Composites (or complex materials) are formed from two or many constituent materials with novel physical or chemical characteristics when integrated.

This work presents the development of flexible dual-mode surface acoustic wave (SAW) sensor based on single crystalline thin film lithium niobate (TF-LN). Numerical modeling is conducted to investigate the SAW propagation and the effects on strain sensitivity. The dependence of strain sensitivity on angles between the applied strain and SAW propagation direction is analyzed numerically and experimentally, showing that the maximum strain sensitivity is at 45° rather than longitudinal direction. 128° Y-cut TF-LN (~50 µ m), obtained by micromachining technique, is utilized as the piezoelectric substrate to fabricate the SAW strain sensors with dual-mode, namely Rayleigh mode and thickness shear mode. The sensor has excellent flexibility and demonstrates remarkable capability for an ultra-wide range strain measurement up to ±3000 . Temperature effects on resonant frequency and strain sensitivity are investigated in the range of 25 °C–100 °C, and similar temperature characteristics are observed for the dual modes. A method of beat frequency between the dual modes is introduced which is able to eliminate the temperature effect on strain sensing, an on-chip temperature influence removing capability. All the results clearly show that this sensor exhibits great potential for applications in flexible electronics and microsystems.

The ultimate image with important information and better visuals can be obtained by fusing infrared and visual images. Several fusion methods consist of complex networks to derive the finest parameters from different source images and do not exploit reliable and salient information completely. To avoid the limitations of information loss and fusion of feature information, we have proposed a multilayer triple dense network for infrared and visible image fusion named as MTDFusion, which completely exploits salient features and utilizes the residual features from the combination of input images adaptively. The encoding network is designed as a triple network of dense blocks that takes inputs as infrared, visible, and the difference extracted from infrared and visible images. The fusion layer exploits the weighted combination of input images, and decoding layers work conventionally. The results obtained by our proposed approach reveal optimal image details and higher contrast. We have improved the loss function by combining smooth-ℒ ₁ and structured similarity index (SSIM) loss functions for our proposed method. Additionally, the different variations of λs in SSIM are examined using qualitative and quantitative assessments. The results achieve optimal fusion performance compared to several state-of-the-art methods. The source code for MTDFusion is available at https://github.com/tgg-77/MTDFusion.

Graphene-based optical sensing devices have been widely studied for their broad band absorption, high carrier mobility, and mechanical flexibility. Due to graphene’s weak light absorption, studies on graphene-based optical sensing thus far have focused on hybrid heterostructure devices to enhance photo-absorption. Such hybrid devices need a complicated integration process and lead to deteriorating carrier mobility as a result of heterogeneous interfaces. Rippled or wrinkled graphene has been studied in electronic and optoelectronic devices. However, concrete demonstrations of the impact of the morphology of nanofilms (e.g., graphite and graphene) associated with light absorption in optical sensing devices have not been fully examined. This study explored the optical sensing potential of a graphite nanofilm surface with ripples induced by a stretchable polydimethylsiloxane (PDMS) supporting layer under different stretch:release ratios and then transferred onto silicon, both under experimental conditions and via simulation. The optical sensing potential of the rippled graphite nanofilm was significantly enhanced (260 mA/W at the stretch–release state of 30%), as compared to the pristine graphite/PDMS (20 mA/W at the stretch–release state of 0%) under laser illumination at a wavelength of 532 nm. In addition, the results of our simulated computation also confirmed the improved light absorption of rippled graphite nanofilm surface-based optical sensing devices, which was comparable with the results found in the experiment.