Acoustic Wave Sensor Research Articles

Imaging of Love Waves and Their Interaction with Magnetic Domain Walls in Magnetoelectric Magnetic Field Sensors (Adv. Electron. Mater. 6/2022)

Surface Acoustic Wave Magnetic Field Sensors In article number 2200033, Jeffrey McCord and co-workers image the propagating shear wave behavior in magnetoelectric surface acoustic wave (SAW) sensor devices by time-resolved magnetooptical microscopy. The discovered complex spatial magnetoelastic interactions set limits on the sensor's noise performance, which need to be overcome for improved magnetoelectric magnetic field sensors with even lower limit of field detection.

Development of ball surface acoustic wave gas chromatograph for environmental monitoring in spacecraft and its application on the ground

For on-site analysis of surface materials on the Moon, planets, and small bodies and for the monitoring of air quality in crewed spacecraft, we have developed a portable gas chromatograph (GC) equipped with a ball surface acoustic wave (SAW) sensor. In this study, we fabricated a 10 cm cube GC that implements the forward flush method using two metal micro-electro-mechanical-system columns coated with different stationary phases in microchannels fabricated by wet etching and diffusion bonding of stainless-steel plates. Using this GC, we succeeded in analyzing 10 kinds of gas within 10 min. In addition, for the application of the ball SAW GC on the ground, we also developed a palm-sized GC with a single metal capillary column and used it in the analysis of the headspace gas of sake. We showed that the ratio of peak areas differed among odorants depending on the brand and brewing process of sake.

Long-term high-temperature behavior of Ti–Al based electrodes for surface acoustic wave devices

The long-term high-temperature behavior of Ti–Al based electrodes for the application in surface acoustic wave (SAW) sensor devices was analyzed. The electrodes were obtained by e-beam evaporation of Ti/Al multilayers on the high-temperature stable piezoelectric Ca3TaGa3Si2O14 (CTGS) substrates and structuring via the lift-off process. AlNO (25 at.% Al; 60 at.% N and 15 at.% O) cover and barrier layers were applied as protection against oxidation from the surrounding atmosphere and to prohibit a chemical reaction with the substrate. The samples were annealed at temperatures up to 600 °C in air for a duration of up to 192 h. Scanning and transmission electron microscopy were used to evaluate the morphology and degradation of the electrodes as well as of the extended contact pads. The results revealed that the Ti–Al based electrodes remained unoxidized after annealing for 192 h at 400 and 500 °C and for 24 h at 600 °C. After the heat treatment for 192 h at 600 °C, a strong oxidation of the structured electrodes occurred, which was less pronounced within the pads. In summary, the investigation showed that Ti–Al based SAW devices are a cost efficient alternative for long-term applications up to at least 500 °C and short- and medium-term applications up to 600 °C.

Damage evaluation of fixed beams at both ends for bridge health monitoring using a combination of a vibration sensor and a surface acoustic wave device

In recent years, the aging of engineering structures such as bridges has become a serious problem, and the development of an inexpensive and mass-producible structural health monitoring (SHM) system is desired. In this study, we proposed an SHM method for bridges using an impedance-loaded surface acoustic wave (SAW) sensor, which is a combination of a SAW sensor and an impedance-changing sensor. The impedance-loaded part comprised a vibration sensor, that is a geophone, and variable-capacitance diode, and vibration experiments were conducted on fixed beams at both ends that simulated a bridge. The results from these experiments were subjected to frequency analysis under the continuous wavelet transform, and it was confirmed that unsupervised machine learning could be used to discriminate damage with high accuracy. Furthermore, it was found that a one-class support vector machine, used for anomaly detection in machine learning, was suitable for the SHM method of bridges.

Piezoelectric Vibration Energy Harvesters with Distinct Interdigital Electrodes Used for Toxic Gas Detection and in a Numerical Simulation for a Glucose Sensor Application

Introduction: In this paper, a surface acoustic wave sensor (SAW) for dangerous gas sensing applications has been designed and experimented. The Glucose Sensor applications has been simulated to show that the piezoelectric material has a high significance for use in detection. Background: Detection of gases is one of the major applications SAW gas sensor extend its services into the field of medical and even in power plants. A Surface Acoustic Wave (SAW) is an acoustic wave that propagates on the surface of an elastic material (usually a solid), with an amplitude that decreases with the depth of the substrate. Objectives: SAW devices typically use electrodes on a piezoelectric material to convert an electrical signal to a SAW. Methods: This paper reviews the significance of piezoelectric materials and focuses on MEMS based SAW, and we investigate the resonance frequencies of a SAW gas sensor, which consists of an Inter Digitated Transducer (IDT) etched onto a piezoelectric substrate and covered with a thin Poly Isobutylene (PIB) film. Results: The mass of the PIB film increases as PIB selectivity adsorbs CH2Cl2 (Dichloromethane, DCM) in the air. This causes a shift in resonance to a slightly lower frequency. Conclutions: Our characterization of the piezoelectric material have shown a high significance when a sensitive layer of gas is etch for detected the dangerous gases, we used it in another application Glucose Sensor to show the importance of our system. The sensor of the Glucose Gensor application has been designed and simulated by a finite element analysis which was conducted on LiNbO3 pellets. This model is applied to verify the sensing properties of DCM and the glucose. On the other hand, the Glucose Sensor and the toxic gas detection allow measurement of the concentration,whether it is for the dangerous gases or glucose. The concentration of external glucose and the current density versus glucose concentration were measured by a finite element.

Simulation Design of Surface Acoustic Wave Sensor Based on Langasite Coplanar Integration with Multiple Parameters

In the harsh environment of high temperature and high rotation, a single parameter is difficult to satisfy the multi-parameter test requirements of aerospace metallurgy. Therefore, a multi-parameter coplanar integrated surface acoustic wave (SAW) sensor based on Langasite (LGS) is proposed. In this paper, the optimal cut for different measurement parameters is analyzed, and the optimal cut to temperature, pressure and vibration are obtained. The simulation results show that (0°, 138.5°, 25°) LGS has superior second-order temperature sensitivity, the edge of the rectangular sealed cavity is more suitable for pressure sensors, and the optimal cut is (0°, 138.5°, 30°). The stress of the vibration sensor cantilever beam is mainly concentrated on the edge of the fixed end, and the optimal cut is (0°, 138.5°, 35°). Based on the optimal sensitive tangential direction of each sensitive element and the symmetry of the Langasite wafer, the reasonable layout of the coplanar integrated structure with the three parameters of temperature, pressure and vibration is determined. Moreover, according to the optimal orientation selection and reasonable structure layout of each parameter, combined with frequency separation rules, the parameters of interdigital electrode were determined, and the idea of multi-parameter integrated design was simulated and verified.

A highly sensitive surface acoustic wave sensor modified with molecularly imprinted hydrophilic PVDF for the selective amino acid detection

Molecularly imprinted templates for L-Tryptophan sensor using hydrophilic poly (vinylidene fluoride) (PVDF) was fabricated through in-situ polymerization of 2-hydroxyethyl methacrylate (HEMA) within PVDF matrix. The work proposes a novel strategy for the integration of antifouling layer modification for water treatment technique into molecular imprinting polymer for surface acoustic wave (SAW) sensor fabrication and demonstrates their effective utilization in direct biological applications. The L-Tryptophan templates were carefully prepared on the delay line area of a SAW device prepared through photolithographic method internally. The imprinted template was qualitatively analysed using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and atomic force microscopy (AFM) to understand the bond formation and distribution of imprinted textures on the receptor surface. Quantitative measurements as the L-Tryptophan receptor was conducted through SAW analysis within range of concentration 0.5–150 ng/ml and the sensor responded linearly with the varying concentration. The limit of detection and limit of quantification calculated to be 0.2 and 0.6 ng/ml, respectively. The mechanism of adsorption was further studied using Langmuir, Freundlich and Redlich-Peterson adsorption isotherm. The MIP modified SAW sensor had high selectivity in the presence of structural analogues such as D- tryptophan, ascorbic acid, leucine and tyrosine. The hydrophilic PVDF coated MIP-SAW sensor exhibited relatively high imprinting factor towards L-tryptophan of about 9.34.

A high-sensitivity MoS2/graphene oxide nanocomposite humidity sensor based on surface acoustic wave

We herein propose a surface acoustic wave (SAW) sensor coating with a MoS2/graphene oxide (GO) nanocomposite. MoS2 nanosheets were prepared using the liquid stripping method, and the MoS2/GO nanoflakes were characterized by scanning electron microscopy. Subsequently, the surface acoustic sensor was prepared using a standard photolithographic technique and the MoS2/GO layers were deposited via drop-casting on the sensor surfaces. The performance of the sensor was then tested from 20% relative humidity (RH) to 95% RH. The sensor exhibits good performance with a sensitivity of 114 ppm/%RH at low humidity (at 20–70% RH) and a sensitivity of 766.8 ppm/%RH at high humidity (at 70–95% RH). The recovery and response time of the sensor were 6.6 s and 3.5 s. Moreover, the sensor exhibited good repeatability and long-term stability. The influence of temperature on sensor response at different values of humidity was also tested, and the relationship between temperature and frequency was almost linear from 25° to 70°C at different humidity levels. Finally, the humidity-sensitive mechanism was analyzed at different humidity values. The humidity sensor has great application potential in medical care, environmental monitoring, and cultural relic preservation.

Resonance Analysis of Piezoelectric Bulk Acoustic Wave Devices Based on YCOB Crystals with Monoclinic Symmetry Excited by Lateral Electric Fields

The monoclinic YCOB crystal still maintains good piezoelectric properties at 800 °C; thus, it has a good application prospect in high-temperature piezoelectric acoustic wave sensors. However, due to the lower symmetry compared crystals in trigonal and tetragonal systems, the exciting characteristics of piezoelectric plates based on monoclinic YCOB crystals are more complicated. The vibration analysis model of lateral-field-excitation (LFE) devices based on monoclinic crystals is scarce; thus, the coupling relationships between different vibration modes and energy-trapping characteristics of the devices are unclear, which hinders the optimal design of devices. In this paper, by using Mindlin plate theory, the high-frequency vibrations of piezoelectric resonators based on monoclinic YCOB crystal plates excited by a lateral electric field are modeled and analyzed. The coupling relationships between the vibration modes of the device are clarified. The influences of the electrode width, electrode/plate mass ratio and electrode gap value on resonances and energy-trapping characteristics of the device are achieved. In addition, the effects of the structure parameters on the mass sensitivity of the monoclinic YCOB LFE devices are investigated, which are further verified by FEM simulations. The results are crucial to obtaining good resonance and sensing characteristics for LFE high-temperature piezoelectric sensors based on crystals with monoclinic symmetry.

Enhanced sensitivity of langasite-based surface acoustic wave CO gas sensor using highly porous Ppy@PEDOT:PSS hybrid nanocomposite

The potential usage of the nano-porous Ppy@PEDOT:PSS hybrid organic nanocomposite polymers has been widely explored in various applications fields. However, the study of Ppy@PEDOT:PSS based room temperature (RT) CO gas sensing utilizing surface acoustic wave (SAW) sensor is still at infancy. Herein, we report a langasite (LGS) based RT CO SAW gas sensor using Ppy@PEDOT:PSS hybrid organic nanocomposite polymer. The Ppy@PEDOT:PSS/LGS SAW sensor exhibited high sensitivity of ~9.38 KHz positive frequency shift (∆f) as compared to the pristine PEDOT:PSS/LGS SAW (~1.76 kHz negative ∆f) sensor with a short response/recovery times (121 s/138 s), good reproducibility, excellent selectivity and low detection limit (~231 ppb) towards CO (100 ppm) gas at RT. Interestingly, our experimental findings demonstrated that the Ppy@PEDOT:PSS/LGS SAW sensor exhibited the positive ∆f which was attributed to the enhanced elastic loading and decrease in the electrical conductivity under CO gas ambient, whereas the PEDOT:PSS/LGS SAW showed negative ∆f, which was due to the mass loading effect. Moreover, the impressive CO gas sensing performances were attributed to the, high porosity, large surface area, abundant OH and amine functionalizing groups. In addition the synergetic effect of the p-p hybrid heterojunction, which can act as active surface sites that further improving the CO gas molecules absorption, and thus leading to the massive changes in elastic stiffness and the electrical conductivity of the Ppy@PEDOT:PSS film. The scientific findings of this work offers a versatile platform to develop high performance hybrid organic polymer nanocomposites based SAW gas-sensing applications.

Fabry-Perot Interference Fiber Acoustic Wave Sensor Based on Laser Welding All-Silica Glass

Due to the small difference between the thermal expansion coefficients of silica optical fiber and silica glass, they are used as probe materials of optical fiber acoustic wave sensors. According to the light absorption characteristics of a pressure-sensitive silica diaphragm and silica glass, the laser welding of an all-silica Fabry–Perot (FP) interference optical fiber acoustic wave sensor with a CO2 laser is proposed. For understanding the influence of thermal expansion of sealing air in an FP cavity and the drift of interference-intensity demodulation working point of a FP interference acoustic wave sensor, we designed a process for the laser welding of an ultra-thin silica diaphragm and sleeve and optical fiber and sleeve. The exhaust hole of the FP cavity is reserved in the preparation process, and an amplified spontaneous emission light source and a tunable optical-fiber FP filter are introduced to stabilize the working point. The sensor is tested with a 40 kHz sound vibration signal. The results show that the sound pressure sensitivity of the sensor to an acoustic source of 0.02–0.1 W/cm2 is 6.59 mV/kPa. The linearity coefficient is 0.99975, indicating good linearity.

Enhanced NO2 sensitivity of SnO2 SAW gas sensors by facet engineering

Surface acoustic wave (SAW) sensors have great advantages in real-time and in-situ gas detection due to the inherent advantages of passive and wireless operation. This inevitably requires the sensor to improve gas sensing performances at room temperature. The facet engineering of metal oxide (MOS) provided an effective way to obtain MOS gas-sensitive materials with superior performance by the facet-dependent properties. Here, we developed a novel strategy to prepare SnO2 quantum wires with different (110) facets ratio by adjusting the synthesis time. The as-prepared SnO2 quantum wires were integrated into SAW devices to optimize the performance of the gas sensor. When the synthesis time was 8 h, the response of the SAW gas sensor was enhanced with the frequency change of 17 kHz, the response and recovery times of 45 s and 96 s, respectively. Our experimental results revealed that the effect of mass loading was the responsible underlying mechanism for the superior NO2 gas sensing performances. In addition, the adsorption and charge transfer properties between the SnO2 surface and NO2 molecules were further discussed by QCM and resistance measurement, respectively. The calculation of density functional theory (DFT) also proves the adsorption energy of NO2 on the SnO2 (110) facet is larger than the (101) facet and (211) facet, indicating that the (110) facet is more beneficial to the adsorption of NO2 molecules. This indicated that the facet engineering of MOS by facet-dependent properties to enhance gas sensitivity may open new opportunities for the design of SAW sensor.

High temperature effects on surface acoustic wave strain sensor

Surface acoustic wave sensors (SAW), as a wireless passive sensor, are susceptible to temperature disturbance for high-temperature strain measurements, which results in poor accuracy and repeatability of the strain sensing. This paper investigates temperature effects on SAW strain sensors. Results show that high temperature not just induces frequency drift, but also reduces strain transfer ratio due to glue softening. The resonant frequencies of the langasite (LGS) SAW sensor have a linear relationship with strain for fixed temperatures, while the strain sensitivity is greatly affected by temperature. The results also show that the difference of measured strain sensitivities between the two resonant frequencies for one device is much less affected by temperature because they suffer the same strain transfer ratio and temperature characteristics, but different strain responses. A method to eliminate temperature effects has been proposed and accurate high-temperature strain measurements with SAW sensor were conducted to extend strain range up to 380 με with high repeatability under 500 °C.

A New Analytical Method to Quantify Ammonia in Freshwater with a Bulk Acoustic Wave Sensor.

A new method to analyse ammonia in freshwater, based on a piezoelectric quartz crystal coated with the metalloporphyrin chloro[5,10,15,20-tetrakis(pentafluorophenyl)porphyrinato] manganese(III) is presented. A 9 MHz quartz crystal coated on both faces with an amount of porphyrin produced a frequency decrease of 21.4 kHz, which allowed ammonia in a 10.00 mL sample to be quantified in concentrations between 5 and 70 µg L−1, with a sensitivity of 0.60 Hz L µg−1, over a period of at least eight months. The proposed method has several advantages over the officially recommended indophenol spectrophotometric method: sample volume was reduced by a factor of 2.5, toxic reagents (phenol and sodium nitroprusside) were eliminated, analysing turbid samples presented no difficulty, and there was not only a significant time saving in solution preparation, but also in sample analysis time, which was reduced from 1 h to 2 min. No statistically significant differences (α = 0.05) were found both in the mean and precision of the results obtained for ammonia in water samples collected from domestic wells, analysed by this new method and by the indophenol spectrophotometric method. Furthermore, the proposed method would allow the individual quantification, with similar sensitivity, of amines and ammonia within a single analytical run.

Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2.

In this research, a compact electronic nose (e-nose) based on a shear horizontal surface acoustic wave (SH-SAW) sensor array is proposed for the NO2 detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH3), benzene (C6H6) and acetone (C3H6O). Carbon-based nanostructured materials (CBNm), such as mesoporous carbon (MC), reduced graphene oxide (rGO), graphene oxide (GO) and polydopamine/reduced graphene oxide (PDA/rGO) are deposited as a sensitive layer with controlled spray and Langmuir–Blodgett techniques. We show the potential of the mass loading and elastic effects of the CBNm to enhance the detection, the classification and the discrimination of NO2 among different gases by using Machine Learning (ML) techniques (e.g., PCA, LDA and KNN). The small dimensions and low cost make this analytical system a promising candidate for the on-site discrimination of sub-ppm NO2.

Interface and Sensitive Characteristics of the Viscoelastic Film Used in a Surface Acoustic Wave Gas Sensor.

The surface morphology of viscoelastic-sensitive films significantly affects sensing characteristics of surface acoustic wave (SAW) sensors. Uniformity and compactness of the film surface directly influences detectability of the SAW sensor toward target gases. Viscoelastic fluoroalcoholpolysiloxane (SXFA) was prepared in this work using spin coating technology on an SAW delay line of 200 MHz and then used as coating for detection of dimethyl methylphosphonate (DMMP). Polarizing, atomic force, and scanning electron microscopies confirmed the uniformity of the SXFA surface. The particle diameter in the cluster region was 10-15 μm. The contact angle (5.72-26.69°), surface tension (21.053-29.155 mN/m), Gibbs free energy (-160.68 to -153.45 J/m2), and spreading coefficient (0.3028-6.9453 J/m2) of different concentrations of SXFA were obtained through experiments, and their relation was analyzed using the Young T equation and Gibbs adsorption isotherm. The glass transition temperature (-19.7 °C) and elasticity of SXFA were also discussed. The consistency of sensor preparation was confirmed by detecting DMMP with five SAW sensors prepared simultaneously. Seven consecutive tests showed that the SAW sensor presents satisfactory repeatability (standard deviation, s, 1.134; coefficient of variance, v, 0.065; and population mean deviation, δ, 0.913) at a concentration of 1.71 mg/m3 and acceptable linear relationship at a concentration range of 0.058-1.92 mg/m3, with a sensitivity of around 1.21 mv/(mg/m3). The sensor exhibited outstanding sensitivity and satisfactory linearity and repeatability to DMMP. Meanwhile, the sensing mechanism in gas adsorption was also discussed in terms of LSER formulation and hydrogen bonding formation between SXFA and DMMP.

Analyte discrimination by SAW sensor variable loop amplification probing

A Surface Acoustic Wave (SAW) sensor with an ‘acoustically thick’ (average 0.5 µm) polyethyleneimine (PEI) chemo-selective active layer is used to demonstrate analyte-specific response for two closely related volatile organic compounds (VOC): ethanol and methanol. By means of variable amplification, we modified the SAW resonant oscillator frequency, allowing a ~ 3 MHz frequency scan. Sets of data consisting in gain-frequency values were further acquired for six analyte concentrations for each of the VOCs, while baselines were acquired for 0 analyte concentration. Following measurements, the frequency vs. gain curves were fitted using an analytical model based on the acoustic response of a visco-elastic thick layer. The variations of the fitting parameters upon sensor exposure to analyte proved highly non-linear and distinct variations for the two analytes, supporting amplificator gain based frequency scan of the SAW sensor as a functional method for analyte discrimination using a single SAW sensor.

Artificial Intelligent Olfactory System for the Diagnosis of Parkinson's Disease.

Background: Currently, Parkinson’s disease (PD) diagnosis is mainly based on medical history and physical examination, and there is no objective and consistent basis. By the time of diagnosis, the disease would have progressed to the middle and late stages. Pilot studies have shown that a unique smell was present in the skin sebum of PD patients. This increases the possibility of a noninvasive diagnosis of PD using an odor profile. Methods: Fast gas chromatography (GC) combined with a surface acoustic wave sensor with embedded machine learning (ML) algorithms was proposed to establish an artificial intelligent olfactory (AIO) system for the diagnosis of Parkinson’s through smell. Sebum samples of 43 PD patients and 44 healthy controls (HCs) from Fourth Affiliated Hospital of Zhejiang University School of Medicine, China, were smelled by the AIO system. Univariate and multivariate methods were used to identify the significant volatile organic compound (VOC) features in the chromatograms. ML algorithms, including support vector machine, random forest (RF), k nearest neighbor (KNN), AdaBoost (AB), and Naive Bayes (NB), were used to distinguish PD patients from HC based on the VOC peaks in the chromatograms of sebum samples. Results: VOC peaks with average retention times of 5.7, 6.0, and 10.6 s, respectively, corresponding to octanal, hexyl acetate, and perillic aldehyde, were significantly different in PD and HC. The accuracy of the classification based on the significant features was 70.8%. Based on the odor profile, the classification had the highest accuracy and F1 of the five models with 0.855 from NB and 0.846 from AB, respectively, in the process of model establishing. The highest specificity and sensitivity of the five classifiers were 91.6% from NB and 91.7% from RF and KNN, respectively, in the evaluating set. Conclusions: The proposed AIO system can be used to diagnose PD through the odor profile of sebum. Using the AIO system is helpful for the screening and diagnosis of PD and is conducive to further tracking and frequent monitoring of the PD treatment process.

A two-stage method for real-time baseline drift compensation in gas sensors

Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

Surface Acoustic Wave (SAW) Sensors: Physics, Materials, and Applications.

Surface acoustic waves (SAWs) are the guided waves that propagate along the top surface of a material with wave vectors orthogonal to the normal direction to the surface. Based on these waves, SAW sensors are conceptualized by employing piezoelectric crystals where the guided elastodynamic waves are generated through an electromechanical coupling. Electromechanical coupling in both active and passive modes is achieved by integrating interdigitated electrode transducers (IDT) with the piezoelectric crystals. Innovative meta-designs of the periodic IDTs define the functionality and application of SAW sensors. This review article presents the physics of guided surface acoustic waves and the piezoelectric materials used for designing SAW sensors. Then, how the piezoelectric materials and cuts could alter the functionality of the sensors is explained. The article summarizes a few key configurations of the electrodes and respective guidelines for generating different guided wave patterns such that new applications can be foreseen. Finally, the article explores the applications of SAW sensors and their progress in the fields of biomedical, microfluidics, chemical, and mechano-biological applications along with their crucial roles and potential plans for improvements in the long-term future in the field of science and technology.