A two-dimensional MoS_(2) array based on artificial neural network learning for high-quality imaging

As the basis of machine vision,the biomimetic image sensing devices are the eyes of artificial intelligence.In recent years,with the development of two-dimensional(2D)materials,many new optoelectronic devices are developed for their outstanding performance.However,there are still little sensing arrays based on 2D materials with high imaging quality,due to the poor uniformity of pixels caused by material defects and fabrication technique.Here,we propose a 2D MoS_(2)sensing array based on artificial neural network(ANN)learning.By equipping the MoS_(2)sensing array with a“brain”(ANN),the imaging quality can be effectively improved.In the test,the relative standard deviation(RSD)between pixels decreased from about 34.3%to 6.2%and 5.49%after adjustment by the back propagation(BP)and Elman neural networks,respectively.The peak signal to noise ratio(PSNR)and structural similarity(SSIM)of the image are improved by about 2.5 times,which realizes the re-recognition of the distorted image.This provides a feasible approach for the application of 2D sensing array by integrating ANN to achieve high quality imaging.

Persistent luminescence encoding for rapid and accurate oral-derived bacteria identification

The dysbiosis of oral microbiota contributes to diseases such as periodontitis and certain cancers by triggering the host inflammatory response.Developing methods for the immediate and sensitive identification of oral microorganism is crucial for the rapid diagnosis and early interventions of associated diseases.Traditional methods for microbial detection primarily include the plate culturing,polymerase chain reaction and enzyme-linked immunosorbent assay,which are either time-consuming or laborious.Herein,we reported a persistent luminescence-encoded multiple-channel optical sensing array and achieved the rapid and accurate identification of oral-derived microorganisms.Our results demonstrate that electrostatic attractions and hydrophobic-hydrophobic interactions dominate the binding of the persistent luminescent nanoprobes to oral microorganisms and the microbial identification process can be finished within 30 min.Specifically,a total of 7 oral-derived microorganisms demonstrate their own response patterns and were differentiated by linear discriminant analysis(LDA)with the accuracy up to 100%both in the solution and artificial saliva samples.Moreover,the persistent luminescence encoded array sensor could also discern the microorganism mixtures with the accuracy up to 100%.The proposed persistent luminescence encoding sensor arrays in this work might offer new ideas for rapid and accurate oralderived microorganism detection,and provide new ways for disease diagnosis associated with microbial metabolism.