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In this work, star-shaped heterostructures of Ag 2 O/BaTiO 3 /ZnO were proposed as resistive-type gas sensors. Through combined analyzes of XRD, SEM, and EDS, the structure, surface morphology, and the elemental composition of the synthesized materials were confirmed. The star-shaped morphology of BaTiO 3 /ZnO was found to be intact even after loading with different quantities of Ag 2 O, as revealed by the SEM analysis. The impact of loading different amounts of Ag 2 O (ranging from 1 % to 3 %) on the sensors' ability to detect volatile organic compounds (VOCs) and carbon dioxide (CO 2) was concretely gauged by their response, response/recovery times, selectivity, and long-term stability. For the detection of ethanol/acetone, CO 2 , and methanol, a 1:2 M ratio of BaTiO 3 /ZnO with 1 % and 2 % Ag 2 O, and a 1:3 M ratio of BaTiO 3 /ZnO with 1 % Ag 2 O were determined as the optimum content, respectively. From the gas sensing analysis, Ag 2 O/BaTiO 3 /ZnO sensors with different amounts of Ag 2 O additive gave different optimal temperatures for different target gases. Lower operating temperatures, i.e. 180 °C, and 260 °C for CO 2 and VOCs, respectively, rapid responsivity with a response time of <1 s for VOCs, and fast recovery times (as low as 10, 13, and 6 s for ethanol, acetone, and methanol vapors, respectively) were observed. More importantly, the sensor exhibited good selectivity for several possible interferences. The underlying reasons for the acceleration of the gas molecules adsorption, hence the fast sensor response, are the high specific surface area established by the star-shaped morphology, and compatible energy band alignment at the p/n interface coupled with the catalytic effect induced by Ag 2 O. [ABSTRACT FROM AUTHOR]

Real‐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. [ABSTRACT FROM AUTHOR]

To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area. [ABSTRACT FROM AUTHOR]

A novel prototype based on the combination of a multi-junction, high-efficiency photovoltaic (PV) module and a supercapacitor (SC) able to self-power a wireless sensor node (WSN) for outdoor air quality monitoring has been developed and tested. A PV module with about an 8 cm2 active area made of eight GaAs-based triple-junction solar cells with a nominal 29% efficiency was assembled and characterized under terrestrial clear-sky conditions. Energy is stored in a 4000 F/4.2 V supercapacitor with high energy capacity and a virtually infinite lifetime (104 cycles). The node power consumption was tailored to the typical power consumption of miniaturized, low-consumption NDIR CO2 sensors relying on an LED as the IR source. The charge/discharge cycles of the supercapacitor connected to the triple-junction PV module were measured under illumination with a Sun Simulator device at selected radiation intensities and different node duty cycles. Tests of the miniaturized prototype in different illumination conditions outdoors were carried out. A model was developed from the test outcomes to predict the maximum number of sensor samplings and data transmissions tolerated by the node, thus optimizing the WSN operating conditions to ensure its self-powering for years of outdoor deployment. The results show the self-powering ability of the WSN node over different insolation periods throughout the year, demonstrating its operation for a virtually unlimited lifetime without the need for battery substitution. [ABSTRACT FROM AUTHOR]

Intelligent packaging has attracted research interest during the last decades. More specifically, food packaging is of great importance due to the utmost need to monitor and maintain food quality until consumption. Thus, there is a high demand for sensors capable of detecting gases such as CO2, emitted by packaged meat or chicken which serve as freshness indicators. In the present work, a sensor based on Cu2O nanocubes was fabricated and tested against CO2 at room temperature. Cu2O nanocubes were synthesized by solution‐based methods and deposited on commercial interdigitated electrodes. Specifically, the Cu2O‐based sensor successfully detected down to 5% CO2 (50,000 ppm) in the ambient atmosphere, at room temperature, with a response time of less than 90 s. This level of CO2 is in the range that indicates the unsuitability of packaged meat for consumption. Furthermore, the sensor was able to maintain its response to CO2 after being stored in the fridge for 20 days, showcasing its endurance under food maintenance conditions. [ABSTRACT FROM AUTHOR]

Carbon dioxide is an important parameter for indoor air quality (IAQ) monitoring and demand controlled ventilation (DCV). Usually, CO2 sensors are wall‐mounted at 0.9–1.8 m (3–6 ft) height as prescribed by LEED, although ASHRAE standards seemed to relax this requirement. In this work, we investigate whether positioning these sensors in the ceiling is effective and advantageous. We studied CO2‐level measurements for HVAC control in configurations with mixing ventilation and found that CO2 from human exhalations experiences buoyancy from several factors. We calculated buoyancy from air properties, and we introduced the notion of "stratification temperature" for exhaled air. By simulation, we test the sensitivity to temperature, and we conducted in situ in vivo measurements to acquire more detailed insights in the feasibility of ceiling sensor positions. Buoyancy calculations show that in exhaled air, the positive buoyancy of H2O approximately compensates for the negative buoyancy of CO2, so that thermal buoyancy is the most dominant factor. Exhaled air, containing CO2 to be measured, will rise towards a ceiling that has a temperature below the stratification temperature. Computational fluid dynamics (CFD) simulations of a small office space indicate that this can also be the case in the presence of air flows induced by a mechanical ventilation system. The measurement results support that using "properly mounted" CO2 sensors in the ceiling gives lower variability in CO2 measurements and faster response than wall‐mounted sensors and yields slightly higher values than wall sensors. Our results highlight the need to update the standards and regulations for sensing CO2 to include ceiling‐mounted sensors. [ABSTRACT FROM AUTHOR]

Metal organic frameworks (MOFs) have tunable chemical structures, orderly pore structures, and modifiable surface functional groups making them a promising material for environmental remediation. However, their rigid powder morphology poses significant challenges for customizing MOF structures with adjustable mechanical properties. Here, we synthesis the stretchable UV‐curable MOF hydrogels through high‐resolution digital light processing 3D printing, and evaluated its adsorption effect on seven common dyesand.The results showed that it had selective adsorption effect on methylene blue (MB), and the adsorption effect increased with the increase of the initial concentration of MB: its adsorption capacity on 6 mgL−1, 12 mgL−1 and 24 mgL−1 MB solution reached 13.55 mgg−1, 26.31 mgg−1 and 50.70 mgg−1 respectively. Compared to powdered MOFs, the 3D‐printed MOF (Cu‐BTC) exhibited superior adsorption for dye and radioactive pollution, with methylene blue adsorption increasing from 8.93 mgg−1 to 25.05 mgg−1 and I2 adsorption from 141.56 mgg−1 to 792.65 mgg−1. Furthermore, the 3D‐printed MOF structure proved easily cleanable with dilute HCl and reusable over five times without degradation. In addition, our study highlighted the suitability of the 3D‐printed MOF for CO2‐sensing devices, demonstrating its broad applicability in environmental remediation and sensing technology. [ABSTRACT FROM AUTHOR]

Realization of high- k dielectric oxide material-based devices such as photodetectors (PDs)/gas sensors fabricated on silicon substrate seems to be the utmost promising as a cost-effective approach to use in next generation optoelectronic field. We have explored metal/CeO 2 /p-Si heterojunction to construct as a broadband (BB) PD and CO 2 gas sensor device. The effect of post-annealing procedure on photodetection and CO 2 gas sensing characteristics were correlated using AFM, XRD, XPS, Raman and UV–Vis studies. At 0 V bias, the fabricated as-deposited Au/CeO 2 /p-Si PD device exhibits outstanding performance with enhanced responsivity of 85 AW−1 (at 910 nm), detectivity of 1.36×1015 Jones (at 850 nm), EQE of 1.3×104 % (at 850 nm), faster rise and fall times of 124 ms and 107 ms (at 900 nm). On the other hand, the photodetection characteristic parameters were slightly deteriorated as a function of annealing with respect to the as-deposited device. One of the causes for this fact was attributed to the increase in rms surface roughness (AFM) and decrease in absorbance of CeO 2 thin film (UV–Vis). Additionally, Ag/CeO 2 /p-Si heterojunction was studied as a CO 2 gas sensor and evaluated response/recovery times as a function of CO 2 gas concentration. Sensing responses of CeO 2 as-deposited, CeO 2 -400, CeO 2 -500, CeO 2 -600 and CeO 2 -700 °C were around 58, 60, 62, 81 and 90 %, respectively, when exposed to 200 ppm of CO 2. Thus, we validate that the prospect of integrating enhanced performance in BB PDs and CO 2 gas sensors using metal/CeO 2 /p-Si heterojunction could serve as a basic building block in near future optoelectronic applications. Furthermore, the effect of post-annealing temperature on the morphological, structural, BB photodetection and CO 2 gas sensing properties were discussed in detail. [ABSTRACT FROM AUTHOR]

Carbon dioxide is a colorless, odorless, and non-flammable gas and is claimed to be the fourth most abundant in the earth's atmosphere. Carbon dioxide emission is mainly generated by human and animal exhalation, decomposition of organic matter, and forest fires. Moreover, human activities in the industrial sector emit high levels of carbon dioxide gas, such as through fossil fuel burning, transportation, and deforestation. It is also an asphyxiant and high exposure to it may lead to health effects in humans such as headaches, breathing difficulty, tiredness, coma, and elevated blood pressure. Therefore, in this paper, a carbon dioxide gas sensor with IoT using TiO2 is proposed to observe varying concentrations of carbon dioxide gas at room temperature. Three similar gas sensors were fabricated via screen-printing technology to compare their performance towards carbon dioxide. The hardware development consisted of an Arduino Uno R3 with ESP 8266 Wi-Fi module, wires, LCD display, red and green LEDs, and a 5V power supply. The ThingSpeak application was integrated with the gas sensor and hardware parts to monitor the carbon dioxide concentration in a real-time system. Gas sensor G1 produced the highest response and highest sensitivity with values of 2.120 and 0.245, respectively. [ABSTRACT FROM AUTHOR]

Open-ventilated greenhouses have reasonable setup costs and low operational costs for growers, which is crucial and most appealing for this research. These attributes fit developing nations like Thailand and other tropical regions. It is challenging to control the equipment intended to obtain an ideal microclimate. This research was conducted in an actual greenhouse setting for data collection and experiments, with a proposed system for adaptive equipment control via web integration. Also, the proposed multivariate multistep LSTM was forecasted over 1 h and cooperated with sensor data. Additional sensors, like a leaf wetness sensor and a CO2 sensor, were installed for detecting plant-level precision for vaporization, rather than greenhouse-level. The proposed system can optimize the indoor temperature within 34.5 to 36 °C with a 39 to 40 °C outdoor temperature. Also, humidity was still at the ideal level of 68 to 70%; more precisely, the wetness value was below 300 throughout the experiment. The model accuracy achieved a sufficient RMSE (0.49) and R2 (0.9788). This proposed system architecture and MM-LSTM model has potential as one dimension of a fully smart greenhouse system development in open-ventilated greenhouse settings in tropical regions and Southeast Asian nations for a better yield rate and less human interaction. [ABSTRACT FROM AUTHOR]

The study comprehensively evaluates low-cost CO2 sensors from different price tiers, assessing their performance against a reference-grade instrument and exploring the possibility of calibration using different machine learning techniques. Three sensors (Sunrise AB by Senseair, K30 CO2 by Senseair, and GMP 343 by Vaisala) were tested alongside a reference instrument (Los Gatos precision greenhouse gas analyzer). The results revealed differences in sensor performance, with the higher cost Vaisala sensors exhibiting superior accuracy. Despite its lower price, the Sunrise sensors still demonstrated reasonable accuracy. Meanwhile, the K30 sensor measurements displayed higher variability and noise. Machine learning models, including linear regression, gradient boosting regression, and random forest regression, were employed for sensor calibration. In general, linear regression models performed best for extrapolating data, whereas decision tree-based models were generally more useful in handling non-linear datasets. Notably, a stack ensemble model combining these techniques outperformed the individual models and significantly improved sensor accuracy by approximately 65%. Overall, this study contributes to filling the gap in intercomparing CO2 sensors across different price categories and underscores the potential of machine learning for enhancing sensor accuracy, particularly in low-cost sensor applications. [ABSTRACT FROM AUTHOR]

The spatial prediction of soil CO2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO2 flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO2 flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO2 flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO2 flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha−1 d−1, R2 = 0.73), showing a stronger fitting ability for the spatial variability of soil CO2 flux. The spatial mapping results of soil CO2 flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO2 flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO2 flux, thereby supporting regional agricultural sustainable development and climate change response strategies. [ABSTRACT FROM AUTHOR]

Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO2) concentration daily. [ABSTRACT FROM AUTHOR]

We developed and tested a complete measurement system to quantify CO2 and CH4 emissions at the scale of an industrial site based on the innovative sensor Airborne Ultra-light Spectrometer for Environmental Application (AUSEA), operated on board uncrewed aircraft vehicles (UAVs). The AUSEA sensor is a new light-weight (1.4 kg) open-path laser absorption spectrometer simultaneously recording in situ CO2 and CH4 concentrations at high frequency (24 Hz in this study) with precisions of 10 ppb for CH4 and 1 ppm for CO2 (when averaged at 1 Hz). It is suitable for industrial operation at a short distance from the sources (sensitivity up to 1000 ppm for CO2 and 200 ppm for CH4). Greenhouse gas concentrations monitored by this sensor throughout a plume cross section downwind of a source drive a simple mass balance model to quantify emissions from this source. This study presents applications of this method to different pragmatic cases representative of real-world conditions for oil and gas facilities. Two offshore oil and gas platforms were monitored for which our emissions estimates were coherent with mass balance and combustion calculations from the platforms. Our method has also been compared to various measurement systems (gas lidar, multispectral camera, infrared camera including concentrations and emissions quantification system, acoustic sensors, ground mobile and fixed cavity ring-down spectrometers) during controlled-release experiments conducted on the TotalEnergies Anomaly Detection Initiatives (TADI) test platform at Lacq, France. It proved suitable to detect leaks with emission fluxes down to 0.01 gs-1 , with 24 % of estimated CH4 fluxes within the - 20 % to + 20 % error range, 80 % of quantifications within the - 50 % to + 100 % error range and all of our results within the - 69 % to + 150 % error range. Such precision levels are better ranked than current top-down alternative techniques to quantify CH4 at comparable spatial scales. This method has the potential to be operationally deployed on numerous sites and on a regular basis to evaluate the space- and time-dependent greenhouse gas emissions of oil and gas facilities. [ABSTRACT FROM AUTHOR]

Salt marshes provide many important ecosystem services, key among them being carbon sequestration. However, a large degree of uncertainty remains in salt marsh carbon budgets, particularly during colder months of the year when salt marsh microbial and vegetative activity is assumed to dormant. We also lack data on urban systems. In this study, we used an easily portable carbon dioxide sensor package to directly measure net carbon dioxide (CO2) fluxes throughout the winter in a temperate, urban salt marsh. We sampled across the dormant season both on normal (cold) temperature days and on days that were anomalously warm (defined here as air temperatures 2.8°C above the long-term average). We demonstrated that median (±mad) daytime CO2 fluxes doubled on the warm days, compared to cold days (1.7 ± 2 mmol m-2 h-1, 0.7 ± 1.3 mmol m-2 h-1, respectively). We also show that net CO2 emissions scaled with soil temperature. The high day-to-day variability, however, implies that infrequent or sparse measurements cannot sufficiently capture the temporal dynamics of dormant season salt marsh net CO2 fluxes. The magnitude of the net CO2 source from our sampling during the dormant season leads us to hypothesize that, as mean annual temperatures continue to increase, dormant season CO2 emissions from salt marshes will increasingly offset growing season carbon dioxide uptake. This change compromises the carbon sequestration capacity, and therefore the climate mitigation potential of these ecosystems. Future studies should focus on quantifying the impact of dormant season CO2, and other greenhouse gases on salt marsh carbon budgets. [ABSTRACT FROM AUTHOR]

Facing the current sustainability challenges requires reduction in building stock energy usage towards achieving the European Green Deal targets. This can be accomplished by adopting techniques such as fault detection and diagnosis and efficiency optimization. Taking an Estonian school as a case study, an occupancy-based algorithm for scheduling ventilation operations in buildings is here developed starting only from energy use data. The aim is optimizing the system's operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use the historical data of a school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time-series methods, then creates 12 occupancy schedules that are either based on classical detection with an on-off method or on occupancy estimation for demand-controlled ventilation. We find that the latter replaces the 60% capacity of current on-off schedules by 30% or even 0%, with energy savings ranging from 3.5% to 66.4%. The corresponding costs are reduced from 18.1% up to 62.6%, while still complying with current national regulations for indoor air quality. Remarkably, our method can immediately be extended to other countries, as it relies only on occupancy schedules that ignore weather and other location-specific factors. [ABSTRACT FROM AUTHOR]

We present a highly sensitive multi-parameter sensor for magnetic and gas detection. The device is based on an in-plane doubly clamped micro-beam micro-resonator, which is electrothermally heated. It acts as a Lorentz force magnetic sensor of high sensitivity, good linearity, good repeatability, and low hysteresis effect. It also functions as a gas-sensor based on the cooling/heating effect of the micro-beam as demonstrated for carbon dioxide (CO2) and methane (CH4) detection. The CO2/CH4 sensor shows high sensitivity and excellent linearity. In addition, we demonstrate simultaneous magnetic and gas detection by tracking the frequency shift of the first two symmetric and anti-symmetric modes at the same time. We show that the sensitivity of the magnetometer is gas-independent and only depends on the frequency shift of the second mode, which is unaffected by variations of the thermal axial load. For the first time, high sensitivity to magnetic fields, CO2, and CH4 is demonstrated using the same device. The demonstrated simultaneous and highly-sensitive multi-parameter sensing platform using a single resonator is promising for smart environmental and monitoring applications. [ABSTRACT FROM AUTHOR]

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

This document is a correction notice for an article titled "Semantic representation of neural circuit knowledge in Caenorhabditis elegans" published in the journal Brain Informatics. The correction addresses errors in the referencing and citation of the original article. It provides a corrected reference list and a table listing all citations in the correct order. The corrected manuscript is available upon request from the corresponding author. The document does not provide any additional information beyond the correction notice. [Extracted from the article]

Fine‐tuning the sensing properties of carbon dioxide (CO2) colorimetric sensors is a challenging task. In this study, a short synthetic route is developed for CO2‐responsive nanoparticles with diverse sensitivity and detection range across a wide range of CO2 concentrations. This involves the addition of oily liquids as inter‐chain spacers, which changes the diffusivity of CO2 gas into the sensing matrices without other chemical modifications. R values and the limit of detection (LOD) are estimated from Euclidean distance (ED) curves, indicating a ten‐fold difference in sensitivity. Optical and physical analyses revealed that these results arise from intercalating inter‐chain spacers between polymeric chains and modifying the gas diffusivity. Furthermore, the sensitivity and detection range can be easily tuned by mixing two CO2‐responsive nanoparticles having different inter‐chain spacers, allowing the fabrication of the sensors with subtle differences in sensitivity and detection range. Based on these results, a CO2 level meter with a horizontal linear gauge that allows visible identification of the level of CO2 over the entire range of CO2 concentrations (from 0% to 100%) is developed. Consequently, the findings will be helpful in achieving a better understanding of gas information and finding an effective way to control the characteristics of colorimetric sensors. [ABSTRACT FROM AUTHOR]

Carbon nanowalls (CNWs) have attracted significant attention for gas sensing applications due to their exceptional material properties such as large specific surface area, electric conductivity, nano- and/or micro-porous structure, and high charge carrier mobility. In this work, CNW films were synthesized and used to fabricate gas sensors for carbon dioxide (CO2) gas sensing. The CNW films were synthesized using an inductively-coupled plasma (ICP) plasma-enhanced chemical vapor deposition (PECVD) method and their structural and morphological properties were characterized using Raman spectroscopy and electron microscopy. The obtained CNW films were used to fabricate gas sensors employing interdigitated gold (Au) microelectrodes. The gas sensors were fabricated using both direct synthesis of CNW films on interdigitated Au microelectrodes on quartz and also transferring presynthesized CNW films onto interdigitated Au microelectrodes on glass. The CO2 gas-sensing properties of fabricated devices were investigated for different concentrations of CO2 gas and temperature-ranges. The sensitivities of fabricated devices were found to have a linear dependence on the concentration of CO2 gas and increase with temperature. It was revealed that devices, in which CNW films have a maze-like structure, perform better compared to the ones that have a petal-like structure. A sensitivity value of 1.18% was obtained at 500 ppm CO2 concentration and 100 °C device temperature. The CNW-based gas sensors have the potential for the development of easy-to-manufacture and efficient gas sensors for toxic gas monitoring. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

The digital age has brought tremendous opportunities for innovation and efficiency. However, it has also exposed businesses, governments, and individuals to a range of cyber threats, such as data breaches, network attacks, ransomware, malicious insiders, and identity theft. This requires the implementation of robust cybersecurity measures to safeguard sensitive information and ensure the uninterrupted operation of all critical IT systems. This paper aims to provide a facial recognition security system for cyber-physical security that incorporates a neural network and intelligent algorithms to assess the severity level of security breaches. The system also includes alarms with severity levels ranging from 1 (low severity) to 4 (critical), based on facial recognition and data from carbon dioxide and temperature sensors. In the event of a security breach, an incident response plan is presented. The proposed system is applicable to offices, workspaces, server rooms, data centers and other areas where information is stored, to enhance physical security and protect against cybersecurity threats. [ABSTRACT FROM AUTHOR]

The presence of an elevated amount of methane (CH4) in exhaled breath can be used as a non-invasive tool to monitor certain health conditions. A compact, inexpensive and transportable CH4 sensor is thus very interesting for this purpose. In addition, if the sensor is also able to simultaneously measure carbon dioxide (CO2), one can extract the end-tidal concentration of exhaled CH4. Here, we report on such a sensor based on a commercial detection module using tunable diode laser absorption spectroscopy. It was found that the measured CH4/CO2 values exhibit a strong interference with water vapor. Therefore, correction functions were experimentally identified and validated for both CO2 and CH4. A custom-built breath sampler was developed and tested with the sensor for real-time measurements of CH4 and CO2 in exhaled breath. As a result, the breath sensor demonstrated the capability of accurately measuring the exhaled CH4 and CO2 profiles in real-time. We obtained minimum detection limits of ~80 ppbv for CH4 and ~700 ppmv for CO2 in 1.5 s measurement time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We measured the column-averaged atmospheric CO2 mixing ratio (XCO2) to a variety of cloud tops with an airborne pulsed multi-wavelength integrated path differential absorption (IPDA) lidar during NASA's 2017 ASCENDS/ABoVE airborne campaign. Measurements of height-resolved atmospheric backscatter profiles allow this lidar to retrieve XCO2 to cloud tops, as well as to the ground, with accurate knowledge of the photon path length. We validated these measurements with those from an onboard in situ CO2 sensor during spiral-down maneuvers. These lidar measurements were 2–3 times better than those from previous airborne campaigns due to our using a wavelength step-locked laser transmitter and a high-efficiency detector for this campaign. Precisions of 0.6 parts per million (ppm) were achieved for 10 s average measurements to mid-level clouds and 0.9 ppm to low-level clouds at the top of the planetary boundary layer. This study demonstrates the lidar's capability to fill in XCO2 measurement gaps in cloudy regions and to help resolve the vertical and horizontal distributions of atmospheric CO2. Future airborne campaigns and spaceborne missions with this capability can be used to improve atmospheric transport modeling, flux estimation and carbon data assimilation. [ABSTRACT FROM AUTHOR]

The creation of highly sensitive optical CO2 sensors in air is an urgent problem, as it allows monitoring of the content of CO2 both indoors and in ambient air, where its level is usually 600...1000 ppm. As a sensitive element of the CO2 sensor, it is proposed to use a liquid crystal substance (cholesteric-nematic mixture based on 5CB with cholesteric impurities), supplemented with multi-layered carbon nanotubes. The spectral characteristics of the liquid crystal mixture in the range of 400...600 nm were studied. The content of carbon nanotubes ranged from 0.1 to 0.5 wt.%. It was established that with an increase in concentration in the range of 10...100 mg/m3, the transmission minimum shifts to the long-wave region of the spectrum. The maximum spectral sensitivity in this range is 6 nm ⁄ mg ⁄ m3. The researched material can be used as a sensitive element of an optical CO2 sensor. Further research should be conducted in the direction of investigating the interaction of such a composite with other gases, as well as finding opportunities to improve the properties of the composite. Perhaps the use of multi-walled carbon nanotubes. [ABSTRACT FROM AUTHOR]

Chemo-resistive gas sensors for CO 2 detection were fabricated using ZnO nanoparticles synthesized by the hydrothermal method. The CO 2 sensors were activated by thermal and/or UV light exposure. While the resistance changes of sensors exposed to CO 2 at high temperatures (150–300 °C) exhibited noise, those activated by UV light remained stable, indicating that UV light can improve CO 2 sensing. Good response, short response time (<10 s), stability, a linear calibration curve, and high sensitivity were obtained under UV light. Therefore, the UV-activated sensor could be applied in practical environments for CO 2 monitoring. Furthermore, the measurements for CO 2 also showed that the p-n transition was observed with changes in temperature. The CO 2 sensor acted as p-type at RT, while it acted as n-type at 80 °C. The p-type conductivity of ZnO at RT, which can be attributed to Zn vacancies acting as acceptors, was confirmed through PL and EDX spectra, as well as Hall Effect measurements. It should be noted that the p-n transition in sensors based on undoped materials under UV irradiation at low temperatures (<100 °C) has been less reported. The n-p transition could offer intriguing opportunities to customize the electronic properties of the nanostructured devices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Volumetric capnography in healthy ventilated neonates showed deformed waveforms, which are supposedly due to technological limitations of flow and carbon dioxide sensors. Aims: This bench study analyzed the role of apparatus dead space on the shape of capnograms in simulated neonates with healthy lungs. Methods: We simulated mechanical breaths in neonates of 2, 2.5, and 3 kg of body weight using a neonatal volumetric capnography simulator. The simulator was fed by a fixed amount of carbon dioxide of 6 mL/kg/min. Such simulator was ventilated in a volume control mode using fixed ventilatory settings with a tidal volume of 8 mL/kg and respiratory rates of 40, 35, and 30 breaths per minute for the 2, 2.5 and 3 kg neonates, respectively. We tested the above baseline ventilation with and without an additional apparatus dead space of 4 mL. Results: Simulations showed that adding the apparatus dead space to baseline ventilation increased the amount of re‐inhaled carbon dioxide in all neonates: 0.16 ± 0.01 to 0.32 ± 0.03 mL (2 kg), 0.14 ± 0.02 to 0.39 ± 0.05 mL (2.5 kg), and 0.13 ± 0.01 to 0.36 ± 0.05 mL (3 kg); (p <.001). Apparatus dead space was computed as part of the airway dead space, and therefore, the ratio of airway dead space to tidal volume increased from 0.51 ± 0.04 to 0.68 ± 0.06, from 0.43 ± 0.04 to 0.62 ± 0.01 and from 0.38 ± 0.01 to 0.60 ± 0.02 in the 2, 2.5 and 3 kg simulated neonates, respectively (p <.001). Compared to baseline ventilation, adding apparatus dead space decreased the ratio of the volume of phase III to VT size from 31% to 11% (2 kg), from 40% to 16% (2.5 kg) and from 50% to 18% (3 kg); (p <.001). Conclusions: The addition of a small apparatus dead space artificially deformed the volumetric capnograms in simulated neonates with healthy lungs. [ABSTRACT FROM AUTHOR]

Detecting CO2 in complex gas mixtures is challenging due to the presence of competitive gases in the ambient atmosphere. Photoelectrochemical (PEC) techniques offer a solution, but material selection and specificity remain limiting. Here, we constructed a hydrogen‐bonded organic framework material based on a porphyrin tecton decorated with diaminotriazine (DAT) moieties. The DAT moieties on the porphyrin molecules not only facilitate the formation of complementary hydrogen bonds between the tectons but also function as recognition sites in the resulting porous HOF materials for the selective adsorption of CO2. In addition, the in‐plane growth of FDU‐HOF‐2 into anisotropic molecular sheets with large areas of up to 23000 μm2 and controllable thickness between 0.298 and 2.407 μm were realized in yields of over 89 % by a simple solution‐processing method. The FDU‐HOF‐2 can be directly grown and deposited onto different substrates including silica, carbon, and metal oxides by self‐assembly in situ in formic acid. As a proof of concept, a screen‐printing electrode deposited with FDU‐HOF‐2 was fabricate as a label‐free photoelectrochemical (PEC) sensor for CO2 detection. Such a signal‐off PEC sensor exhibits low detection limit for CO2 (2.3 ppm), reusability (at least 30 cycles), and long‐term working stability (at least 30 days). [ABSTRACT FROM AUTHOR]

In this paper, a facile one-step drop method is developed to convert reduced graphene oxide into graphene oxide nanoparticles. An aqueous solution of reduced graphene oxide is poured through the pores of anodic aluminum oxide templates to form graphene oxide nanospheres at low temperature (120 (C) and low pressure (0.8 bar) preparation conditions. Electrochemical exfoliation of graphite and reduced graphene oxide nanospheres (not hollow) were adopted to fabricate reduced graphene oxide within the pores of an anodic aluminum oxide template. The prepared reduced graphene oxide nanospheres show uniform diameters (~42.5 nm) and a low O/C ratio (8%). Graphene nanospheres achieve higher sensitivity to CO2 gas in air (at room temperature) compared to graphene oxide nanosheets (i.e., in contrast to carbon dioxide gas sensors in graphene sheets). Its sensitivity increased to 35% by shaping the nanosheets to nanospheres of reduced graphene oxide which is attributed to a surface area expansion of 7.4 105 times. Moreover, graphene oxide, reduced graphene oxide, and reduced graphene oxide nanospheres show a fast response time of 0.19 s, 0.24 s, and 0.31 s, and short recovery times of 1.14 s, 1.17 s, and 1.22 s, respectively, with higher sensitivity of reduced graphene oxide nanospheres. [ABSTRACT FROM AUTHOR]

Background: Identification of the causative agent is an essential requirement for better treatment of bloodstream infection. The BacT/Alert 3D (BioMérieux, Marcy l'Étoile, France), is a blood culture system equipped with CO2 sensors to monitor the growth of microorganisms in blood culture bottles designed to optimize bacterial growth. The aim of this study was to determine the performance of this equipment in detecting bacterial pathogens from patients with bloodstream infection in the context of low-and-middle-income countries (LMICs), with Bobo-Dioulasso Teaching Hospital as a case study. Methodology: A cross-sectional study was conducted over a period of 5 months at the Sourô Sanou University Hospital, Bobo-Dioulasso, Burkina Faso, a low-income country. Blood samples from a total of 231 patients with clinical suspicion of bloodstream infections were collected and processed according to the manufacturer's instructions. Results: Sixty-nine of the 231 blood culture samples of patients were positive, giving a bacteriological yield of 29.9%. Escherichia coli, Salmonella spp, and Staphylococcus aureus were the top three bacterial species isolated. Conclusion: The implementation of the BacT/Alert 3D system has significantly enhanced the diagnosis of bacteraemia in the Bobo-Dioulasso Teaching Hospital. This enhancement is marked by time savings in patient care, reduced staff workload, and an increased bacteriological yield over time. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a micro-ring resonator-based highly sensitive carbon dioxide sensor. For this purpose, a valley is created in the core of the ring, and then PbSe quantum dots (QDs) are deposited in the valley and the sensor is exposed to CO2 gas. In this way, the refractive index of the PbSe QDs increases with an increase in the concentration of CO2 gas flow, which shifts the resonant frequency of the micro-ring resonator accordingly. The designed sensor operates almost linearly over a wide range of CO2 gas concentrations and shows a high shift in resonance frequency at different concentrations of CO2 gas. The detection limit for the designed sensor is 10 ppm of CO2 gas, which is more sensitive than previously reported sensors based on microring resonators. The resonance frequency shifts are investigated by changing the width of the valley. The minimum width of the valley was determined for the evanescent field in which only the outer core of the ring affects the resonant frequency. Also, the modal analysis of the waveguide of the designed micro-ring resonator is investigated to determine the core width. The radius of the micro-ring resonator is also investigated to determine the length of the straight waveguide at 193.41 THz. [ABSTRACT FROM AUTHOR]

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The paper studies autonomous monitoring systems for climate-soil parameters equipped with humidity sensors, atmospheric pressure sensors, air/soil temperature sensors, and carbon dioxide sensors. The hard- and software are proposed in this work. The operating principle of the monitoring system is described herein. Research results concern the dynamics of soil temperature changes in agricultural fields. [ABSTRACT FROM AUTHOR]

The long-wavelength infrared region provides opportunities for selective and sensitive measurements in various gas-sensing applications. In this work, a mid-infrared laser-based sensor is designed and demonstrated for trace detection of benzene, acetylene, and carbon dioxide at ambient conditions. The sensor is based on a distributed-feedback quantum cascade laser emitting near 14.84 μm. Scanned-wavelength absorption spectroscopy and a multidimensional linear regression algorithm were employed to enable selective measurements of the target species. The laser wavelength was scanned over 673.8–675.1 cm−1 by a sine-wave injection current at 1 kHz repetition rate. Noise-limited absorbance measurement was used to calculate minimum detection limits of 0.22, 5.92, and 8.32 ppm for benzene, acetylene, and carbon dioxide, respectively, at a laser path length of 26 cm. The current measurements are limited to mixtures prepared in the lab to demonstrate the superiority of this long wavelength region for high sensitivity and interference-free multi-species measurements. Future work will apply this sensor in field measurements. [ABSTRACT FROM AUTHOR]

As an urgent international challenge, the sudden change in climate due to global warming needs to be addressed in the near future. This can be achieved through a reduction in fossil fuel utilization and through carbon sequestration, which reduces the concentration of CO2 in the atmosphere. In this study, a self-sustainable impact sensor is proposed through implementing a triboelectric nanogenerator with a CaCO3 contact layer fabricated via a CO2 absorption method. The triboelectric polarity of CaCO3 with the location between the polyimide and the paper and the effects of varying the crystal structure are investigated first. The impact sensing characteristics are then confirmed at various input frequencies and under applied forces. Further, the high mechanical strength and strong adherence of CaCO3 on the surface of the device are demonstrated through enhanced durability compared to the unmodified device. For the intended application, the as-fabricated sensor is used to detect the turning state of the paper Ddakji in a slap match game using a supervised learning algorithm based on a support vector machine presenting a high classification accuracy of 95.8%. The robust CaCO3-based triboelectric device can provide an eco-friendly advantage due to its self-powered characteristics for impact sensing and carbon sequestration. [ABSTRACT FROM AUTHOR]

Low-cost particulate matter (PM) sensors continue to grow in popularity, but issues such as aerosol sizedependent sensitivity drive the need for effective calibration schemes. Here we devise a time-evolving calibration method for the Plantower PMS5003 PM2.5 mass concentration measurements. We use 2 years of measurements from the Berkeley Environmental Air-quality and CO2 Network sensors deployed in San Francisco and Los Angeles in our analysis. The calibration uses a hygroscopic growth correction factor derived from k-Köhler Theory, where the calibration parameters are determined empirically using EPA AQS reference data at co-location sites during the period from 2021--2022. The parameters are found to vary cyclically through the seasons, and the seasonal cycles match changes in sulfate and elemental carbon PM composition fractions throughout the year. In both regions, the seasonal RH dependence calibration performs better than the uncalibrated data and data calibrated with the EPA's national Plantower calibration algorithm. In the San Francisco Bay Area, the seasonal RH dependence calibration reduces the RMSE by ~40% from the uncalibrated data and maintains a mean bias much smaller than the EPA National Calibration scheme (--0.90 vs --2.73 µg/m³). We also find that calibration parameters forecasted beyond those fit with the EPA reference data continue to outperform the uncalibrated data and EPA calibration data, enabling real-time application of the calibration scheme even in the absence of reference data. While the correction greatly improves the data accuracy, non-Gaussian distribution of the residuals suggests that other processes besides hygroscopic growth can be parameterized for future improvement of this calibration. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) gas diffusion is an important component of carbon cycling in soils. This process is particularly relevant in karst landscapes, which contain easily weathered rock, subsurface fractures, and cave networks. We instrumented three soil profiles--the shoulder, back slope, and toe slope of a sinkhole--above a karst cave in Virginia. Each profile had solidstate CO2 sensors and soil water content/temperature sensors at 20 and 60 cm depth that collected hourly measurements from 2017 to 2019. We calculated CO2 fluxes using Fick's first law along with measured soil and assumed atmospheric CO2 concentrations. With this approach, we identified occasional near-surface zero-flux planes, in which CO2 likely diffused both upward and downward. All profiles had upward CO2 fluxes during warm-season months, with maximum fluxes of 1.2 lmol CO2 m22 s21 in the shoulder and back slope versus 2.0 lmol CO2 m22 s21 in the toe slope. During cool-season months, upward CO2 fluxes were smaller (0-0.3 lmol CO2 m22 s21) and were often counteracted by downward fluxes in the toe slope, possibly driven by ventilation into the underlying cave. The toe slope had a cumulative annual efflux of 14.5 mol CO2 m22, which was .3 times greater than the other profiles. Fluxes were sensitive to soil porosity, with an order-of-magnitude difference when porosity was assumed to be 0.40 versus 0.56 cm3 cm23. The results of this study offer new insight into short-term and seasonal variations in diffusive CO2 gas transport in karst soils, and they may inform other investigations of non-uniform diffusion processes. [ABSTRACT FROM AUTHOR]

For a safe environment, harmful-gas sensors of low cost and high performance are essential. For CO2 gas sensing applications, Ba-doped CuO thin films with 4 mol% and 6 mol% Ba were produced on glass substrates using the successive ionic layer adsorption and reaction approach. Utilizing various techniques, crystallographic structures, nanomorphologies, and elemental compositions were examined to assess the impact of doping on the characteristics of the films. According to the structural and morphological analyses, the nanocrystalline films consisted of irregularly shaped nanoparticles, which assembled to form a rough surface with unequal grain sizes. Because of its nanoporous nature, the CuO:6% Ba thin film exhibited the most substantial nanomorphological change and the highest gas sensing capability. At varied CO2 gas flow rates, the maximum sensor response (9.4%) and Rair/RCO2 ratio (1.12) at room temperature (RT = 30 °C) were observed at 100 SCCM. By optimizing the sensor's operating temperature, the sensor response value reached 82.2% at 150 °C, which is approximately eight times the value at RT. Selectivity, reusability, repeatability, detection limit, and quantification limit were all tested. It shows excellent response and recovery times of 5.6 and 5.44 s. In comparison to prior literature, the improved sensor is suited for use in industrial applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the field of embedded systems, energy efficiency is a critical requirement, particularly for battery-powered devices. RISC-V processors have gained popularity due to their flexibility and open-source nature, making them an attractive choice for embedded applications. However, not all RISC-V processors are equally energy-efficient, and evaluating their performance in specific use cases is essential. This paper presents RisCO2, an RISC-V implementation optimized for energy efficiency. It evaluates its performance compared to other RISC-V processors in terms of resource utilization and energy consumption in a signal processing application for nondispersive infrared (NDIR) CO2 sensors.The processors were implemented in the PULPino SoC and synthesized using Vivado IDE. RisCO2 is based on the RV32E_Zfinx instruction set and was designed from scratch by the authors specifically for low-power signal demodulation in CO2 NDIR sensors. The other processors are Ri5cy, Micro-riscy, and Zero-riscy, developed by the PULP team, and CV32E40P (derived from Ri5cy) from the OpenHW Group, all of them widely used in the RISC-V community. Our experiments showed that RisCO2 had the lowest energy consumption among the five processors, with a 53.5% reduction in energy consumption compared to CV32E40P and a 94.8% reduction compared to Micro-riscy. Additionally, RisCO2 had the lowest FPGA resource utilization compared to the best-performing processors, CV32E40P and Ri5cy, with a 46.1% and a 59% reduction in LUTs, respectively. Our findings suggest that RisCO2 is a highly energy-efficient RISC-V processor for NDIR CO2 sensors that require signal demodulation to enhance the accuracy of the measurements. The results also highlight the importance of evaluating processors in specific use cases to identify the most energy-efficient option. This paper provides valuable insights for designers of energy-efficient embedded systems using RISC-V processors. [ABSTRACT FROM AUTHOR]

In the search for precursors to earthquakes, correlation has been found between geochemical characteristics of soil gases and seismic activity. In this paper we present evidence that seismic waves can trigger emission of soil radon (Rn) and carbon dioxide (CO2). An active experiment was performed in two fault zones in China, the Annighe fault in Sichuan province and the Xiadian fault in Heibei province. An active seismic source was used to generate seismic waves at 10 m depth in wells within bedrock. Rn and CO2 detectors were placed around the wells at a distance of ∼1 m for observing the effects of the seismic waves on the emission of the gases. The observations confirm that the seismic waves have a significant and direct effect on the concentration and flux of soil radon and carbon dioxide. When the seismic events were triggered, the observed concentrations of Rn and CO2 immediately increased and reached peak values within 5–50 min and 30–60 min, with corresponding increases of Rn and CO2 concentrations by 10.5%–238.7% and 3.1%–54.1%, respectively. The measured concentrations and flux of CO2 and Rn after the passage of the seismic waves showed strong correlation, confirming the suggestion that CO2 is the carrier gas for Rn. To the best of our knowledge this is the first direct, in‐situ measurement of gas emission caused by the passage of seismic waves and provides important constraints for better understanding of geochemical earthquake precursors. Plain Language Summary: Observed geochemical properties of soil gases migrated from the deep Earth can be used to survey seismicity, volcanic activity and emission of greenhouse gases. However, due to difficulty in natural earthquake prediction, most observations on the geochemical effects linked to seismic activity are conducted after or away from naturally occurring events, with the effects normally inferred from seismic parameters. In this study, an active seismic source based on methane gaseous detonation was employed to artificially produce seismic events along two fault zones, and in‐situ measurements of concentrations and flux of radon (Rn) and carbon dioxide (CO2) were conducted. These observations showed strong correlation between concentrations and flux of CO2 and Rn after the events, in agreement with the hypothesis that CO2 is the carrier gas for Rn in tectonically active settings. Key Points: Soil CO2 and Rn emission triggered by a new type active source that excited at 10 m deep well in bedrock are observed at first timeQuantitative effect of seismicity on the concentration and flux of soil CO2 and Rn are presentedEmission of CO2 and Rn after the seismicity show strong correlation and proved that CO2 is the carrier gas for Rn [ABSTRACT FROM AUTHOR]