CO2 Sensor Research Articles

The River Runner: a low-cost sensor prototype for continuous dissolved greenhouse gas measurements

Abstract. Freshwater ecosystems are sources of the two most relevant greenhouse gases (GHGs): CO2 and CH4. Understanding the importance of freshwater ecosystems in the global carbon cycle and their role in global warming trends requires the accurate quantification of gas fluxes from the water phase to the atmosphere. These fluxes depend on the gas exchange velocity and the concentration gradient between the phases, which both cause high spatio-temporal variability in fluxes. On a global scale, the estimation of fluxes is limited by the lack of cheap and accurate methods to measure dissolved gas concentrations. Low-cost sensors, as an alternative to expensive gas analysers, are available; however, to date, the in situ performance of such sensors has been poorly examined. Here, we present an inexpensive data-logging sensor prototype that provides continuous measurements of dissolved CO2 and CH4 in submerged environments. Gas measurements are done in a confined gas space, which is rapidly equilibrated with the water phase through a single-layer polytetrafluoroethylene (PTFE) membrane, by a miniature non-dispersive infrared (NDIR) sensor for CO2 (Sunrise sensor, Senseair, Sweden) and a cheap metal oxide sensor for CH4 (TGS2611-E, Figaro Engineering Inc., Japan). Pressure, temperature and humidity are measured to correct raw sensor readings. For freshwater, the dissolved gas concentration is directly obtained from the measured molar fraction and temperature and pressure readings. In air, we measured the molar fraction of CO2 in a range from 400 to 10 000 ppm and the molar fraction of CH4 in a range from 2 to 50 ppm with an accuracy of ± 58 and ± 3 ppm respectively. We successfully used our prototype to measure diurnal variations in dissolved CO2 in a natural stream. We further calibrated the CH4 sensor for in situ use at concentrations ranging from 0.01 to 0.3 µmol L−1. Underwater, we were able to measure the molar fraction of CH4 in the prototype head with an accuracy of ± 13 ppm in the range from 2 to 172 ppm. The underwater measurement error of CH4 is always higher than for the same concentration range in air, and CH4 is highly overestimated below 10 ppm. At low CH4, humidity was the most important influence on the TGS2611-E sensor output in air, whereas temperature became the predominant factor underwater. We describe the response behaviour of low-cost sensors in submerged environments and report calibration methods to correct for temperature and humidity influence on the sensor signal if used underwater. Furthermore, we provide do-it-yourself instructions to build a sensor for submerged continuous measurements of dissolved CO2 and CH4. Our prototype does not rely on an external power source, and we anticipate that such robust low-cost sensors will be useful for future studies of GHG emissions from freshwater environments.

Effect of proton irradiation on the sensitivity of CO2 sensors based on SnO2 and SnO–SnO2 heterojunctions

Gas sensors are integral to space exploration and development projects. However, few studies have examined the effects of proton irradiation on the performance of semiconductor gas sensors. This study fills this gap by investigating the effect of proton irradiation on the sensitivity of CO2 semiconducting sensors, specifically SnO2 and SnO–SnO2 heterojunction types. In SnO2-based sensors, sensitivity was indicated to remain stable at low fluence and increase at higher fluences owing to proton-induced oxygen vacancy formations, mainly. Meanwhile, in SnO–SnO2 heterojunction sensors, it was found to decrease at low fluences and increase significantly at higher fluences owing to changes in the electrical properties of SnO. These findings suggest that proton irradiation can enhance sensor sensitivity, enabling potential applications in radiation-prone environments, such as outer space. This study contributes to the understanding of the effects of proton irradiation on semiconductor gas sensors and paves the way for their development.

Controlling the structure and composition of SnO2-based thin film with reactive sputtering to improve the sensitivity of semiconductor CO2 sensor

The sensitivity of SnO2 thin film-based CO2 gas sensors was enhanced by controlling the surface structure employing reactive sputtering during the deposition process to carefully adjust the oxygen partial pressure to modify the surface structure of the SnO2 films. This process increased the sensitivity, primarily due to larger surface area and improved gas adsorption capabilities. Furthermore, the effect of heterojunctions between p-type SnO and n-type SnO2 on the sensitivity was investigated using a model diagram. Both theoretical analysis and experimental data consistently demonstrated that the number of heterojunction interfaces contributes significantly to the sensitivity of SnO-SnO2 heterojunction gas sensors. These findings highlight the effectiveness of controlling the surface structure and composition ratio of thin films through reactive sputtering to enhance sensitivity. This study offers valuable insights for optimizing SnO2 thin-film-based gas sensors for CO2 detection.

Ppb‐level CO2 sensor based on a miniature multipass cell with eight‐petaled spot pattern

AbstractThis paper presents an innovative carbon dioxide sensor leveraging a dense‐pattern multipass absorption cell. This novel design dramatically compacts an extensive optical path of approximately 11.4 m into a 70 mm inter‐mirror distance, effectively enhancing the system's sensitivity and reducing its size. Utilizing a 2004 nm diode laser, the sensor accurately targets a potent CO2 absorption line at 4989.9 cm−1. Experimental validation reveals remarkable linearity (R² = .999) and a detection limit of 59 ppb with 1 s integration time. The incorporation of Kalman filtering techniques further refines the sensor's performance, reducing the detection limit to 30 ppb—a substantial improvement of 49%. This advancement in sensor technology marks a significant step forward in efficient and precise CO2 monitoring.

Research Progress on End-Tidal Carbon Dioxide Detection Technology Based on Non-Dispersive Infrared Method

The concentration of end-tidal carbon dioxide is one of the important indicators for evaluating whether the human respiratory system is normal. Accurately detecting of end-tidal carbon dioxide is of great significance in clinical practice. With the continuous promotion of the localization of end-tidal carbon dioxide monitoring technology, its application in clinical practice in China has become increasingly widespread in recent years. The study is based on the non-dispersive infrared method and comprehensively elaborates on the detection principle, gas sampling methods, key technologies, and technological progress of end-tidal carbon dioxide detection technology. It comprehensively introduces the current development status of this technology and provides reference for application promotion and further improvement.

Developing of Tomato Distribution Process Monitoring System Using Internet of Things (IoT)

Technological developments are used to provide product quality assurance and provide goods and services to consumers. Product quality is a condition related to the product, where the product could meet the needs and satisfaction of consumers. Product quality assurance, could be supported from raw materials, processes to distribution. This condition requires a tool that is able to control or monitor agricultural products by utilizing technology, namely the Internet of Things (IoT) using temperature, humidity, vibration, carbon dioxide (CO2) sensors and GPS that is able to show the location when the distribution process takes place in real time. The purpose of this study was to design and performance monitoring tools for temperature, humidity, vibration, carbon dioxide (CO2) and location during the distribution process. The results of the field test showed that the GPS sensor was able to show the location in real time according to the delivery route. The results of recording the highest temperature data was 30.8°C and the lowest temperature was 27.8°C, while the lowest humidity was 80% RH and the highest was 94% RH. The recording results from the SW-420 sensor showed that 51.04% for small vibration, 19.79% for medium vibration, and 28.64% for large vibration. The recording results from the MQ-2 sensor was able to show the carbon dioxide value around the tomatoes, with the lowest carbon dioxide with the value of 10% and the highest with the value of 23%.

Reaction-induced headspace strategy toward efficient available chlorine analysis in bleaching water products

In this study, with reaction-induced headspace strategy, a novel and convenient method for quantifying available chlorine in bleaching water product was developed. The analytical technique relies on the detection of carbon dioxide generated from the oxidation–reduction reaction between available chlorine substances (i.e., hypochlorite) and oxalate in the presence of a strong acid. The formed carbon dioxide is accurately measured using gas chromatography (GC) in a closed headspace vial. The results demonstrated that this analytical technique has high precision and and accuracy in detecting available chlorine. Additionally, the method offers low detection limits (limit of quantification (LOQ) = 0.19 μg/mL), and the recoveries ranged from 98.78 to 101.20% for added standards. With its efficiency and automation, this proposed reaction-induced headspace technique offers a fast and simple method for the routine estimation of available chlorine in bleaching water products, making it suitable for a wide range of applications including processing and quality monitoring.

The PiGas: A low-cost approach to volcanic gas sampling

Instruments which are designed to measure multiple gases are widely used across several industries with a common focus on environmental gas sampling. Reliable instrumentation and gas sensors are therefore vital for the monitoring of air quality in cities and in more challenging scenarios, for example volcanic gas release. However, such instrumentation is commonly expensive and can be difficult to deploy, meaning that large instrument networks - needed to assess spatial air quality differences - is inhibitive. A bespoke do-it-yourself approach can therefore be beneficial. Here, we detail a new instrument, the PiGas, which is modelled on the successful “Multi-GAS” technique (Shinohara, 2005; Aiuppa et al. 2005) for the measurement of carbon dioxide (CO2), sulphur dioxide (SO2), hydrogen sulphide (H2S), and water vapour (H2O). Indeed, CO2 is a common gas of interest yet its measurement at high accuracy can be expensive. We demonstrate a cost reduction to <£500 by adapting parts used from non-air sampling specific industries and through use of a lower-cost and lower-power requirement CO2 sensor (the CozIR A-H). We demonstrate the versatility of the PiGas for monitoring indoor and outdoor air quality as a diffuse and pumped sensor. We also test the instrument in two extreme volcanic environments at the high altitude Alitar and Volcán Lastarria (Chile). Overall, the instrumentation shows potential for more widespread co-ordinated use to improve spatial sampling distributions in low concentration scenarios as a supplement to higher accuracy and higher precision instruments in volcanic environments.

Fiber-Optic Photoacoustic CO Sensor for Gas Insulation Equipment Monitoring Based on Cantilever Differential Lock-In Amplification and Optical Excitation Enhancement.

A fiber-optic photoacoustic CO sensor for gas insulation equipment is proposed, which relies on F-P interferometric cantilever-based differential lock-in amplification and optical multipass excitation enhancement. The sensor has excellent characteristics of high sensitivity, antielectromagnetic interference, fast response, and long-distance detection. The photoacoustic pressure waves in the two resonators of the differential photoacoustic cell (DPAC) are simultaneously detected by two fiber-optic interferometric cantilevers and processed differentially; thereby, the gas flow noise is effectively suppressed. Based on the comprehensive analysis of the superposition of photoacoustic excitation and multipass absorption, the diameter of the resonator is determined to be 6 mm. The optical power emitted by the 1566.6 nm distributed feedback laser is increased to 500 mW by an erbium-doped fiber amplifier. The near-infrared light is reflected 30 times in the multipass cell, which improves the order of magnitude of optical effective excitation. Due to the low sound velocity of SF6 gas, the resonant frequency of the DPAC with a resonator length of 80 mm is 760 Hz. The response time to CO/SF6 gas is 93 s with a flow rate of 500 sccm. The detection limit of the CO sensor is 53 ppb, which realizes the accurate and timely perception of the SF6 decomposition derivative CO and provides technical support for trouble-free operation of gas insulation equipment.

Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models

Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical–chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains.

Characterizing indoor environmental quality in Portuguese office buildings for designing an intervention program

Intervention studies have been explored to identify actions to effectively remediate indoor environmental quality (IEQ) problems and to improve people's health, well-being, comfort, and productivity. This study assessed a comprehensive set of IEQ indicators related to ventilation, air pollution, thermal comfort, illuminance, and noise for the first time in Portuguese office buildings. The purpose was to derive evidence-based corrective measures for a further environmental intervention program. The study monitored and surveyed 15 open-space offices from six modern office buildings in Porto (Portugal) during a workday between September and December 2022. Illuminance was of most concern among the assessed IEQ indicators since the measured levels were below the minimum limit required in 27% of the evaluated workplaces. For CO2, although mean concentrations were below 1000 ppm, absolute values exceeding that level were consistently registered in 20% of the offices during the afternoon period. Mean levels of PM2.5, PM10, and ultrafine particles exceeding the WHO guidelines were found in 13%, 7%, and 7% of the offices, respectively. The assessed thermal comfort levels were typically neutral, corresponding to an estimated mean of 6% of dissatisfied people. Based on the findings, an intervention plan was designed to be implemented in the further stages of this work. The priority interventions to test include relocation of printers (PM source removal), optimisation of ventilation rates (using real-time data from CO2 sensors), adjustment of desk positions to improve illuminance, and introduction of indoor plants.

Inferring ventilation rates with quantified uncertainty in operational rooms using point measurements of carbon dioxide: Classrooms as a case study

We present a robust integral method to estimate the daily mean per-person ventilation rate Q¯pp based on carbon dioxide (CO2) concentration measurements in operational spaces, and limited other data. The method makes no assumptions regarding the ventilation provision throughout the day, nor requires the room to be in a steady state, nor the air within to be well-mixed. We demonstrate that several integral parameters remain reliably close to a value of unity, despite large variations in room conditions. Evaluating the likely distributions of integral parameters provides a method to quantify the uncertainty bounds and therefore assess the reliability of these ventilation estimates. Taking school classrooms as a case study, estimates of Q¯pp based on measured CO2 are shown to exhibit uncertainty bounds (of 95% confidence intervals) of approximately ±24% if no other data than the classroom timetable is available. Deploying four CO2 sensors within a classroom is expected to halve the uncertainty bounds to around ±12%. Moreover, the framework presented herein evidences that when the same classroom experiences similar usage on two different days, the relative per-person ventilation rate achieved during these two days can be simply determined by the ratio of their integral excess CO2 concentrations. These significant findings offer great scope to facilitate more reliable ventilation estimates, particularly from large-scale data sets of CO2 measured in operational spaces, to better inform assessments of indoor air quality.

Design and fabrication of poly-Si/SiO2 Fabry–Perot filter for nondispersive IR CO2 sensors

The relationship between the transmittance and FWHM of a Fabry–Perot filter for a nondispersive carbon dioxide (CO2) sensor was investigated as a function of the number of distributed Bragg reflector (DBR) pairs consisting poly-Si and SiO2 thin films. Given the significant prior research on the fabrication of high-performance Fabry–Perot filters, this study is focused on the relationship between the transmittance and FWHM that can be achieved by controlling the reflectance of the DBR pairs. Each layer of the filter was simulated adequately as the poly-Si and SiO2-based DBR pairs, and poly-Si and SiO2 were deposited on the soda–lime substrate by RF sputtering and low-pressure chemical vapor deposition based on the simulation results. The fabricated filter showed a transmittance of 43.7% and FWHM of 125 nm at 4.26 μm. The NDIR CO2 sensor with Fabry–Perot filter showed enhanced selectivity to CH4 and CO compared with normalized CO2 response.

A comparative DFT study on the adsorption properties of lithium batteries thermal runaway gases CO, CO2, CH4 and C2H4 on pristine and Au doped CdS monolayer

The thermal runaway gases, such as CO, CO2, CH4, and C2H4, will leak from the battery electrolyte when lithium batteries in extreme discharge or thermal runaway conditions. The structural properties, differential charge density (DCD), density of state (DOS), gas adsorption properties, desorption time, work function and front-orbit theory calculation of pristine and Au doped CdS monolayer are explored and compared by first-principle calculation. The adsorption energy of CO, CO2, CH4 and C2H4 on the surface of Au–CdS are –0.89 eV, –0.40 eV, –0.14 eV and –0.77 eV, respectively. After doping, the adsorption type of CO and C2H4 are chemical type. Partial density of states shows that the gas molecules react with doped precious metal atoms. The adsorption ability of Au–CdS can be ranged as CO>C2H4>CO2>CH4. C2H4 on the surface of Au–CdS can desorb within 10 s at room temperature. As a result, Au–CdS exhibits tremendous prospective application as CO or C2H4 gas sensor. This study demonstrates that the adsorption and sensing properties of CdS monolayer can be improved effectively by the introduction of precious metal.

Influence of varying carbon oxides concentrations on the selectivity of an electrical sensor utilizing graphene nanoribbons

We have considered a graphene nano ribbon-based two probe-device working on the principle of alteration of current-voltage (I–V) characteristic, upon interactions of its channel material with the adsorbed gas molecules. The analysis of sensing parameters alongside the electron transport mechanism was carried out by using density functional theory (DFT) formulated on the Hohenberg-Kohen paradigm in conjunction with the non-equilibrium green functions (NEGF) method. Electronic transport through similar work reported earlier reveals that the combination of these two techniques coordinates extremely well and yields convincing electronic results. The nanoribbon-based sensing device was employed to evaluate its capability to detect Carbon dioxide (CO2) and Carbon monoxide (CO) molecules. Analysis of the computed result reveals significant modifications in the electronic property of the device upon adsorption of carbon dioxide and carbon monoxide entities on the channel surface. One such significant and dramatic change in the characteristic that we observed is the nature of the channel that transforms from metallic to semi-metallic. Another observed change is the signal amplification capability relative to the pristine device. The signal gets amplified on increasing number of molecular entities from one to six.The transformation in sensor performance is additionally supported by examining variations in multiple parameters, including adsorption energy, charge transfer, differential resistance, and the figure of merit, also known as the peak-to-valley ratio (PVR). The investigation unveiled that the currently designed zigzag graphene nanoribbon (ZGNR) model exhibits high sensitivity to both CO2 and CO gases. This phenomenon is substantiated by the elevated adsorption energy, increased charge transfer, elevated peak-to-valley ratio (PVR), and heightened sensor response. The substantial sensor response, registering at a magnitude of 8.3, underscores the efficacy of the current system in detecting both low and high concentrations of CO and CO2 molecules. The current study emphasizes the notion that understanding the electronic and transport behaviour of zigzag graphene nanoribbons (ZGNR) and analogous materials could offer valuable insights for addressing the threat of environmental pollution.

The effect of high ethanol concentration on E-nose response for diabetes detection in exhaled breath: Laboratory studies

The growing number of people with diabetes has paved the way for the creation of non-invasive devices to measure blood glucose levels and enable the detection of diabetes non-invasively, for example, through exhaled air. This paper presents the development of a breath detection system using multiple sensors (e-nose system) for the non-invasive estimation of blood glucose levels. The system employs commercially available ethanol, CO2, and acetone sensors, with synthetic breath mixtures used for testing, including four scenarios with high ethanol concentrations (0–570 ppm) as an influence factor. Results showed a mean absolute error of 0.245 ppm when analysing commonly observed acetone concentrations in diabetic breath using four sensors and the XGBoost Regressor. In mixtures with high ethanol concentrations and varying acetone concentrations (0–8.62 ppm), the CatBoost Regressor outperformed other machine learning algorithms with a mean absolute error of 0.568 ppm. This study emphasises the significant impact of ethanol on acetone detection and the need to consider ethanol levels in the developing of non-invasive devices for blood glucose prediction based on the exhaled acetone measurements. The research shows that a set of three gas sensors is optimal for estimating acetone concentrations in gas mixtures. The presented results constitute a preliminary step towards developing a non-invasive device for estimating blood glucose levels based on breath analysis, with the novelty of considering alcohol intake as a potential influencing factor.

Effects of UAS Rotor Wash on Air Quality Measurements.

Laboratory and field tests examined the potential for unmanned aircraft system (UAS) rotor wash effects on gas and particle measurements from a biomass combustion source. Tests compared simultaneous placement of two sets of CO and CO2 gas sensors and PM2.5 instruments on a UAS body and on a vertical or horizontal extension arm beyond the rotors. For 1 Hz temporal concentration comparisons, correlations of body versus arm placement for the PM2.5 particle sensors yielded R2 = 0.85 and for both gas sensor pairs exceeded R2 of 0.90. Increasing the timestep to 10 s average concentrations throughout the burns improved the R2 value for the PM2.5 to 0.95 from 0.85. Finally, comparison of whole-test average concentrations further increased the correlations between body- and arm-mounted sensors, exceeding R2 of 0.98 for both gases and particle measurements. Evaluation of PM2.5 emission factors with single factor ANOVA analyses showed no significant differences between the values derived from the arm, either vertical or horizontal, and those from the body. These results suggest that rotor wash effects on body- and arm-mounted sensors are minimal in scenarios where short duration, time-averaged concentrations are used to calculate emission factors and whole-area flux values.

Winter Carbon Dioxide Measurement in Honeybee Hives

Sensor technologies have sufficiently advanced to provide low-cost devices that can quantify carbon dioxide levels in honeybee hives with high temporal resolution and in a small enough package for hive deployment. Recent publications have shown that summer carbon dioxide levels vary throughout the day and night over ranges that typically exceed 5000 ppm. Such dramatic changes in a measurable parameter associated with bee physiology are likely to convey information about the colony health. In this work, we present data from four UK-based hives collected through the winter of 2022/2023, with a focus on seeing if carbon dioxide can indicate when colonies are at risk of failure. These hives have been fitted with two Sensirion SCD41 photoacoustic non-dispersive infrared (NDIR) carbon dioxide sensors, one in the queen excluder, at the top of the brood box, and one in the crown board, at the top of the hive. Hive scales have been used to monitor the hive mass, and internal and external temperature sensors have been included. Embedded accelerometers in the central frame of the brood box have been used to measure vibrations. Data showed that the high daily variation in carbon dioxide continued throughout the coldest days of winter, and the vibrational data suggested that daily fanning may be responsible for restoring lower carbon dioxide levels. The process of fanning will draw in colder air to the hive at a time when the bees should be using their energy to maintain the colony temperature. Monitoring carbon dioxide may provide feedback, prompting human intervention when the colony is close to collapse, and a better understanding may contribute to discussions on future hive design.

Digital Technology: A Step to Protect Cave Art of Indian Subcontinent as National Heritage – A Review

Abstract The integration of digital technology, particularly virtual reality, is proving to be a pivotal tool in preserving and sharing the rich cultural heritage found within Cave art sites. This article deliberates on the use and application of digital technology in the field of cave painting conservation that provides an authentic and effective method of Cave art preservation for the future. Employing advanced techniques of photogrammetry, terrestrial laser scanning, structured light scanning, and high-resolution photography can digitally preserve the intricate details of Cave art, while the utilization of humidity, temperature, and CO2 sensors may provide a comprehensive digital monitoring system to track the condition of Cave art over time. This paper deals with the evolving landscape of technologies and their application to safeguard Cave art from environmental degradation and anthropogenic factors. The digitization of cave art has the potential to accurately conserve a site if used in an effective manner while also allowing visitors to experience this art form in ways never before feasible. The application of advanced digital technology in cave art conservation is not merely a choice but a necessity, considering the impermanent nature of these paintings in their natural environment. This paper underscores the urgency and significance of leveraging digital tools to ensure the enduring legacy of Cave art, an example of how digital technologies, as they evolve, will play an increasingly essential role in the conservation and dissemination of our world’s extraordinary cultural heritage.

Sensitivity Enhancement of CO2 Sensors at Room Temperature Based on the CZO Nanorod Architecture.

Cobalt-doped ZnO (CZO) thin films were deposited on glass substrates at room temperature by radio frequency (RF) magnetron sputtering of a single target prepared with ZnO and Co3O4 powders. Changes in the crystallinity, morphology, optical properties, and chemical composition of the CZO thin films were investigated at various sputtering powers of 45, 60, and 75 W. All samples presented a hexagonal wurtzite-type structure with a preferential c-axis at the (002) plane, along with a distinct change in the strain values through X-ray diffraction patterns. Scanning electron and atomic force microscopy revealed uniform and dense deposition of nanorod CZO samples with a high surface roughness (RMS). The Hall mobility and carrier concentration increased with the introduction of Co+ ions into the ZnO matrix, as seen from the Hall effect study. The gradual increase of the power applied on the target source significantly affected the morphology of the CZO thin film, which is reflected in the CO2-sensing performance. The best gas response to CO2 was recorded for CZO-60 W with a response of 1.45 for 500 ppm CO2, and the response/recovery times were 72 and 35 s, respectively. The distinguishing feature of the CZO sensor is its ability to effectively and rapidly detect the CO2 target gas at room temperature (∼27 °C, RT).