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In recent years, bone tissue engineering (BTE) has made significant progress in promoting the direct and functional connection between bone and graft, including osseointegration and osteoconduction, to facilitate the healing of damaged bone tissues. Herein, we introduce a new, environmentally friendly, and cost-effective method for synthesizing reduced graphene oxide (rGO) and hydroxyapatite (HAp). The method uses epigallocatechin-3-O-gallate (EGCG) as a reducing agent to synthesize rGO (E-rGO), and HAp powder is obtained from Atlantic bluefin tuna (Thunnus thynnus). The physicochemical analysis indicated that the E-rGO/HAp composites had exceptional properties for use as BTE scaffolds, as well as high purity. Moreover, we discovered that E-rGO/HAp composites facilitated not only the proliferation, but also early and late osteogenic differentiation of human mesenchymal stem cells (hMSCs). Our work suggests that E-rGO/HAp composites may play a significant role in promoting the spontaneous osteogenic differentiation of hMSCs, and we envision that E-rGO/HAp composites could serve as promising candidates for BTE scaffolds, stem-cell differentiation stimulators, and implantable device components because of their biocompatible and bioactive properties. Overall, we suggest a new approach for developing cost-effective and environmentally friendly E-rGO/HAp composite materials for BTE application.

We propose a novel, to the best of our knowledge, distance recovery method via swept frequency mixing for frequency-modulated continuous wave (FMCW) light detection and ranging (LiDAR) to overcome the Nyquist limit and obtain high data efficiency. A one dimensional (1D) experiment was conducted to recover the optical fiber length; in addition, a 3D image was obtained by recovering the distances of several targets in free space. Compared to conventional methods based on fast Fourier transform (FFT), beat frequency up to 14 times the Nyquist limit for sampling frequency was successfully measured without aliasing. The proposed method dramatically increases the data efficiency in FMCW LiDAR by reducing the number of complex algorithms and experimental resources required.