthma attacks for the test dataset. The experiment was carried out on 10 different supervised machine learning [SML] models as well as semi-supervised machine learning (SSML) models. The SML models have been further refined through hyper-parameter tuning, and better results have been obtained in the case of some ML models. The result has been compared with the existing literature considering the same external environment from where the meteorological data was collected, and similar ML models have been used. The research outperformed the existing literature, which is depicted in the result and analysis section of the article. Methods: The study evaluated ML models, both supervised and semi-supervised, to assess pollution levels. Relevant features were selected while less relevant ones were discarded. Accuracy levels of different ML algorithms werecompared in the results. The most effective model for an IoT system was chosen to maximize accuracy. In semi-supervised learning, feature selection followed supervised learning, but testing was akin to unsupervised learning. Results were compared with supervised learning data, enhancing reliability. Results: The result employing various classifiers werepresented across tables after the independent parameter Ozone was removed. Following the output of several classifiers, the results were verified using the k-fold validation method, with k being set to 5 or 10, accordingly. Tables display the best outcome, which is indicated in bold characters. method: In this research work the researcher considered 9 different ML models and used them as supervised as well as semi supervised model to determine the pollution level of the certain area. In this research work the researcher also selected the most relevant features and discarded the less relevant features. In case of SML algorithm, the accuracy level of the different ML algorithm has been determined and depicted in the result analysis section. The most effective ML model has been chosen for the proposed embedded system so that accuracy could be achieved at most. In case of semi supervised algorithm the feature selection is done as per the supervised algorithm. In this case the training is done same as the SML algorithm, but the testing phase is done like unsupervised machine learning algorithm where the decision parameter is predicted and ultimately matched with the previously achieved data of SML algorithm. The reliability of this approach is much more effective than simple SML algorithm. Conclusion: This study focused on predicting asthma risk in the Asansol-Durgapur industrial belt, India, using low-cost PM and gas sensors. Data calibration with meteorological inputs enhanced accuracy. ML models predicted risk and were refined through hyper-parameter tuning. Comparative analysis showed superior performance, emphasizing the importance of precise air quality monitoring. While offering a robust framework for future research, the study’s limitation lies in its area-specific dataset.

(Invited) Past, Present and Future for Electrochemical Gas Sensors in Energy Applications

The markets for modern low-cost electrochemical gas sensors have been growing for longer than sensors have been around. Energy markets for gas sensors include the oil, gas and electricity industries as well as the new developing and fast growing green energy sectors. The primary gases to detect include combustible hydrocarbons, oxygen, and toxic gases. Specifically, we are interested in methane (blue and green), ammonia, and H2 for the renewable energy sectors but include hydrocarbon fuels and CO2 as a greenhouse gas emission. The reasons for monitoring crosscut all areas of the energy business and include applications in production, transport, storage and use. Primary sensor uses include: 1) health and safety (people and assets), 2) leak detection and isolation to help mitigate product losses, 3) monitoring to protect the environment and 4) measurements for process control and efficacy. Each of these areas of sensing have special requirements and demand cost-effective and time efficient sensor performance in many and varied real world scenarios.Electrochemical gas sensors have a long and rich history starting with the commercialization of the first practical potentiometric CO2 gas sensor by Severinghaus in 1954 and the O2 amperometric sensor by Clark in 1956 that launched the modern blood gas analysis industry. Additional milestones include the introduction of the “diffusion electrode” in 1968 creating the modern amperometric sensor which has produced a large array of sensors for toxic and hazardous gases including H2, ammonia, and other energy gases. Progress has been made in the field of electrochemical gas sensors, not only in improving performance, sensitivity, selectivity, response time, and stability, but also in logistical properties including miniaturization, lower power consumption, low cost as well as communication and computation to make automated-operational systems. New high-volume production has contributed to lower costs commensurate with a chip-based sensor mentality. The evolution of one of the gas sensor technologies is given below (the room temperature AGS or amperometric gas sensor) and similar progress has been seen in mixed potential and solid state gas sensors.The growing understanding of the sensor’s fundamental electrocatalytic reactions has led to tailored designs of electrode-electrolyte combinations and packages for the various applications. Often ignored in sensor publications of performance, the fundamental electrocatalytic studies are poised to make significant advances in energy gas sensor selectivity and sensitivity. The additional implementation of intelligent algorithms (AI/ML) to make “smart” sensors and sensor arrays complements advanced nano-materials and designs for improving sensor performance. One major improvement is our understanding of the sensor response mechanisms at the electrocatalytic level. This new research will enable new electrochemical sensor advances that are poised to impact the health and wellbeing of both people and the planet. Ideas and concepts that significantly contribute to the safe and efficient rollout of the newest green energy platforms are presented. Figure 1

SnO2 Nanowire/MoS2 Nanosheet Composite Gas Sensor in Self-Heating Mode for Selective and ppb-Level Detection of NO2 Gas

The development of low-cost and low-power gas sensors for reliable NO2 gas detection is important due to the highly toxic nature of NO2 gas. Herein, initially, SnO2 nanowires (NWs) were synthesized through a simple vapor–liquid–solid growth mechanism. Subsequently, different amounts of SnO2 NWs were composited with MoS2 nanosheets (NSs) to fabricate SnO2 NWs/MoS2 NS nanocomposite gas sensors for NO2 gas sensing. The operation of the sensors in self-heating mode at 1–3.5 V showed that the sensor with 20 wt.% SnO2 (SM-20 nanocomposite) had the highest response of 13 to 1000 ppb NO2 under 3.2 V applied voltage. Furthermore, the SM-20 nanocomposite gas sensor exhibited high selectivity and excellent long-term stability. The enhanced NO2 gas response was ascribed to the formation of n-n heterojunctions between SnO2 NWs and MoS2, high surface area, and the presence of some voids in the SM-20 composite gas sensor due to having different morphologies of SnO2 NWs and MoS2 NSs. It is believed that the present strategy combining MoS2 and SnO2 with different morphologies and different sensing properties is a good approach to realize high-performance NO2 gas sensors with merits such as simple synthesis and fabrication procedures, low cost, and low power consumption, which are currently in demand in the gas sensor market.

Smart mid-infrared metasurface microspectrometer gas sensing system

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm3) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

A concave octahedral hollow SnO2 nanocage for enhancing typical electrolyte (EMC) leakage gas sensing performance in lithium-ion batteries

The safety issues caused by lithium-ion battery (LIB) failures are the pain points that constrain the development of LIB. Developing a high sensitivity and low-cost gas sensor targeting trace electrolyte vapors, such as methyl ethyl carbonate (EMC), can help achieve early warning in accidents. Tin dioxide (SnO2) has always been one of the most widely used gas sensitive materials. Herein, the concave octahedral hollow SnO2 nanocages (SnO2-Hoc) are quickly prepared by template-engaged coordinating etching. The response value of its sensor to 10 ppm EMC is 7.24 at 140℃ and it shows sufficient sensitivity in LIB leakage simulation tests. The hollow concave octahedral structure promotes the EMC diffusion, and the lower energy barrier of EMC cleavage is the key to selective response. Overall, this work synthesized SnO2-Hoc sensor to directly detect trace EMC, which is expected to provide new guarantees for the LIB safe application.

Methane (CH4) Detection System Using The TGS2611 Sensor and MQ-4 Sensor

Abstract This research focused on cow dung digesters as measurement samples, utilizing the TGS2611 and MQ-4 sensors, both known for their affordability. The objective was to develop a low-cost gas sensor system for monitoring methane gas concentrations through a straightforward web-based data acquisition platform. The DHT11 sensor was employed to measure air temperature and humidity. The electronic sensor circuit in this research utilized a voltage divider circuit. Testing the performance of the low-cost gas sensor involved integrating it with an anaerobic digestion system. For both the TGS2611 and MQ-4 sensors, the graph illustrating the relationship between sensor voltage (volts) and methane gas concentration (ppm) formed a logarithmic or non-linear pattern. The measurement range for the system was around 4700 ppm to 6300 ppm for TGS2611 and 4000 ppm to 5100 ppm for MQ-4. The sensor sensitivity was determined as 0.5433 Volts/ppm for TGS2611 and 0.5639 Volt/ppm for MQ-4. The TGS2611 sensor exhibited a higher response to changes in methane gas concentration, while the MQ-4 sensor successfully detected methane gas within its designated range. Interestingly, the TGS2611 sensor accurately detected methane gas concentrations above its specified range. The gas sampling procedure involved automatic gas sampling in a vacuum chamber for measurement, a strategy potentially enhancing sensor robustness. This research successfully amalgamated sensor advantages, circuit control, and data analysis to realize an economical yet reliable monitoring system.

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, we propose the use of Machine Learning (ML) to predict accurate concentrations of pollutant gases acquired by LCS integrated into an embedded Internet of Things platform. However, a key challenge is to optimize an accurate ML design under low memory and computation power constraints of microcontrollers (MCUs) while maintaining accurate ML scores. After data analysis and pre-processing, we assess and analyze the performance of five ML algorithms to predict the concentration of pollutants gases from multiple specifications (weather, presence of other gases, etc.). To support the experiments, datasets from three sources are used: (1) VOCSens, (2) Belgian Interregional Environment Agency cell, and (3) Visual-Crossing. Once the best model was optimized and validated, multiple hard constraints were added to the selected ML structure to satisfy material and expert requirements. Trained models were ported to be implemented locally in a MCU after comparing several porting libraries. The assembled code obtained is evaluated based on two metrics: storage memory consumption and inference time, relative to the highest attainable capacities. The improved random forest is the best ML model for the used dataset with an R2 score meeting of 0.72 and Root Means Square Error of 0.0028 ppm. The best generated Tiny-ML model needs 3% of RAM and 98% of Flash storage. The empirical results prove that the developed ML algorithm applied to LCS provides high accuracy to predict pollutant gases. This algorithm can also be used to adjust the LCS systems to provide calibrated data in real time, even if the platform being used is not particularly advanced or powerful.

Concept Drift Mitigation in Low-Cost Air Quality Monitoring Networks.

Future air quality monitoring networks will integrate fleets of low-cost gas and particulate matter sensors that are calibrated using machine learning techniques. Unfortunately, it is well known that concept drift is one of the primary causes of data quality loss in machine learning application operational scenarios. The present study focuses on addressing the calibration model update of low-cost NO2 sensors once they are triggered by a concept drift detector. It also defines which data are the most appropriate to use in the model updating process to gain compliance with the relative expanded uncertainty (REU) limits established by the European Directive. As the examined methodologies, the general/global and the importance weighting calibration models were applied for concept drift effects mitigation. Overall, for all the devices under test, the experimental results show the inadequacy of both models when performed independently. On the other hand, the results from the application of both models through a stacking ensemble strategy were able to extend the temporal validity of the used calibration model by three weeks at least for all the sensor devices under test. Thus, the usefulness of the whole information content gathered throughout the original co-location process was maximized.

Nitrogen-doped carbon nano-onions/polypyrrole nanocomposite based low-cost flexible sensor for room temperature ammonia detection

One of the frontier research areas in the field of gas sensing is high-performance room temperature-based novel sensing materials, and new family of low-cost and eco-friendly carbon nanomaterials with a unique structure has attracted significant attention. In this work, we propose a novel low-cost flexible room temperature ammonia gas sensor based on nitrogen-doped carbon nano-onions/polypyrrole (NCNO-PPy) composite material mounted low-cost membrane substrate was synthesized by combining hydrothermal and in-situ chemical polymerization methods. The proposed flexible sensor revealed high sensing performance when employed as the sensing material for ammonia detection at room temperature. The NCNO-PPy ammonia sensor exhibited 17.32% response for 100 ppm ammonia concentration with a low response time of 26 s. The NCNO-PPy based flexible sensor displays high selectivity, good repeatability, and long-term durability with 1 ppm as the lower detection limit. The proposed flexible sensor also demonstrated remarkable mechanical robustness under extreme bending conditions, i.e., up to 90° bending angle and 500 bending cycles. This enhanced sensing performance can be related to the potential bonding and synergistic interaction between nitrogen-doped CNOs and PPy, the formation of defects from nitrogen doping, and the presence of high reactive sites on the surface of NCNO-PPy composites. Additionally, the computational study was performed on optimized NCNO-PPy nanocomposite for both with and without NH3 interaction. A deeper understanding of the sensing phenomena was proposed by the computation of several electronic characteristics, such as band gap, electron affinity, and ionization potential, for the optimized composite.

Wearable Low-Cost and Low-Energy Consumption Gas Sensor with Machine Learning to Recognise Outdoor Areas

Wearable Low-Cost and Low-Energy Consumption Gas Sensor with Machine Learning to Recognise Outdoor Areas

A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification.

This study introduces numerous low-cost gas sensors and a real-time alcoholic beverage classification system based on machine learning. Dogs possess a superior sense of smell compared to humans due to having 30 times more olfactory receptors and three times more olfactory receptor types than humans. Thus, in odor classification, the number of olfactory receptors is a more influential factor than the number of receptor types. From this perspective, this study proposes a system that utilizes distinctive data patterns resulting from heterogeneous responses among numerous low-cost homogeneous MOS-based sensors with poor gas selectivity. To evaluate the performance of the proposed system, learning data were gathered using three alcoholic beverage groups including different aged whiskeys, Korean soju with 99% same compositions, and white wines made from the Sauvignon blanc variety, sourced from various countries. The electronic nose system was developed to classify alcoholic samples measured using 30 gas sensors in real time. The samples were injected into a gas chamber for 60 seconds, followed by a 60-second injection of clean air. After preprocessing the time-series data into four distinct datasets, the data were analyzed using a machine learning algorithm, and the classification results were compared. The results showed a high classification accuracy of over 99%, and it was observed that classification performance varied depending on data preprocessing. As the number of gas sensors increased, the prediction accuracy improved, reaching up to 99.83 ± 0.21%. These experimental results indicated that the proposed electronic nose system's classification performance was comparable to that of commercial electronic nose systems. Additionally, the implementation of an alcoholic beverage classification system based on a pretrained LDA model demonstrated the feasibility of real-time classification using the proposed system.

A Kalman Filter Scheme for the Optimization of Low-Cost Gas Sensor Measurements

Air pollution, which is mainly caused by industrialization, intensive transportation, and the heating of buildings, is one of the most important problems in large cities because it seriously harms the health and the quality of life of their citizens. This is why air quality is monitored not only by governmental organizations and official research institutions through the use of sophisticated monitoring systems but also by citizens through the use of low-cost air quality measurement devices. However, the reliability of the measurements derived from low-cost sensors is questionable, so the measurement errors must be eliminated. This study experimentally investigated the impact of the use of a Kalman filter on the accuracy of the measurements of low-cost air quality sensors. Specifically, measurements of air pollutant gases were carried out in the field in real ambient air conditions. This study demonstrates not only the optimization of the measurements through the application of a Kalman filter but also the behavior of the filter coefficients and their impact on the predicted values.

Promoting quality in low-cost gas sensor devices for real-world applications

Recent advancements in metal oxide semiconductor sensors enable system integrators to make sensor devices without the traditional complications of operating barebone sensor components. Anyone, so it seems, can put together multi-sensor-systems by combining an integrated sensor subsystem with a small piece of digital infrastructure, resulting in low-cost sensor systems or sensor networks for all kinds of applications. With low energy consumption, the deployment of close meshed sensor networks is becoming a reality, promising high density data for big data models. However, data quality is not necessarily a feature of such devices, since the device output signals are heavily processed and the insight into the actual operating technique is black-boxed by the respective sensor manufacturer. High volume production of a sensor model requires widely applicable output information, yet this mainstreaming negatively impacts quality control efforts on the system level as well as niche applications that require specialized operation modes. This article exemplarily examines the measurement chain of typical metal oxide semiconductor sensor applications and deduces requirements for a technically sound advancement of the subject for research and commercial purposes. Equivalent considerations can be made for any low-cost sensor principle and their respective challenges. The conclusion urges all stakeholders participating in the development and marketing of sensor devices to advance a scientifically valid state of knowledge and to educate their customers and even the general public accordingly.

Low-cost urban carbon monitoring network and implications for china: a comprehensive review.

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented.

Evaluation of Suitability of Low-Cost Gas Sensors for Monitoring Indoor and Outdoor Urban Areas

Evaluation of Suitability of Low-Cost Gas Sensors for Monitoring Indoor and Outdoor Urban Areas

Laboratory Performance Evaluation of a Low-Cost Electrochemical Formaldehyde Sensor.

Formaldehyde is a known human carcinogen and an important indoor and outdoor air pollutant. However, current strategies for formaldehyde measurement, such as chromatographic and optical techniques, are expensive and labor intensive. Low-cost gas sensors have been emerging to provide effective measurement of air pollutants. In this study, we evaluated eight low-cost electrochemical formaldehyde sensors (SFA30, Sensirion®, Staefa, Switzerland) in the laboratory with a broadband cavity-enhanced absorption spectroscopy as the reference instrument. As a group, the sensors exhibited good linearity of response (R2 > 0.95), low limit of detection (11.3 ± 2.07 ppb), good accuracy (3.96 ± 0.33 ppb and 6.2 ± 0.3% N), acceptable repeatability (3.46% averaged coefficient of variation), reasonably fast response (131-439 s) and moderate inter-sensor variability (0.551 intraclass correlation coefficient) over the formaldehyde concentration range of 0-76 ppb. We also systematically investigated the effects of temperature and relative humidity on sensor response, and the results showed that formaldehyde concentration was the most important contributor to sensor response, followed by temperature, and relative humidity. The results suggest the feasibility of using this low-cost electrochemical sensor to measure formaldehyde concentrations at relevant concentration ranges in indoor and outdoor environments.

3D Printed Fused Deposition Modeling (FDM) Capillaries for Chemiresistive Gas Sensors.

This paper discusses the possible use of 3D fused deposition modeling (FDM) to fabricate capillaries for low-cost chemiresistive gas sensors that are often used in various applications. The disadvantage of these sensors is low selectivity, but 3D printed FDM capillaries have the potential to increase their selectivity. Capillaries with 1, 2 and 3 tiers with a length of 1.5 m, 3.1 m and 4.7 m were designed and manufactured. Food and goods available in the general trade network were used as samples (alcohol, seafood, chicken thigh meat, acetone-free nail polish remover and gas from a gas lighter) were also tested. The "Vodka" sample was used as a standard for determining the effect of capillary parameters on the output signal of the MiCS6814 sensor. The results show the shift of individual parts of the signal in time depending on the parameters of the capillary and the carrier air flow. A three-tier capillary was chosen for the comparison of gas samples with each other. The graphs show the differences between individual samples, not only in the height of the output signal but also in its time characteristic. The tested 3D printed FDM capillaries thus made it possible to characterize the output response by also using an inexpensive chemiresistive gas sensor in the time domain.

Effective and low-cost gas sensor based on a light intensity analysis of a webcam image: Gas enriched polymers under high pressure

Gas detection and monitoring play important roles in ensuring safety in many aspects of technology and industry. An effective and low-cost gas sensor with high performance was developed by using light intensity measurements from images. This simple technique is based on volumetric measurements of released gas combined with light intensity analyses of bright-water-level images by employing a webcam and diffusion analysis. The gas emitted from gas enriched polymers under high pressure reduces the water volume level in a graduated cylinder, producing measurable changes in the pixel distance in the webcam controlled with LabView software; this phenomenon allows for the detection and monitoring of the released gas concentration. As a result, the gas sensor responds instantly and measures the gas concentration from 1 part per million (ppm) to 1500 wt∙ppm with an adjustable sensitivity and measuring range. Additionally, various performance tests for polymer materials verify that the sensor has satisfactory reliability, a broad measuring range, and insensitivity against variations in temperature and pressure. The developed low-cost sensor is applied for the detection and characterization of any other gasses, such as He, O2, N2 and Ar, through simultaneous real-time monitoring and in situ measurements. The present developed webcam sensor is designed for application of pure gas at high pressure.

Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm.

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.