To monitor the health of large-scale structures, a wireless measurement system, such as a bridge, is required. One of the methods of monitoring the health of large-scale structures involves the application of an impedance-loaded wireless surface acoustic wave (SAW) sensor. Additionally, a pressure-sensor-loaded SAW sensor can detect the vibration of a cantilever. In this study, a continuous wavelet transform (CWT) is adopted to analyze the sensor responses. The CWT results obtained were classified into two categories based on the attenuation at each frequency, which include the exponential or linear type. Furthermore, machine learning was applied to evaluate cantilever damage. The results indicate that a high accuracy evaluation of damage is feasible with the proposed method.

We report that membrane filtration can replace centrifugation as a highly efficient size classification process of colloidal quantum dots (QDs) after chemical synthesis. The production of colloidal QDs requires the separation of the targeted QDs dispersed in organic solvents from other by-products. The separation process has been conventionally performed by centrifugation. We investigated replacing the centrifugation with filtration using organic solvent-resistant polyamide hollow fiber membranes (HFMs). By choosing the pore size of HFMs, QDs of arbitrary size were classified. It was also demonstrated that not only large QDs but also small QDs could be separated simultaneously by using HFM with a dense layer of polyamide inside. Consequently, highly monodisperse QDs were easily obtained in a single filtration operation using HFMs.