Flexible and wearable acoustic wave technology has recently attracted tremendous attention due to their wide-range applications in wearable electronics, sensing, acoustofluidics, and lab-on-a-chip, attributed to its advantages such as low power consumption, small size, easy fabrication, and passive/wireless capabilities. Great effort has recently been made in technology development, fabrication, and characterization of rationally designed structures for next-generation acoustic wave based flexible electronics. Herein, advances in fundamental principles, design, fabrication, and applications of flexible and wearable acoustic wave devices are reviewed. Challenges in material selections (including both flexible substrate and piezoelectric film) and structural designs for high-performance flexible and wearable acoustic wave devices are discussed. Recent advances in fabrication strategies, wave mode theory, working mechanisms, bending behavior, and performance/evaluation are reviewed. Key applications in wearable and flexible sensors and acoustofluidics, as well as lab-on-a-chip systems, are discussed. Finally, major challenges and future perspectives in this field are highlighted.

There are great concerns for sensing using flexible acoustic wave sensors and lab-on-a-chip, as mechanical strains will dramatically change the sensing signals (e.g., frequency) when they are bent during measurements. These strain-induced signal changes cannot be easily separated from those of real sensing signals (e.g., humidity, ultraviolet, or gas/biological molecules). Herein, we proposed a new strategy to minimize/eliminate the effects of mechanical bending strains by optimizing off-axis angles between the direction of bending deformation and propagation of acoustic waves on curved surfaces of layered piezoelectric film/flexible glass structure. This strategy has theoretically been proved by optimization of bending designs of off-axis angles and acoustically elastic effect. Proof-of-concept for humidity and ultraviolet-light sensing using flexible SAW devices with negligible interferences are achieved within a wide range of bending strains. This work provides the best solution for achieving high-performance flexible acoustic wave sensors under deformed/bending conditions.

Surface acoustic wave (SAW) technology has been widely developed for ultraviolet (UV) detection due to its advantages of miniaturization, portability, potential to be integrated with microelectronics, and passive/wireless capabilities. To enhance UV sensitivity, nanowires (NWs), such as ZnO, are often applied to enhance SAW-based UV detection due to their highly porous and interconnected 3D network structures and good UV sensitivity. However, ZnO NWs are normally hydrophilic, and thus, changes in environmental parameters such as humidity will significantly influence the detection precision and sensitivity of SAW-based UV sensors. To solve this issue, in this work, we proposed a new strategy using ZnO NWs wrapped with hydrophobic silica nanoparticles as the effective sensing layer. Analysis of the distribution and chemical bonds of these hydrophobic silica nanoparticles showed that numerous C-F bonds (which are hydrophobic) were found on the surface of the sensitive layer, which effectively blocked the adsorption of water molecules onto the ZnO NWs. This new sensing layer design minimizes the influence of humidity on the ZnO NW-based UV sensor within the relative humidity range of 10–70%. The sensor showed a UV sensitivity of 9.53 ppm (mW/cm2)−1, with high linearity (R2 value of 0.99904), small hysteresis (<1.65%) and good repeatability. This work solves the long-term dilemma of ZnO NW-based sensors, which are often sensitive to humidity changes.

Thin film-based surface acoustic wave (SAW) technology has been extensively explored for physical, chemical, and biological sensors. However, these sensors often show inferior performance for a specific sensing in complex environments, as they are affected by multiple influencing parameters and their coupling interferences. To solve these critical issues, we propose a methodology to extract critical information from the scattering parameter and combine the machine learning method to achieve multi-parameter decoupling. We used the AlScN film-based SAW device as an example in which the highly c-axis orientated and low stress AlScN film was deposited on silicon substrate. The AlScN/Si SAW device showed a Bode quality factor value of 228 and an electromechanical coupling coefficient of ∼2.3%. Two sensing parameters (i.e., ultraviolet or UV and temperature) were chosen for demonstration, and the proposed machine learning method was used to distinguish their influences. Highly precision UV sensing and temperature sensing were independently achieved without their mutual interferences. This work provides an effective solution for decoupling of multi-parameter influences and achieving anti-interference effects in thin film-based SAW sensing.

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementation of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor for confidential information. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide-based hydrogel sensors. It offers fast response and good sensitivity and allows high-precision recognition of handwritten content from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from seven participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (∼91.30%). Our developed handwriting recognition system has great potential in advanced human–machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.