Comparative Study of the Effective Properties of 0-3 and Gyroid Triply Periodic Minimal Surface Cement-Piezocomposites.

In the present numerical simulation work, effective elastic and piezoelectric properties are calculated and a comparative study is conducted on a cement matrix-based piezocomposite with 0-3 and gyroid triply periodic minimal surface (TPMS) inclusions. The present study compares the effective properties of different piezoelectric materials having two different types of connectivity of the inclusions namely, 0-3 inclusions where the inclusions are physically separated from each other and are embedded within the matrix and the second one is TPMS inclusions having interpenetrating phase type connectivity. Effective properties are calculated for four different materials at five different volume fractions namely, 10%, 15%, 20%, 25%, and 30% volume fractions of inclusion by volume. In terms of effective properties and direct piezoelectric effect, TPMS piezocomposite is found to perform better compared to 0-3 piezocomposite. Lead-free piezoelectric material 0.5Ba(Ca0.8Zr0.2)O3 - 0.5(Ba0.7Ca0.3)TiO3 demonstrates better performance compared to all other material inclusions studied. The present study attempts to highlight improved piezoelectric effective properties of lead-free material-based piezocomposites with TPMS inclusions.

Effect of Poling on Photocatalysis, Piezocatalysis, and Photo-Piezo Catalysis Performance of BaBi4Ti4O15 Ceramics.

This study focuses on analyzing the poling effect of BaBi4Ti4O15 (BBT) on the basis of photo and piezo-catalysis performance. BBT powder is prepared via a solid state reaction followed by calcination at 950 °C for 4 h. BBT is characterized by an X-ray diffractometer, scanning electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy. The optical bandgap of BBT is evaluated with the help of Tauc's plot and found to be 3.29 eV, which comes in the photon energy range of ultra-violet radiation. BBT powder is poled by using Corona poling in the presence of 2 kV mm-1 of electric field. An aqueous solution of methyl blue (MB) dye in the presence of UV radiation is used to evaluate the photo/piezocatalysis performance. Photocatalysis, piezocatalysis, and photo-piezo catalysis degradation efficiencies of poled and unpoled BBT powder are tested for 120 min of UV light irradiation. Photo-piezocatalysis shows degradation efficiencies of 62% and 40% for poled and unpoled BBT powder, respectively. Poling of BBT powder shows significant enhancement in degradation performance of MB dye in aqueous solution. Scavenger tests are also performed to identify reactive species.

An epifluidic electronic patch with spiking sweat clearance for event-driven perspiration monitoring.

Sensory neurons generate spike patterns upon receiving external stimuli and encode key information to the spike patterns, enabling energy-efficient external information processing. Herein, we report an epifluidic electronic patch with spiking sweat clearance using a sensor containing a vertical sweat-collecting channel for event-driven, energy-efficient, long-term wireless monitoring of epidermal perspiration dynamics. Our sweat sensor contains nanomesh electrodes on its inner wall of the channel and unique sweat-clearing structures. During perspiration, repeated filling and abrupt emptying of the vertical sweat-collecting channel generate electrical spike patterns with the sweat rate and ionic conductivity proportional to the spike frequency and amplitude over a wide dynamic range and long time (> 8 h). With such 'spiking' sweat clearance and corresponding electronic spike patterns, the epifluidic wireless patch successfully decodes epidermal perspiration dynamics in an event-driven manner at different skin locations during exercise, consuming less than 0.6% of the energy required for continuous data transmission. Our patch could integrate various on-skin sensors and emerging edge computing technologies for energy-efficient, intelligent digital healthcare.

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis.

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.