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The patterning of polydimethylsiloxane (PDMS) into complex two-dimensional (2D) or 3D shapes is a crucial step for diverse applications based on soft lithography. Nevertheless, mould replication that incorporates time-consuming and costly photolithography processes still remains the dominant technology in the field. Here we developed monolithic quasi-3D digital patterning of PDMS using laser pyrolysis. In contrast with conventional burning or laser ablation of transparent PDMS, which yields poor surface properties, our successive laser pyrolysis technique converts PDMS into easily removable silicon carbide via consecutive photothermal pyrolysis guided by a continuous-wave laser. We obtained high-quality 2D or 3D PDMS structures with complex patterning starting from a PDMS monolith in a remarkably low prototyping time (less than one hour). Moreover, we developed distinct microfluidic devices with elaborated channel architectures and a customizable organ-on-a-chip device using this approach, which showcases the potential of the successive laser pyrolysis technique for the fabrication of devices for several technological applications. A laser-based patterning method enables the fast fabrication of high-quality two- and three-dimensional features in polydimethylsiloxane for microfluidics and biomedical applications.

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.

Many gaze data visualization techniques intuitively show eye movement together with visual stimuli. The eye tracker records a large number of eye movements within a short period. Therefore, visualizing raw gaze data with the visual stimulus appears complicated and obscured, making it difficult to gain insight through visualization. To avoid the complication, we often employ fixation identification algorithms for more abstract visualizations. In the past, many scientists have focused on gaze data abstraction with the attention map and analyzed detail gaze movement patterns with the scanpath visualization. Abstract eye movement patterns change dramatically depending on fixation identification algorithms in the preprocessing. However, it is difficult to find out how fixation identification algorithms affect gaze movement pattern visualizations. Additionally, scientists often spend much time on adjusting parameters manually in the fixation identification algorithms. In this paper, we propose a gaze behavior-based data processing method for abstract gaze data visualization. The proposed method classifies raw gaze data using machine learning models for image classification, such as CNN, AlexNet, and LeNet. Additionally, we compare the velocity-based identification (I-VT), dispersion-based identification (I-DT), density-based fixation identification, velocity and dispersion-based (I-VDT), and machine learning based and behavior-based modelson various visualizations at each abstraction level, such as attention map, scanpath, and abstract gaze movement visualization.

Purpose The purpose of this study is to classify the discoid lateral meniscus (DLM) according to the signal and shape in magnetic resonance imaging (MRI), and to provide information not only in diagnosis but also in treatment. Materials and Methods We reviewed 162 cases who diagnosed with DLM by MRI and underwent arthroscopic procedures from April 2010 to March 2018. Three observers reviewed MRI findings of all cases and predicted arthroscopic tear using three MRI criteria (criterion 1,2 and 3). Among three criteria, the criterion that most accurately predicts arthroscopic tear was selected. Using this criterion, the cases of predicted tear were named group 1. In addition, group 1 was divided into three subgroups (group 1a, 1b and 1c) by deformation or displacement on MRI and arthroscopic type of tear and procedures were analyzed according to these subgroups. Results The intra-meniscal signal change itself (criterion 3) on MRI showed the highest agreement with the arthroscopic tear. No meniscal deformation and displacement on MRI (group 1a) showed no specific type of tear and more cases of meniscal saucerization. The meniscal deformation on MRI (group 1b) showed more simple horizontal tears and more cases of meniscal saucerization. The meniscal displacement on MRI (group 1c) showed more peripheral tears and more cases of meniscal repair and subtotal meniscectomy. Comparing arthroscopic type of tear and type of arthroscopic procedure between three subgroups, there were significant differences in three groups ( P  < .05). Conclusions Intra-meniscal signal change itself on MRI is the most accurate finding to predict arthroscopic tear in symptomatic DLM. In addition, subgroup analysis by deformation or displacement on MRI is helpful to predict the type of arthroscopic tear and procedures.

Sustainable power sources for outdoor wearable electronics are essential for the continuous operation of wearable devices. However, the current lack of engineering design that can harvest energy regardless of weather conditions presents a significant challenge. In this regard, this study introduces a wearable, breathable all‐weather usable dual energy harvester (AWuDEH) that can seamlessly generate electrical energy regardless of weather conditions. In this study, the AWuDEH integrated with the thermoelectric generator and the droplet‐based electricity generator is demonstrated. The AWuDEH, especially engineered with a bi‐functional top substrate for radiative cooling and electrification, achieves sustainable energy harvesting outdoors, thereby addressing the conventional challenge associated with the necessity for separate energy harvesters tailored to outdoor usage contingent on weather conditions. The device reaches a maximum power output of 14.6 µW cm−2 under simulated sunny conditions and generates a much more enhanced thermoelectric power of 74.78 µW cm−2 and a droplet‐based electric power of 256.25 mW m−2 in rainy conditions. As proof, this study developed self‐powered wearable electronics capable of acquiring physiological signals in simulated outdoor scenarios. This study presents a promising advancement in wearable technology, offering a potent solution for sustainable energy harvesting independent of weather conditions.  

Liquid crystal elastomers hold promise in various fields due to their reversible transition of mechanical and optical properties across distinct phases. However, the lack of local phase patterning techniques and irreversible phase programming has hindered their broad implementation. Here we introduce laser-induced dynamic crosslinking, which leverages the precision and control offered by laser technology to achieve high-resolution multilevel patterning and transmittance modulation. Incorporation of allyl sulfide groups enables adaptive liquid crystal elastomers that can be reconfigured into desired phases or complex patterns. Laser-induced dynamic crosslinking is compatible with existing processing methods and allows the generation of thermo- and strain-responsive patterns that include isotropic, polydomain and monodomain phases within a single liquid crystal elastomer film. We show temporary information encryption at body temperature, expanding the functionality of liquid crystal elastomer devices in wearable applications. Lack of local phase patterning in liquid crystal elastomers has hindered their broad implementation. The authors report a laser-induced dynamic crosslinking approach with allyl sulfide groups to achieve reconfigurable high-resolution patterning of multiple liquid crystalline phases in a single film.

Existing wearable technologies rely on physical coupling to the body to establish optical 1 , 2 , fluidic 3 , 4 , thermal 5 , 6 and/or mechanical 7 , 8 measurement interfaces. Here we present a class of wearable device platforms that instead relies on physical decoupling to define an enclosed chamber immediately adjacent to the skin surface. Streams of vapourized molecular substances that pass out of or into the skin alter the properties of the microclimate defined in this chamber in ways that can be precisely quantified using an integrated collection of wireless sensors. A programmable, bistable valve dynamically controls access to the surrounding environment, thereby creating a transient response that can be quantitatively related to the inward and outward fluxes of the targeted species by analysing the time-dependent readings from the sensors. The systems reported here offer unique capabilities in measuring the flux of water vapour, volatile organic compounds and carbon dioxide from various locations on the body, each with distinct relevance to clinical care and/or exposure to hazardous vapours. Studies of healing processes associated with dermal wounds in models of healthy and diabetic mice and of responses in models using infected wounds reveal characteristic flux variations that provide important insights, particularly in scenarios in which the non-contact operation of the devices avoids potential damage to fragile tissues. A non-contact wearable device that defines and modulates a microclimate adjacent to the skin can measure incoming and outgoing streams of vapourized substances, offering valuable insights into physiological health, wound healing and environmental exposures.

Soft robots, capable of safe interaction with delicate objects through their flexibility and compliance, are attracting attention in various real-world applications as manipulators, biomedical devices and wearable tools. As these technologies advance, the ability to perform complex tasks in a robust and reliable way becomes essential. Thus, the incorporation of embedded intelligence in soft robots, which enables them to perceive external environments and generate appropriate actions, is increasingly important. Inspiration from sophisticated biological systems, which exhibit optimized behaviours through the acquisition of external information, promotes the development of intelligent soft robots. Here, we introduce biomimicry strategies for intelligent soft robotics and highlight progress in how soft robots interact with their environment and perform tasks. First, we discuss sensors inspired by the sensory nervous systems and soft actuators inspired by the musculoskeletal systems. Furthermore, we investigate various applications such as manipulation, exploration, wearable devices, biomedical devices and imperceptible devices. We conclude discussing the challenges and offering a perspective on the future direction of this field.Soft robots are evolving to perform increasingly complex tasks, with biomimicry having a fundamental role in their development. This Review details biomimetic strategies and pivotal advances in sensors, actuators and applications of intelligent soft robotics.

Although the microstructure of coexistence phase provides direct insights of the nucleation mechanism and their change is substantial in the phase transition, their study is limited due to the lack of suitable tools capturing the thermodynamically unstable transient states. We resolve this problem in computational study by introducing a generalized canonical ensemble simulation and investigate the morphological change of the nucleus during the water evaporation and condensation. We find that at very low pressure, where the transition is first order, classical nucleation theory holds approximately. A main nucleus is formed in the supersaturation near spinodal and the overall shape of the nucleus is finite and compact. On increasing the pressure of the system, more nuclei are formed even before spinodal. They merge into a larger nuclei with a smaller free energy penalty to form ramified shapes. We suggest order parameters to describe the extent of fluctuation and their relation to the free energy profile.

This work demonstrates the synthesis and characterization of novel nanoporous silicified phospholipid bilayers assembled inorganic powders. The materials are obtained by silicification process with silica precursor at the hydrophilic region of phospholipid bilayers. This process involves the co-assembly of a chemically active phospholipids bilayer within the ordered porosity of a silica matrix and holds promise as a novel application for controlled drug release or drug containers with a high level of specificity and throughput. The controlled release application of the synthesized materials was achieved to glycolic acid, and obtained a zero-order release pattern due to the nanoporosity.