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Abstract The development of all‐in‐one devices for artificial visual systems offers an attractive solution in terms of energy efficiency and real‐time processing speed. In recent years, the proliferation of smart sensors in the growth of Internet‐of‐Things (IoT) has led to the increasing importance of in‐sensor computing technology, which places computational power at the edge of the data‐flow architecture. In this study, a prototype visual sensor inspired by the human retina is proposed, which integrates ferroelectricity and photosensitivity in two‐dimensional (2D) α‐In2Se3 material. This device mimics the functions of photoreceptors and amacrine cells in the retina, performing optical reception and memory computation functions through the use of electrical switching polarization in the channel. The gate‐tunable linearity of excitatory and inhibitory functions in photon‐induced short‐term plasticity enables to encode and classify 12 000 images in the Mixed National Institute of Standards and Technology (MNIST) dataset with remarkable accuracy, achieving ≈94%. Additionally, in‐sensor convolution image processing through a network of phototransistors, with five convolutional kernels electrically pre‐programmed into the transistors is demonstrated. The convoluted photocurrent matrices undergo straightforward arithmetic calculations to produce edge and feature‐enhanced scenarios. The findings demonstrate the potential of ferroelectric α‐In2Se3 for highly compact and efficient retinomorphic hardware implementation, regardless of ambipolar transport in the channel.

Reservoir computing (RC) architecture which mimics the human brain is a fundamentally preferred method to process dynamical systems that evolve with time. However, the difficulty in generating rich reservoir states using two‐terminal devices remains challenging, which hinders its hardware implementation. Herein, the 1D array of ferroelectric field‐effect transistor (Fe‐FET) based on α‐In2Se3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in α‐In2Se3 is sufficiently investigated by polarization dynamic model. The proposed Fe‐FET is capable of experimentally classifying images using MNIST dataset with a high accuracy of 91%. Furthermore, time‐series real‐life chaotic system, for example, Earth's weather, can be accurately forecasted using our Ferro‐RC based on the Jena climate dataset recorded in a 1 year period. Remarkable determination coefficient (R 2) of 0.9983 and normalized root mean square error (NRMSE) of 8.3 × 10−3 are achieved using a minimized readout network. The demonstration of integrated memory and computation opens a route for realizing a compact RC hardware system.

Abstract Background Biomaterial scaffolds that deliver growth factors such as recombinant human bone morphogenetic proteins‐2 (rhBMP‐2) have improved clinical bone tissue engineering by enhancing bone tissue regeneration. This approach could be further improved if the controlled delivery of bioactive rhBMP‐2 were sustained throughout the duration of osteogenesis from fibrous scaffolds that provide control over dose and bioactivity of rhBMP‐2. In nature, heparan sulfate attached to core proteoglycans serves as the co‐receptor that delivers growth factors to support tissue morphogenesis. Methods To mimic this behavior, we conjugated heparan sulfate decorated recombinant domain I of perlecan/HSPG2 onto an electrospun poly(ε‐caprolactone) (PCL) scaffold, hypothesizing that the heparan sulfate chains will enhance rhBMP‐2 loading onto the scaffold and preserve delivered rhBMP‐2 bioactivity. Results In this study, we demonstrated that covalently conjugated perlecan domain I increased loading capacity of rhBMP‐2 onto PCL scaffolds when compared to control unconjugated scaffolds. Additionally, rhBMP‐2 released from the modified scaffolds enhanced alkaline phosphatase activity in W20–17 mouse bone marrow stromal cells, indicating the preservation of rhBMP‐2 bioactivity indicative of osteogenesis. Conclusions We conclude that this platform provides a sophisticated and efficient approach to deliver bioactive rhBMP‐2 for bone tissue regeneration applications.