Optimal bilayer composites for temperature-tracking wireless electronics.

Modern silicone-based epidermal electronics engineered for body temperature sensing represent a pivotal development in the quest for advancing preventive medicine and enhancing post-surgical monitoring. While these compact and highly flexible electronics empower real-time monitoring in dynamic environments, a noteworthy limitation is the challenge in regulating the infiltration or obstruction of heat from the external environment into the surface layers of these electronics. The study presents a cost-effective temperature sensing solution by embedding wireless electronics in a multi-layered elastomeric composite to meet the dual needs of enhanced thermal insulation for encapsulation in contact with air and improved thermal conductivity for the substrate in contact with the skin. The encapsulating composite benefits from the inclusion of hollow silica microspheres, which reduce the thermal conductivity by 40%, while non-spherical aluminum nitride enhances the thermal conductivity of the substrate by 370%. The addition of particles to the respective composites inevitably leads to an increase in modulus. Two composite elements are engineered to coexist while maintaining a matching low modulus of 3.4 MPa and a stretchability exceeding 30%, all without compromising the optimized thermal properties. Consecutive thermal, electrical, and mechanical characterization confirms the sensor's capacity for precise body temperature monitoring during a single day's lifespan, while also assessing the influence of behavioral factors on body temperature.

High-Energy-Density Zinc-Air Microbatteries with Lean PVA-KOH-K2CO3 Gel Electrolytes.

Small-scale, primary electrochemical energy storage devices ("microbatteries") are critical power sources for microelectromechanical system (MEMS)-based sensors and actuators. However, the achievable volumetric and gravimetric energy densities of microbatteries are typically insufficient for intermediate-term applications of MEMS-enabled distributed internet-connected devices. Further, in the increasing subset of Internet of Things (IoT) nodes, where actuation is desired, the peak power density of the microbattery must be simultaneously considered. Metal-air approaches to achieving microbatteries are attractive, as the anode and cathode are amenable to miniaturization; however, further improvements in energy density can be obtained by minimizing the electrolyte volume. To investigate these potential improvements, this work studied very lean hydrogel electrolytes based on poly(vinyl alcohol) (PVA). Integration of high potassium hydroxide (KOH) loading into the PVA hydrogel improved electrolyte performance. The addition of potassium carbonate (K2CO3) to the KOH-PVA gel decreased the carbonation consumption rate of KOH in the gel electrolyte by 23.8% compared to PVA-KOH gel alone. To assess gel performance, a microbattery was formed from a zinc (Zn) anode layer, a gel electrolyte layer, and a carbon-platinum (C-Pt) air cathode layer. Volumetric energy densities of approximately 1400 Wh L-1 and areal peak power densities of 139 mW cm-2 were achieved with a PVA-KOH-K2CO3 electrolyte. Further structural optimization, including using multilayer gel electrolytes and thinning the air cathode, resulted in volumetric and gravimetric energy densities of 1576 Wh L-1 and 420 Wh kg-1, respectively. The batteries described in this work are manufactured in an open environment and fabricated using a straightforward layer-by-layer method, enabling the potential for high fabrication throughput in a MEMS-compatible fashion.

Intensity inhomogeneity correction of MRI images using InhomoNet.

Intensity inhomogeneity is one of the major artifacts in magnetic resonance imaging (MRI). Bias field present in MRI images alters true pixel value and produces spurious varying pixel intensities. This artifact affects the diagnosis by radiologists in a detrimental manner and also degrades the performance of computer-aided diagnosis algorithms such as segmentation. The present work proposes a novel network called InhomoNet for intensity inhomogeneity correction of MRI image. The generator architecture of InhomoNet consists of a new multi-scale local information module at each encoder block that helps to capture features at multiple scales. The horizontal and vertical kernels help to reduce the problems like loss of neighborhood information, gridding issues caused due to large dilated convolution operations. The attention-driven skip connections in the generator network are utilized to transfer optimal semantic and spatial localization information from the encoder to decoder blocks. Further, the present work proposes two new losses functions, i.e. histogram correlation and 3D pixel loss. These losses help to realize pixel consistency across different regions of brain MRI. The inculcation of the L1 loss provides guidance to the upsampling process as it compares the prediction from each decoder block with the ground truth. The proposed method is evaluated on simulated and real MRI data. The comparative analysis with popular state-of-the-art methods depicts the ability of the proposed method to perform intensity inhomogeneity correction accurately.

Facile and quantitative estimation of strain in nanobubbles with arbitrary symmetry in 2D semiconductors verified using hyperspectral nano-optical imaging.

When layers of van der Waals materials are deposited via exfoliation or viscoelastic stamping, nanobubbles are sometimes created from aggregated trapped fluids. Though they can be considered a nuisance, nanobubbles have attracted scientific interest in their own right owing to their ability to generate large in-plane strain gradients that lead to rich optoelectronic phenomena, especially in the semiconducting transition metal dichalcogenides. Determination of the strain within the nanobubbles, which is crucial to understanding these effects, can be approximated using elasticity theory. However, the Föppl-von Kármán equations that describe strain in a distorted thin plate are highly nonlinear and often necessitate assuming circular symmetry to achieve an analytical solution. Here, we present an easily implemented numerical method to solve for strain tensors of nanobubbles with arbitrary symmetry in 2D crystals. The method only requires topographic information from atomic force microscopy and the Poisson ratio of the 2D material. We verify that this method reproduces the strain for circularly symmetric nanobubbles that have known analytical solutions. Finally, we use the method to reproduce the Grüneisen parameter of the E' mode for 1L-WS2 nanobubbles on template-stripped Au by comparing the derived strain with measured Raman shifts from tip-enhanced Raman spectroscopy, demonstrating the utility of our method for estimating localized strain in 2D crystals.

Unsupervised smoke to desmoked laparoscopic surgery images using contrast driven Cyclic-DesmokeGAN.

In laparoscopic surgery, energized dissecting devices and laser ablation causes smoke, which degrades the visual quality of the operative field. This paper proposes an unsupervised approach to desmoke laparoscopic images called Cyclic-DesmokeGAN. In the generator, multi-scale residual blocks help to alleviate the smoke component at multiple scales, while refinement module helps to obtain desmoked images with sharper boundaries. As the presence of smoke degrades contrast and fine structure, the proposed method utilizes high boost filtered image at each encoder layer. The contrast loss improves overall contrast, thereby reducing the smoke, while Unsharp Regularization loss helps to stabilize the network. The proposed Cyclic-DesmokeGAN is tested on 200 smoke images obtained from Cholec80 dataset consisting of videos of cholecystectomy surgeries. The results depict effectiveness, as proposed approach achieved 3.47±0.09 Contrast-Distorted Images Quality, 4.15±0.74 Naturalness Image Quality Evaluator, and 0.23±0.00 Fog Aware Density Evaluator, these indexes are best in comparison to other state-of-the-art methods.