CO2 Sensor Research Articles

Investigation of indicators for personal exposure and occupancy in offices by using smart sensors

Despite the advent of smart building sensors, real-time methods for capturing inhalation exposure and occupancy are limited. This study utilized stationary and wearable environmental sensors, along with Decision Tree and correlation analyses, to determine the optimal set of indicators for approximating dynamic inhalation exposures and occupancy in office environments. In a 2-week field campaign in two Swiss office buildings, real-time measures of air temperature, relative humidity, CO2, and size-resolved particle concentrations were taken at two scales: personal – via personalized vests with sensors for personal exposure detection; and room – via stationary sensors at sidewalls and office desks. Occupancy and activity profiles were collected at three scales: personal – via smartwatches; room – via visual inspections; and building – via a cloud-based location service system. A desk-mounted CO2 sensor in the center of an office was an effective indicator of personal exposures to CO2, PM2.5, and PM10. Sidewall CO2 measurements accurately captured room occupancy in open-plan offices, while desk-mounted PM10 sensors were effective in areas with transient occupancy (e.g., cafeteria). Findings from this study aid improved understanding of the complex dynamics of air pollutants in offices, and could support the development of refined methods for smart building monitoring and ventilation control.

Minimum sensor grid density and configuration to enable CO2-based demand-controlled ventilation in an office building

CO2-based demand-controlled ventilation (DCV) requires placement of CO2 sensors in air handling units (AHUs) as well as in individual zones. In complex multi-zone systems or across building portfolios, the installation and maintenance costs of these sensors are non-negligible. This study explores how CO2 sensor grids can be configured in a sparser manner to allow for CO2-based DCV of spaces adjacent to zones with CO2 sensors that may not have sensing infrastructure themselves. A simulation-based study of a 26-zone office building was conducted under a variety of occupancy schedules, number and placement of zone-level CO2 sensors, and with and without motion detectors to determine how RP-1747 DCV impacted heating, ventilation, and air conditioning (HVAC) energy use and zone CO2 concentrations when implemented traditionally or with adjacent control, for a total of 29,730 simulations. It was found that CO2 sensors installed in approximately 31% of zones could effectively enable CO2-based DCV across the entire floor plate with a negligible impact on the number of hours where CO2 concentrations were elevated, resulting in 7.3% to 17.4% higher HVAC energy savings compared to individual control of spaces in the case study building.

Physisorption-Mediated Charge Transfer in TiS2 Nanodiscs: A Room Temperature Sensor for Highly Sensitive and Reversible Carbon Dioxide Detection.

Real-time and high-performance monitoring of trace carbon dioxide (CO2) has become a necessity due to its substantial impact on the global climate, human health, indoor occupancy, and crop productivity. Two-dimensional materials such as transition metal dichalcogenides (TMDs) have gained significant interest in gas sensing applications owing to their intrinsically high surface-to-volume ratio. However, the research has been limited to prominent TMDs such as WS2 and MoS2. Specifically, the chemiresistive sensing performance of titanium disulfide (TiS2) has rarely been investigated. We present an electric-field-assisted TiS2 nanodisc assembly for the fabrication of a low-cost, low-power CO2 gas sensor based on charge transfer between physisorbed CO2 analyte molecules and TiS2 nanodiscs operating at room temperature. The physiochemical properties of the synthesized TiS2 nanodiscs were investigated via scanning electron microscopy (SEM), electron diffraction spectroscopy (EDS), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Raman spectroscopy. The fabricated sensor demonstrated an ultra-high sensor response of 60%, a fast response time of 37 s toward 500 ppm CO2 gas, and the lowest detection limit of 5 ppm under ambient conditions. The low adsorption energies and vdW interaction between CO2 molecules and TiS2 resulted in easy desorption, allowing the sensor to self-recover without the need for external stimuli, which is hardly been witnessed in other 2D material analogues. Furthermore, the sensor has excellent reproducibility and stability for successive analyte exposures, as well as excellent selectivity for CO2 over other interfering gases. This reported sensor based on 2D TMDs is the first of its type to integrate such a broad range of sensor characteristics (such as high sensor response and sensitivity, rapid response and recovery times, a high signal-to-noise ratio, and excellent selectivity at room temperature) into a single, revolutionary device for CO2 detection.

Experimental Campaign of Massive CO2 Atmospheric Releases in an Urban Area

Over recent decades, several campaigns have been carried out to collect data regarding the release and atmospheric dispersion of dense chemical products in an open field. All these experimental data are valuable information to challenge the predictions of numerical tools (Gaussian, integral-type, and CFD) and, if needed, to improve the code itself and the way we are using it. On the other hand, little attention has been paid to atmospheric dispersion releases with massive flow rates in a complex urban environment. To fill this gap, Ineris carried out an experimental campaign intended to study the atmospheric dispersion of massive CO2 releases on the CENZUB site (an action training center in an urban area located in Sissonne, France). Three CO2 releases were performed with mass flow rates of about 7 kg/s in three different configurations: one axial street release and two impacting releases (against a small and high-rise building). Several technologies of CO2 sensors were used to ensure better measurement accuracy. The main experimental campaign features and preliminary data analysis are presented. The results demonstrated the influence of the built environment on dispersion patterns.

A promising ultra-sensitive CO2 sensor at varying concentrations and temperatures based on Fano resonance phenomenon in different 1D phononic crystal designs

Detecting of the levels of greenhouse gases in the air with high precision and low cost is a very urgent demand for environmental protection. Phononic crystals (PnCs) represent a novel sensor technology, particularly for high-performance sensing applications. This study has been conducted by using two PnC designs (periodic and quasi-periodic) to detect the CO2 pollution in the surrounding air through a wide range of concentrations (0–100%) and temperatures (0–180 °C). The detection process is physically dependent on the displacement of Fano resonance modes. The performance of the sensor is demonstrated for the periodic and Fibonacci quasi-periodic (S3 and S4 sequences) structures. In this regard, the numerical findings revealed that the periodic PnC provides a better performance than the quasi-periodic one with a sensitivity of 31.5 MHz, the quality factor (Q), along with a figure of merit (FOM) of 280 and 95, respectively. In addition, the temperature effects on the Fano resonance mode position were examined. The results showed a pronounced temperature sensitivity with a value of 13.4 MHz/°C through a temperature range of 0–60 °C. The transfer matrix approach has been utilized for modeling the acoustic wave propagation through each PnC design. Accordingly, the proposed sensor has the potential to be implemented in many industrial and biomedical applications as it can be used as a monitor for other greenhouse gases.

First principles investigation of CO and CO2 adsorption on graphene nanoribbon modified by ZrOx

Carbon dioxide (CO2) is normally emitted from anthropogenic and natural sources, and it is a greenhouse gas that is directly linked to climate change. CO is a main sink of OH molecules in the troposphere to produce CO2. Precise evaluation of the concentrations of the two gases is essential for controlling their emission. The influence of Armchair-graphene nanoribbon (GNR) modification by ZrOx (where x=0,1,or2) on its adsorption of CO and CO2 is inspected in this investigation. First principles computations that employ density functional theory (DFT) are utilized to assess gas adsorption by evaluation of the adsorption energy (Ega) and length (D), exchange of charge between the gas and the structure (∆QT), density of states (DOS), along with the band structure. The modification of GNR is established by atomic substitution (doping) or deposition on GNR structure (decoration). The results indicate outstanding enhancement of CO and CO2 adsorption on the modified GNR structures. However, doping is more efficient than decoration for adsorption of both gases. In particular, the Zr doped GNR has the highest capacity for both gases' adsorption, where the adsorption energy for CO and CO2 increases 18.4 and 16.5 times, respectively, reference to the pristine GNR. The outcomes of this investigation promote the utilization of ZrOx doping of GNR as an approach for the fabrication of highly sensitive and selective environmental CO and CO2 sensors.

Effect of nanomodification of cement pastes on the CO2 uptake rate

This study investigates the effects of nano-TiO2 addition on the CO2 uptake rate of cement pastes as a function of CO2 concentration. 16x4x4 cm prismatic samples of 0.5 water/binder (w/b) cement paste with and without nano-TiO2 particles were used. These samples were placed inside a controlled environment with three initial CO2 concentrations (25%, 50%, and 100% CO2) during eight 1-hour cycles. CO2 concentration in the chamber was constantly monitored with a CO2 sensor, and was refilled each hour to the targeted concentration. CO2 uptake results showed that nano-TiO2 addition enhances the CO2 uptake rate of cement pastes, enabling the capture of more CO2 with lower CO2 concentrations, thus making the process more efficient. Using nanoparticles reduces the porosity of cement pastes. Typically, a decline in porosity produces a decrease in the samples' diffusion of CO2, implying a lower carbonation rate coefficient. However, data showed that nanomodified samples absorb more CO2 faster while the porosity is decreased, which is counterintuitive. 3D X-ray results suggest that the nanomodification of cement pastes increases the pore surface area while reducing the overall porosity. Thus, the carbonation rate coefficient does not directly depend on the pore volume but on the pore surface area.

Room-Temperature Monitoring of CH4 and CO2 Using a Metal-Organic Framework-Based QCM Sensor Showing Inherent Analyte Discrimination.

The detection of methane and carbon dioxide is of growing importance due to their negative impact on global warming. This is true for both environmental monitoring and leak detection in industrial processes. Although solid-state sensors are technologically mature, they have limitations that prohibit their use in certain situations, e.g., explosive atmospheres. Thus, there is a need to develop new types of sensor materials. Herein, we demonstrate a simple, low-cost, metal-organic framework (MOF)-based gas leak detection sensor. The system is based on gravimetric sensing by using a quartz crystal microbalance. The quartz crystal is functionalized by layer-by-layer growth of a thin metal-organic framework film. This film shows selective uptake of methane or carbon dioxide under atmospheric conditions. The hardware has low cost, simple operation, and theoretically high sensitivity. Overall, the sensor is characterized by simplicity and high robustness. Furthermore, by exploiting the different adsorption kinetics as measured by multiple harmonic analyses, it is possible to discriminate whether the response is due to methane or carbon dioxide. In summary, we demonstrate data relevant toward new applications of metal-organic frameworks and microporous hybrid materials in sensing.

English

Aerobic stability is one of the important factors affecting the nutritive value of silages for livestock and determined by various methods. The aim of the current experiment was to design a microcontroller based low cost device using CO2 sensor for determination of aerobic stability of silage and compare with the conventional temperature measurement system. It was hypothesised that the time taken to increase the silage temperature by 2oC above room temperature and produce 10 g CO2 kg- 1 DM might be similar or comparable for the same silages when the silage is exposed to air simultaneously. The microcontroller based low cost device was designed and allowed discrimination between silages in terms of aerobic stability as in the temperature measurement system. The data obtained by microcontroller based low cost device is consistent with data obtained by the temperature measurement system. The time taken to increase silage temperature by 2oC above room temperature and produce 10 g CO2 kg-1 dry matter (DM) of silage were similar in both measurement systems. It was also found that the high correlation coefficients between CO2 production and temperature until reflection point of temperature obtained and ranged from 0.990 to 0.995 for sugar beet pulp silage (SNPS) and corn silage (CS). The CO2 sensor has provided the opportunity in designing a simple and handy device with an affordable cost to measure the aerobic stability of silage as accurately as temperature measurement sys

Functionalized carbon nanotubes enabled flexible and scalable CO2 sensors

Functionalized carbon nanotubes enabled flexible and scalable CO2 sensors

Study on Local Power Plant Emissions Using Multi-Frequency Differential Absorption LIDAR and Real-Time Plume Tracking

We present a new fiber-amplifier-based differential absorption light detection and ranging (DIAL) system for range-resolved detection of carbon dioxide (CO2) and water vapor (H2O) over a range of a few kilometers. The fiber amplifier chain is seeded with a 7.14 kHz fast-switching high-spectral purity wavelength source near 1572 nm to cover ten different frequencies across the CO2/H2O line pair in ≈1.4 ms. We demonstrate the system in a study of CO2 emissions from a local power plant in Boulder, CO, USA. We use real-time wind information to predict the plume location for tracking and modeling of the CO2 emission rate to compare with the reported data from the power plant over a 13 h period. There is overall agreement with the reported burn rate, but we see periods of bias towards underestimation of the CO2 emission rate. We attribute the dropout periods to uncertainties between the measured and the plant’s local wind speed data that impact both the tracking location and the plume model predictions. Upcoming studies that will make use of real-time Doppler wind data are expected to significantly decrease these uncertainties.

Measurement and Evaluation of Greenhouse Gases with Cloud Based Logging

Abstract: Robust measurements of natural Greenhouse Gases emissions are vital for evaluating regional to global carbon budgets and for assessing climate feedbacks on natural emissions to improve climate models. To capture and analyze the high temporal variability of these fluxes in a well-defined footprint, we designed and developed an inexpensive device. In addition to automatically collect gas samples from footprint for subsequent various analyses in the laboratory, this device also utilizes a low cost carbon dioxide sensor, Temperature and Humidity Sensor, GPS sensor to measure Greenhouse Gases (GHG). Each of the devices modules were equipped with an ESP32 Wi-Fi, NodeMCU and Transceiver module to enable a local radio communication with the ground receiving Station for onward processing to cloud. This study shows the potential of a low cost and low instrument, open source software for devices development as automatic sensor network system to study GHG fluxes. Results obtained from research is shown as follows: CO2 (360 to 455 ppm), Temperature (26.9 OC to 30.9OC) and Humidity (35.5% to 36.5%) which is good for human health. However, this research proves human capacity building and promotion of open data access for research.

GO/CuO Nanohybrid-Based Carbon Dioxide Gas Sensors with an Arduino Detection Unit.

A gas sensor is a device that detects the presence of gases in a specific area. This research work demonstrates the effectiveness of gas sensors based on graphene oxide (GO) and copper oxide (CuO) semiconductor nanomaterials for the detection of carbon dioxide. GO and CuO were prepared by the modified Hummer's method and precipitation method using CuCl2 as a precursor, respectively. These materials are made into a hybrid using poly(vinyl alcohol) (PVA)/poly(vinylpyrrolidone) (PVP) polymer solutions of low concentrations and are spin coated onto the pattern-etched copper-clad substrate. The sensor is tested using a source measurement unit (SMU) to obtain the change in the resistance of the sensor in open air and in a carbon dioxide environment. The fabricated sensor with an Arduino microcontroller detection unit showed a good sensing response of 60%.

Pt-and Pd-doped carbon nanotubes with different diameters as CO sensors: A comparative DFT study

The density functional theory (DFT) is employed to assess the electronic characteristics and vibrational frequencies of CO adsorbed by several Zigzag carbon nanotubes with different diameters that are substitutionally doped using Pt and Pd. Computations were based on the Quantum Espresso (PBE) and Gaussian 16/6-31G(d,p) or LanL2DZ basis sets. The formation energy, adsorption energy, and interaction distances between CO and carbon nanotube as a function of the tube diameter were evaluated for Pt- and Pd- doped carbon nanotubes. Such doped nanotubes have relatively higher adsorption energy at smaller diameters, amounting to −2.784 eV and −2.635 eV for Pd-doped and Pt-doped carbon nanotubes, respectively. The adsorption energies of CO gas on Larger tubes Pt and Pd doped carbon nanotube are −2.207 eV, and −2.583 eV eV, respectively. Following CO adsorption, the smaller diameter Pt-doped carbon nanotube has a relatively greater change in electrical conductivity and chemical reactivity than other Pd- or Pt-doped nanotubes with relatively higher diameters. These changes are attributed to energy gaps concerning the alpha and beta HOMO and LUMO molecular orbitals. The computed Raman active mode frequencies extensively depend on diameter pre- and post-adsorption on the Pd- and Pt -nanotubes. This work offers several scientific suggestions concerning the use of Pt and Pd doped carbon nanotubes with different diameters for CO molecule sensor use cases. Such sensor materials having charge movement-based sensing might be employed as acceptors or donors.

Whole lung lavage decreases physiological dead space in patients with pulmonary alveolar proteinosis: two case reports

BackgroundPulmonary alveolar proteinosis (PAP) is a rare disease characterized by progressive accumulation of the alveolar surfactant. Whole lung lavage (WLL) using a high volume of warmed saline remains the standard therapy. However, no established bedside monitoring tool can evaluate the physiological effect of WLL in the perioperative period. Indirect calorimetry, which is generally used to measure resting energy expenditure, can detect carbon dioxide (CO2) production and mixed-expired partial pressure of CO2 breath by breath. In this physiological study, we calculated CO2 elimination per breath (VTCO2,br) and Enghoff’s dead space using indirect calorimetry and measured the extravascular lung water index to reveal the effect of WLL.Case presentationWe measured VTCO2,br, Enghoff’s dead space, and the extravascular lung water and cardiac indices before and after WLL to assess the reduction in shunt by washing out the surfactant. A total of four WLLs were performed in two PAP patients. The first case involved an Asian 62-year-old man who presented with a 3-month history of dyspnea on exertion. The second case involved an Asian 48-year-old woman with no symptoms. VTCO2,br increased, and the Enghoff’s dead space decreased at 12 h following WLL. An increase in the extravascular lung water was detected immediately following WLL, leading to a transient increase in Enghoff’s dead space.ConclusionWLL can increase efficient alveolar ventilation by washing out the accumulated surfactant. However, the lavage fluid may be absorbed into the lung tissues immediately after WLL and result in an increase in the extravascular lung water.

Real-Time Monitoring of Cement Paste Carbonation with In Situ Optical Fiber Sensors

Reinforced concrete structures are prevalent in infrastructure and are of significant economic and social importance to humanity. However, they are prone to decay from cement paste carbonation. pH sensors have been developed to monitor cement paste carbonation, but their adoption by the industry remains limited. This work introduces two new methods for monitoring cement paste carbonation in real time that have been validated through the accelerated carbonation of cement paste samples. Both configurations depart from traditional pH monitoring. In the first configuration, the carbonation depth of a cement paste sample is measured using two CO2 optical fiber sensors. One sensor is positioned on the surface of the sample, while the other is embedded in the middle. As the carbonation depth progresses and reaches the embedded CO2 sensor, the combined response of the sensors changes. In the second configuration, a multimode fiber is embedded within the paste, and its carbonation is monitored by observing the increase in reflected light intensity (1.6–18%) resulting from the formation of CaCO3. Its applicability in naturally occurring carbonation is tested at concentrations of 3.2% CO2, and the influence of water is positively evaluated; thus, this setup is suitable for real-world testing and applications.

A laser-based sensor for selective detection of benzene, acetylene, and carbon dioxide in the fingerprint region

A laser-based sensor for selective detection of benzene, acetylene, and carbon dioxide in the fingerprint region

Observations on the ultrastructure of the oral appendages of Glenea cantor Fabricius (Coleoptera: Lamiinae).

Using scanning electron microscopy, we examined the gross and ultrastructure morphology of the mandibles, labial palpi, and mandibular palpi of adult male and female Glenea cantor beetles. The morphology of these parts, both in their gross and ultrastructure, varied significantly between males and females. The lengths of the mandible, labial palpi, and mandibular palpi were clearly noticeably longer in females than in men, which is mostly related to the oviposition mechanism. In terms of the ultrastructural morphology of the mandibles, labial palpi, and mandibular palpi, seven types of sensilla were found on these parts: sensilla twig basiconica (STB I, II, III, and IV), sensilla chaetica (Scheme IV and V), Böhm's bristles (Bb), sensilla placodea (SP), sensilla trichodea (ST II), sensilla plate (SP), sensilla coeloconica (SC), and sensilla campaniformia (SCa). Females have significantly more ST I and Scheme III on their mandibles than males. The mechanical sensors SCh and Bb, olfactory sensor ST II, taste sensor STB IV, and carbon dioxide sensor and temperature and humidity sensors SC, SP, and SCa were much more developed on the labial and maxillary palpi of females compared to those of males, which further clarified the significance of the oral appendages in oviposition behavior. These findings will advance information-based technique design and the creation of information-based pest control strategies by assisting in our understanding of the host preference and oviposition behavior of adult G. cantor. RESEARCH HIGHLIGHTS: The external morphology and distribution of the oral appendages of Glenea cantor between sexes has been researched through scanning electron microscopy (SEM) for the first time. Several significant differences between males and females have been found by analyzing the oral appendages of G. cantor with SEM. The differences in the structure of oral appendages of G. cantor between sexes reflect functional differences in reproductive behaviors.

Estimating the number of occupants and activity intensity in large spaces with environmental sensors

Recently, occupant-centered control models have been widely discussed, with intelligent control models looking for more refined and dynamic regulation based on occupant information. This article introduced a novel method for using environmental sensors and machine learning algorithms to identify the number of occupants and activity intensity in large spaces. A multi-functional space was monitored for approximately two months using PIR, CO2, sound decibel, temperature and humidity sensors. To address the challenge of identifying the number of occupants in large spaces, the study proposed a non-uniformly distributed interval of occupant numbers that offsets the small fluctuations in the number of people. The study also demonstrated that PIR and CO2 level measurements could be used to estimate the headcount interval with an accuracy rate of 84.5%. Furthermore, the study employed K-means clustering to identify low-, medium-, and high-level activities in the studied space, achieving an overall accuracy rate of 89.3%. A new metric of activity intensity was introduced to measure the activities carried out indoors, which incorporated CO2 and sound decibel levels, PIR readings, and the number of occupants. This proposed metric was found to be appropriate for quantifying the activity intensity in the studied space. Overall, the method presented in this study provided a promising approach for enabling occupant-based control strategies that leverage advanced sensor data to optimize building service systems in large spaces.

Optimization of indoor thermal comfort values with fuzzy logic and genetic algorithm

It is known that in crowded environments such as educational institutions and workplaces, keeping indoor air quality and climate within certain limits contributes to success and production. For this purpose, a system has been developed to ensure air quality well-being in working environments. In our study, the Arduino processor managed by the fuzzy logic control system (FLC) starts to work with the trigger of the motion sensor HC-SR501. The inputs of the FLC system are defined as LM-35 sensor for temperature, DHT-11 for humidity, MQ-135 for air quality, MQ-9 sensor for CO and explosive gas. The designed system evaluates the instantaneous data obtained from the fuzzy logic system rule base and decides which of the output air filter, heater and alarm systems will operate at what speed. In order to increase system efficiency, fuzzy logic input membership values are optimized by genetic algorithm.