Non-dispersive Infrared Research Articles

Winter Carbon Dioxide Measurement in Honeybee Hives

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

Prospects for Low-Resolution NDIR Sensors to Discern Ignition Properties of Fuels

Abstract The cetane number (CN) is an important fuel property to consider for compression ignition engines as it is a measure of a fuel's ignition delay. Derived cetane number (DCN) already varies significantly within jet fuels. With the expected increasing prevalence of alternative jet fuels, additional variability is expected. DCN is usually assigned to fuels using ASTM methods that use large equipment like the ignition quality tester (IQT), which consumes a lot of fuel and is cumbersome to operate. Over the last decade, there have been advances in the development of chemometric models, which use machine learning to correlate infrared spectra of fuels to fuel properties like DCN, density, and C/H ratio, among many others. These techniques have certain advantages over the ASTM methods, and previous studies performed on samples of diesel fuels have shown high accuracies in DCN prediction. However, this accuracy is generally a result of high resolution, making the equipment expensive, relatively large for handheld sensors, and power-hungry. On the other hand, nondispersive infrared (NDIR) sensors, despite having a low resolution, are attractive because they can be compact, inexpensive, and power efficient. These characteristics are important for handheld or onboard fuel sensors. However, one would anticipate a tradeoff between these advantages and accuracy. This study investigates this tradeoff and the feasibility of low-resolution NDIR sensors to discern fuel properties such as DCN by using Machine Learning models trained on real FTIR data, and DCNs obtained from IQT. DCN predictions were made for blends of ATJ/F-24, CN fuels, and neat Jet A1, A2, and JP 5, with an error limit of 10%. It was found that there seems to be sufficient variability in the near infrared (NIR) range to discern DCN with a feasible number of channels, but the channels have to be narrow (e.g., FWHMs as narrow as 60 nm). For the dataset in the study, the performance of linear models was better than the nonlinear model. Finally, NIR region beyond 1050 nm was found to be more important in DCN prediction, primarily the regions consisting of the first and second C-H overtones and the C-H combination band.

Designing an optical gas chamber with stepped structure for non-dispersive infrared methane gas sensor

Compared to traditional semiconductor and electrochemistry gas sensors, the non-dispersive infrared (NDIR) gas detection technology presents notable advantages in terms of stability and selectivity. However, the high-performance NDIR gas sensor generally requires a long optical path, resulting in a large gas chamber. In this short communication, a simple and relatively small stepped optical gas chamber (45 mm × 20 mm × 30 mm) with long optical path (94.92 mm) is designed combining simulation method. Furthermore, the stepped optical gas chamber based on the simulation structure is fabricated by a computer number control technique, and used to fabricate a NDIR gas sensor. The results show that the NDIR gas sensor based on the stepped optical gas chamber can be successfully used for CH4 gas detection with a wide detection range and good repeatability. This work provides an optional reference for developing compact NDIR gas sensor.

Precipitation fuels dissolved greenhouse gas (CO2, CH4, N2O) dynamics in a peatland-dominated headwater stream: results from a continuous monitoring setup

Stream ecosystems are actively involved in the biogeochemical cycling of carbon (C) and nitrogen (N) from terrestrial and aquatic sources. Streams hydrologically connected to peatland soils are suggested to receive significant quantities of particulate, dissolved, and gaseous C and N species, which directly enhance losses of greenhouse gases (GHGs), i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), and fuel in-stream GHG production. However, riverine GHG concentrations and emissions are highly dynamic due to temporally and spatially variable hydrological, meteorological, and biogeochemical conditions. In this study, we present a complete GHG monitoring system in a peatland stream, which can continuously measure dissolved GHG concentrations and allows to infer gaseous fluxes between the stream and the atmosphere and discuss the results from March 31 to August 25 at variable hydrological conditions during a cool spring and warm summer period. Stream water was continuously pumped into a water-air equilibration chamber, with the equilibrated and actively dried gas phase being measured with two GHG analyzers for CO2 and N2O and CH4 based on Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) and Non-Dispersive Infra-Red (NDIR) spectroscopy, respectively. GHG measurements were performed continuously with only shorter measurement interruptions, mostly following a regular maintenance program. The results showed strong dynamics of GHGs with hourly mean concentrations up to 9959.1, 1478.6, and 9.9 parts per million (ppm) and emissions up to 313.89, 1.17, and 0.40 mg C or N m−2h−1 for CO2, CH4, and N2O, respectively. Significantly higher GHG concentrations and emissions were observed shortly after intense precipitation events at increasing stream water levels, contributing 59% to the total GHG budget of 762.2 g m−2 CO2-equivalents (CO2-eq). The GHG data indicated a constantly strong terrestrial signal from peatland pore waters, with high concentrations of dissolved GHGs being flushed into the stream water after precipitation. During drier periods, CO2 and CH4 dynamics were strongly influenced by in-stream metabolism. Continuous and high-frequency GHG data are needed to assess short- and long-term dynamics in stream ecosystems and for improved source partitioning between in-situ and ex-situ production.

Experimental Preliminary Measurements of CO2 Flux for Exploring Hidden Geothermal Systems

Geothermal energy is a promising renewable energy source, and to enhance its use, identifying Hidden Geothermal Systems (HGS) without thermal manifestations on the surface is a challenging subject. Soil CO2 flux monitoring has become an effective method for detecting HGS, different from traditional methods that target thermal indicators. Expensive portable CO2 gas analyzers are commonly used for this purpose, but their high cost prevents wide applications. Thus, this study tries to design and test a cost-effective solution for measuring CO2 flux while keeping high accuracy and reliability of measured data. The method incorporates a self-made accumulation chamber connected to a relatively inexpensive CO2 portable meter, the GasLab Pro Carbon Dioxide Sampling Data Logger CM-1000. The device uses non-dispersive infrared (NDIR) to detect CO2 and is equipped with a data logger for continuous monitoring. The CO2 flux measurement is performed using the accumulation chamber method. The reliability of this tool for detecting CO2 flux is evaluated, and the experimental results are verified by comparing them with an intelligent gas flow meter, the Shimadzu Intelligent Flow Meter DFM-1000. The tool is tested in various conditions, with CO2 flux values ranging from 3.30 to 1013.02 g m-2 day-1, proving capable of measuring CO2 flux up to 1000 g m-2 day-1. Field tests were conducted at 60 sites to evaluate the tool’s performance. The results suggest that the lower measurement limit of the tool is approximately 0.1 g m-2 day-1. Overall, the cost-effective solution holds promise as a reliable tool for investigating HGS, with potential applications in other environments with similar or higher CO2 flux rates. In addition, conducting further comparison studies with a common sophisticated automatic flux tool such as LI-COR 850 can help improve the accuracy and reliability of the tool.

Compact, real-time exhaust gas recirculation rate sensor for use in natural gas combustion engine control

Natural gas fueled engines offer a 25% reduction in greenhouse gas emissions compared to diesel. However, achieving similar fuel efficiency to diesel while also minimizing harmful emissions is a challenge. Emissions and efficiency targets can be simultaneously achieved for natural gas by using a three way catalyst and exhaust gas recirculation (EGR), but this requires that the air-fuel ratio and EGR rate be measured and precisely controlled to avoid knocking combustion while maximizing efficiency. Such on-engine control can be achieved with high speed, precise, and robust measurements of EGR rate at a size and cost level compatible with onboard control systems. In this work, we present a compact, real-time laser absorption spectroscopy-based EGR sensor with potential for low-cost, on-engine control. We develop a new, computationally efficient data fitting algorithm called Integral Hashing to enable fully autonomous, low latency, real-time operation of the laser sensor at 954Hz. The complete sensor, including lasers and drivers, data acquisition, and data processing is contained in a single 25 × 25 × 8cm enclosure supplied with 15W of power. We validate the sensor to within 2% EGR rate against an extractive non-dispersive infrared (NDIR) gas sensor on a 250kW 6 cylinder natural gas engine by simultaneously measuring a set of steady state EGR rates between 0% and 25%. Additionally, we measure transient EGR rate fluctuations that are too fast to detect with the NDIR measurement. The sensor system presented here could be used to precisely control the EGR rate, improving the efficiency, emissions, and overall adoption of natural gas engines.

Characterization of a Waveguide with 45 Degrees Reflectors for the Detection of Carbon Dioxide

This paper investigates the characteristics of the angle of incidence of a waveguide with 45 degrees reflectors. Non-dispersive infrared (NDIR) gas sensors consist of an infrared source, a waveguide, and an infrared detector. The waveguide especially plays a part in an infrared path and gas cell. According to the Beer-Lambert law, the NDIR sensor with longer ray traveling path shows a higher response to the target gas. Also of particular interest is that the normal incidence ratio toward an infrared detector enhances the NDIR sensor selectivity for the target gases as well as the sensitivity. While there has been significant research on the effect on sensitivity by designing waveguide structures or ray traveling distance, this paper focuses on understanding the incidence of light toward the infrared detector by the waveguide with 45 degrees reflector and how this affects the selectivity of the sensor. A Ray tracing simulation is adopted to analyze the effect of the number of 45 degrees reflectors on the distribution of the incidence. Furthermore, a non-dispersive infrared gas sensor is applied to measure how the number of the reflectors affect to the selectivity for the target gas. The results reveal the enhancement of the selectivity by the reflectors.AcknowledgementsThis work was supported by the Korea Innovation Foundation(INNOPOLIS) grant funded by the Korea government(MSIT) (2020-DD-UP-0348). This study has been conducted with the support of the Korea Institute of Industrial Technology as "Development and commercialization for clean hydrogen production/storage and CO2 monitoring system in the field of industrial complex" (Kitech EH-23-0012) This work was supported by the Technology Innovation Program (155736, Development of Miniaturized Sensor Module Technology for Non-invasive Arterial Blood Carbon Dioxide Real-time Monitoring) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea). Figure 1

Carbon Corrosion Behavior of Carbon Support Materials for PGM-Based Catalysts in PEM Fuel Cells

The proton exchange membrane fuel cell (PEMFC) is a promising device for the transportation sector of a fossil free society, using green hydrogen from water electrolysis as a clean and sustainable option. However, sluggish kinetics as well as durability issues limit the implementation until this day. Therefore, modifications to the platinum catalysts [1] and the carbon supports [2] are made to improve the overall performance and durability of the cathode catalyst system.Herein, we present the investigation of the carbon corrosion behavior of different carbon support materials in PEMFCs using online nondispersive infrared (NDIR).The different carbon supports used for this study are commercial materials such as Vulcan, Ketjenblack EC-300J and Ketjenblack EC-600JD as well as self-synthesized mesoporous carbon nanodendrites (MCND). [3] The MCND support material is a highly porous high surface area carbon, which makes it a suitable candidate for fuel cell applications. Onto the support materials, Pt nanoparticles were deposited using a customized fluidized bed reduction reactor [4], resulting in similar Pt particle size and distribution onto the support material, thus making a comparison between different supports possible. Beginning of life (BOL) measurements in a single cell setup, as shown in Figure 1, show promising performance of the platinum catalyst supported on MCND (Pt/CMCND) compared to the platinum catalysts on commercial supports. Furthermore, we will present the investigated carbon corrosion behavior of the different support materials conducted by accelerated stress tests (AST) based on the DOE support degradation protocol. The online quantification of the evolved CO and CO2 from the cathode catalyst layer is measured using our online NDIR giving insight in the carbon corrosion resistivity of the different support materials. Together with the performance measurements, we develop a better understanding of the impact of the carbon support material on the performance and durability of the cathode catalyst layer.Literature:[1] Strasser, P; Kühl, S. Nano Energy 2016, 29, 166.[2] Ott, S. et. al., Nat. Mater. 2020, 19 (1), 77-85.[3] Numao, S. et al., Carbon 2009, 47, 306-312.[4] Hornberger, E. et. Al. J. Electrochem. Soc. 2020, 167, 114509. Figure 1: Air polarization curves of the four different catalysts under the following conditions: 80° C, 100% RH, 150 kPaabs backpressure, stoich.: 1.5 (H2) and 2 (air), min. flow: 50 ml/min (H2) and 800 ml/min (air). Figure 1

Novel pyroelectric single crystals PIN-PMN-PT and their applications for NDIR gas detectors

Ternary manganese-doped (1−x−y)Pb(In1/2Nb1/2)O3-xPb(Mg1/3Nb2/3)O3-yPbTiO3 (Mn-doped PIN-PMN-PT) single crystals have demonstrated remarkable pyroelectric properties alongside enhanced temperature stability. These attributes hold substantial promise for the advancement of high precision nondispersive IR (NDIR) applications. In this study, Mn-doped 0.21Pb(In1/2Nb1/2)O3-0.49Pb(Mg1/3Nb2/3)O3-0.30PbTiO3 single crystals were introduced and carefully investigated. A compensated structure of pyroelectric IR detector based on Mn-doped PIN-PMN-PT single crystal was designed and fabricated, and a CO2 gas concentration monitoring module was built. Results showed that Mn-doped PIN-PMN-PT single crystals exhibit high pyroelectric coefficient, and better thermal stability than binary (1−x)Pb(Mg1/3Nb2/3)O3-xPbTiO3 system. The compensated pyroelectric IR detectors using Mn-doped PIN-PMN-PT single crystals as element chips showcase a performance that is approximately fourfold higher than that of commercial LiTaO3 pyroelectric detectors. The NDIR CO2 gas sensor module was assembled and exhibited remarkable accuracy along with an impressively low minimum detection concentration. These outcomes underscores its substantial potential for practical utilization in gas monitoring applications.

Development of continuous monitoring of ambient carbon dioxide using near-infrared quartz-enhanced photoacoustic spectroscopy

We demonstrate continuous measurement of carbon dioxide (CO2) in the atmosphere using quartz-enhanced photoacoustic spectroscopy (QEPAS) at 2.008 μm with a fiber-coupled distributed-feedback laser. The QEPAS sensor is composed of a quartz tuning fork with a resonance frequency f 0 of 12.4 kHz. The photoacoustic second-harmonic (2f) signal resulting from CO2 absorption was detected using a digital lock-in amplifier. The photoacoustic signal is proportional to the laser power. The limit of detection at an absorption line of 4980.13161 cm−1 with a line strength of 3.87 × 10−22 cm2 molecule−1 cm−1 was 12 parts per million with an integration time of 10 s. The results of the continuous monitoring of ambient CO2 using the QEPAS sensor corresponded to those obtained using a nondispersive IR sensor. The QEPAS sensor exhibited a suitable performance for field measurements of the CO2 mixing ratio.

Development of a compact NDIR CO2 gas sensor for harsh environments

We present a compact non-dispersive infrared (NDIR) gas sensor that can be adopted in high-moisture and low temperature environments. The NDIR gas sensor was formed by a Micro-Electro-Mechanical Systems (MEMS) emitter working at 400℃ with a broadband radiation spectrum, a MEMS thermopile detector that integrated with an optical filter, a compact gas-cell with high optical coupling efficiency of ∼ 48 %, and an imbedded miniature thermostat. The footprint of the sensor is 24 mm × 8 mm × 8 mm (length × width × height), making it just 30 % of the size of conventional NDIR gas sensors with equivalent detection resolution, and its heat capability is around 7.29 J/K that promise a cost-effective thermoregulation. The sensor is capable of accurately detecting gas, such as Carbon dioxide, within a concentration range spanning from 0.5 % to 20 % with a reading error of ± 0.1 % covering a working temperature from −20 ℃ to 60 ℃. Because the sensor is equipped with a waterproof breathable film at the air hole, the sensor can operate in the humidity range from 0 to 100 %RH, and the maximum detection reading error is 0.1 % ± 230 ppm. It is possible to be applied for both low temperature environments (e.g. food preservation cabinet, biological cabinet, etc) and high-moisture environments (e.g. greenhouse, humidity chambers, etc.).

Determination of difluoromethane (R32) by non-dispersive infrared (NDIR) spectroscopy with temperature compensation by an improved whale optimization algorithm (IWOA)

To reduce the influence of temperature on difluoromethane (R32) gas sensors using non-dispersive infrared (NDIR), a temperature compensation method based on improved whale optimization algorithm (IWOA) and back propagation neural network (BPNN) is proposed. The IWOA that uses tent chaotic mapping to optimize the initial population of the whale optimization algorithm (WOA) and incorporates adaptive weights in the population iterations is used to optimize the weights and thresholds between nodes in each layer of the BPNN. The optimized BPNN is used to train the temperature compensation model. The experimental results show that the back propagation neural network optimized with improved whale optimization algorithm (IWOA-BPNN) reduces the maximum relative error to 1.33%, compared to 12.46 and 11.66% for the traditional BPNN and the back propagation neural network optimized with whale optimization algorithm (WOA-BPNN), respectively. IWOA-BPNN is also compared with previously reported algorithms, and the results show that the algorithm improves the accuracy of the sensor.

Enhancing methane sensing with NDIR technology: Current trends and future prospects

Abstract This study presents an in-depth review of non-dispersive infrared (NDIR) sensors for methane detection, focusing on their principles of operation, performance characteristics, advanced signal processing techniques, multi-gas detection capabilities, and applications in various industries. NDIR sensors offer significant advantages in methane sensing, including high sensitivity, selectivity, and long-term stability. The underlying principles of NDIR sensors involve measuring the absorption of infrared radiation by the target gas molecules, leading to precise and reliable methane concentration measurements. Advanced signal processing techniques, such as single-frequency filtering and wavelet filtering algorithms, have been explored to improve the performance of the sensor by reducing noise, enhancing the signal-to-noise ratio, and achieving more accurate results. In the context of multi-gas detection, NDIR sensors face challenges due to overlapping absorption spectra. However, various solutions, including narrow-band optical bandpass filters, gas filter correlation techniques, and machine learning algorithms, have been proposed to address these issues effectively. This study delves into specific applications of NDIR sensors in various industries, such as coal mines, wastewater treatment plants, and agriculture. In these settings, NDIR sensors have demonstrated their reliability, accuracy, and real-time monitoring capabilities, contributing to environmental protection, safety, and energy recovery. Furthermore, the anticipated future trends and developments in NDIR methane detection technology are explored, including increased miniaturization, integration with artificial intelligence, improvements in power efficiency, and the development of multi-gas NDIR sensors. These advancements are expected to further enhance the capabilities and widespread adoption of NDIR sensors in methane detection applications.

Infrared Non‐Invasive Exhaled Biomarker Sensing: A Review

AbstractExhaled breath analysis is considered an effective tool for diagnosing the patient's metabolic status because of its non‐invasive nature. Gas chromatography‐mass spectrometry (GC‐MS) and other mass spectrometry techniques are widely used for exhaled breath analysis. Nevertheless, the requirement of a handheld device for real‐time analysis and rapid processing time of multiple samples leads to advancing infrared (IR) based breath gas sensing techniques. Herein, the IR gas sensing technologies are discussed with a focus on non‐dispersive infrared (NDIR), photoacoustic (PAS), and tunable diode laser absorption spectroscopy (TDLAS) gas sensing technologies because of their highly selective gas detection among compound gases (mixture of VOCs), as well as their corresponding sensing mechanisms and characteristics, for real‐time monitoring of exhaled biomarkers. Carbon dioxide, acetone, ammonia, nitric oxide, methane, and ethylene are the significant biomarkers elaborated in this work with their respective clinical applications and the significance of non‐invasive real‐time monitoring using IR detectors. Challenges in selectivity and clinical trials leads to focus on this review. Current market analysis, present status of different techniques to detect specific biomarkers and other challenges with their possible solutions discussed in this review aid in developing highly selective IR‐based handheld breath VOC analyzers for early diagnosis and screening.

Hydrofluoroolefins Leakage Detection by Nondispersive Infrared Gas Sensor Using InAsSb Light Emitting Diodes and Photodiodes

Hydrofluoroolefins Leakage Detection by Nondispersive Infrared Gas Sensor Using InAsSb Light Emitting Diodes and Photodiodes

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.

Calibration Assessment of Low-Cost Carbon Dioxide Sensors Using the Extremely Randomized Trees Algorithm.

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.

Dynamic Modeling of Carbon Dioxide Transport through the Skin Using a Capnometry Wristband.

The development of a capnometry wristband is of great interest for monitoring patients at home. We consider a new architecture in which a non-dispersive infrared (NDIR) optical measurement is located close to the skin surface and is combined with an open chamber principle with a continuous circulation of air flow in the collection cell. We propose a model for the temporal dynamics of the carbon dioxide exchange between the blood and the gas channel inside the device. The transport of carbon dioxide is modeled by convection-diffusion equations. We consider four compartments: blood, skin, the measurement cell and the collection cell. We introduce the state-space equations and the associated transition matrix associated with a Markovian model. We define an augmented system by combining a first-order autoregressive model describing the supply of carbon dioxide concentration in the blood compartment and its inertial resistance to change. We propose to use a Kalman filter to estimate the carbon dioxide concentration in the blood vessels recursively over time and thus monitor arterial carbon dioxide blood pressure in real time. Four performance factors with respect to the dynamic quantification of the CO2 blood concentration are considered, and a simulation is carried out based on data from a previous clinical study. These demonstrate the feasibility of such a technological concept.

A selective feature optimized multi-sensor based e-nose system detecting illegal drugs validated in diverse laboratory conditions

Detecting illegal drugs, such as cannabis and methamphetamine, with high accuracy and speed is a complex problem that requires an innovative solution. To address this challenge, we propose a new method that utilizes a newly developed electronic nose (e-nose) system with an unprecedented total of 56 sensors, including four different types: metal-oxide-semiconductor (MOS), electrochemical (EC), non-dispersive infrared (NDIR), and photoionization detector (PID). Previous studies on gas sensors have typically validated results in a single controlled laboratory condition. In contrast, our study evaluated the performance of our system in different environments from the original training setting. To evaluate the detection performance of our system in unfamiliar environments and its robustness, we diluted the drug gas with normal air from six different laboratory environments. In addition, we evaluated the detection accuracy of our method using forward-feature selection, which allowed us to evaluate the impact of different combinations of sensors. By selectively optimizing sensors based on their ability to capture unique features of different drugs, our proposed method reduced the number of optimal sensors to less than half of 56 (24 selected). The proposed method achieved a detection accuracy of 93.03% and reduced the error rate from 12.23% to 6.97% using 5166 datasets including cannabis, methamphetamine, and tobacco. Our research not only provides rapid and enhanced accuracy, but also has the potential to be an effective tool for detecting illegal drugs in various settings, which could greatly contribute to strengthening national and social security.

A Monitoring System for Carbon Dioxide in Honeybee Hives: An Indicator of Colony Health

Non-dispersive infra-red (NDIR) detectors have become the dominant method for measuring atmospheric CO2, which is thought to be an important gas for honeybee colony health. In this work we describe a microcontroller-based system used to collect data from Senserion SCD41 NDIR sensors placed in the crown boards and queen excluders of honeybee colonies. The same sensors also provide relative humidity and temperature data. Several months of data have been recorded from four different hives. The mass change measurements, from hive scales, when foragers leave the hive were compared with the data from the gas sensors. Our data suggest that it is possible to estimate the colony size from the change in measured CO2, however no such link with the humidity is observed. Data are presented showing the CO2 decreasing over many weeks as a colony dies.