Editorial: Recent trends in acoustic sensor applications

Mostafa M. Gouda

doi:10.59400/sv3188

Editorial: Recent trends in acoustic sensor applications

Mostafa M. Gouda College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; Department of Nutrition & Food Science, National Research Centre, Giza 12622, Egypt

Article ID: 3188

DOI: https://doi.org/10.59400/sv3188

Keywords: acoustic sensors; QCM; SAW; piezoelectric materials; biomolecule sensation

Abstract

Acoustic sensor technology opens opportunities in various fields, particularly quartz crystal microbalance (QCM) and surface acoustic wave (SAW) technologies, which are known for high sensitivity, real-time detection, and non-invasive features. These sensors use mechanical waves to respond to external disturbances like mass changes or viscoelastic alterations, making them effective for monitoring chemical and biological processes, including biomolecules, volatile organic compounds, and pollutants. This adaptability enables applications. Therefore, in the Sound and Vibration journal, we invite the authors to submit related topics to attract interested scientists.

Published

2025-05-20

How to Cite

Gouda, M. M. (2025). Editorial: Recent trends in acoustic sensor applications. Sound & Vibration, 59(2), 3188. https://doi.org/10.59400/sv3188

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Issue

Vol. 59 No. 2 (2025)

Section

Editorial

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Gouda M, El-Din Bekhit A, Tang Y, et al. Recent innovations of ultrasound green technology in herbal phytochemistry: A review. Ultrasonics Sonochemistry. 2021; 73: 105538. doi: 10.1016/j.ultsonch.2021.105538

[2]Huang H, Su S, Wu N, et al. Graphene-Based Sensors for Human Health Monitoring. Frontiers in Chemistry. 2019; 7. doi: 10.3389/fchem.2019.00399

[3]Wang J, Fang Z, Liu W, et al. Light-Boosting Highly Sensitive and Ultrafast Piezoelectric Sensor Based on Composite Membrane of Copper Phthalocyanine and Graphene Oxide. International Journal of Molecular Sciences. 2024; 25(12): 6713. doi: 10.3390/ijms25126713

[4]Gouda MM, Elsharkawy ER, He Y, Li X. Importance of Advanced Detection Methodologies from Plant Cells to Human Microsystems Targeting Anticancer Applications. International Journal of Molecular Sciences. 2025; 26(10):4691. doi: 10.3390/ijms26104691

[5]Wang G, Tao LQ, Peng Z, et al. Nomex paper-based double-sided laser-induced graphene for multifunctional human-machine interfaces. Carbon. 2022; 193: 68–76. doi: 10.1016/j.carbon.2022.03.026

[6]Cui B, Wang W, Cheng L, et al. Acoustic impedance-based surface acoustic wave chip for gas leak detection and respiratory monitoring. Communications Engineering. 2025; 4(1). doi: 10.1038/s44172-025-00347-z

[7]George SM, Kandasubramanian B. Advancements in MXene-Polymer composites for various biomedical applications. Ceramics International. 2020; 46(7): 8522–8535. doi: 10.1016/j.ceramint.2019.12.257

[8]Richard B, Shahana C, Vivek R, et al. AR, Rasheed PA. Acoustic platforms meet MXenes – a new paradigm shift in the palette of biomedical applications. Nanoscale. 2023; 15(45): 18156–18172. doi: 10.1039/d3nr04901a

[9]Fadahunsi AA, Li C, Khan MI, et al. MXenes: state-of-the-art synthesis, composites and bioapplications. Journal of Materials Chemistry B. 2022; 10(23): 4331–4345. doi: 10.1039/d2tb00289b

[10]Li X, Feng Y, Long J, et al. MXene-activated graphene oxide enhancing NO2 capture and detection of surface acoustic wave sensors. Sensors and Actuators B: Chemical. 2024; 401: 135006. doi: 10.1016/j.snb.2023.135006

[11]Zheng F, Li M, Li C, et al. Wireless surface acoustic wave humidity sensor with chitosan/porous cyclodextrin–TiO2 composites for monitoring air and human respiration. Sensors and Actuators B: Chemical. 2023; 379: 133235. doi: 10.1016/j.snb.2022.133235

[12]Hung TT, Pan CL, Lai SK, et al. Detecting low-concentration ammonia with a surface acoustic wave sensor using a silver nanoparticles–graphene–polypyrrole hybrid nanocomposite film. Synthetic Metals. 2024; 308: 117710. doi: 10.1016/j.synthmet.2024.117710

[13]Dinu LA, Buiculescu V, Baracu AM. Recent Progress on Nanomaterials for NO2 Surface Acoustic Wave Sensors. Nanomaterials. 2022; 12(12): 2120. doi: 10.3390/nano12122120

[14]Jean F, Khan MU, Alazzam A, et al. Advancement in piezoelectric nanogenerators for acoustic energy harvesting. Microsystems & Nanoengineering. 2024; 10(1). doi: 10.1038/s41378-024-00811-4

[15]Aleixandre M, Horrillo MC. Recent Advances in SAW Sensors for Detection of Cancer Biomarkers. Biosensors. 2025; 15(2): 88. doi: 10.3390/bios15020088

[16]Saleh S, Alkalamouni H, Antar K, et al. “Quartz crystal microbalance-based biosensor for rapid and ultrasensitive SARS-CoV-2 detection”. Journal of Pharmaceutical and Biomedical Analysis Open. 2025; 5: 100071. doi: 10.1016/j.jpbao.2025.100071

[17]Alshraim A, Rahman S, Al-Gawati M, et al. Investigating a quartz crystal microbalance sensor functionalized with MXene-Cu/Cu2O/C nanocomposites for glucose detection. International Journal of Electrochemical Science. 2025; 20(5): 100980. doi: 10.1016/j.ijoes.2025.100980

[18]Serratos IN, Luviano AS, Millan-Pacheco C, et al. Quartz Crystal Microbalance Application and In Silico Studies to Characterize the Interaction of Bovine Serum Albumin with Plasma Polymerized Pyrrole Surfaces: Implications for the Development of Biomaterials. Langmuir. 2023; 39(32): 11213–11223. doi: 10.1021/acs.langmuir.3c00308

[19]Gouda M, Ghazzawy HS, Alqahtani N, et al. The Recent Development of Acoustic Sensors as Effective Chemical Detecting Tools for Biological Cells and Their Bioactivities. Molecules. 2023; 28(12): 4855. doi: 10.3390/molecules28124855

[20]Alanazi N, Almutairi M, Alodhayb AN. A Review of Quartz Crystal Microbalance for Chemical and Biological Sensing Applications. Sensing and Imaging. 2023; 24(1). doi: 10.1007/s11220-023-00413-w

[21]Damiati S. Acoustic Biosensors for Cell Research. In: Thouand G (editor). Handbook of Cell Biosensors. Cham: Springer International Publishing; 2022.

[22]Mao GJ, Yang TT, Gong Y, et al. Hypochlorous Acid-Activatable NIR Fluorescence/Photoacoustic Dual-Modal Probe with High Signal-to-Background Ratios for Imaging of Liver Injury and Plasma Diagnosis of Sepsis. ACS Sensors. 2025; 10(2): 1032–1042. doi: 10.1021/acssensors.4c02872

[23]Weis JA, Rauh JL, Ellison MA, et al. Photoacoustic imaging for non-invasive assessment of biomarkers of intestinal injury in experimental necrotizing enterocolitis. Pediatric Research. 2024; 97(1): 169–177. doi: 10.1038/s41390-024-03358-2

[24]Wang K, Jiang S, Wang W, et al. Dual-miRNA guided in-vivo imaging and multimodal nanomedicine approaches for precise hepatocellular carcinoma differentiation and synergistic cancer theranostics using DNA hairpins and dual-ligand functionalized zirconium-MOF nanohybrids. Biomaterials. 2025; 321: 123330. doi: 10.1016/j.biomaterials.2025.123330

[25]Knox HJ, Chan J. Acoustogenic Probes: A New Frontier in Photoacoustic Imaging. Accounts of Chemical Research. 2018; 51(11): 2897–2905. doi: 10.1021/acs.accounts.8b00351

[26]Gouda M, Lv JM, Huang Z, et al. Bioprobe-RNA-seq-microRaman system for deep tracking of the live single-cell metabolic pathway chemometrics. Biosensors and Bioelectronics. 2024; 261: 116504. doi: 10.1016/j.bios.2024.116504

[27]Chu H, Zhang C, Wang M, et al. Hyperspectral imaging with shallow convolutional neural networks (SCNN) predicts the early herbicide stress in wheat cultivars. Journal of Hazardous Materials. 2022; 421: 126706. doi: 10.1016/j.jhazmat.2021.126706

[28]Shi Q, Denolle MA, Ni Y, et al. Denoising Offshore Distributed Acoustic Sensing Using Masked Auto‐Encoders to Enhance Earthquake Detection. Journal of Geophysical Research: Solid Earth. 2025; 130(2). doi: 10.1029/2024jb029728

[29]Yin Y, Xu H, Zhang Z, et al. Fiber-optic distributed acoustic sensing signal enhancement based on data fusion of premium sensing channels. Optics Communications. 2025; 577: 131403. doi: 10.1016/j.optcom.2024.131403

[30]Shao L, Zhang J, Chen X, et al. Artificial intelligence-driven distributed acoustic sensing technology and engineering application. PhotoniX. 2025; 6(1). doi: 10.1186/s43074-025-00160-z

[31]Ul Hassan I, Panduru K, Walsh J. Predictive Maintenance in Industry 4.0: A Review of Data Processing Methods. Procedia Computer Science. 2025; 257: 896–903. doi: 10.1016/j.procs.2025.03.115

This paper introduces a universal framework for understanding the vibration responses of systems subjected to harmonic excitation. By examining a simplified cylinder-spring-damper model, the study refurbishes traditional scaling methods for the excitation frequency ratio and critical damping ratio. The findings indicate that in damped systems, the maximum amplitude of vibration does not align with the natural frequency. This observation leads to the introduction of a new scaling method for reduced frequency. This new approach aligns resonance peaks at the new reduced velocity of 1.0 across different damping ratios, providing a consistent characterization of vibration behavior. A new critical damping ratio of 0.707 is identified for an excited system as opposed to the traditional damping ratio of 1.0 for an unexcited system. Key properties such as maximum amplitude, phase lag, bandwidth, and quality factor are analyzed, demonstrating that the proposed reduced frequency and critical damping ratio effectively capture the dynamics of both damped and undamped excited systems. The findings offer significant insights for practical applications in engineering and various scientific fields.

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.