Photoionization Detector Research Articles

Real-time indoor sensing of volatile organic compounds during building disinfection events via photoionization detection and proton transfer reaction mass spectrometry

Photoionization detectors (PIDs) are low-cost sensors that are widely used for real-time volatile organic compound (VOC) monitoring in buildings. Performance assessment of PIDs is often based on reference VOC measurements derived from offline, time-integrated sample analysis. Such assessment techniques lack valuable real-time information that is needed to evaluate how PIDs track transient VOC emission events. The objective of this study is to evaluate the real-time performance of a PID in sensing indoor VOC mixtures during building disinfection events through co-location measurements with a state-of-the-art proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS). Field measurements were conducted in a tiny house laboratory using an isobutylene-calibrated PID and a PTR-TOF-MS during the use of spray-based disinfectants. The PID and PTR-TOF-MS measurements demonstrated that chemical disinfectants are a major source of VOCs in buildings, with the latter revealing the presence of a large diversity of VOCs. In general, the PID was successful in identifying VOC emission events during the application of the disinfectants. Thus, PIDs may be suitable for integration with building automation systems for ventilation control. The PID response was much less than the PTR-TOF-MS response, suggesting that the PID is not efficiently detecting many components of the emitted VOC mixtures. The PID performed best for alcohol-based disinfectants, moderately well for lactic acid- and botanical-based disinfectants, and poorly for disinfectants featuring peracetic and acetic acid. Detailed correlation analysis between the PID and PTR-TOF-MS responses provides a basis for improving the reliability of PIDs in estimating VOC concentrations through application of product-specific correction factors.

Qualification and optimisation of a gas mixing apparatus for complex trace gas mixtures

Abstract A gas mixing apparatus (GMA) can provide well-defined gas mixtures, which are necessary to calibrate gas sensors for quantitative measurements. This article presents a novel modular GMA and especially the representative measurements carried out to qualify and quantify its performance. New methods were developed to optimise the GMA’s settling speed and to enable self-monitoring. The GMA is able to provide up to 14 individual test gases, each in a large concentration range from sub-parts-per-billion to hundreds of parts-per-million. Additionally, the relative humidity can be set. The system is able to provide stable gas mixtures in approximately 6 min. The entire control software is realised in Python, which determines the control setpoints of all relevant system components based on the concentration input of the user. A major focus was placed on minimising the time of a gas mixture exchange to confirm design and additional software optimisation. Also, a number of techniques are shown for the analytical quantification of the system, including analysis of thermal desorption tubes by an accredited laboratory, showing maximal contaminations of e.g. only 4.18 ppb ethanol. Finally, self-monitoring of the system using photoionization detectors (PID) is demonstrated as an addition to flow readback for internal leakage detection.

Prediction of the Response of a Photoionization Detector to a Complex Gaseous Mixture of Volatile Organic Compounds Produced by α-Pinene Oxidation.

Photoionization detectors (PIDs) are lightweight and respond in real time to the concentrations of volatile organic compounds (VOCs), making them suitable for environmental measurements on many platforms. However, the nonselective sensing mechanism of PIDs challenges data interpretation, particularly when exposed to the complex VOC mixtures prevalent in the Earth's atmosphere. Herein, two approaches to this challenge are investigated. In the first, quantum-chemistry calculations are used to estimate photoionization cross sections and ionization potentials of individual species. In the second, machine learning models are trained on these calculated values, as well as empirical PID response factors, and then used for prediction. For both approaches, the resulting information for individual species is used to model the overall PID response to a complex VOC mixture. In complement, laboratory experiments in the Harvard Environmental Chamber are carried out to measure the PID response to the complex molecular mixture produced by α-pinene oxidation under various conditions. The observations show that the measured PID response is 15% to 30% smaller than the PID response modeled by quantum-chemistry calculations of the photoionization cross section for the photo-oxidation experiments and 15% to 20% for the ozonolysis experiments. By comparison, the measured PID response is captured within a 95% confidence interval by the use of machine learning to model the PID response based on the empirical response factor in all experiments. Taken together, the results of this study demonstrate the application of machine learning to augment the performance of a nonselective chemical sensor. The approach can be generalized to other reactive species, oxidants, and reaction mechanisms, thus enhancing the utility and interpretability of PID measurements for studying atmospheric VOCs.

Application of Unsupervised/Supervised Machine Learning to Harmful Gas Detection/Classification Based on Mutually-Interacting Semiconductor Metal Oxide Gas Sensors

Chemical and/or physical sensors are employed at domestic and industrial sites with the aim to perform a variety of functions, such as prediction, forecasting, prognostics, remaining useful life estimation, anomaly detection, and trend analysis. The sensing information contain the analog signals originating from gas-generated signals, pressure, shear, strain, torsion, temperature, humidity, illumination, electromagnetic radiation, sound, and vibration. Chemical gas sensors can be categorized mainly as semiconductor metal oxide (SMO), electrochemical, or photo-ionization detection (PID) sensors. SMO gas sensors are based on adsorption and desorption of gas molecules onto/from semiconductor metal oxides and however, suffer from the low selectivity to diverse gas species, i.e., the mutually interacting features.Here, we report a high-performance machine learning strategy for gas detection/discrimination against harmful gas combination and adopt the synergistic integration of supervised and unsupervised learning by exploiting a SMO gas sensor array. A 4 SMO sensor array was constructed for detecting carbon monoxide and ethyl alcohol (C2H5OH) mixtures using 15 different gas combinations. Gas detection/discrimination performance was probed in terms of different numbers of gas sensor integrated as an array and machine learning algorithms. Unsupervised K-Means clustering was successfully applied to the rational identification of the similarity features of targeted gases among 4 different groups, which are listed as matrix gas, two single-component gases, and one two-gas by employing only unlabeled voltage-based gas sensing information. Detailed classification was performed through a multitude of supervised algorithms, i.e., 2-layer artificial neural networks (ANNs), 4-layer deep neural networks (DNNs), 1-dimensional convolutional neural networks (1D CNNs), and 2-dimensional convolutional neural networks (2D CNNs). The numerical-based DNNs and image-based CNNs are evaluated to be excellent approaches for gas detection/classification, as corroborated by the highest accuracy and lowest loss parameters.Through the systematic investigation on the influence of the number of sensors on the arrayed gas sensor system, the applicability of machine learning methodology to an arrayed SMO gas sensor system is justified by the four unique features, i.e., i) a data augmentation methodology, ii) machine learning approach of combining K-means clustering and neural networks, and iii) a systematic approach to optimized sensor combinations, potentially recommending the minimized sensor systems based on chemical gas sensors against harmful gas species. Even two SMO sensor combinations are shown to be highly effective in performing gas discrimination against harmful gas species assisted through either numeric-based DNNs or image-based 1D CNNs, overcoming the simple clustering proposed through the unsupervised K-means clustering.

Detection of Propionic Acids Trapped in Thin Zeolite Layer Using Thermal Desorption Analysis.

Volatile organic compounds (VOCs) have recently received considerable attention for the analysis and monitoring of different biochemical processes in biological systems such as humans, plants, and microorganisms. The advantage of using VOCs to gather information about a specific process is that they can be extracted using different types of samples, even at low concentrations. Therefore, VOC levels represent the fingerprints of specific biochemical processes. The aim of this work was to develop a sensor based on a photoionization detector (PID) and a zeolite layer, used as an alternative analytic separation technique for the analysis of VOCs. The identification of VOCs occurred through the evaluation of the emissive profile during the thermal desorption phase, using a stainless-steel chamber for analysis. Emission profiles were evaluated using a double exponential mathematical model, which fit well if compared with the physical system, describing both the evaporation and diffusion processes. The results showed that the zeolite layer was selective for propionic acid molecules if compared to succinic acid molecules, showing linear behavior even at low concentrations. The process to define the optimal adsorption time between the propionic acid molecules was performed in the range of 5 to 60 min, followed by a thermal desorption process at 100 °C. An investigation of the relationship between the evaporation and diffusion rates showed that the maximum concentration of detected propionic acid molecules occurred in 15 min. Other analyses were performed to study how the concentration of VOCs depended on the desorption temperature and the volume of the analysis chamber. For this purpose, tests were performed using three analysis chambers with volumes of 25 × 10-6, 50 × 10-6, and 150 × 10-6 m3 at three different desorption temperatures of 20 °C, 50 °C, and 100 °C, respectively. The results demonstrated that the evaporation rate of the VOCs increased rapidly with an increasing temperature, while the diffusion rate remained almost constant and was characterized by a slow decay time. The diffusion ratio increased when using a chamber with a larger volume. These results highlight the capabilities of this alternative technique for VOC analysis, even for samples with low concentrations. The coupling of a zeolite layer and a PID improves the detection selectivity in portable devices, demonstrating the feasibility of extending its use to a wide range of new applications.

Performance evaluation of an ambient volatile organics sensor based on mesoporous silica preconcentrator and a photoionization detector

Performance evaluation of an ambient volatile organics sensor based on mesoporous silica preconcentrator and a photoionization detector

Flow and contaminant transfer at the interface of an aerodynamic containment barrier subjected to the wake of a moving obstacle

This contribution is an experimental and numerical study on the breach of a containment barrier subject to the transient wake of a moving obstacle. In the context of a 100 m3 laboratory with high air exchange rate (8.5 h−1), a passive gaseous contaminant is emitted at a controlled flow rate from the bottom surface of an open cavity (0.8×0.34 m2 wide) swept by a continuous airflow drawn from the lab. Starting from a steady and turbulent airflow situation, a human-sized obstacle passes in front of the cavity and disturbs the established flow. The containment breach is then analyzed by recording time-dependent gas concentrations and velocity components, measured at the cavity open surface, with photoionization detectors and 2D3C PIV, respectively. A penalty method coupled with LES is used to model the experimental setup and predictions are compared to measurements. The numerical model is found to be able to reproduce the contaminant concentrations, air velocities and main POD modes of the flow within experimental inaccuracies. This modeling approach appears to be suitable to study, in-silico, the effect of drafts induced by human motion on aerodynamic barriers. CFD results also show that pollutant leakage is inherently variable, even under stable ventilation conditions. Up to 100% variation of contaminant concentration peaks can be observed, depending on the initial turbulent state of the room.

Synthesis and Characterization of MCM-41 Nanomaterials Containing Titanium and Application for Catalytic Oxidation of BTEX

The TiO<sub>2</sub>/MCM-41 nanomaterials were synthesized by impregnation with an excess solvent with different percentages of titanium dioxide. They were used for catalytic degradation of Benzene Toluene Ethylbenzene and Xylenes (BTEX) in the presence of hydrogen peroxide in aqueous media. The obtained materials were characterized by X-ray Diffraction, nitrogen adsorption-desorption using the BET method and Fourier Transform Infrared Spectroscopy. The nanostructured phase of the hexagonal ordered materials was obtained even after modification with titanium oxide and calcination. The characterizations have proven the effectiveness of the synthesis method used to incorporate titanium with anatase structure impregnated in the nanoporous of the MCM-41 material. Anatase is the main active phase of TiO<sub>2</sub> to oxidize organic compounds. The catalytic evaluation wascarried out in a semi-bath reactor with 20 mL of a mixture containing BTEX (100 mg/L), hydrogen peroxide (2.0 mol/L) and TiO<sub>2</sub>/MCM-41 (2.0 g/L) in aqueous media. The reactions were carried out at a temperature of 60°C for 5 hours, and the analyses were performed by gas chromatography with a photoionization detector and headspace sampler. The catalytic tests showed satisfactory results with more than 95% of conversion, where the catalyst 48%TiO<sub>2</sub>/MCM-41 presented higher performance.

Colorimetric Signal Readout for the Detection of Volatile Organic Compounds Using a Printable Glass-Based Dielectric Barrier Discharge-Type Helium Plasma Detector.

In this paper, we report on a printable glass-based manufacturing method and a new proof-of-concept colorimetric signal readout scheme for a dielectric barrier discharge (DBD)-type helium plasma photoionization detector. The sensor consists of a millimeter-sized glass chamber manufactured using a printable glass suspension. Plasma inside the chip is generated using a custom-built power supply (900 V and 83.6 kHz), and the detector uses ∼5 W of power. Our new detection scheme is based on detecting the change in the color of plasma after the introduction of target gases. The change in color is first captured by a smartphone camera as a video output. The recorded video is then processed and converted to an image light intensity vs retention time plot (gas chromatogram) using three standard color space models (red, green, blue (RGB), hue, saturation, lightness (HSL), and hue, saturation, value (HSV)) with RGB performing the best among the three models. We successfully detected three different categories of volatile organic compounds using our new detection scheme and a 30-m-long gas chromatography column: (1) straight-chain alkanes (n-pentane, n-hexane, n-heptane, n-octane, and n-nonane), (2) aromatics (benzene, toluene, and ethylbenzene), and (3) polar compounds (acetone, ethanol, and dichloromethane). The best limit of detection of 10 ng was achieved for benzene at room temperature. Additionally, the device showed excellent performance for different types of sample mixtures consisting of three and five compounds. Our new detector readout method combined with our ability to print complex glass structures provides a new research avenue to analyze complex gas mixtures and their components.

Application of homemade portable gas chromatography coupled to photoionization detector for the detection of volatile organic compounds in an industrial park

Traditional offline detection of volatile organic compounds (VOCs) requires complex and time-consuming pre-treatments including gas sampling in containers, pre-concentrations, and thermal desorption, which hinders its application in rapid VOCs monitoring. Developing a cost-effective instrument is of great importance for online measurement of VOCs. Recently, photoionization detectors (PID) are received great attention due to their fast response time and high sensitivity. This study a portable gas chromatography coupled to PID (pGC-PID) was developed and optimized experimental parameters for the application in online monitoring of VOCs at an industrial site. The sampling time, oven temperature and carrier gas flow rate were optimized as 80 s, 50 °C and 60 ml·min−1, respectively. The sampling method is direct injection. Poly tetra fluoroethylene (PTFE) filter membranes were selected to remove particulate matter from interfering with PID. The reproducibility and peak separation were good with relative standard deviations (RSD) ≤ 7%. Good linearities of 27 VOCs standard curves were achieved with R2 ≥ 0.99, and the detection limits were ≤10 ppb with the lowest being 2 ppb for 1,1,2-Trichloroethane. Finally, the pGC-PID is successfully applied in online VOCs monitoring at an industrial site. A total of 17 VOCs species was detected and their diurnal variations were well obtained, indicating pGC-PID is well suited for online analysis in field campaign.

Ultrathin Silica Integration for Enhancing Reliability of Microfluidic Photoionization Detectors.

Microfluidic photoionization detectors (μPIDs) based on silicon chips can rapidly and sensitively detect volatile compounds. However, the applications of μPID are limited by the manual assembly process using glue, which may outgas and clog the fluidic channel, and by the short lifetime of the vacuum ultraviolet (VUV) lamps (especially, argon lamps). Here, we developed a gold-gold cold welding-based microfabrication process to integrate ultrathin (10 nm) silica into μPID. The silica coating enables direct bonding of the VUV window to silicon under amicable conditions and works as a moisture and plasma exposure barrier for VUV windows that are susceptible to hygroscopicity and solarization. Detailed characterization of the silica coating was conducted, showing that the 10 nm silica coating allows 40-80% VUV transmission from 8.5 to 11.5 eV. It is further shown that the silica-protected μPID maintained 90% of its original sensitivity after 2200 h of exposure to ambient (dew point = 8.0 ± 1.8 °C), compared to 39% without silica. Furthermore, argon plasma inside an argon VUV lamp was identified as the dominant degradation source for the LiF window with color centers formation in UV-vis and VUV transmission spectra. Ultrathin silica was then also demonstrated effective in protecting the LiF from argon plasma exposure. Lastly, thermal annealing was found to bleach the color centers and restore VUV transmission of degraded LiF windows effectively, which will lead to future development of a new type of VUV lamp and the corresponding μPID (and PID in general) that can be mass produced with a high yield, a longer lifetime, and better regenerability.

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.

Review and Perspective: Gas Separation and Discrimination Technologies for Current Gas Sensors in Environmental Applications.

Presently, numerous state-of-the-art approaches are being adapted for gas sensing and monitoring. These include hazardous gas leak detection as well as ambient air monitoring. Photoionization detectors, electrochemical sensors, and optical infrared sensors are a few of the commonly widely used technologies. Extensive reviews on the current state of gas sensors have been summarized. These sensors, which are either nonselective or semiselective, are affected by unwanted analytes. On the other hand, volatile organic compounds (VOCs) can be heavily mixed in many vapor intrusion situations. To determine the individual VOCs in a highly mixed gas sample using nonselective or semiselective gas sensors, gas separation and discrimination technologies are highly warranted. These technologies include gas permeable membranes, metal-organic frameworks, microfluidics and IR bandpass filters for different sensors, respectively. The majority of these gas separation and discrimination technologies are currently being developed and evaluated in laboratory-controlled environments and have not yet been extensively utilized in the field for vapor intrusion monitoring. These technologies show promise for continued development and application in the field for more complex gas mixtures. Hence, the present review focuses on the perspectives and a summary of the existing gas separation and discrimination technologies for the currently popular reported gas sensors in environmental applications.

Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds.

Molecular ionization potentials (IP) and photoionization cross sections (σ) can affect the sensitivity of photoionization detectors (PIDs) and other sensors for gaseous species. This study employs several methods of machine learning (ML) to predict IP and σ values at 10.6 eV (117 nm) for a dataset of 1251 gaseous organic species. The explicitness of the treatment of the species electronic structure progressively increases among the methods. The study compares the ML predictions of the IP and σ values to those obtained by quantum chemical calculations. The ML predictions are comparable in performance to those of the quantum calculations when evaluated against measurements. Pretraining further reduces the mean absolute errors (ε) compared to the measurements. The graph-based attentive fingerprint model was most accurate, for which εIP = 0.23 ± 0.01 eV and εσ = 2.8 ± 0.2 Mb compared to measurements and computed cross sections, respectively. The ML predictions for IP correlate well with both the measured IPs (R 2 = 0.88) and with IPs computed at the level of M06-2X/aug-cc-pVTZ (R 2 = 0.82). The ML predictions for σ correlated reasonably well with computed cross sections (R 2 = 0.66). The developed ML methods for IP and σ values, representing the properties of a generalizable set of volatile organic compounds (VOCs) relevant to industrial applications and atmospheric chemistry, can be used to quantitatively describe the species-dependent sensitivity of chemical sensors that use ionizing radiation as part of the sensing mechanism, such as photoionization detectors.

Porous silica preconcentrator for selective ambient volatile organic compounds detection: Effects of surface functionalization and humidity

With the increased demand for hand-held and ambient gas sensors, it is imperative to develop sensors that can offer both selective and sensitive detection. Gas preconcentration is a widely tried and tested method to increase the sensitivity of gas detectors. While it effectively lowers the limit of detection, it does not impact the selectivity of the detector. Therefore, preconcentrator materials have mostly been used in conjunction with selective detectors. In this work, we use the preconcentration method with a nonselective small and portable photoionization detector to introduce selectivity. For this purpose, we use a relatively slow heating rate, that allows for gradual desorption and analytes from the preconcentrator material–nanoporous silica. The characteristic desorption temperature of the volatile organic compounds (VOC) from the preconcentrator allows selective detection of the VOC. In this work, we study the effect of surface functionalization, to make it hydrophobic, and observe the adsorption–desorption behavior of polar (isopropyl alcohol) and non-polar (octane) gas molecules. The hydrophobic silica surface was found to improve the adsorption of non-polar octane, while it reduced the adsorption of polar isopropyl alcohol. The desorption temperature for isopropanol remained unchanged for both functionalized and non-functionalized preconcentrators; however, the desorption temperature for octane increased by 10 °C when the functionalized hydrophobic pSiO2 was used. We also observed the presence of humidity, a known interferent, did not heavily impact the sensing performance. These results are promising evidence that functionalized porous silica integrated with a photoionization detector can be used for selective gas detection in the ambient atmosphere.

Pulmonary and neurological health effects associated with exposure to representative composite manufacturing emissions and corresponding alterations in circulating metabolite profiles.

Cured-in-place pipe (CIPP) technology is increasingly being utilized to repair aging and damaged pipes, however, there are concerns associated with the public health hazards of emissions. CIPP installation involves the manufacture of a new plastic composite pipe at the worksite and includes multiple variable components including resin material, curing methods, and operational conditions. We hypothesize styrene-based composite manufacturing emissions (CMEs) will induce greater pulmonary inflammatory responses and oxidative stress, as well as neurological toxicity compared with nonstyrene CMEs. Further, these CME-toxicological responses will be sex- and time-dependent. To test the hypothesis, representative CMEs were generated using a laboratory curing chamber and characterized using thermal desorption-gas chromatography-mass spectrometry and photoionization detector. Styrene was released during staying, isothermal curing, and cooling phases of the process and peaked during the cooling phase. Male and female C57BL6/J mice were utilized to examine alterations in pulmonary responses and neurotoxicity 1 day and 7 days following exposure to air (controls), nonstyrene-CMEs, or styrene-CMEs. Serum styrene metabolites were increased in mice exposed to styrene-CMEs. Metabolic and lipid profiling revealed alterations related to CIPP emissions that were resin-, time-, and sex-dependent. Exposure to styrene-CMEs resulted in an influx of lymphocytes in both sexes. Expression of inflammatory and oxidative stress markers, including Tnfα, Vcam1, Ccl2, Cxcl2, Il6, Cxcl1, Tgfβ1, Tgmt2, and Hmox1, displayed alterations following exposure to emissions. These changes in pulmonary and neurological markers of toxicity were dependent on resin type, sex, and time. Overall, this study demonstrates resin-specific differences in representative CMEs and alterations in toxicity endpoints, which can potentially inform safer utilization of composite manufacturing processes.

Lightweight Gas Sensor Based on MEMS Pre-Concentration and Infrared Absorption Spectroscopy Inside a Hollow Fiber

This paper reports on a compact and lightweight sensor for analysis of gases/vapors by means of a MEMS-based pre-concentrator coupled to a miniaturized infrared absorption spectroscopy (IRAS) module. The pre-concentrator was utilized to sample and trap vapors in a MEMS cartridge filled with sorbent material and to release them once concentrated by fast thermal desorption. It was also equipped with a photoionization detector for in-line detection and monitoring of the sampled concentration. The vapors released by the MEMS pre-concentrator are injected into a hollow fiber, which acts as the analysis cell of the IRAS module. The miniaturized internal volume of the hollow fiber of about 20 microliters keeps the vapors concentrated for analysis, thus allowing measurement of their infrared absorption spectrum with a signal to noise ratio high enough to identify the molecule, despite the short optical path, starting from sampled concentration in air down to parts per million. Results obtained for ammonia, sulfur hexafluoride, ethanol and isopropanol are reported to illustrate the sensor detection and identification capability. A limit of identification (LoI) of about 10 parts per million was validated in the lab for ammonia. The lightweight and low power consumption design of the sensor allowed operation onboard unmanned aerial vehicles (UAVs). The first prototype was developed within the EU Horizon 2020 project ROCSAFE for the remote assessment and forensic examination of a scene in the aftermath of industrial or terroristic accidents.

Personal VOCs Exposure with a Sensor Network Based on Low-Cost Gas Sensor, and Machine Learning Enabled Indoor Localization

Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.

Study on Denoising Method of Photoionization Detector Based on Wavelet Packet Transform

Aiming at the task of noise suppression caused by the photoionization detector (PID) monitoring signal of volatile organic compounds (VOCs) due to local non-uniformity of the photocathode surface of PID in the ionization chamber, this paper proposes an analytical method of a PID signal with the adaptive weight of the small wave package decomposition node. The PID signal is transmitted to the upper machine software through the single-chip microcontroller. The appropriate wavelet packet decomposition level is determined according to the time frequency characteristics of the original signal of the PID, and the optimal wavelet packet base is selected through the polynomial fitting of the signal quality evaluation index. By comparing the quality of signals processed by the traditional wavelet packet denoising method and the denoising method presented in this paper, the superiority of the proposed method in the denoising signals of PID was verified. This method can eliminate the noise generated by local non-uniformity on the photocathode surface of the PID ionization chamber in a high humidity environment, which lays a foundation for the accurate monitoring of VOCs in a high humidity environment.

Highly Integrated μGC Based on a Multisensing Progressive Cellular Architecture with a Valveless Sample Inlet

Microscale gas chromatographs (μGCs) promise in-field analysis of volatile organic compounds (VOCs) in environmental and industrial monitoring, healthcare, and homeland security applications. As a step toward addressing challenges with performance and manufacturability, this study reports a highly integrated monolithic chip implementing a multisensing progressive cellular architecture. This architecture incorporates three μGC cells that are customized for different ranges of analyte volatility; each cell includes a preconcentrator and separation column, two complementary capacitive detectors, and a photoionization detector (PID). An on-chip carrier gas filter scrubs ambient air for the analysis. The monolithic chip, with all 16 components, is 40.3 × 55.7 mm2 in footprint. To accommodate surface adsorptive and low-volatility analytes, the architecture eliminates the commonly used inlet valve, eliminating the need for chemically inactive surfaces in the valves and pumps, allowing the use of standard parts. Representative analysis is demonstrated from a nonpolar 14-analyte mixture, a polar 12-analyte mixture, and a 3-phosphonate ester mixture, covering a wide vapor pressure range (0.005-68.5 kPa) and dielectric constant range (1.8-23.2). The three types of detectors show highly complementary responses. Quantitative analysis is shown in the tens to hundreds ppb range. With 200 mL samples, the projected detection limits reach 0.12-4.7 ppb. Limited tests performed at 80% humidity showed that the analytes with vapor pressures <12 kPa were unaffected. A typical full run takes 28 min and consumes 2.3 kJ energy for the fluidic elements (excluding electronics). By eliminating chip-to-chip fluidic interconnections and requiring just one custom-fabricated element, this work presents a path toward high-performance and highly manufacturable μGCs.