We report on the response of a monolithic high Tc transition-edge bolometer to about 3-mm-wave for the first time. The detector structure consisting of 400 nm YBa2Cu3O7−x (YBCO) film on buffered Yttria Stabilized Zirconia substrate without any coupled antenna, shows bolometric type responsivity to the 3-mm-waves radiation at its transition temperature. The YBCO thin film and Ce0.9La0.1O2 buffer layer are both fabricated by the metal-organic deposition method. The meander line pattern of the bolometer is designed for obtaining maximum absorption and responsivity possible when the polarized radiation of the source is aligned with the pattern. Meander lines are 50 micrometers wide and 1.5 mm long. We have measured amplitude and phase of the response versus modulation frequency of the detector to the linearly polarized 95 GHz source, and the detector was biased at 5 distinct temperatures at the transition corresponding to five different electrical conductivities of the YBCO film. When the meander lines of the device are parallel to the incident beam polarization, the YBCO pattern is speculated to act as a dissipative antenna resulting in higher absorption leading to high magnitude of the response as observed. The results from the measured phase of the response versus modulation frequency are also in agreement with the discussed absorbed mechanism. The absorption of the YBCO pattern is also measured to depend on the electrical conductivity of the YBCO film and our results show that there is an optimum electrical conductivity for having maximum absorption for this detector. Simulation results for this structure confirm the experiments showing that at electrical conductivity value of 1.33 × 10 5 S/m we have the maximum absorption for our device. These observations promise design of versatile THz and millimeter-wave detectors with potentials for applications in medical and security imaging.

Increasing demand for wearable devices has resulted in the development of soft sensors; however, an excellent soft sensor for measuring stretch, twist, and pressure simultaneously has not been proposed yet. This paper presents a novel, fully 3D, microfluidic-oriented, gel-based, and highly stretchable resistive soft sensor. The proposed sensor is multi-functional and could be used to measure stretch, twist, and pressure, which is the potential of using a fully 3D structure in the sensor. Unlike previous methods, in which almost all of them used EGaIn as the conductive material, in this case, we used a low-cost, safe (biocompatible), and ubiquitous conductive gel instead. To show the functionality of the proposed sensor, FEM simulations and a set of designed experiments were done, which show linear (99%), accurate (> 94.9%), and durable (tested for a whole of four hours) response of the proposed sensor. Then, the sensor was put through its paces on a female test subject’s knee, elbow, and wrist to show the potential application of the sensor as a body motion sensor. Also, a fully 3D active foot insole was developed, fabricated, and evaluated to evaluate the pressure functionality of the sensor. The result shows good discrimination and pressure measurement for different foot sole areas. The proposed sensor has the potential to be used in real-world applications like rehabilitation, wearable devices, soft robotics, smart clothing, gait analysis, AR/VR, etc.

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Fatigue and rutting are two common damage types in asphalt pavements. Reclaimed asphalt pavement (RAP), as a sustainable approach in the pavement industry, deals with the foregoing damage. Fatigue and rutting characteristics of asphalt pavement are generally assessed using laboratory tests, taking a long time and consuming significant amounts of raw material. This study aims to propose a novel approach for predicting fatigue and rutting performance of RAP mixtures. A new ensemble prediction method, named COA-KNN, is introduced by combining the coyote optimization algorithm and K-nearest neighbor to increase the accuracy of fatigue and rutting prediction. In order to evaluate the accuracy, the proposed method was compared against robust prediction methods, including random forest (RF), gradient boosting (GB), decision tree regression (DT), and multiple linear regression (MLR). Afterward, the influence of each variable on the mentioned damages is examined, and the variables are ranked based on their relative influence on the mentioned damages. The results suggest that COA-KNN outperformed other prediction techniques when comparing different performance indicators. Total binder content in asphalt mixes and the PG span of the virgin binder added to the recycled asphalt mixture had the highest relative influence on fatigue and rutting performance, respectively.