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

Volatile organic compounds: A threat to the environment and health hazards to living organisms - A review

Volatile organic compounds (VOCs) are the organic compounds having a minimum vapor pressure of 0.13 kPa at standard temperature and pressure (293 K, 101 kPa). Being used as a solvent for organic and inorganic compounds, they have a wide range of applications. Most of the VOCs are non-biodegradable and very easily become component of the environment and deplete its purity. It also deteriorates the water quality index of the water bodies, impairs the physiology of living beings, enters the food chain by bio-magnification and degrades, decomposes and manipulates the physiology of living organisms. To unveil the adverse impacts of volatile organic compounds (VOCs) and their rapid eruption and interference in the living world, a review has been designed. This review presents an insight into the currently available VOCs, their sources, applications, sampling methods, analytic procedures, imposition on the health of aquatic and terrestrial communities and their contamination of the environment. Elaboration has been done on representation of toxicological effects of VOCs on vertebrates, invertebrates, and birds. Subsequently, the role of environmental agencies in the protection of environment has also been illustrated.

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.

Temperature-modulated sensing characteristics of ultrafine Au nanoparticle-loaded porous ZnO nanobelts for identification and determination of BTEX

The identification and determination of benzene, toluene, ethylbenzene, and xylene (BTEX) has always been a formidable challenge for chemiresistive metal oxide sensors owing to their structural similarity and low reactivity, as well as the intrinsic cross sensitivity of metal oxides. In this paper, a temperature-modulated sensing strategy is proposed for the identification and determination of BTEX using a high-performance chemiresistive sensor. Ultrafine Au nanoparticle-loaded porous ZnO nanobelts as sensing materials were synthesized through an exchange reaction followed by thermal oxidation, which exhibited high response toward BTEX. Under dynamic modulation of working temperature, the distinguishable characteristic curves were demonstrated for each BTEX compound. By employing the linear discrimination and convolutional neural network analyses, highly effective BTEX identification was achieved among all investigated volatile organic compounds, which is difficult to realize for single chemiresistive sensors at constant working temperatures. Furthermore, quantitative analysis of BTEX concentrations was accomplished by establishing the relationship between concentration and response at specific points on their response curves. This developed strategy is expected to pave a new way for constructing highly sensitive gas sensors for the identification and analysis of hazardous gases, thereby enhancing their applicability in environmental monitoring.

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.

Four levels of in-sensor computing in bionic olfaction: from discrete components to multi-modal integrations

Sensing and computing are two important ways in which humans attempt to perceive and understand the analog world through digital devices. Analog-to-digital converters (ADCs) discretize analog signals while the data bus transmits digital data between the components of a computer. With the increase in sensor nodes and the application of deep neural networks, the energy and time consumption limit the increment of data throughput. In-sensor computing is a computing paradigm that integrates sensing, storage, and processing in one device without ADCs and data transfer. According to the integration degree, herein, we summarize four levels of in-sensor computing in the field of artificial olfactory. In the first level, we show that different functions are conducted by using discrete components. Next, the data conversion and transfer are exempt within the in-memory computing architecture with necessary data encoding. Subsequently, in-sensor computing is integrated into a single device. Finally, multi-modal in-sensor computing is proposed to improve the quality and reliability of the classification results. At the end of this minireview, we provide an outlook on the use of metal nanoparticle devices to achieve such in-sensor computing for bionic olfaction.

Portable FBAR based E-nose for cold chain real-time bananas shelf time detection

Being cheap, nondestructive, and easy to use, gas sensors play important roles in the food industry. However, most gas sensors are suitable more for laboratory-quality fast testing rather than for cold-chain continuous and cumulative testing. Also, an ideal electronic nose (E-nose) in a cold chain should be stable to its surroundings and remain highly accurate and portable. In this work, a portable film bulk acoustic resonator (FBAR)-based E-nose was built for real-time measurement of banana shelf time. The sensor chamber to contain the portable circuit of the E-nose is as small as a smartphone, and by introducing an air-tight FBAR as a reference, the E-nose can avoid most of the drift caused by surroundings. With the help of porous layer by layer (LBL) coating of the FBAR, the sensitivity of the E-nose is 5 ppm to ethylene and 0.5 ppm to isoamyl acetate and isoamyl butyrate, while the detection range is large enough to cover a relative humidity of 0.8. In this regard, the E-nose can easily discriminate between yellow bananas with green necks and entirely yellow bananas while allowing the bananas to maintain their biological activities in their normal storage state, thereby showing the possibility of real-time shelf time detection. This portable FBAR-based E-nose has a large testing scale, high sensitivity, good humidity tolerance, and low frequency drift to its surroundings, thereby meeting the needs of cold-chain usage.

A review of piezoelectric MEMS sensors and actuators for gas detection application

Piezoelectric microelectromechanical system (piezo-MEMS)-based mass sensors including the piezoelectric microcantilevers, surface acoustic waves (SAW), quartz crystal microbalance (QCM), piezoelectric micromachined ultrasonic transducer (PMUT), and film bulk acoustic wave resonators (FBAR) are highlighted as suitable candidates for highly sensitive gas detection application. This paper presents the piezo-MEMS gas sensors' characteristics such as their miniaturized structure, the capability of integration with readout circuit, and fabrication feasibility using multiuser technologies. The development of the piezoelectric MEMS gas sensors is investigated for the application of low-level concentration gas molecules detection. In this work, the various types of gas sensors based on piezoelectricity are investigated extensively including their operating principle, besides their material parameters as well as the critical design parameters, the device structures, and their sensing materials including the polymers, carbon, metal-organic framework, and graphene.

Trends and Applications of Surface and Bulk Acoustic Wave Devices: A Review

The past few decades have witnessed the ultra-fast development of wireless telecommunication systems, such as mobile communication, global positioning, and data transmission systems. In these applications, radio frequency (RF) acoustic devices, such as bulk acoustic waves (BAW) and surface acoustic waves (SAW) devices, play an important role. As the integration technology of BAW and SAW devices is becoming more mature day by day, their application in the physical and biochemical sensing and actuating fields has also gradually expanded. This has led to a profusion of associated literature, and this article particularly aims to help young professionals and students obtain a comprehensive overview of such acoustic technologies. In this perspective, we report and discuss the key basic principles of SAW and BAW devices and their typical geometries and electrical characterization methodology. Regarding BAW devices, we give particular attention to film bulk acoustic resonators (FBARs), due to their advantages in terms of high frequency operation and integrability. Examples illustrating their application as RF filters, physical sensors and actuators, and biochemical sensors are presented. We then discuss recent promising studies that pave the way for the exploitation of these elastic wave devices for new applications that fit into current challenges, especially in quantum acoustics (single-electron probe/control and coherent coupling between magnons and phonons) or in other fields.

Recent Progress on Flexible Room-Temperature Gas Sensors Based on Metal Oxide Semiconductor

With the rapid development of the Internet of Things, there is a great demand for portable gas sensors. Metal oxide semiconductors (MOS) are one of the most traditional and well-studied gas sensing materials and have been widely used to prepare various commercial gas sensors. However, it is limited by high operating temperature. The current research works are directed towards fabricating high-performance flexible room-temperature (FRT) gas sensors, which are effective in simplifying the structure of MOS-based sensors, reducing power consumption, and expanding the application of portable devices. This article presents the recent research progress of MOS-based FRT gas sensors in terms of sensing mechanism, performance, flexibility characteristics, and applications. This review comprehensively summarizes and discusses five types of MOS-based FRT gas sensors, including pristine MOS, noble metal nanoparticles modified MOS, organic polymers modified MOS, carbon-based materials (carbon nanotubes and graphene derivatives) modified MOS, and two-dimensional transition metal dichalcogenides materials modified MOS. The effect of light-illuminated to improve gas sensing performance is further discussed. Furthermore, the applications and future perspectives of FRT gas sensors are also discussed.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

A prototype portable instrument employing micro-preconcentrator and FBAR sensor for the detection of chemical warfare agents

The presence of chemical warfare agents (CWAs) in the environment is a serious threat to human safety, but there are many problems with the currently available detection methods for CWAs. For example, gas chromatography–mass spectrometry cannot be used for in-field detection owing to the rather large size of the equipment required, while commercial sensors have the disadvantages of low sensitivity and poor selectivity. Here, we develop a portable gas sensing instrument for CWA detection that consists of a MEMS-fabricated micro-preconcentrator (μPC) and a film bulk acoustic resonator (FBAR) gas sensor. The μPC is coated with a nanoporous metal–organic framework material to enrich the target, while the FBAR provides rapid detection without the need for extra carrier gas. Dimethyl methylphosphonate (DMMP), a simulant of the chemical warfare agent sarin, is used to test the performance of the instrument. Experimental results show that the μPC provides effective sample pretreatment, while the FBAR gas sensor has good sensitivity to DMMP vapor. The combination of μPC and FBAR in one instrument gives full play to their respective advantages, reducing the limit of detection of the analyte. Moreover, both the μPC and the FBAR are fabricated using a CMOS-compatible approach, and the prototype instrument is compact in size with high portability and thus has potential for application to in-field detection of CWAs.

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.

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.

TiO2/Cu2O/CuO Multi-Nanolayers as Sensors for H2 and Volatile Organic Compounds: An Experimental and Theoretical Investigation

TiO₂/Cu₂O/CuO multi-nanolayers highly sensitive toward volatile organic compounds (VOCs) and H₂ have been grown in various thicknesses by a cost-effective and reproducible combined spray-sputtering-annealing approach. The ultrathin TiO₂ films were deposited by spray pyrolysis on top of sputtered-annealed Cu₂O/CuO nanolayers to enhance their gas sensing performance and improve their protection against corrosion at high operating temperatures. The prepared heterostructures were investigated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and ultraviolet visible (UV-vis) and micro-Raman spectroscopy. The gas sensing properties were measured at several operating temperatures, where the nanolayered sensors with oxide thicknesses between 20 and 30 nm (Cu₂O/CuO nanolayers) exhibited a high response and an excellent selectivity to ethanol vapor after thermal annealing the samples at 420 °C. The results obtained at an operating temperature of 350 °C demonstrate that the CuO/Cu₂O nanolayers with thicknesses between 20 and 30 nm are sensitive mainly to ethanol vapor, with a response of ∼150. The response changes from ethanol vapors to hydrogen gas as the thickness of the CuO/Cu₂O nanolayers changes from 50 to 20 nm. Density functional theory-based calculations were carried out for the geometries of the CuO(1̅11)/Cu₂O(111) and TiO₂(111)/CuO(1̅11)/Cu₂O(111) heterostructures and their sensing mechanism toward alcohols of different chain lengths and molecular hydrogen. The reconstructed hexagonal Cu₂O(111) surface and the reconstructed monoclinic CuO(1̅11) and TiO₂(111) facets, all of which terminate in an O layer, lead to the lowest surface energies for each isolated material. We studied the formation of the binary and ternary heteroepitaxial interfaces for the surface planes with the best-matching lattices. Despite the impact of the Cu₂O(111) substrate in lowering the atomic charges of the CuO(1̅11) adlayer in the binary sensor, we found that it is the different surface structures of the CuO(1̅11)/Cu₂O(111) and TiO₂(111)/CuO(1̅11)/Cu₂O(111) devices that are fundamental in driving the change in the sensitivity response observed experimentally. The experimental data, supported by the computational results, are important in understanding the use of the multi-nanolayered films tested in this work as reliable, accurate, and selective sensor structures for the tracking of gases at low concentrations.

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.

Recent advances in acoustic wave biosensors for the detection of disease-related biomarkers: A review

In the past several decades, acoustic wave biosensors, as an emerging kind of biosensors, have been developed and widely used for the detection of mass, viscosity, conductivity and density. Varieties of applications have been explored such as medical diagnosis, drug screening, environmental monitoring, food analysis and biochemical assay. Among them, the detection of disease-related biomarkers based on acoustic sensors has aroused great research interest all over the world. In this review, the classification and characteristics of acoustic wave biosensors are briefly introduced. Then, some classical studies and recent advances in disease-related biomarker detection utilizing these biosensors are summarized and detailed, respectively. Here, the disease-related biomarkers mainly include antigens, small molecular proteins, cancer cells, viruses and VOCs. Finally, challenges and future trends of these typical acoustic wave biosensors are discussed. Compared with other reviews of acoustic wave sensors, this review highlights the great potential of typical acoustic wave biosensors for early disease screening and diagnosis compared with widely-used medical imaging. Moreover, they are integrated with other technologies for the design of multi-analyte, multi-parameter and intelligent devices, collecting more comprehensive information from biomarkers. This review provides a new perspective on the applications and optimization of acoustic wave biosensors to develop more reliable platforms for disease-related biomarker detection and disease diagnosis.

Switching-peptides for one-step immunoassay and its application to the diagnosis of human hepatitis B

Herein, we present switching-peptides for a one-step immunoassay, without the need for additional antibody treatment or washing steps to detect antigen-antibody interactions. Fluorescently labeled switching-peptides were dissociated from the immobilized antibody soon after the antigens were bound to the binding pockets. In this study, four different parts of the antibody (IgG) frame regions were chemically synthesized, and these peptides were bound to immobilized antibodies as switching-peptides. We presented the design principle of switching-peptides and used Pymol software, based on the changes in thermodynamic parameters, to study the interaction between antibodies and switching-peptides. The binding properties of switching-peptides were analyzed based on Förster resonance energy transfer between switching-peptides as well as between switching-peptides and antibodies (IgGs) isolated from different animals. The binding constants of the four switching-peptides to antibodies were estimated to be in the range of 1.48-3.29 μM. Finally, the feasibility of using switching-peptides for the quantitative one-step immunoassay was demonstrated by human hepatitis B surface antigen (hHBsAg) detection and statistical comparison of the assay results with those of conventional ELISA. The limit of detection for HBsAg was determined to be 56 ng/mL, and the dynamic range was estimated to be 136 ng/mL-33 μg/mL. These results demonstrate the feasibility of the one-step immunoassay for HBsAg.

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.

Surface Engineering of Metal-Organic Framework Prepared on Film Bulk Acoustic Resonator for Vapor Detection

Gravimetric resonators based on micro/nanoelectromechanical systems (M/NEMS) are potential candidates in developing smaller, less expensive, and higher-performance gas sensors. Metal-organic frameworks (MOFs) with high surface areas have recently come into focus as advanced nanoporous sensitive materials in microgravimetric gas sensors. The surface of MOFs on those sensors is critical in offering water stability and varying absorption behaviors. However, the influences of the surface on sensing performance are less explored and the strategy to tune surface properties of MOFs mounted on gravimetric resonators is still rare. In this paper, a straightforward strategy to engineer surface properties of MOFs, specifically Cu₃(benzenetricarboxylate)₂ (known as HKUST-1), is reported and the surface hydrophilicity/hydrophobicity of HKUST-1 is tuned by chemical vapor deposition combined with monolayer self-assembly. It was found that the hybrid inorganic and organic surface engineering strategy not only preserves the absorption capacity of inner MOFs but also significantly enhances the sensor's stability toward water.

Short period sinusoidal thermal modulation for quantitative identification of gas species

The field of chemical (gas) sensing has witnessed an unprecedented increase in device sensitivity with single molecule detection now becoming a reality. In contrast to this, the ability to distinguish or discriminate between gas species has lagged behind. This is problematic and results in a high rate of false alarms. Here, we demonstrate a short period sinusoidal thermal modulation strategy to quantitatively and rapidly identify two industrially relevant gases (hydrogen sulfide (H₂S) and sulfur dioxide (SO₂)) by using a single semiconducting metal oxide sensor device. By applying sinusoidal heating voltages with a fixed short period, we were able to simultaneously obtain distinct patterns of dynamic responses. These characteristic patterns were adopted to build and validate a gas recognition library. Our approach does not rely on large-scale sensor arrays and complex algorithms and is amenable for real-time and low-power gas monitoring.

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.