CO2 Sensor Research Articles

Aquality32: A low-cost, open-source air quality monitoring device leveraging the ESP32 and google platform

We present AQuality32, an open-source air quality monitoring device designed to address the challenges faced by small research teams in accessing affordable and versatile air quality monitoring solutions. The device utilizes an ESP32 System on Module (SoM) and integrates sensors for CO2 (SDC30 from Sensirion) and particulate matter (HM3301 from Seeed) measurement, along with temperature, relative humidity, and extensive sensing possibilities. It can be powered by a battery and is suitable for both stationary and on-the-go measurements. The paper details the hardware description, building instructions, programming, calibration procedures, and data collection setup for AQuality32. Validation experiments assess communication stability, geolocation accuracy, and environmental monitoring capabilities. The results demonstrate the device’s reliability, affordability, and suitability for various applications in environmental sciences, public health, and indoor/outdoor air quality monitoring. The paper emphasizes the importance of robust solutions, openness, and easy documentation for widespread adoption and impact in air quality research and monitoring.

How to improve the perceived health, comfort, and well-being of primary school teachers? A quantitative self-reported survey during the COVID-19 pandemic in Scotland

Teachers are among the most stressed professionals, for whom the built environment has an influence. In addition, the COVID-19 pandemic has increased the pressure on schools, where enhanced ventilation is deemed essential to help reduce virus-laden particles in classrooms. Good Indoor Air Quality (IAQ) is required to maintain an adequate level of comfort, health, and well-being. Therefore, solutions to improve IAQ quickly and cheaply are essential. As such, the Scottish Government has funded Local Authorities to purchase CO2 sensors for school classrooms. This study explores two interventions designed to improve the quality of indoor air. The first one by raising the awareness of the teachers on ventilation strategies via a webinar. The second one by deploying devices that visually inform the occupants of the indoor conditions: Temperature, Relative Humidity, and CO2 levels in the classrooms. The novelty of this study is that it evaluated the influence of engaging teachers in the management of their working indoor environments. This paper presents the results of the perceived health, comfort, and well-being of teachers from two primary schools built before 1919 located in Edinburgh, Scotland. Visual feedback sensors improved the perceived air quality of teachers in their classrooms but increased pressure on their workload and were a potential distraction for their pupils. In contrast, raising the awareness of the teachers via the webinar improved their perception of their indoor environment without added pressure. Therefore, awareness programs should be devised to educate school staff on Indoor Air Quality in addition to the development of sensors with visual feedback.

Gas Adsorption on the Co2Te3 Monolayer: Density Functional Theory Study.

We report a numerical investigation of the chemical adsorption of acetone and carbon dioxide gases by a cobalt telluride (III) 2D layer. Calculations were performed using first principles calculations within the density functional theory to evaluate the adsorbed gas impact on the material's conductivity. The adsorption of carbon dioxide was found to significantly affect the Co2Te3 monolayer electronic structure and its electrical conductivity, which is not observed in the case of acetone. This letter demonstrates that the calculated bond formation energy for carbon dioxide is approximately 2 times higher than that for the acetone. It proved that the quasi-2D cobalt telluride (III) could be introduced as a selective electrochemical sensor for carbon dioxide in quick medical diagnoses and other technological applications.

Enhanced and Sustainable Indoor Carbon Dioxide Monitoring by Using Ambient Light to Power Advanced Biological Sensors

Enhancing carbon dioxide (CO2) detection is crucial for improving indoor air quality and environmental surveillance. Traditional CO2 sensors face drawbacks like high costs, large sizes, environmental impact, and reliance on external power, limiting their practicality for continuous indoor monitoring. In this research, an innovative indoor CO2‐sensing system using a self‐powered bio‐solar cell (BSC) platform is introduced. Utilizing cyanobacteria as a sensitive biocatalyst and sustainable power source, the system offers a cost‐effective, eco‐friendly, and maintenance‐free alternative to conventional sensors. It operates by monitoring electron‐transfer processes in cyanobacteria during photosynthesis, converting CO2 and water into oxygen and chemical energy, enabling accurate CO2 level monitoring. The system responds to CO2 fluctuations and issues alerts when levels are outside the recommended range of 500–1000 ppm for human health and productivity. A self‐sustaining configuration of eight BSCs—one for sensing and others for power generation—ensures continuous operation without external power. An integrated energy‐harvesting board efficiently manages power distribution to a microcontroller and display system for real‐time data visualization, with the array producing up to 400 μW. Additionally, a machine‐learning model interprets BSC outputs to accurately quantify CO2 levels, enhancing the sensor's adaptive performance.

A Click-generated chalcone allied triazole sensor for Co (II) with INHIBIT logic gate construction and its antioxidant properties

This article presents the synthesis of chalcone allied 1,2,3-triazole via an aldol-click reaction. The newly synthesized compound chalcogenyl based 1,2,3-triazole (7) selectively detects Co (II) ion over other metal cations by UV–visible photophysical study with the limit of detection value of 1.24 × 10-8 M. Job’s plot confirms that compound (7) and the Co (II) metal ion have 1:1 binding stoichiometry. The binding mechanism between Co (II) and sensor was confirmed by using 1H NMR spectroscopy, mass spectrometry and DFT studies. The reversible behaviour of 7 towards Co (II) was used to construct a molecular logic gate. The compound (7) demonstrated good antioxidant activity with an IC50 value of 3.04 µM in comparison to 1.31 µM of DPPḢ. Further, to explore the antioxidant effect of ligand 7, the molecular docking analysis was performed with xanthine oxidase protein and the obtained binding energy was −11.08 kcal/mol.

Micro-Incubator Protocol for Testing a CO2 Sensor for Early Warning of Spontaneous Combustion

A protocol for detecting the potential occurrence of spontaneous combustion (SC) in stored cottonseeds and peanuts using a micro-incubator is described. The protocol indicates how to quantify CO2 production rates and final CO2 levels in wet versus dry cottonseed and peanut samples, which can provide crucial data for the early detection of SC risk in storage facilities. The experimental design utilizes a micro-incubator to simulate conditions found in large bulk crop storage. Parameters monitored include CO2 concentration, temperature, and relative humidity. The protocol includes preparation methods, experimental procedures for both control (dry) and wet seed tests, and test termination criteria that allow for safe experimentation of likely pathogenic fungi. The protocol has three replicates for wet and dry conditions. The protocol is intended to facilitate future experimental studies and ultimately contribute to the development of a consistently reliable early warning fire detection system for SC in cottonseed and peanut warehouse facilities. A consistently reliable fire detection system would address a critical need in the cotton and peanut industry for improved fire risk management and insurability of storage facilities.

Characterization and enhanced carbon dioxide sensing performance of spin-coated Na- and Li-doped and Co-doped cobalt oxide thin films.

Recognizing the substantial effects of carbon dioxide on human health and the environment, monitoring CO2 levels has become increasingly vital. Owing to energy constraints and the widespread application of CO2 gas sensors, it is important to design cost-effective, more efficient, and faster response CO2 gas sensors that operate at room temperature and involve a low-cost technique. This study aims to develop a cost-effective and efficient CO2 gas detector that functions at room temperature and uses less power than traditional high-temperature CO2 sensors. In this study, we achieved this by employing innovative Co3O4 thin films with optimized spinel-structured p-type semiconductors through spin-coating, facilitated by Li and Na doping as well as Li/Na codoping. Doping with 3% Li/Na reduced the crystallite size from 92.4 to 8.03 nm and increased the band gap from 3.31 to 3.69 eV. At room temperature (30 °C), the sensor response improved significantly, increasing from 50% to 345.01% for 3% Li-Co3O4 upon the addition of 3% Na at a concentration of 9990 ppm. This performance surpasses that of most metal-oxide-based CO2 sensors reported in the literature. Additionally, this optimized sensor demonstrated a very short response time of 18.8 s and a recovery time of 16.4 s at a CO2 concentration of 9990 ppm diluted with air. It outperformed other films in terms of sensitivity, stability, response and recovery times, and performance across a wide range of relative humidity levels (43-90%). The sensor exhibited superior selectivity for CO2 than for N2, H2, and NH3. Overall, the 3% Li, Na-Co3O4 sensor is well-suited for climate change mitigation and industrial applications.

Mechanistic Study of the Electrochemical Reduction of CO2 in Aprotic Ionic Liquid in Air.

The capture and electrochemical conversion of dilute CO2 in air is a promising approach to mitigate global warming. Aiming to increase the efficiency of the electrochemical reduction of CO2, we fabricated electrodes and developed a custom-designed sealed electrochemical reaction system to study the mechanism of this conversion. The performance of three metal electrodes, Ag, Cu, and SUS 316 L, was compared in an aprotic ionic liquid as the electrolyte to monitor the CO2 concentration and chemical reactions using a CO2 sensor and diffuse reflectance infrared Fourier transform spectroscopy and Raman spectroscopy in CO2/N2 (400 ppm CO2 and 99.96 % N2) or synthetic air (400 ppm CO2, 21 % O2, and 79 % N2). The CO2 concentration decreased at negative potentials and was more drastic in synthetic air than in CO2/N2. At negative potential in synthetic air, IR revealed carbon monoxide, carbonate, or peroxydicarbonate on the Ag, Cu, or SUS 316L electrodes, respectively. Reaction intermediates were identified using Raman spectroscopy. Superoxide (O2⋅-), produced by the reduction of O2 on each electrode, promotes the electrochemical reduction of CO2 whose reduction potential is higher on the negative side than that of O2. This research deepens our understanding of the electrochemical capture/release and conversion of dilute CO2.

PH-controlled-ion-mobility of Laponite-phosphate dispersion for physical hydrogel with improved mechanical properties and sensitivity to CO2

Self-assembled physical nanocomposite hydrogels based on bio-based poly(itaconic acid) and Laponite® nanoclays were prepared and tested as possible CO2 sensors in smart food packaging. The structure and mechanical properties of the fabricated hydrogels were controlled by changing the initial pH of the aqueous Laponite® dispersions stabilised by tetrasodium pyrophosphate. The dispersions were studied by SAXS and analysed using circular disc and fractal model to determine clay agglomeration tendency. Sodium and pyrophosphate ions mobility was investigated by 23Na and 31P 1D as well as spin-spin relaxation times T2 NMR measurements. The rheological behaviour of the produced hydrogels was studied by oscillatory shear measurements. It was found that decreasing the pH of the Laponite® nanodispersions up to 8 increases the strength of the physical interactions between the nanoclays, dispersant, and polyelectrolyte chains, which enables production of dimensionally stable and mechanically robust hydrogels. Their potential application as the CO2-sensitive matrix for construction of environmentally friendly colorimetric sensors was demonstrated showing the CO2 sorption capacity of 0.07 mmol CO2/g. A prototype device of hydrogel sensor was built and tested in a food packaging application to monitor the plum fruit respiration processes.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests’ detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

Compact and full-range carbon dioxide sensor using photoacoustic and resonance dependent modes

Compact and full-range carbon dioxide sensor using photoacoustic and resonance dependent modes

Mining PANI/Ti3AlC2/CeO2 ternary composite CO sensor: Material synthesis, gas sensing performance and mechanism research

To address the issue of poor gas sensitivity in existing mining semiconductor CO sensor materials like PANI at room temperature, this study employed an ice-water bath in conjunction with an in-situ polymerization method to incorporate 2D sheet-like Ti3AlC2 and nano-CeO2 materials with oxygen vacancies into PANI. As a result, nanometer-sized PANI/Ti3AlC2/CeO2 ternary composite nanomaterial was synthesized. The morphology, synthesis mechanism, and thermal stability of the ternary composite material were investigated using scanning electron microscopy-energy dispersive analysis (SEM-EDS), transmission electron microscopy analysis (TEM), spectral analysis, and thermogravimetric methods. Furthermore, the CO gas sensing mechanism of the ternary composite material was also examined. The study discovered that the nano-PANI/Ti3AlC2/CeO2 ternary composite nanomaterial, when deposited on the Au interdigital electrode of the IDE substrate, exhibits superior gas transmission of 400 ppm CO compared to gas sensors based on nano-PANI, nano-PANI/Ti3AlC2, and nano-PANI/CeO2. The sensing response was enhanced by 1.75 times, 1.4 times, and 1.2 times, respectively. This improvement in sensing mechanism can be attributed to the pn heterojunction and interfacial interaction forces between the components. Hence, the nano-PANI/Ti3AlC2/CeO2 ternary composite nanomaterial holds potential as a high-performance CO detection sensing material at room temperature. Additionally, it can serve as a theoretical basis for further research on low-power, integrable sensor materials in coal mines.

Expanding seawater carbon dioxide and methane measuring capabilities with a Seaglider

Abstract. Warming, ocean acidification, and deoxygenation are increasingly putting pressure on marine ecosystems. At the same time, thawing permafrost and decomposing hydrates in Arctic shelf seas may release large amounts of methane (CH4) into the water column, which could accelerate local ocean acidification and contribute to climate change. The key parameters to observing and understanding these complex processes and feedback mechanisms are vastly undersampled throughout the oceans. We developed carbon dioxide (CO2) and CH4 gliders, including standard operational procedures, with the goal that CO2 and CH4 measurements will become more common for glider operations. The Seagliders with integrated Contros HydroC CO2 or CH4 sensors also include conductivity, temperature, depth, oxygen, chlorophyll a, backscatter, and fluorescent dissolved organic matter sensors. Communication via satellite allows for near-real-time data transmission, sensor adjustments, and adaptive sampling. Several sea trials with the CO2 Seaglider in the Gulf of Alaska and data evaluation with discrete water and underway samples suggest nearly “weather-quality” CO2 data as defined by the Global Ocean Acidification Network. A winter mission in Resurrection Bay, Alaska, provided the first insights into the water column inorganic carbon dynamics during this otherwise undersampled season. The CH4 Seaglider passed its flight trials in Resurrection Bay but needs to be tested during a field mission in an area with CH4 concentrations beyond background noise. Both sensing systems are available to the science community through the industry partners (Advanced Offshore Operations and -4H-JENA engineering GmbH) of this project.

Indoor Environmental Quality in Portuguese Office Buildings: Influencing Factors and Impact of an Intervention Study

Office workers spend a considerable part of their day at the workplace, making it vital to ensure proper indoor environmental quality (IEQ) conditions in office buildings. This work aimed to identify significant factors influencing IEQ and assess the effectiveness of an environmental intervention program, which included the introduction of indoor plants, carbon dioxide (CO2) sensors, ventilation, and printer relocation (source control), in six modern office buildings in improving IEQ. Thirty office spaces in Porto, Portugal, were randomly divided into intervention and control groups. Indoor air quality, thermal comfort, illuminance, and noise were monitored before and after a 14-day intervention implementation. Occupancy, natural ventilation, floor type, and cleaning time significantly influenced IEQ levels. Biophilic interventions appeared to decrease volatile organic compound concentrations by 30%. Installing CO2 sensors and optimizing ventilation strategies in an office that mainly relies on natural ventilation effectively improved air renewal and resulted in a 28% decrease in CO2 levels. The implementation of a source control intervention led to a decrease in ultrafine particle and ozone concentrations by 14% and 85%, respectively. However, an unexpected increase in airborne particle levels was detected. Overall, for a sample of offices that presented acceptable IEQ levels, the intervention program had only minor or inconsistent impacts. Offices with declared IEQ problems are prime candidates for further research to fully understand the potential of environmental interventions.

Ultra-compact dual-channel integrated CO2 infrared gas sensor

Expiratory CO2 concentrations can directly reflect human physiological conditions, and their detection is highly important in the treatment and rehabilitation of critically ill patients. Existing respiratory gas analyzers suffer from large sizes and high power consumption due to the limitations of the internal CO2 sensors, which prevent them from being wearable to track active people. The internal and external interference and sensitivity limitations must be overcome to realize wearable respiratory monitoring applications for CO2 sensors. In this work, an ultra-compact CO2 sensor was developed by integrating a microelectromechanical system emitter and thermopile detectors with an optical gas chamber; the power consumption of the light source and ambient temperature of the thermally sensitive devices were reduced by heat transfer control; the time to reach stabilization of the sensor was shortened; the humidity resistance of the sensor was improved by a dual-channel design; the light loss of the sensor was compensated by improving the optical coupling efficiency, which was combined with the amplitude trimming network to equivalently improve the sensitivity of the sensor. The minimum size of the developed sensor was 12 mm × 6 mm × 4 mm, and the reading error was <4% of the reading from −20 °C to 50 °C. The minimum power consumption of the sensor was ~33 mW, and the response time and recovery time were 10 s (@1 Hz), and the sensor had good humidity resistance, stability, and repeatability. These results indicate that the CO2 sensor developed using this strategy has great potential for wearable respiratory monitoring applications.

Intramolecular Charge Transfer Inhibition Strategy toward a Desired Solvatochromic Fluorescent Platform: Visualization of Duple Organelles and Detection of Carbon Dioxide.

Solvatochromic fluorescent probes are crucial molecular tools to investigate and aggregate proteins' fold, visualize fine structures in biomembranes, and label different organelles in dual emission colors. However, solvatochromic fluorogens often displayed a weak emission at high polarity, hindering their bioimaging applications. To resolve this problem, herein, we propose an intramolecular charge transfer (ICT) inhibition strategy. The probe was designed with a single electronic donor and two acceptors in order to split and inhibit the ICT procedure. As a result, the probe displayed an intense emission at both low and high polarities and showed a large emission shift (84 nm) upon polarity change. Using the probe, we successfully imaged lipid droplets and the endoplasmic reticulum in different fluorescence colors. Moreover, the different degrees of lipid accumulation by oleic acid, stearic acid, and cholesterol (oleic acid > stearic acid > cholesterol) have been revealed. The lipid accumulation induced by the three lipids could be rapidly consumed under lipid-less conditions, and the lipids with stearic acid were the most difficult to be consumed. The biological results could facilitate the understanding and treatment of lipid accumulation and obesity. Furthermore, utilizing the polarity increase of diethylamine after the reaction with CO2, the ratiometric detection of CO2 has been achieved for the first time with the probe.

Enhancing Ultra-High Temperature Milk Quality: A Novel Approach to Microbial Contamination Detection Using the BD BACTEC™ FX System

The demand for effective detection methods to ensure the safety and quality of Ultra-High Temperature (UHT) milk remains crucial in the food industry. Traditional techniques for detecting microorganisms are time-consuming and labor-intensive, leading to a need for a rapid and sensitive method for detecting microbial growth in UHT milk. This study evaluates the efficacy of the BD BACTEC™ FX system for microbial detection in UHT milk samples. The system utilizes internal fluorescent CO2 sensors to detect the metabolic activity of bacteria growing in the culture broth. The investigation comprises two stages: a controlled laboratory experiment with UHT milk samples spiked with Bacillus spizizenii inoculated at different population levels (from 0 to 4.0 log10 CFU/mL), and an industrial-scale assessment of commercial UHT milk. The microbial detection system detected the lowest B. spizizenii count (0 log CFU/mL) inoculated into UHT milk after 13 h of incubation. The 13-hour incubation period was also sufficient to detect microbial contamination in commercial UHT milk samples. Results indicate that this system offers heightened sensitivity compared to conventional methods, detecting microbial contamination in a significantly shorter time frame (6–13 h). Taxonomic identification of contaminants revealed the presence of Cellulomonas spp. and Enterococcus spp. in UHT commercial samples. The findings emphasize the critical importance of robust detection techniques in ensuring the safety and quality of UHT milk products.

Visual and Gravimetric CO2 Sensing at High Humidity Levels Enabled by MOF‐804 Cofunctionalized with Ionic Liquid and m‐Cresol Purple

AbstractReal‐time monitoring of carbon dioxide (CO2) is imperative for medical diagnosis and effective environmental preservation. Despite the formidable challenge posed by the inherent chemical inertness of CO₂ molecules, a pioneering CO2 sensor based on MOF‐804 cofunctionalized with ionic liquid (IL) and m‐cresol purple (mCP) is successfully developed. By ingeniously integrating hydrogen bonding, electrostatic interactions, and hydrophobic properties within the sensitive layer, the sensor achieves a state‐of‐the‐art sensitivity (Δf = 384 Hz), an exceptionally vast detection range spanning 400–80 000 ppm, and remarkable stability with minimal sensitivity drift even at relative humidity (RH) levels exceeding 80%. Furthermore, the inherent gasochromic property, stemming from the zwitterionic mechanism, paves the way for innovative self‐sustaining CO2 test strips and groundbreaking applications, including CO2 tracking and CO2‐encrypted security labeling technology. Collectively, the realization of this ultra‐sensitive and robust CO2 monitoring approach, coupled with its CO2‐triggered visual and multifunctional capabilities, opens up novel avenues for dynamic CO2 detection, advanced military encryption strategies, and enhanced health management systems.

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

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

Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence

Fires in commercial buildings can lead to significant casualties and property damage. Thus, rapidly and accurately identifying the source of a fire and its intensity is crucial for guiding rescue operations. This study aims to explore the use of distributed fiber optic temperature sensing systems, combined with deep learning algorithms, to predict key information about fires in commercial buildings, including the location of the fire source, the intensity of fire development, and the critical planar distribution of carbon monoxide. Based on the Fire Dynamic Simulation (FDS) data, a dual-agent model was developed, and with a sensor sampling interval of 3 s, 30 s of historical data achieved prediction accuracies of 94.23 % for fire intensity and 99.28 % for fire location. Upon reducing the intervals for fire location, the accuracy reached 93.56 %. Additionally, the Random Forest algorithm for mapping carbon monoxide distribution demonstrated an overall Mean Absolute Percentage Error (MAPE) of 5.87 %. Within the 0–150 ppm range, the MAPE was 2.46 %, far below the error levels of existing CO sensors. The database configurations and the sensors' spatiotemporal arrangement were also optimized to rapid and reliable fire prediction. This research demonstrates the feasibility of using artificial intelligence for critical fire scene information detection.