CO2 Sensor Research Articles

An IoT-Based Hydroponic Monitoring and Control System for Sustainable Food Production

The agricultural sector, historically the main source of food globally, faces unprecedented challenges due to decreasing soil fertility and unsustainable farming practices. This study presents a smart farming method, a tailored approach to cultivating food and arable crops, as a sustainable and innovative farming method. Hydroponics involves growing plants in nutrient water solutions rather than soil, establishing a controlled environment free from external conditions. We developed a sophisticated hydroponics system equipped with sensors monitoring temperature, humidity, pH, and light levels. Using IoT technologies, we enabled real-time data transmission from sensors to a dedicated web server - Amazon web services (AWS) and an Android mobile application powered by a Raspberry Pi3. This setup allows remote monitoring and control of the hydroponic system, reducing human intervention. The experiment validated the sensor network's efficiency in accurately transmitting data. The findings suggest that widespread adoption of hydroponic farming could substantially enhance global food security. Future advancements in this method could include real-time monitoring of plant growth rates by incorporating additional sensors, like carbon dioxide (CO2) sensors. This research is a significant step toward sustainable agriculture. It offers a technologically advanced solution capable of transforming food production methods and ensuring a more secure and sustainable food supply worldwide.

Desain dan Realisasi Alat Ukur Kadar CO2 Portabel dengan Notifikasi Suara Berbasis Mikrokontroler Arduino

Increasing levels of carbon dioxide (CO2) in the environment is a global issue that has an impact on climate change and air quality. Easily accessible CO2 measurement is important for monitoring and mitigating these impacts. This research aims to design and realize a portable CO2 level measuring instrument with sound notifications, using an Arduino microcontroller. The method used in this study is the development of microcontrollers. CO2 analyzers are designed using CO2 sensors, microcontrollers and sound output modules. Data processing and calibration are performed to ensure measurement accuracy. Tests are conducted at various locations to assess the performance of the tool in real conditions. This portable device is integrated with an audible notification system that provides immediate alerts to users when CO2 levels exceed unnatural limits. Concern for urban communities efforts to increase awareness and response to adverse air conditions, especially in open spaces and urban environments. The results showed that portable CO2 analyzers were able to measure CO2 levels with good accuracy according to the specifications of the components used. The sound notification system works to provide alerts in various conditions. Portability, ease of use make this tool useful for individuals and simple to carry to monitor and respond to changes in air quality.

Advancement of microcontroller-based fire suppression system for enhanced safety in sugar cane plantations

The occurrence of accidental fires in sugarcane plantations is a significant cause of pre-harvest loss in the sugar industry, particularly during arid seasons. This paper introduces the conceptualization and implementation of a microcontroller-based prototype system for mitigating accidental fires in sugar cane plantations. The proposed system encompasses two distinct subsystems: the field monitoring and the fire control unit. The Field monitoring system, deployed within the sugarcane field, assumes responsibility for fire detection via smoke detectors, infrared detectors, as well as carbon dioxide and carbon monoxide detectors, which are accompanied by a fire extinguishing mechanism. Conversely, the fire control unit, installed at the fire brigade office, establishes wireless communication with the field monitoring system through the global system for mobile communication (GSM) network. Upon detecting a fire, the microcontroller promptly dispatches a fire alert to the fire brigade while simultaneously activating water pumps to suppress the fire until the brigade arrives to fully extinguish it. Furthermore, the fire control unit triggers the fire alarm and exhibits the global positioning system (GPS) coordinates of the burning field to facilitate precise localization. The proposed system uses the Arduino-Mega and Arduino- Leonardo microcontroller platforms and is programmed using the C++ programming language in Visual-Studio-Code software and the Arduino integrated development environment. Designed prototype can detect fires and alert the fire brigade in less than twenty seconds and activate water pumps in less than one second after fire detection.

Advance Multi-Gas Detection System for Optimal Safety

Indoor air quality is a growing concern, particularly the concentration of carbon dioxide (CO2) and other hazardous gases, which can pose significant health risks and diminish safety. Recognizing the critical nature of this issue, this paper detailsthe development of an advanced environmental monitoring system designed to detect elevated levels of CO2 and other potentially dangerous gases. Our approach synthesizes various research methods, including requirement gathering, literature review, and user surveys, to inform system conception and implementation. The resulting device integrates highly sensitive sensors for CO2, CO, LPG, smoke, and additional hazardous gas detection, all governed by a microcontroller at the system's nucleus. This technology is augmented by a comprehensive alarm strategy—encompassing audio alerts, text messages, emails, and mobile application notifications—together with a user-friendly display interface. The system is also endowed with Internet of Things (IoT) capabilities, offering users the convenience of real-time monitoring and control through a web dashboard and a dedicated mobile application. Moreover, the device boasts the ability to monitor environmental parameters like temperature and humidity, providing a holistic view of indoor air conditions. Key results indicate a significant improvement in detection response time and accuracy, with the enhanced communication features facilitating swift and effective alerts to users. These advancements mark a stride toward safer indoor environments, potentially mitigating the risks associated with poor air quality.

Sensing ability of carbon nitride (C6N8) for the detection of carbon monoxide (CO) and carbon dioxide (CO2)

Carbon nitride (C6N8) is explored as an adsorption material for harmful industrial gases such as carbon monoxide (CO) and carbon dioxide (CO2). The complexation of CO and CO2 with C6N8 (analyte@C6N8) is deeply studied by the analyses of interaction energy (IE), frontier molecular orbital (FMO), non-covalent interactions (NCI), natural bond orbital (NBO), Bader’s quantum theory of atoms in molecules (QTAIM), density of state (DOS), reduced density gradient (RDG), and electron localization function (ELF). The results of interaction energy (−18.870 kJ/mol to −14.991 kJ/mol) showed the adsorption strength of CO and CO2 gases on the C6N8 surface. NCI and QTAIM investigations demonstrated that non-covalent interactions occurred in CO2 @C6N8 and CO@C6N8 complexes. The NCI and RDG analyses indicated surface interaction with CO and CO2 gases. The emergence of novel energy levels in the DOS spectrum highlighted the system's susceptibility to contaminants and the development of new excited states in the PDOS spectrum demonstrated the chemical reactivity of complexes. Moreover, the results of QNBO show that surface (C6N8) extracted a relatively greater amount of charge from CO (0.018 e−1) as compared to CO2 (0.006 e−1). At 300 K, the analyte@C6N8 complexes recovery time (4.06 ×10−10 s and 1.92 ×10−9 s) showed sensing behavior of surface for CO and CO2 gases. Our results suggested that the C6N8 surface is a superb option for CO and CO2 gases.

Characterization of a Waveguide with 45 Degrees Reflectors for the Detection of Carbon Dioxide

This paper investigates the characteristics of the angle of incidence of a waveguide with 45 degrees reflectors. Non-dispersive infrared (NDIR) gas sensors consist of an infrared source, a waveguide, and an infrared detector. The waveguide especially plays a part in an infrared path and gas cell. According to the Beer-Lambert law, the NDIR sensor with longer ray traveling path shows a higher response to the target gas. Also of particular interest is that the normal incidence ratio toward an infrared detector enhances the NDIR sensor selectivity for the target gases as well as the sensitivity. While there has been significant research on the effect on sensitivity by designing waveguide structures or ray traveling distance, this paper focuses on understanding the incidence of light toward the infrared detector by the waveguide with 45 degrees reflector and how this affects the selectivity of the sensor. A Ray tracing simulation is adopted to analyze the effect of the number of 45 degrees reflectors on the distribution of the incidence. Furthermore, a non-dispersive infrared gas sensor is applied to measure how the number of the reflectors affect to the selectivity for the target gas. The results reveal the enhancement of the selectivity by the reflectors.AcknowledgementsThis work was supported by the Korea Innovation Foundation(INNOPOLIS) grant funded by the Korea government(MSIT) (2020-DD-UP-0348). This study has been conducted with the support of the Korea Institute of Industrial Technology as "Development and commercialization for clean hydrogen production/storage and CO2 monitoring system in the field of industrial complex" (Kitech EH-23-0012) This work was supported by the Technology Innovation Program (155736, Development of Miniaturized Sensor Module Technology for Non-invasive Arterial Blood Carbon Dioxide Real-time Monitoring) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea). Figure 1

A smart sensor for monitoring antimicrobial interventions in wound infections

Effective therapeutic monitoring of wound infections is crucial for wound care. We explore a novel approach using a previously described 3D-printed, colourimetric indicator to non-invasively monitor antimicrobial interventions in wound infection. The indicator contains a pH-sensitive dye which detects carbon dioxide production from wound pathogens by changing colour from blue to yellow. The sensor accurately reported the minimum inhibitory concentration of antibiotic against Pseudomonas aeruginosa in a sealed 96-well plate. Subinhibitory antibiotic concentrations resulted in the sensor changing colour, whilst remaining blue for inhibitory concentrations. The sensor was applied to a porcine skin wound model with Pseudomonas aeruginosa biofilms, testing several antimicrobial treatments: antibiotic monotherapy, phage monotherapy, and a combination of both. After treatment, the sensor, sealed with the wound, enabled visual monitoring of antimicrobial efficacy. Through comparing post-treatment bioburden and sensor response, greater log-fold reductions in P. aeruginosa following antimicrobial treatment correlated with longer sensor response times. Sensors monitoring unsuccessfully treated biofilms changed colour, while those treated at or above the minimum biofilm eradication concentration elicited no colour change, indicating P. aeruginosa eradication. This sensor holds promise for monitoring wound infection treatment efficacy, improving patient outcomes, and promoting undisturbed wound healing as a non-invasive point-of-care tool.

Self-calibrating dual-sensing electrochemical sensors for accurate detection of carbon dioxide in blood.

A paper-based electrochemical dual-function biosensor capable of determining pH and TCO2 was synthesized for the first time using an iridium oxide pH electrode and an all-solid-state ion electrode (ASIE). In the study, to obtain highly reliable results, the biosensor was equipped with a real-time pH correction function before TCO2 measurements. Compared to traditional liquid-filling carbon dioxide detection sensors, the utilization of ferrocene endows our novel sensor with abundant positive sites, and thus greatly improves its performance. Conversely, the introduction of MXene with conductivity close to that of metals reduces electrode resistance, which is beneficial for accelerating the electrochemical reaction of the sensor and reducing LOD. After optimization, the detection range of TCO2 is 0.095 nM-0.66 M, with a detection limit of as low as 0.023 nM. In addition, the sensor was used in real serum sample-spiked recovery experiments and comparison experiments with existing clinical blood gas analyzers, which confirmed the effectiveness of its clinical application. This study provides a method for the rational design of paper-based electrochemical biosensors and a new approach for the clinical detection of blood carbon dioxide.

Simultaneous real-time detection of fractional exhaled nitric oxide and end-tidal carbon dioxide by quantum cascade laser absorption spectroscopy

The simultaneous detection of fractional exhaled nitric oxide (FeNO) and end-tidal carbon dioxide (ETCO2) is of great importance for the distinguishing and diagnosis of asthma and chronic obstructive pulmonary disease (COPD), providing more comprehensive information on respiratory disorders. This work demonstrates a simultaneous ETCO2 and FeNO detection system based on quantum cascade laser absorption spectroscopy (QCLAS) technology was presented. The system employs wavelength modulation spectroscopy (WMS) technology and the Herriott multi-pass cell, achieving a detection limit of 2.82 ppb for nitric oxide (NO) and 0.05 % for carbon dioxide (CO2). Real-time exhalation measurements were performed on volunteers with varying ETCO2 and FeNO levels, and the results of the test can accurately distinguish whether the corresponding volunteer was healthy, had asthma or COPD. The effect of exhalation flow rate on the concentration of the two gases was explored. A range of expiratory flow rates were tested in the flow rate interval from 1 to 4 L/min, and there was always an inverse relationship between expiratory flow rate and FeNO concentration, but flow rate changes did not affect ETCO2 concentration. The results indicate that this detection system can simultaneously and effectively measure ETCO2 and FeNO concentrations in real-time.

Design, Develop, Fabricate, and Statistical Quantification of Air Quality System Along with Web Application

Our Air Quality Index (AQI) measurement and analytics system is specifically designed for household, office, or single-room use, addressing concerns related to pollution's impact on respiratory health and visibility. The system incorporates dedicated sensors such as the PM Sensor (PMS5003), the ozone sensor (MQ- 131), the VOC Sensor (MP503), the CO2 Sensor (MH- Z19B), and the temperature and humidity sensor (DHT22). These sensors communicate with a web application server through the ESP8266-01 Wi-Fi module. The sensor values are displayed on a TFT (Thin Film Transistor) display, ensuring continuous monitoring with a time delay. The Arduino Pro Mini Board, powered by an FTDI (Future Technology Devices International Ltd.) chip, facilitates the sensor readings by operating on specific protocols. The ESP-01 module provides internet capability, enabling the system to host a custom web server. The sensor data is utilized to generate graphs on a dashboard, presenting the air quality information in a visually accessible manner. The displayed values are aligned with the standard guidelines set by the Central Pollution Control Board (CPCB). Moreover, the system supports historical data retrieval through integration with a PostgreSQL database, allowing users to analyze air quality trends over time. By providing real-time monitoring, comprehensive analytics, and historical data insights, our AQI measurement and analytics system empowers users to make informed decisions to improve air quality and mitigate the potential risks associated with pollution.

A novel method for the bronchial occlusion of prolonged air leakage using customized endobronchial plugs.

The management of prolonged air leakage (PAL) is a significant clinical challenge, particularly for patients who are unfit for surgical treatment. The use of endoscopic interventions with occlusive devices has been reported previously; however, local availability and cost may represent potential hurdles, especially in developing countries. In this study, we presented a conical endobronchial plug of our design and evaluated the efficacy of making use of that in the treatment of PAL as a novel method. This retrospective study included a total of 23 patients with PAL who were not suitable for surgery and underwent bronchial occlusion using customized plugs. The responsible bronchi were identified by the balloon occlusion test or by end-tidal carbon dioxide detection. In each case, the plug was grasped at the knot of the tip with forceps and then inserted into the target bronchus with a flexible bronchoscope. Of the 23 patients with intractable pneumothorax, 1 to 3 plugs (median =1) were successfully inserted into the affected bronchi of each patient for bronchial occlusion. Air leakage showed complete cessation in 13 patients (56.5%) and reduction in seven patients (30.4%). A total of 20 patients (87.0%) experienced successful removal of the drainage tube following plug occlusion or presented with additional pleurodesis. Complications included expectoration of the plug (n=1) and fever (n=1); no other severe complications were observed. Bronchial occlusion using customized endobronchial plugs appears to be an effective and simple option for the management of PAL in patients who are not suitable for surgery, especially in developing countries, as the customized plug is a cost-effective alternative.

Building Ventilation Optimization Through Occupant-Centered Computer Vision Analysis

Abstract Buildings consume about 40 % of all energy. Ventilation plays a significant role in both the energy consumption of buildings and the comfort of occupants. To achieve energy efficiency and comfort, smarter ventilation control algorithms can be employed, such as those with feedback based on CO2 levels. Furthermore, by knowing the current number of people in a space, ventilation can theoretically be adjusted to maintain a constant CO2 level without wasting energy when people are not present. An additional benefit of such control could arise due to occupants’ habits. For example, if a person senses elevated CO2 levels, even if the ventilation system has started operating more intense, they might choose to open a window, potentially compromising energy efficiency. Therefore, if the control algorithm were to maintain a constant CO2 level, occupants may be less likely to open windows. In our work, we explore a model in combination with a custom monitoring system based on computer vision to implement such control. The monitoring system combines outside and inside CO2 sensors with precise people counting based on computer vision to provide data to the model. The model relies on the mass balance equation for CO2 and considers the historical data of the number of occupants and their activities to estimate the overall CO2 generation in indoor spaces. The results suggest that the model can effectively forecast CO2 dynamics with an absolute deviation of 40 ppm. However, it was observed that the analysis of the actual air exchange level could be compromised by several factors.

Dynamic Data Enhancing Battery Efficiency Through Collection Scheduling in IQRF Wireless Sensor Networks

Abstract In this study, we explore innovative strategies for enhancing energy efficiency in Wireless Sensor Networks (WSNs), with a focus on the IQRF network. Our approach integrates dynamic sleep scheduling and data collection methods to optimize battery usage and extend the network’s operational lifespan. We introduce a battery life estimation model, taking into account various factors such as data collection frequency and network size. This model is instrumental in predicting battery longevity under different operational scenarios. Additionally, we develop a practical tool in the form of an API and an online calculator, aimed at assisting network designers in planning and maintaining energy-efficient WSNs. Our results, derived from a case study involving a CO2 sensor network, demonstrate the effectiveness of our methodologies in real-world applications. The study concludes that implementing dynamic data collection and sleep scheduling significantly enhances battery life, offering a valuable contribution to the sustainability and reliability of WSNs.

Diel variability of carbon dioxide concentrations and emissions in a largest urban lake, Central China: Insights from continuous measurements

Accurately quantifying the carbon dioxide (CO2) emissions from lakes, especially in urban areas, remains challenging due to constrained temporal resolution in field monitoring. Current lake CO2 flux estimates primarily rely on daylight measurements, yet nighttime emissions is normally overlooked. In this study, a non-dispersive infrared CO2 sensor was applied to measure dissolved CO2 concentrations over a 24-h period in a largest urban lake (Tangxun Lake) in Wuhan City, Central China, yielding extensive data on diel variability of CO2 concentrations and emissions. We showed the practicality and efficiency of the sensor for real-time continuous measurements in lakes. Our findings revealed distinct diurnal variations in CO2 concentrations (Day: 38.58 ± 23.8 μmol L−1; Night: 42.01 ± 20.2 μmol L−1) and fluxes (Day: 7.68 ± 10.34 mmol m−2 d−1; Night: 9.68 ± 9.19 mmol m−2 d−1) in the Tangxun Lake. The balance of photosynthesis and respiration is of utmost importance in modulating diurnal CO2 dynamics and can be influenced by nutrient loadings and temperature. A diel variability correction factor of 1.14 was proposed, suggesting that daytime-only measurements could underestimate CO2 emissions in urban lakes. Our data suggested that samplings between 11:00 and 12:00 could better represent the average diel CO2 fluxes. This study offered valuable insights on the diel variability of CO2 fluxes, emphasizing the importance of in situ continuous measurements to accurately quantify CO2 emissions, facilitating selections of sampling strategies and formulation of management strategies for urban lakes.

In2O3-based thin-film transistors with a (400) polar surface for CO2 gas detection at 150 °C

Because of increasing demand for CO2 detection, CO2 sensors capable of low-temperature operation are desired. However, conventional oxide semiconductor sensors still require high operating temperatures. In this study, we fabricated a CO2 sensor that can operate at 150 °C. The sensor is based on an open-channel-type thin-film transistor (TFT) structure with an In2O3(400) polar plane as a channel. Because the In2O3(400) surface is polar, many OH groups are adsorbed onto the as-prepared surface. When CO2 gas is introduced, CO2 molecules react with the OH groups and the TFT characteristics change. As a result, CO2 can be detected with a sensitivity 2.9 times greater than that under an inert N2 atmosphere despite the operating temperature of only 150 °C. In a TFT with nonpolar In2O3(222) planes, the sensitivity remains at 1.3 times. We therefore believe that TFTs fabricated with the polar surface of an oxide semiconductor are useful for gas-sensing applications.

A neural network filter based high-sensitive MIR CO2 sensor

A low-power consumption, small-size and high measurement sensitivity mid-infrared (MIR) carbon dioxide (CO2) sensor was developed based on wavelength modulation spectroscopy (WMS) technology, utilizing a mid-infrared distributed feedback interband cascade laser (DFB-ICL) operating around 2682 nm. The sensor incorporated two key components, a home-made multi-pass gas cell with base length of 10 cm achieving 1.5 m optical path and a highly integrated FPGA board, which were used to reduce the size of the sensor. A novel second harmonic-neural network filter (SH-NNF) was also proposed and implemented in this sensor. The sensor’s performance was evaluated through a 20 mins continuous experiment using calibrated gas with 500 ppm concentration. The results, before and after SH-NNF, demonstrated an improvement in sensitivity and detection precision from 1.68 ppm (1 σ) to 1.17 ppm and from 2.14 ppm to 1.45 ppm, respectively. The optimum limit of detection of 0.392 ppm with integration time of 218 s was obtained. The ppm detection and compact characteristics of the developed MIR sensor show its potential for detecting CO2 in the ambient air.

A multimodal lithium niobate-based SAW gas sensor for accurate detection of carbon dioxide at elevated temperature

Carbon dioxide (CO2) detection is crucial for safety control, monitoring of the environment, and other industrial applications. The diverse fields of applications make the detection of CO2 a challenging task. In this paper, a study on a multimodal surface acoustic wave (SAW) CO2 sensing system was conducted to scrutinize the sensitivity of the lithium niobate-based multimodal SAW sensor towards CO2 with temperature compensation. The study focused on developing and evaluating a SAW temperature-compensated gas sensor using time-of-flight measurements. The sensor exhibited good repeatability and sensitivity toward different concentrations of CO2.

Network Optimization of Carbon Monoxide Sensor Nodes in the Metropolitan Region of São Paulo

Air pollution is one of the biggest problems affecting large urban areas. Better monitoring of regions suffering from this type of pollution is in the interest of public health. Although many cities employ sensors to monitor air pollution, a current concern is how to establish the ideal number of sensors to monitor a given geographical region. To address this concern, this research proposes a method to optimize the number of sensors in an air pollution monitoring network to cover a given region efficiently and precisely and uses the metropolitan region of São Paulo, Brazil, and CO sensors as an example. The model of Fragmentation into Groups via Routes is proposed to distribute sensors within micro-regions that display similar air pollution characteristics. A network of virtual sensors is created, and the output of each sensor is established using a method of spatial interpolation called IDW. To identify the optimum sensor configuration, a genetic algorithm is used to assess the topology with the lowest variance of data spread. A lesser number of sensor stations to be treated leads to faster responses to sudden changes in urban conditions. Therefore, municipality authorities can take quick measures to improve the population’s wellness.

MOF-derived Co3O4 nanoparticles over direct grown ZnO nanoflower on ceramic for CO sensor with high selectivity

ZnO nanoflower was directly fabricated on ceramic and then Co3O4 was grown in-situ on ZnO nanoflower by a MOF-derived method. Co3O4 nanoparticles were successfully coated on the surface of ZnO nanoflower to develop a sensor for the detection of carbon monoxide. Notably, the Co3O4/ZnO sensor exhibits an outstanding response of 38 % to 100 ppm CO at 210 ℃ with a fast response/recovery time (38 s/50 s). In addition, the sensor showed excellent selectivity for CO, which is 14 times better than for the second gas (500 ppm CO and NH3). In the meantime, the sensor's response remained a stable value when the 100 ppm CO is mixed with 100 ppm H2, CH4 and HN3, respectively. The prominent CO sensing performance of the sensor is attributed to its unique structure charaterastics of nanoflower and the constructed heterostructure, which provides a large surface area and abundant active sites. Therefore, the sensor based on MOF-derived Co3O4 nanoparticles directly on ZnO nanoflower have promising application for constructing high performance CO sensor in the detection domain.

2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces.

The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates.