Self-powered transparent photodetector for subretinal visual functions of wide-field-of-view and broadband perception

Natural photoreceptors enable color vision in humans,wherein the eyes detect colors and their corresponding intensities via cone and rod photoreceptors,respectively.Herein,we developed an artificial broadband photoreceptor with light-color intensity detection similar to that of natural photoreceptors.The developed photoreceptor operates in the self-powered mode and is capable of broadband perception(365–940 nm).The designed metal-oxide heterojunction(n-ZnO/p-NiO)photoreceptor with a thin tin sulfide layer embedded in between is capable of perceiving various colors.It exhibits good transparency in the visible range and displays excellent integration with flexible substrates,highlighting its potential for use in flexible electronics.The fabricated structure has an exceptional response time(≈1 ms)and a wide-field-of-view(150?)compared to the human eye's sensing range(50–100 ms and 108?).The transparent photorecep-tor mimics cones and rods to detect a various wavelength-dependent signals and explicitly differentiate between the intensities of the detected signals,respectively.This is further illustrated by employing the developed photoreceptor to detect colors in real time by generating unique signals corresponding to each color.The demonstration provides the proof of concept for self-biased flexible bioelectronics emulating high-performing visual functions of artificial eyes.

Estimation of Chlorophyll-a Concentration in Lake Taihu from Gaofen-1 Wide-Field-of-View Data through a Machine Learning Trained Algorithm

Wide-field-of-view(WFV) imager that observes the earth environment with four solar reflective bands in a spatial resolution of 16 m is equipped on board Gaofen-1(GF-1) satellite. Chlorophyll-a(Chl-a) concentration in Lake Taihu, China from 2018 to 2019 is collected and collocated with GF-1 satellite data. This study develops a general and reliable estimation of Chl-a concentration from GF-1 WFV data under turbid inland water conditions. The collocated data are classified according to season and used in random forest(RF) regression to train models for retrieving the lake Chl-a concentration. A composite index is developed to select the most important variables in the models. The models trained for each season show a better performance than the model trained by using the whole year data in terms of the coefficient of determination(R^(2)) between retrievals and observations. Specifically, the R2 values in spring, summer, autumn, and winter are 0.88, 0.88, 0.94, and 0.74, respectively;whereas that using the whole year data is only 0.71. The Chl-a concentration in Lake Taihu exhibits an obvious seasonal change with the highest in summer, followed by autumn and spring, and the lowest in winter. The Chl-a concentration also displays an obvious spatial variation with season. A high concentration occurs mainly in the northwest of the lake. The temporal and spatial changes of Chl-a concentration are almost consistent with the changes in the areas and times of cyanobacteria blooms based on Moderate Resolution Imaging Spectroradiometer(MODIS) data. The proposed algorithm can be operated without a priori knowledge on atmospheric conditions and water quality. Our study also demonstrates that GF-1 data are increasingly valuable for monitoring the Chl-a concentration of inland water bodies in China at a high spatial resolution.

Marine aquaculture mapping using GF-1 WFV satellite images and full resolution cascade convolutional neural network

Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions,which may cause severe coastal water problems without adequate environmental management.Effective mapping of mariculture areas is essential for the protection of coastal environments.However,due to the limited spatial coverage and complex structures,it is still challenging for traditional methods to accurately extract mariculture areas from medium spatial resolution(MSR)images.To solve this problem,we propose to use the full resolution cascade convolutional neural network(FRCNet),which maintains effective features over the whole training process,to identify mariculture areas from MSR images.Specifically,the FRCNet uses a sequential full resolution neural network as the first-level subnetwork,and gradually aggregates higher-level subnetworks in a cascade way.Meanwhile,we perform a repeated fusion strategy so that features can receive information from different subnetworks simultaneously,leading to rich and representative features.As a result,FRCNet can effectively recognize different kinds of mariculture areas from MSR images.Results show that FRCNet obtained better performance than other classical and recently proposed methods.Our developed methods can provide valuable datasets for large-scale and intelligent modeling of the marine aquaculture management and coastal zone planning.