Flicker Noise in Resistive Gas Sensors-Measurement Setups and Applications for Enhanced Gas Sensing.

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS2, WS2, and ZrS3) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing.

Optimum Choice of Randomly Oriented Carbon Nanotube Networks for UV-Assisted Gas Sensing Applications.

We investigated the noise and photoresponse characteristics of various optical transparencies of nanotube networks to identify an optimal randomly oriented network of carbon nanotube (CNT)-based devices for UV-assisted gas sensing applications. Our investigation reveals that all of the studied devices demonstrate negative photoconductivity upon exposure to UV light. Our studies confirm the effect of UV irradiation on the electrical properties of CNT networks and the increased photoresponse with decreasing UV light wavelength. We also extend our analysis to explore the low-frequency noise properties of different nanotube network transparencies. Our findings indicate that devices with higher nanotube network transparencies exhibit lower noise levels. We conduct additional measurements of noise and resistance in an ethanol and acetone gas environment, demonstrating the high sensitivity of higher-transparent (lower-density) nanotube networks. Overall, our results indicate that lower-density nanotube networks hold significant promise as a viable choice for UV-assisted gas sensing applications.

Clinical studies of detecting COVID-19 from exhaled breath with electronic nose.

The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.

Organic Vapor Sensing Mechanisms by Large-Area Graphene Back-Gated Field-Effect Transistors under UV Irradiation.

The gas sensing properties of graphene back-gated field-effect transistor (GFET) sensors toward acetonitrile, tetrahydrofuran, and chloroform vapors were investigated with the focus on unfolding possible gas detection mechanisms. The FET configuration of the sensor device enabled gate voltage tuning for enhanced measurements of changes in DC electrical characteristics. Electrical measurements were combined with a fluctuation-enhanced sensing methodology and intermittent UV irradiation. Distinctly different features in 1/f noise spectra for the organic gases measured under UV irradiation and in the dark were observed. The most intense response observed for tetrahydrofuran prompted the decomposition of the DC characteristic, revealing the photoconductive and photogating effect occurring in the graphene channel with the dominance of the latter. Our observations shed light on understanding surface processes at the interface between graphene and volatile organic compounds for graphene-based sensors in ambient conditions that yield enhanced sensitivity and selectivity.

Embedded gas sensing setup for air samples analysis.

This paper describes a measurement setup (eNose) designed to analyze air samples containing various volatile organic compounds (VOCs). The setup utilizes a set of resistive gas sensors of divergent gas selectivity and sensitivity. Some of the applied sensors are commercially available and were proposed recently to reduce their consumed energy. The sensors detect various VOCs at sensitivities determined by metal oxide sensors' technology and operating conditions. The setup can utilize prototype gas sensors, made of resistive layers of different compositions, as well. Their properties can be modulated by selecting operating temperature or using UV light irradiation. The unit is controlled by an embedded system M5Stack Core2 ESP32 IoT. We used this development kit to program the measurement procedure and data recording fastly. The setup utilizes an aluminum gas chamber of a volume of 220 ml, a set of electrical valves to introduce there an air sample with the help of an electrical micropump. The handling of the setup was simplified to a selection of a few operations by touch screen only without a necessity of extra training. The recorded data are saved in a memory card for further processing. The evolved setup can be upgraded to apply more advanced data processing by utilizing WiFi or Bluetooth connection. The control program was prepared using the Arduino IDE software environment and can be further advanced with ease. The applied materials and the established measurement procedure can use various air samples, including exhaled breath samples for patients' screening check-ups. We applied the same time of 10 min for response and recovery, acceptable for practical use.

Risk Analysis by a Probabilistic Model of the Measurement Process.

The aim of the article is presentation of the testing methodology and results of examination the probabilistic model of the measurement process. The case study concerns the determination of the risk of an incorrect decision in the assessment of the compliance of products by measurement. Measurand is characterized by the generalized Rayleigh distribution. The model of the measurement process was tested in parallel mode by six risk metrics. An undesirable effect in the reconstruction building block of the model was detected, consisting in the distortion of probability distribution at the edges of the measuring range. The paper gives guidelines on how to use the model, to obtain the analytical risk assessment consistent with the results of the Monte Carlo method. The study can be useful in product quality control, test design, and fault diagnosis.

Utilizing pulse dynamics for non-invasive Raman spectroscopy of blood analytes.

Non-invasive measurement methods offer great benefits in the field of medical diagnostics with molecular-specific techniques such as Raman spectroscopy which is increasingly being used for quantitative measurements of tissue biochemistry in vivo. However, some important challenges still remain for label-free optical spectroscopy to be incorporated into the clinical laboratory for routine testing. In particular, non-analyte-specific variations in tissue properties introduce significant variability of the spectra, thereby preventing reliable calibration. For measurements of blood analytes such as glucose, we propose to decrease the interference from individual tissue characteristics by exploiting the known dynamics of the blood-tissue matrix. We reason that by leveraging the natural blood pulse rhythm, the signals from the blood analytes can be enhanced while those from the static components can be effectively suppressed. Here, time-resolved measurements with subsequent pulse frequency estimation and phase-sensitive detection are proposed to recover the Raman spectra correlated with the dynamic changes at blood-pulse frequency. Pilot in vivo study results are presented to establish the benefits as well as outline the challenges of the proposed method in terms of instrumentation and signal processing.

Assessment of Electronic Sensing Techniques for the Rapid Identification of Alveolar Echinococcosis through Exhaled Breath Analysis.

Here we present a proof-of-concept study showing the potential of a chemical gas sensors system to identify the patients with alveolar echinococcosis disease through exhaled breath analysis. The sensors system employed comprised an array of three commercial gas sensors and a custom gas sensor based on WO3 nanowires doped with gold nanoparticles, optimized for the measurement of common breath volatile organic compounds. The measurement setup was designed for the concomitant measurement of both sensors DC resistance and AC fluctuations during breath samples exposure. Discriminant Function Analysis classification models were built with features extracted from sensors responses, and the discrimination of alveolar echinococcosis was estimated through bootstrap validation. The commercial sensor that detects gases such as alkane derivatives and ethanol, associated with lipid peroxidation and intestinal gut flora, provided the best classification (63.4% success rate, 66.3% sensitivity and 54.6% specificity) when sensors' responses were individually analyzed, while the model built with the AC features extracted from the responses of the cross-reactive sensors array yielded 90.2% classification success rate, 93.6% sensitivity and 79.4% specificity. This result paves the way for the development of a noninvasive, easy to use, fast and inexpensive diagnostic test for alveolar echinococcosis diagnosis at an early stage, when curative treatment can be applied to the patients.

Analysis of Vibration and Acoustic Signals for Noncontact Measurement of Engine Rotation Speed.

The non-contact measurement of engine speed can be realized by analyzing engine vibration frequency. However, the vibration signal is distorted by harmonics and noise in the measurement. This paper presents a novel method for the measurement of engine rotation speed by using the cross-correlation of vibration and acoustic signals. This method can enhance the same frequency components in engine vibration and acoustic signal. After cross-correlation processing, the energy centrobaric correction method is applied to estimate the accurate frequency of the engine's vibration. This method can be implemented with a low-cost embedded system estimating the cross-correlation. Test results showed that this method outperformed the traditional vibration-based measurement method.

Non-Gaussian Resistance Fluctuations in Gold-Nanoparticle-Based Gas Sensors: An Appraisal of Different Evaluation Techniques.

Volatile organic compounds, such as formaldehyde, can be used as biomarkers in human exhaled breath in order to non-invasively detect various diseases, and the same compounds are of much interest also in the context of environmental monitoring and protection. Here, we report on a recently-developed gas sensor, based on surface-functionalized gold nanoparticles, which is able to generate voltage noise with a distinctly non-Gaussian component upon exposure to formaldehyde with concentrations on the ppm level, whereas this component is absent, or at least much weaker, when the sensor is exposed to ethanol or to pure air. We survey four different statistical methods to elucidate a non-Gaussian component and assess their pros and cons with regard to efficient gas detection. Specifically, the non-Gaussian component was clearly exposed in analysis using level-crossing parameters, which require nothing but a modest computational effort and simple electronic circuitry, and analogous results could be reached through the bispectrum function, albeit with more intense computation. Useful information could be obtained also via the Lévy-stable distribution and, possibly, the second spectrum.

Fluctuation-enhanced sensing with organically functionalized gold nanoparticle gas sensors targeting biomedical applications.

Detection of volatile organic compounds is a useful approach to non-invasive diagnosis of diseases through breath analysis. Our experimental study presents a newly developed prototype gas sensor, based on organically-functionalized gold nanoparticles, and results on formaldehyde detection using fluctuation-enhanced gas sensing. Formaldehyde was easily detected via intense fluctuations of the gas sensor's resistance, while the cross-influence of ethanol vapor (a confounding factor in exhaled breath, related to alcohol consumption) was negligible.

Detection of denatonium benzoate (bitrex) remnants in noncommercial alcoholic beverages by Raman spectroscopy.

Illegal alcoholic beverages are often introduced into market using cheap technical alcohol, which is contaminated by denatonium benzoate (Bitrex) of very small concentration. Bitrex is the most bitter chemical compound and has to be removed before alcohol consumption. The home-made methods utilize sodium hypochlorite to disintegrate particles of denatonium benzoate in alcohol and to remove bitter taste before trading. In this experimental studies, we propose a novel method that detects in a fast way the remnants of denatonium benzoate in dubious alcohol samples by Raman spectroscopy. This method applies a portable Raman spectrometer of excitation wavelength 785 nm and utilizes the effect of surface-enhanced Raman spectroscopy (SERS) to recognize the suspected alcoholic beverages. High effectiveness (over 98%) of YES/NO classification of the investigated samples was observed when the nonlinear algorithm support vector machine (SVM) was exploited at carefully adjusted detection parameters. The method can identify illicit alcohol within minutes.

Anatomy of noise in quantitative biological Raman spectroscopy.

Raman spectroscopy is a fundamental form of molecular spectroscopy that is widely used to investigate structures and properties of molecules using their vibrational transitions. It relies on inelastic scattering of monochromatic laser light irradiating the specimen. After appropriate filtering the scattered light is dispersed onto a detector to determine the shift from the excitation wavelength, which appears in the form of characteristic spectral patterns. The technique can investigate biological samples and provide real-time diagnosis of diseases. However, despite its intrinsic advantages of specificity and minimal perturbation, the Raman scattered light is typically very weak and limits applications of Raman spectroscopy due to measurement (im)precision, driven by inherent noise in the acquired spectra. In this article, we review the principal noise sources that impact quantitative biological Raman spectroscopy. Further, we discuss how such noise effects can be reduced by innovative changes in the constructed Raman system and appropriate signal processing methods.

Investigation of noise-induced instabilities in quantitative biological spectroscopy and its implications for noninvasive glucose monitoring.

Over the past decade, optical spectroscopy has been employed in combination with multivariate chemometric models to investigate a wide variety of diseases and pathological conditions, primarily due to its excellent chemical specificity and lack of sample preparation requirements. Despite promising results in several proof-of-concept studies, its translation to the clinical setting has often been hindered by inadequate accuracy of the conventional spectroscopic models. To address this issue and the possibility of curved (nonlinear) effects in the relationship between the concentrations of the analyte of interest and the mixture spectra (due to fluctuations in sample and environmental conditions), support vector machine-based least-squares nonlinear regression (LS-SVR) has been recently proposed. In this paper, we investigate the robustness of this methodology to noise-induced instabilities and present an analytical formula for estimating modeling precision as a function of measurement noise and model parameters. This formalism can be readily used to evaluate uncertainty in information extracted from spectroscopic measurements, particularly important for rapid-acquisition biomedical applications. Subsequently, using field data (Raman spectra) acquired from a glucose clamping study on an animal model subject, we perform the first systematic investigation of the relative effect of additive interference components (namely, noise in prediction spectra, calibration spectra, and calibration concentrations) on the prediction error of nonlinear spectroscopic models. Our results show that the LS-SVR method gives more accurate results and is substantially more robust to additive noise when compared with conventional regression methods such as partial least-squares regression (PLS), when careful selection of the LS-SVR model parameters are performed. We anticipate that these results will be useful for uncertainty estimation in similar biomedical applications where the precision of measurements and its response to noise in the data set is as important, if not more so, than the generic accuracy level.

Improving AFM images with harmonic interference by spectral analysis.

Atomic force microscopy (AFM) is one of the most sensitive tools for nanoscale imaging. As such, it is very sensitive to external noise sources that can affect the quality of collected data. The intensity of the disturbance depends on the noise source and the mode of operation. In some cases, the internal noise from commercial AFM controllers can be significant and difficult to remove. Thus, a new method based on spectrum analysis of the scanned images is proposed to reduce harmonic disturbances. The proposal is a post-processing method and can be applied at any time after measurements. This article includes a few methods of harmonic cancellation (e.g., median filtering, wavelet denoising, Savitzky-Golay smoothing) and compares their effectiveness. The proposed method, based on Fourier transform of the scanned images, was more productive than the other methods mentioned before. The presented data were achieved for images of conductive layers taken in a contact AFM mode.