Non-dispersive Infrared CO2 Sensors Research Articles

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

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

RisCO2: Implementation and Performance Evaluation of RISC-V Processors for Low-Power CO2 Concentration Sensing.

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.

Increased CO2 levels in the operating room correlate with the number of healthcare workers present: an imperative for intentional crowd control

The air in an operating room becomes more contaminated as the occupancy of the room increases. Individuals residing in a room can potentially emit infectious agents. In order to inhibit and better understand the epidemiology of surgical site infections, it is important to develop procedures to track room occupancy level and respiration. Exhaled CO2 provides a respiratory byproduct that can be tracked with IR light and is associated with human occupancy. Exhaled CO2 can also be used as an indirect measure of the potential release and level of infectious airborne agents. We show that non-dispersive infrared CO2 sensors can be used to detect CO2 in operating room air flow conditions of 20 air changes per hour and a positive pressure of 0.03 in. H2O. The CO2 concentration increased consecutively for occupation levels of one to four individuals, from approximately 65 ppm above the background level when one individual occupied the operating room for twenty minutes to approximately 300 ppm above the background when four individuals were present for twenty minutes. The amount of CO2 detected increases as the number of occupants increase, the activity level increases, the residency time increases and when the ventilation level is reduced.

Wake respirometry allows breath-by-breath assessment of ventilation and CO2 production in unrestrained animals

SummaryQuantifying stress and energetic responses in animals are major challenges, as existing methods lack temporal resolution and elevate animal stress. We propose “wake respirometry,” a new method of quantifying fine-scale changes in CO2 production in unrestrained animals, using a nondispersive infrared CO2 sensor positioned downwind of the animal, i.e., in its wake. We parameterize the dispersion of CO2 in wakes using known CO2 flow rates and wind speeds. Tests with three bird species in a wind tunnel demonstrated that the system can resolve breath-by-breath changes in CO2 concentration, with clear exhalation signatures increasing in period and integral with body size. Changes in physiological state were detectable following handling, flight, and exposure to a perceived threat. We discuss the potential of wake respirometry to quantify stress and respiratory patterns in wild animals and provide suggestions for estimating behavior-specific metabolic rates via full integration of CO2 production across the wake.

Design of non-dispersive infrared CO2 sensor with temperature compensation

Design of non-dispersive infrared CO2 sensor with temperature compensation

A Miniaturised, Fully Integrated NDIR CO2 Sensor On-Chip.

In this paper, we present a fully integrated Non-dispersive Infrared (NDIR) CO2 sensor implemented on a silicon chip. The sensor is based on an integrating cylinder with access waveguides. A mid-IR LED is used as the optical source, and two mid-IR photodiodes are used as detectors. The fully integrated sensor is formed by wafer bonding of two silicon substrates. The fabricated sensor was evaluated by performing a CO2 concentration measurement, showing a limit of detection of ∼750 ppm. The cross-sensitivity of the sensor to water vapor was studied both experimentally and numerically. No notable water interference was observed in the experimental characterizations. Numerical simulations showed that the transmission change induced by water vapor absorption is much smaller than the detection limit of the sensor. A qualitative analysis on the long term stability of the sensor revealed that the long term stability of the sensor is subject to the temperature fluctuations in the laboratory. The use of relatively cheap LED and photodiodes bare chips, together with the wafer-level fabrication process of the sensor provides the potential for a low cost, highly miniaturized NDIR CO2 sensor.

Detection of Li-ion battery failure and venting with Carbon Dioxide sensors

Abstract Li-ion battery thermal runaway is a critical safety issue for Electric Vehicles. The proposed global technical regulation No. 20 by the United Nations on Electric Vehicle Safety requires an advanced warning 5 minutes prior to the evolution of hazardous conditions caused by thermal runaway. To achieve this 5-min advanced warning, a robust and sensitive detection methodology is required. Gas venting is often a precursor of thermal runaway, and therefore the use of gas-based detection method was evaluated in this paper to explore its response and implementation within a battery pack. The composition of battery vent-gas during a thermal runaway event includes CO2, CO, H2 and volatile organic compounds (VOCs). Among these gas species, there is still some debate about which is most suitable for detection. To resolve this debate, the composition of vent-gas under different testing conditions is summarized from the literature and CO2 is proposed as the target gas species due to its significant presence and early occurrence in all venting events. After evaluating available sensors, the Non-Dispersive Infrared (NDIR) CO2 sensor is considered due to its robustness and cost effectiveness. To further clarify the responsiveness of the NDIR CO2 sensor, an overcharging experiment leading to cell venting was conducted with a prototype gas sensor suite. The measured CO2 concentrations of over 30,000 ppm were detected with the gas sensor. Lastly, we demonstrate how a representative venting experiment of a single cell can be used to guide and set the sensed CO2 threshold that will trigger an alarm in a battery pack volume.

Sensing Elevated Carbon Dioxide Levels for Detecting Battery Cell Venting in Packs

Li-ion battery thermal runaway is a critical safety issue for large-scale battery packs, and gas venting of a battery cell is a precursor of an imminent thermal runaway. The proposed global technical regulation No.20 on Electric Vehicle Safety requires an advance warning 5 minutes prior to hazardous conditions caused by a thermal runaway event. Voltage-based detection methods are less effective in large packs with many parallel connected cells, and temperature-based methods provide slow and sparse information when fewer than 10% of the cells are instrumented. To achieve immediate detection for thermal runaway, vent-gas detection methods are promising due to their fast response and easy implementation in a pack. The composition of battery vent-gas during faults that can lead to a thermal runaway event include CO2, CO, H2, and volatile organic compounds (VOCs). The best gas species to target for a detection strategy is still debated. The composition of the vent-gas depends on cell chemistry, abuse condition, and cell state of charge. For robustness, the detection strategy needs to be sensitive to all possible failure mechanisms, be inexpensive, and require no maintenance. A Non-Dispersive Infrared (NDIR) CO2 sensor can meet these requirements, because the detection can be made immediately when the presence of CO2 is observed from the first venting event, and the sensor type has low cost and with some sensor’s lifetime over 15 years.To check the NDIR sensor responsiveness, an overcharging test for a prismatic 4.9 Ah cell was performed and the gas venting is observed before thermal runaway is triggered. Quickly after the occurrence of gas venting, the NDIR CO2 sensor showed a strong response with maximum gas concentration exceeding 30,000 ppm as shown in the figure. In a battery pack, the fast velocity of the ejected gas from cell venting ensured the fast response of the gas sensor. To apply the gas detection strategies for large battery packs, the volume and average CO2 concentration can be modeled to set the gas detection threshold. Figure 1

On-Chip Non-Dispersive Infrared CO2 Sensor Based On an Integrating Cylinder.

In this paper, we propose a novel, miniaturized non-dispersive infrared (NDIR) CO2 sensor implemented on a silicon chip. The sensor has a simple structure, consisting of a hollow metallic cylindrical cavity along with access waveguides. A detailed analysis of the proposed sensor is presented. Simulation with 3D ray tracing shows that an integrating cylinder with 4 mm diameter gives an equivalent optical path length of 3.5 cm. The sensor is fabricated using Deep Reactive Ion Etching (DRIE) and wafer bonding. The fabricated sensor was evaluated by performing a CO2 concentration measurement, showing a limit of detection of ∼100 ppm. The response time of the sensor is only ∼2.8 s, due to its small footprint. The use of DRIE-based waveguide structures enables mass fabrication, as well as the potential co-integration of flip-chip integrated midIR light-emitting diodes (LEDs) and photodetectors, resulting in a compact, low-power, and low-cost NDIR CO2 sensor.

A low cost MEMS based NDIR system for the monitoring of carbon dioxide in breath analysis at ppm levels

The molecules in our breath can provide a wealth of information about the health and well-being of a person. The level of carbon dioxide (CO2) is not only a sign of life but also when combined with the level of exhaled oxygen provides valuable health information in the form of our metabolic rate. We report upon the development of a MEMS-based non-dispersive infrared CO2 sensor for inclusion in a hand held portable breath analyser. Our novel sensor system comprises a thermopile detector and low power MEMS silicon on insulator (SOI) wideband infrared (IR) emitter. A lock-in amplifier design permits a CO2 concentration of 50ppm to be detected on gas bench rig. Different IR path lengths were studied with gases in dry and humid (25% and 50% RH) in order to design a sensor suitable for detecting CO2 in breath with concentrations in the range of 4–5%. A breath analyser was constructed from acetal and in part 3D printed with a side-stream sampling mechanism and tested on a range of subjects with two data-sets presented here. The performance of the novel MEMS based sensor was validated using a reference commercial breath-by-breath sensor and produced comparable results and gave a response time of 1.3s. Further work involves the detection of other compounds on breath for further metabolic analysis and reducing the overall resolution of our MEMS sensor system from ca. 250ppm to 10ppm.

Processing and Performance of MOF (Metal Organic Framework)-Loaded PAN Nanofibrous Membrane for CO2 Adsorption

The objective of this experimental study is to produce a nanofibrous membrane functionalized with adsorbent particles called metal organic framework (MOF) in order to adsorb CO2 from a gas source. Therefore, Polyacrylonitrile (PAN) was chosen as the precursor for nanofibers and HKUST-1, a Cu-based MOF, was chosen as adsorbent. The experimental process consists of electrospinning PAN solution blended with HKUST-1 to produce a nanofibrous mat as working substrates. The fibers were collected in a cylindrical canister model. SEM image of this mat showed nanofibers with the presence of small adsorbent particles, impregnated into the as-spun fibers discretely. To increase the amount of MOF particles for effectual gas adsorption, a secondary solvothermal process of producing MOF particles on the fibers was required. This process consists of multiple growth cycles of HKUST-1 particles by using a sol-gel precursor. SEM images showed uniform distribution of porous MOF particles of 2-4 µm in size on the fiber surface. Energy dispersive spectroscopy report of the fiber confirmed the presence of MOF particles through the identification of characteristic Copper elemental peaks of HKUST-1. To determine the thermal stability of the fibrous membrane, Thermogravimetric analysis of HKUST-1 consisting of PAN fiber was performed where a total weight loss of 40% between 210 and 360 °C was observed, hence proving the high-temperature durability of the synthesized membrane. BET surface area of the fiber membrane was measured as 540.73 m2/g. The fiber membrane was then placed into an experimental test bench containing a mixed gas inflow of CO2 and N2. Using non-dispersive infrared CO2 sensors connected to the inlet and outlet port of the bench, significant reduction of CO2 in concentration was measured. Comparative IR spectroscopic analysis between the gas-treated and gas untreated fiber samples showed the presence of characteristic peak in the vicinity of 2300 and 2400 cm−1 which verifies the adsorption of CO2.

Performance Evaluation of a Smart CoAP Gateway for Remote Home Safety Services

In this paper, a smart constrained application protocol (CoAP)-based gateway with a border router is proposed for home safety services to remotely monitor the trespass, fire, and indoor air quality. The smart CoAP gateway controls a home safety sensor node with a pyroelectric infrared motion sensor, a fire sensor, a humidity and temperature sensor, and a non-dispersive infrared CO2 sensor and gathers sensing data from them. In addition, it can convert physical sensing data into understandable information and perform packet conversion as a border router for seamless connection between a low-power wireless personal area network (6LoWPAN) and the Internet (IPv6). Implementation and laboratory test results verify the feasibility of the smart CoAP gateway which especially can provide about 97.20% data throughput.

NDIR Gas Sensor for Spatial Monitoring of Carbon Dioxide Concentrations in Naturally Ventilated Livestock Buildings

The tracer gas ratio method, using CO2 as natural tracer, has been suggested as a pragmatic option to measure emissions from naturally ventilated (NV) barns without the need to directly estimate the ventilation rate. The aim of this research was to assess the performance of a low-cost Non-Dispersive Infra-Red (NDIR) sensor for intensive spatial field monitoring of CO2 concentrations in a NV dairy cow house. This was achieved by comparing NDIR sensors with two commonly applied methods, a Photo-Acoustic Spectroscope (PAS) Gas Monitor and an Open-Path laser (OP-laser). First, calibrations for the NDIR sensors were obtained in the laboratory. Then, the NDIR sensors were placed in a dairy cow barn for comparison with the PAS and OP-laser methods. The main conclusions were: (a) in order to represent the overall barn CO2 concentration of the dairy cow barn, the number of NDIR sensors to be accounted for average concentration calculation was dependent on barn length and on barn area occupation; and (b) the NDIR CO2 sensors are suitable for multi-point monitoring of CO2 concentrations in NV livestock barns, being a feasible alternative for the PAS and the OP-laser methods to monitor single-point or averaged spatial CO2 concentrations in livestock barns.

Enrichment-layer Based Miniaturized Non-dispersive Infrared CO2 Sensor

A new CO2 “hybrid” IR sensor concept for indoor air quality (IAQ) combining infrared detection and CO2 sorbent material is presented. The preparation and characterization of the CO2 sensitive layers are described and the CO2 sorption process, measured with IR absorption technique, is illustrated.

Vertical soil–air CO2 dynamics at the Takayama deciduous broadleaved forest AsiaFlux site

At the Takayama deciduous broadleaved forest Asiaflux site in Japan, the ecosystem carbon dynamics have been studied for more than two decades. In 2005, we installed non-dispersive infrared CO2 sensors in the soil below the site's flux tower to systematically study vertical soil–air CO2 dynamics and explain the behavior of soil surface CO2 efflux. Soil–air CO2 concentrations measured from June 2005 through May 2006 showed sinusoidal variation, with maxima in July and minima in winter, similar to the soil CO2 effluxes measured simultaneously using open-flow chambers. Soil–air CO2 concentrations increased with soil depth from 5 to 50 cm: from 2,000 to 8,000 ppm in the summer and from 2,000 to 3,000 ppm in the winter under snow. Summer soil–air CO2 concentrations were positively correlated with soil moisture on daily and weekly scales, indicating that the Oi, Oe, and A horizons, where decomposition is accelerated by high-moisture conditions, contributed substantially to CO2 emissions. This result is consistent with the short residence time (about 2 h) of CO2 in the soil and larger emissions in shallow soil layers based on our diffusion model. We revealed for the first time that soil–air CO2 concentrations in winter were correlated with both snow depth and wind speed. CO2 transfer through the snow was hundreds of times the gas diffusion rates in the soil. Our estimate of the CO2 efflux during the snow-cover season was larger than previous estimates at TKY, and confirmed the important contribution of the snow-cover season to the site's carbon dynamics.

Atmospheric CO<sub>2</sub> monitoring with single-cell NDIR-based analyzers

Abstract. We describe CO2 concentration measurement systems based on relatively inexpensive single-cell non-dispersive infrared CO2 sensors. The systems utilize signal averaging to obtain precision (1-σ in 100 s) of 0.1 parts per million dry air mole fraction (ppm), frequent calibrations and sample drying in order to achieve state-of-the-art compatibility, and can run autonomously for months at a time. Laboratory tests indicate compatibility among four to six systems to be ±0.1 ppm (1-σ), and field measurements of known reference-gases yield median errors of 0.01 to 0.17 ppm with 1-σ variance of ±0.1 to 0.2 ppm. From May to August 2007, a system co-located with a NOAA-ESRL dual-cell NDIR system at the WLEF tall tower in Wisconsin measured daytime-only daily averages of CO2 that differ by 0.26 ± 0.15 ppm (median ± 1 σ), and from August 2005 to April 2011 a system co-located with weekly NOAA-ESRL network flask collection at Niwot Ridge, Colorado measured coincident CO2 concentrations that differed by −0.06 ± 0.30 ppm (n = 585). Data from these systems are now supporting a wide range of analyses and this approach may be applicable in future studies where accuracy and initial cost of the sensors are priorities.

An innovative carbon-atom-balance-based method for diesel particulate measurement

This paper describes an innovative carbon-atom-balance-based method for diesel particulate measurement using a single non-dispersive infrared CO2 sensor. The diesel particulate sampler (DPS) uses two integrated steps of operation, i.e. collection and analysis of diesel particulate. In the collection operation, the diesel exhaust is channelled through a heated sampling line to a quartz filter where soot particulates, unburned hydrocarbons, water vapour, etc are trapped. Upon completion of particulate collection, the DPS system is isolated from the surroundings. This is followed by an analysis of the diesel-particulate sample by either one of two analysis modes, namely, the regular and ramp modes. In the regular mode, the quartz filter is heated at a constant temperature for a defined dwelling period. Multiple sessions of heating at constant temperatures with individual dwelling periods are allowed for each analysis. In the ramp mode, the temperature controller is programmed to achieve a specified heating rate. The lowest possible rate is 2 °C min-1. Some preliminary results, in terms of mass concentrations of particulates, were obtained from engine emission sampling using this DPS system.