Rational Design of QCM-D Virtual Sensor Arrays Based on Film Thickness, Viscoelasticity, and Harmonics for Vapor Discrimination

Presenting dual-functional peptides on implant surface to direct in vitro osteogenesis and in vivo osteointegration

The complex biological process of osseointegration and the bio-inertness of bone implants are the major reasons for the high failure rate of long-term implants, and have also promoted the rapid development of multifunctional implant coatings in recent years. Herein, through the special design of peptides, we use layer-by-layer assembly technology to simultaneously display two peptides with different biological functions on the implant surface to address this issue. A variety of surface characterization techniques (ellipsometry, atomic force microscopy, photoelectron spectroscopy, dissipation-quartz crystal microbalance) were used to study in detail the preparation process of the dual peptide functional coating and the physical and chemical properties, such as the composition, mechanical modulus, stability, and roughness of the coating. Compared with single peptide functional coatings, dual-peptide functionalized coatings had much better performances on antioxidant, cellular adhesion in early stage, proliferation and osteogenic differentiation in long term, as well as in vivo osteogenesis and osseointegration capabilities. These findings will promote the development of multifunctional designs in bone implant coatings, as a coping strategy for the complexity of biological process during osteointegration.

Investigation of sorptive interactions between volatile organic compounds and supramolecules at dynamic oscillation using bulk acoustic wave resonator virtual sensor arrays

Supramolecules are considered as promising materials for volatile organic compounds (VOCs) sensing applications. The proper understanding of the sorption process taking place in host-guest interactions is critical in improving the pattern recognition of supramolecules-based sensing arrays. Here, we report a novel approach to investigate the dynamic host-guest recognition process by employing a bulk acoustic wave (BAW) resonator capable of producing multiple oscillation amplitudes and simultaneously recording multiple responses to VOCs. Self-assembled monolayers (SAMs) of β-cyclodextrin (β-CD) were modified on four BAW sensors to demonstrate the gas-surface interactions regarding oscillation amplitude and SAM length. Based on the method, a virtual sensor array (VSA) type electronic nose (e-nose) can be realized by pattern recognition of multiple responses at different oscillation amplitudes of a single sensor. VOCs analysis was realized respectively by using principal component analysis (PCA) for individual VOC identification and linear discriminant analysis (LDA) for VOCs mixtures classification.

A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.

Hydrophobic MOF/PDMS-Based QCM Sensors for VOCs Identification and Quantitative Detection in High-Humidity Environments

Metal-organic frameworks (MOFs) have great potential in quartz crystal microbalance (QCM) platforms for volatile organic compound (VOCs) detection and recognition due to their unique properties. However, the MOFs' hydrophilicity degrades performance in high-humidity environments, limiting reliable VOC sensing in complex environments. Herein, we propose a novel VOC virtual sensor array (VSA) using a single QCM sensor with an adsorption layer composed of MIL-101(Cr) MOF and polydimethylsiloxane (PDMS), realizing stable sensing and accurate identification for different VOCs under various relative humidity (RH) conditions. The hydrophobic PDMS layer improves the moisture resistance of the sensor to 4 and 14 times in terms of shifts in resonant frequency and scattering parameters, respectively. In addition, performance is maintained over 2 days of water treatment, demonstrating superior water resistance. The highest sensitivity of 2.68 mdB ppm-1 is achieved for isopropanol detection, with the lowest limit of detection of 20.06 ppm for acetone. Combining resonant signals and lumped parameters, the proposed VSA technique effectively discriminates four VOCs (ethanol, 2-propanol, acetone, and acetonitrile) with a high accuracy of 95.3% under both 60% and 90% RH backgrounds. The studies provide a promising solution for reliable low-concentration VOC detection using QCM sensors in high-humidity environments such as underground spaces.

A single recognition unit-based virtual sensor Array: Applying 3D fluorescence spectroscopy to inner filter effect-based sensing

A convenient, fast, low-cost detection and discrimination method is demanded for environmental monitoring but still it remains more technological challenges. Herein, we demonstrate that the inner filter effect (IFE), in combination with three-dimensional fluorescence spectroscopy, can offer a virtual sensor array (VSA) as apropersolution. And with the aid of pattern recognition techniques, it is feasible to recognize compounds with structural similarities economically and effectively. In this study, with the help of visual clustering plots of principal component analysis (PCA), a prediction model based on hierarchical strategy was made using support vector machine (SVM) method for the qualitative profiling of aromatic pollutants. The VSA was constructed by a single metal-organic framework (MOF) recognition unit (MOF-74 (Zn)) with the excitation wavelength as external regulatory factors. Pattern characteristics of four aromatics with very similar structures (phenylamine, chlorobenzene, nitrobenzene, and phenol), both single analyte and binary mixtures, were acquired. The primary constituents of multi-dimensional spectral signals were subsequently extracted and fed into a vector machine to construct a prediction model through 10-fold cross-validation optimization, resulting in a classification accuracy of 100% for single analytes and 96% for mixtures. Quantitative research has shown that, except for chlorobenzene, all three other analytes can be predicted in concentration within an acceptable error range, and the mixture can be predicted proportionally. Moreover, the VSA can be used to distinguish these pollutants in tap and river water also. We propose for the first time a new tack for the construction of VSA in a general manner, namely using three-dimensional full range fluorescence scanning for IFE based sensing to get multiple times of information resulting from different weak interaction between analyte and sensor for decision-making.

Influence of humidity on accuracy of QCM - IR780-based GUMBOS sensor arrays

Herein, hydrophobic coating materials are reported for QCM detection of VOCs under dry and humid conditions. In this study, IR780-based GUMBOS ([IR780][OTf] and [IR780][NTf2]) were synthesized using an ion exchange reaction and the anions trifluoromethanesulfonimide ([OTf]) and bisperfluoromethanesulfonimide ([NTf2]). The parent iodide salts and GUMBOS ([IR780][I]), [IR780][OTf], and [IR780][NTf2]) were characterized using several analytical techniques. These salts were then employed as sensor coatings on quartz crystal resonators using an electrospray coating method. These sensors were exposed to four flow ratios of five common VOCs in the absence and presence of 10 vol% water. Fundamental frequency responses were recorded and further employed as input variables to develop highly accurate multi-sensor arrays (MSAs). Accuracy was better than 78.3% without water, and better than 91.7% in the presence of water. When multi-harmonic responses were evaluated as input variables to assess discrimination ability for each sensor, highly accurate virtual sensor arrays (VSAs) were developed using each GUMBOS coating. In the case of [IR780][NTf2], a slight improvement in discrimination was achieved in the presence of water (95%) versus the absence of water. Moreover, this study highlights development of readily synthesized hydrophobic coatings of IR780-based GUMBOS for potential detection and discrimination of VOCs in aqueous systems.

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

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

Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication

People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM_2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring.

A Family of 2D-MXenes: Synthesis, Properties, and Gas Sensing Applications

Gas sensors, capable of detecting and monitoring trace amounts of gas molecules or volatile organic compounds (VOCs), are in great demand for numerous applications including diagnosing diseases through breath analysis, environmental and personal safety, food and agriculture, and other fields. The continuous emergence of new materials is one of the driving forces for the development of gas sensors. Recently, 2D materials have been gaining huge attention for gas sensing applications, owing to their superior electrical, optical, and mechanical characteristics. Especially for 2D MXenes, high specific area and their rich surface functionalities with tunable electronic structure make them compelling for sensing applications. This Review discusses the latest advancements in the 2D MXenes for gas sensing applications. It starts by briefly explaining the family of MXenes, their synthesis methods, and delamination procedures. Subsequently, it outlines the properties of MXenes. Then it describes the theoretical and experimental aspects of the MXenes-based gas sensors. Discussion is also extended to the relation between sensing performance and the structure, electronic properties, and surface chemistry. Moreover, it highlights the promising potential of these materials in the current gas sensing applications and finally it concludes with the limitations, challenges, and future prospects of 2D MXenes in gas sensing applications.

Virtual Sensor Array Based on Piezoelectric Cantilever Resonator for Identification of Volatile Organic Compounds

Piezoelectric cantilever resonator is one of the most promising platforms for real-time sensing of volatile organic compounds (VOCs). However, it has been a great challenge to eliminate the cross-sensitivity of various VOCs for these cantilever-based VOC sensors. Herein, a virtual sensor array (VSA) is proposed on the basis of a sensing layer of GO film deposited onto an AlN piezoelectric cantilever with five groups of top electrodes for identification of various VOCs. Different groups of top electrodes are applied to obtain high amplitudes of multiple resonance peaks for the cantilever, thus achieving low limits of detection (LODs) to VOCs. Frequency shifts of multiple resonant modes and changes of impedance values are taken as the responses of the proposed VSA to VOCs, and these multidimensional responses generate a unique fingerprint for each VOC. On the basis of machine learning algorithms, the proposed VSA can accurately identify different types of VOCs and mixtures with accuracies of 95.8 and 87.5%, respectively. Furthermore, the VSA has successfully been applied to identify the emissions from healthy plants and "plants with late blight" with an accuracy of 89%. The high levels of identifications show great potentials of the VSA for diagnosis of infectious plant diseases by detecting VOC biomarkers.

Operando Characterization of Organic Mixed Ionic/Electronic Conducting Materials

Operando characterization plays an important role in revealing the structure-property relationships of organic mixed ionic/electronic conductors (OMIECs), enabling the direct observation of dynamic changes during device operation and thus guiding the development of new materials. This review focuses on the application of different operando characterization techniques in the study of OMIECs, highlighting the time-dependent and bias-dependent structure, composition, and morphology information extracted from these techniques. We first illustrate the needs, requirements, and challenges of operando characterization then provide an overview of relevant experimental techniques, including spectroscopy, scattering, microbalance, microprobe, and electron microscopy. We also compare different in silico methods and discuss the interplay of these computational methods with experimental techniques. Finally, we provide an outlook on the future development of operando for OMIEC-based devices and look toward multimodal operando techniques for more comprehensive and accurate description of OMIECs.

Overview of Quartz Crystal Microbalance Behavior Analysis and Measurement

Quartz Crystal Microbalance (QCM) is one of the many acoustic transducers. It is the most popular and widely used acoustic transducer for sensor applications. It has found wide applications in chemical and biosensing fields owing to its high sensitivity, robustness, small sized-design, and ease of integration with electronic measurement systems. However, it is necessary to coat QCM with a sensing film. Without coating materials, its selectivity and sensitivity are not obtained. At present, this is not an issue, mainly due to the advancement of oscillator circuits and dedicated measurement circuits. Since a new researcher may seek to understand QCM sensors, we provide an overview of QCM from its fundamental knowledge. Then, we explain some of the recent QCM applications both in gas-phase and liquid-phase. Next, the theory of QCM is introduced by using piezoelectric stress equations and the Mason equivalent circuit, which explains how the QCM behavior is obtained. Then, the conventional equations that govern QCM behaviors in terms of resonant frequency and resistance are described. We show the behavior of QCM with a viscous film based on the acoustic wave equation and Mason equivalent circuit. Then, we present various existing QCM electronic measurement methods. Furthermore, we describe the experiment on QCM with viscous loading and its interpretation based on the Mason equivalent circuit. Lastly, we review some theoretical models to describe QCM behavior with various models.

Virtual Sensor Array Based on Butterworth-Van Dyke Equivalent Model of QCM for Selective Detection of Volatile Organic Compounds

Recently virtual sensor arrays (VSAs) have been developed to improve the selectivity of volatile organic compound (VOC) sensors. However, most reported VSAs rely on detecting single property change of the sensing material after their exposure to VOCs, thus resulting in a loss of much valuable information. In this work, we propose a VSA with the high dimensionality of outputs based on a quartz crystal microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical and electrical properties of the MXene film are utilized in the detection of the VOCs. We take the changes of parameters of the Butterworth-van Dyke model for the QCM-based sensor operated at multiple harmonics as the responses of the VSA to various VOCs. The dimensionality of the VSA's responses has been expanded to four independent outputs, and the responses to the VOCs have shown good linearity in multidimensional space. The response and recovery times are 16 and 54 s, respectively. Based on machine learning algorithms, the proposed VSA accurately identifies different VOCs and mixtures, as well as quantifies the targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover, we demonstrate the capacity of the VSA to identify "patients with diabetic ketosis" from volunteers with an accuracy of 95%, based on the detection of their exhaled breath. The QCM-based VSA shows great potential for detecting VOC biomarkers in human breath for disease diagnosis.

Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity

Odors are one of the most diverse and complicated gaseous mixtures so that their discrimination is challenging yet attractive because of the rich information about their origin. The more similar the properties of odors are, the more difficult the discrimination becomes. The practical applications, however, often demand such discrimination, especially with a compact sensing platform. In this paper, we show that a nanomaterial designed for a specific type of odors can clearly discriminate them even with a single nanomechanical sensing channel. Fuel oils and their mixture are used as a model target that has similar chemical properties but different compositions mainly consisting of paraffinic, olefinic, naphthenic, and aromatic hydrocarbons. We demonstrate using octadecyl functionalized silica-titania nanoparticles that the difference in the compositions is successfully picked up based on their high affinity for the aliphatic hydrocarbons and alkyl chain length dependent nonlinear viscoelastic behavior. Such a properly designed material is proved to derive sufficient information from a series of analytes to discriminate them even with a single sensing element. This approach provides a guideline to prepare various sensors whose response properties are distinct and optimized depending on applications.

A flexible virtual sensor array based on laser-induced graphene and MXene for detecting volatile organic compounds in human breath

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases.

Coating-Based Quartz Crystal Microbalance Detection Methods of Environmentally Relevant Volatile Organic Compounds

Volatile organic compounds (VOCs) that evaporate under standard atmospheric conditions are of growing concern. This is because it is well established that VOCs represent major contamination risks since release of these compounds into the atmosphere can contribute to global warming, and thus, can also be detrimental to the overall health of worldwide populations including plants, animals, and humans. Consequently, the detection, discrimination, and quantification of VOCs have become highly relevant areas of research over the past few decades. One method that has been and continues to be creatively developed for analyses of VOCs is the Quartz Crystal Microbalance (QCM). In this review, we summarize and analyze applications of QCM devices for the development of sensor arrays aimed at the detection of environmentally relevant VOCs. Herein, we also summarize applications of a variety of coatings, e.g., polymers, macrocycles, and ionic liquids that have been used and reported in the literature for surface modification in order to enhance sensing and selective detection of VOCs using quartz crystal resonators (QCRs) and thus QCM. In this review, we also summarize novel electronic systems that have been developed for improved QCM measurements.

Determination of quasi-primary odors by endpoint detection

It is known that there are no primary odors that can represent any other odors with their combination. Here, we propose an alternative approach: "quasi" primary odors. This approach comprises the following condition and method: (1) within a collected dataset and (2) by the machine learning-based endpoint detection. The quasi-primary odors are selected from the odors included in a collected odor dataset according to the endpoint score. While it is limited within the given dataset, the combination of such quasi-primary odors with certain ratios can reproduce any other odor in the dataset. To visually demonstrate this approach, the three quasi-primary odors having top three high endpoint scores are assigned to the vertices of a chromaticity triangle with red, green, and blue. Then, the other odors in the dataset are projected onto the chromaticity triangle to have their unique colors. The number of quasi-primary odors is not limited to three but can be set to an arbitrary number. With this approach, one can first find "extreme" odors (i.e., quasi-primary odors) in a given odor dataset, and then, reproduce any other odor in the dataset or even synthesize a new arbitrary odor by combining such quasi-primary odors with certain ratios.

Point-of-care test system for detection of immunoglobulin-G and -M against nucleocapsid protein and spike glycoprotein of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) epidemic continues to ravage the world. In epidemic control, dealing with a large number of samples is a huge challenge. In this study, a point-of-care test (POCT) system was successfully developed and applied for rapid and accurate detection of immunoglobulin-G and -M against nucleocapsid protein (anti-N IgG/IgM) and receptor-binding domain in spike glycoprotein (anti-S-RBD IgG/IgM) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Any one of the IgG/IgM found in a sample was identified as positive. The POCT system contains colloidal gold-based lateral flow immunoassay test strips, homemade portable reader, and certified reference materials, which detected anti-N and anti-S-RBD IgG/IgM objectively in serum within 15 min. Receiver operating characteristic curve analysis was used to determine the optimal cutoff values, sensitivity, and specificity. It exhibited equal to or better performances than four approved commercial kits. Results of the system and chemiluminescence immunoassay kit detecting 108 suspicious samples had high consistency with kappa coefficient at 0.804 (P < 0.001). Besides, the levels and alterations of the IgG/IgM in an inpatient were primarily investigated by the POCT system. Those results suggested the POCT system possess the potential to contribute to rapid and accurate serological diagnosis and epidemiological survey of COVID-19.

Highly Selective Gas Sensor Based on Hydrophobic Silica Decorated with Trimethoxyoctadecylsilane

Trimethoxyoctadecylsilane (OTMS) was successfully used to decorate mesoporous silica with a self-assembly method to enhance the relative gas selectivity. A quartz crystal microbalance was employed to measure the gas-sensing properties. The content of OTMS was the crucial factor that greatly affected the adsorption capacity (q) of silica, which could be converted to relative selectivity (S) to study the sensing mechanism. With increasing OTMS content, q was far higher for small-molecule gases compared to volatile organic compounds (VOCs), which could be explained by the polarity of the bonding objects, and S reached a maximum value of 45.71%. When exposed to VOCs, S was always greater than 0 among the three alcohols. The sensing mechanisms of undecorated silica and OTMS-decorated silica were quite different; the three-state mechanism was proposed to explain the sensing mechanism of OTMS-decorated silica. When exposed to small-molecule gases, the atoms that bonded with carbon atoms on OTMS greatly influenced q. With increasing OTMS content, the bonding energy of OTMS with CO₂ was far less than that with other molecules, resulting in a relative selectivity as high as 38.69%. Furthermore, macroperformance and microproperties were combined in three-dimensional coordinates, which could be applied to predict the sensing performance of silica.

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning"

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

Gas sensors based on mass-sensitive transducers. Part 2: Improving the sensors towards practical application

Within the framework outlined in the first part of the review, the second part addresses attempts to increase receptor material performance through the use of sensor systems and chemometric methods, in conjunction with receptor preparation methods and sensor-specific tasks. Conclusions are then drawn, and development perspectives for gravimetric sensors are discussed.

Quartz Crystal Microbalance Based Sensor Arrays for Detection and Discrimination of VOCs Using Phosphonium Ionic Liquid Composites

Herein, we examine two sensing schemes for detection and discrimination of chlorinated volatile organic compounds (VOCs). In this work, phosphonium ionic liquids (ILs) were synthesized and vapor sensing properties examined and compared to phosphonium IL-polymer composites. Pure IL sensors were used to develop a QCM-based multisensory array (MSA), while IL-polymer composites were used to develop an MSA and virtual sensor arrays (VSAs). It was found that by employing the composite MSA, five chlorinated VOCs were accurately discriminated at 95.56%, which was an increase in accuracy as compared to pure ILs MSA (84.45%). Data acquired with two out of three VSAs allowed discrimination of chlorinated VOCs with 100% accuracy. These studies have provided greater insight into the benefits of incorporating polymers in coating materials for enhanced discrimination accuracies of QCM-based sensor arrays. To the best of our knowledge, this is the first report of a QCM-based VSA for discrimination of closely related chlorinated VOCs.

One novel calix[4]arene based QCM sensor for sensitive, selective and high performance-sensing of formaldehyde at room temperature

This work designs the synthesis of a novel amino morpholine schiff base functionalized calix[4]arene cage (SCC), its deposition onto Quartz Crystal Microbalance (QCM) crystal surface, and usage for the selective detecting of formaldehyde (HCHO). The SCC modified QCM sensor has been characterized by contact angle measurements and microscopy images. Initial experiments revealed that the frequency response decreased significantly which means that there was a good interaction between the SCC molecules and HCHO. The proposed sensor exhibited a linear response towards HCHO in different concentrations ranging from 1.85 to 9.25 ppm, having the high sensitivity (S) and low limit of detection (LOD) being 18.324 Hz/ppm and 0.67 ppm, respectively. Furthermore, the adsorption behavior and mechanism of HCHO onto the QCM sensor were investigated for this sensing system and the adsorption data exhibited a good correlation with the Freundlich and Langmuir-Freundlich adsorption models in terms of the regression coefficient. The QCM sensor showed outstanding selective performance to HCHO among %97 RH and some a number of interfering volatile organic compounds (VOCs) such as chloroform, dichloromethane, acetone, n-hexane, methanol, xylene, and ammonia. Thus, real-time, sensitive, selective and effective recognition of HCHO by the sensor can be explained some adsorption mechanisms such as size-fit concept, three-dimensional structures of molecules and interaction between moieties of the sensible film layer and analyte molecules such as hydrogen bonding interactions.

Bulk and Surface Acoustic Wave Sensor Arrays for Multi-Analyte Detection: A Review

Bulk acoustic wave (BAW) and surface acoustic wave (SAW) sensor devices have successfully been used in a wide variety of gas sensing, liquid sensing, and biosensing applications. Devices include BAW sensors using thickness shear modes and SAW sensors using Rayleigh waves or horizontally polarized shear waves (HPSWs). Analyte specificity and selectivity of the sensors are determined by the sensor coatings. If a group of analytes is to be detected or if only selective coatings (i.e., coatings responding to more than one analyte) are available, the use of multi-sensor arrays is advantageous, as the evaluation of the resulting signal patterns allows qualitative and quantitative characterization of the sample. Virtual sensor arrays utilize only one sensor but combine it with enhanced signal evaluation methods or preceding sample separation, which results in similar results as obtained with multi-sensor arrays. Both array types have shown to be promising with regard to system integration and low costs. This review discusses principles and design considerations for acoustic multi-sensor and virtual sensor arrays and outlines the use of these arrays in multi-analyte detection applications, focusing mainly on developments of the past decade.

Detection and Discrimination of Volatile Organic Compounds using a Single Film Bulk Acoustic Wave Resonator with Temperature Modulation as a Multiparameter Virtual Sensor Array

This paper describes the detection and discrimination of volatile organic compounds (VOCs) using an e-nose system based on a multiparameter virtual sensor array (VSA), which consists of a single-chip temperature-compensated film bulk acoustic wave resonator (TC-FBAR) coated with 20-bilayer self-assembled poly(sodium 4-styrenesulfonate)/poly(diallyldimethylammonium chloride) thin films. The high-frequency and microscale FBAR multiparameter VSA was realized by temperature modulation, which can greatly reduce the cost and complexity compared to those of a traditional e-nose system and can allow it to operate at different temperatures. The discrimination effect depends on the synergy of temperature modulation and the sensing material. For proof-of-concept validation purposes, the TC-FBAR was exposed to six different VOC vapors at six different gas partial pressures by real-time VOC static detection and dynamic detection. The resulting frequency shifts and impedance responses were measured at different temperatures and evaluated using principal component analysis and linear discriminant analysis, which revealed that all analytes can be distinguished and classified with more than 97% accuracy. To the best of our knowledge, this report is the first on an FBAR multiparameter VSA based on temperature modulation, and the proposed novel VSA shows great potential as a compact and promising e-nose system integrated in commercial electronic products.

Gas sensors based on mass-sensitive transducers part 1: transducers and receptors-basic understanding

The scientific interest in gas sensors is continuously increasing because of their environmental, medical, industrial, and domestic applications. This has resulted in an increasing number of investigations being reported in the literature and communicated at conferences. The present review, organized in two parts, addresses the peculiarities of gas sensors based on mass-sensitive transducers, starting with their structure and functionality and progressing to implementation and specific use. In this first part of the review, we discuss the constructional peculiarities and operation regions and the physical and chemical processes governing the reception and transduction functions and the way in which they influence the sensor sensing parameters/features. Scientific outcomes and trends in research into gas sensors based on mass sensitive transducers are also considered.

Multi-modal, ultrasensitive, wide-range humidity sensing with Ti3C2 film

Gravimetric, direct-current electrical, and electrical impedance sensing modes were used to measure response of high surface area 2D Ti3C2 MXene film to water vapor pressures spanning 3 orders of magnitude (20 mTorr-20 Torr). The Ti3C2 film exhibited reproducible reversible response in 0.1%-95% relative humidity (RH) range with a detection limit of <20 mTorr H2O partial pressure (<0.1% RH). DC electrical current-based sensing with 3 mV operating voltage and 0.8 pW power consumption was demonstrated. The highest normalized sensitivity was shown for gravimetric sensing modalities which scale with the overtone number, reaching highest sensitivity of about 12 Hz/% RH at the 9th crystal overtone (45 MHz oscillation).

Volatile organic compounds discrimination based on dual mode detection

We report on a volatile organic compound (VOC) sensor that can provide concentration-independent signals toward target gases. The device is based on a dual-mode detection mechanism that can simultaneously record the mechanical (resonant frequency, f _r) and electrical (current, I) responses of the same gas adsorption event. The two independent signals form a unique I-f _r trace for each target VOC as the concentration varies. The mechanical response (frequency shift, Δf _r) resulting from mass load on the device is directly related to the amount of surface adsorptions, while the electrical response (current variation, ΔI) is associated with charge transfer across the sensing interface and changes in carrier mobility. The two responses resulting from independent physical processes reflect intrinsic physical properties of each target gas. The ΔI-Δf _r trace combined with the concentration dependent frequency (or current) signals can therefore be used to achieve target both recognition and quantification. The dual-mode device is designed and fabricated using standard complementary metal oxide semiconductor (CMOS) compatible processes. It exhibits consistent and stable performance in our tests with six different VOCs including ethanol, methanol, acetone, formaldehyde, benzene and hexane.

Label-Free Bioanalyte Detection from Nanometer to Micrometer Dimensions-Molecular Imprinting and QCMs †

Modern diagnostic tools and immunoassay protocols urges direct analyte recognition based on its intrinsic behavior without using any labeling indicator. This not only improves the detection reliability, but also reduces sample preparation time and complexity involved during labeling step. Label-free biosensor devices are capable of monitoring analyte physiochemical properties such as binding sensitivity and selectivity, affinity constants and other dynamics of molecular recognition. The interface of a typical biosensor could range from natural antibodies to synthetic receptors for example molecular imprinted polymers (MIPs). The foremost advantages of using MIPs are their high binding selectivity comparable to natural antibodies, straightforward synthesis in short time, high thermal/chemical stability and compatibility with different transducers. Quartz crystal microbalance (QCM) resonators are leading acoustic devices that are extensively used for mass-sensitive measurements. Highlight features of QCM devices include low cost fabrication, room temperature operation, and most importantly ability to monitor extremely low mass shifts, thus potentially a universal transducer. The combination of MIPs with quartz QCM has turned out as a prominent sensing system for label-free recognition of diverse bioanalytes. In this article, we shall encompass the potential applications of MIP-QCM sensors exclusively label-free recognition of bacteria and virus species as representative micro and nanosized bioanalytes.

Data-driven nanomechanical sensing: specific information extraction from a complex system

Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realizes the specific information extraction, e.g. alcohol content quantification as a proof-of-concept, from the smells of liquors. A newly developed nanomechanical sensor platform, a Membrane-type Surface stress Sensor (MSS), was utilized. Each MSS channel was coated with functional nanoparticles, covering diverse analytes. The smells of 35 liquid samples including water, teas, liquors, and water/EtOH mixtures were measured using the functionalized MSS array. We selected characteristic features from the measured responses and kernel ridge regression was used to predict the alcohol content of the samples, resulting in successful alcohol content quantification. Moreover, the present approach provided a guideline to improve the quantification accuracy; hydrophobic coating materials worked more effectively than hydrophilic ones. On the basis of the guideline, we experimentally demonstrated that additional materials, such as hydrophobic polymers, led to much better prediction accuracy. The applicability of this data-driven nanomechanical sensing is not limited to the alcohol content quantification but to various fields including food, security, environment, and medicine.

Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors

This review provides analysis of advances in multivariable sensors based on monolayer-protected nanoparticles and several principles of signal transduction that result in building non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors.

Binary coded identification of industrial chemical vapors with an optofluidic nose

An artificial nose system for the recognition and classification of gas-phase analytes and its application in identifying common industrial gases is reported. The sensing mechanism of the device comprises the measurement of infrared absorption of volatile analytes inside the hollow cores of optofluidic Bragg fibers. An array of six fibers is used, where each fiber targets a different region of the mid-infrared in the range of 2-14 μm with transmission bandwidths of about 1-3 μm. The quenching in the transmission of each fiber due to the presence of analyte molecules in the hollow core is measured separately and the cross response of the array allows the identification of virtually any volatile organic compound (VOC). The device was used for the identification of seven industrial VOC vapors with high selectivity using a standard blackbody source and an infrared detector. The array response is registered as a unique six digit binary code for each analyte by assigning a threshold value to the fiber transmissions. The developed prototype is a comprehensive and versatile artificial nose that is applicable to a wide range of analytes.

Assessment of QCM array schemes for mixture identification: citrus scented odors

A comparative study of QCM based MSAs, VSAs, and V-MSAs for complex mixture analysis.

Detection of volatile organic compounds (VOCs) from exhaled breath as noninvasive methods for cancer diagnosis

The detection of cancer at an early stage is often significant in the successful treatment of the disease. Tumor cells have been reported to generate unique cancer volatile organic compound (VOC) profiles which can reflect the disease conditions. The detection and analysis of VOC biomarkers from exhaled breath has been recognized as a new frontier in cancer diagnostics and health inspections owing to its potential in developing rapid, noninvasive, and inexpensive cancer screening tools. To detect specific VOCs of low concentrations from exhaled breath, and to enhance the accuracy of early diagnosis, many breath collection and analysis approaches have been developed. This paper will summarize and critically review the exhaled-breath VOC-related sampling, collection, detection, and analytical methods, especially the recent development in VOC sensors. VOC sensors are commonly inexpensive, portable, programmable, easy to use, and can obtain data in real time with high sensitivities. Therefore, many sensor-based VOC detection techniques have huge potential in clinical point-of-care use.

Quartz-Crystal Microbalance (QCM) for Public Health: An Overview of Its Applications

Nanobiotechnologies, from the convergence of nanotechnology and molecular biology and postgenomics medicine, play a major role in the field of public health. This overview summarizes the potentiality of piezoelectric sensors, and in particular, of quartz-crystal microbalance (QCM), a physical nanogram-sensitive device. QCM enables the rapid, real time, on-site detection of pathogens with an enormous burden in public health, such as influenza and other respiratory viruses, hepatitis B virus (HBV), and drug-resistant bacteria, among others. Further, it allows to detect food allergens, food-borne pathogens, such as Escherichia coli and Salmonella typhimurium, and food chemical contaminants, as well as water-borne microorganisms and environmental contaminants. Moreover, QCM holds promises in early cancer detection and screening of new antiblastic drugs. Applications for monitoring biohazards, for assuring homeland security, and preventing bioterrorism are also discussed.