Plasmon Induced Heat Funneling from Au to Cu in the Bimetallic Au@Cu Core-Shell Nanoparticles

The bimetallic nanostructures that mix a plasmonic metal with a transition metal in the form of the core-shell nanoparticles are promising to promote catalytic performance.But it is still unclear how the heat(hot electrons and phonons)transfers on the interface between two metals.We have designed and synthesized Au@Cu bimetallic nanoparticles with Au as core and Cu as shell.By using transient absorption spectroscopy,we find that there are two plasmon induced heat funneling processes from Au core to Cu shell.One is the electron temperature equilibrium(electron heat transfer)with equilibration time of~560 fs.The other is the lattice temperature equilibrium(lattice heat transfer)with equilibration time of~13 ps.This plasmon induced heat funneling may be universal in similar bimetallic nanostructures,so our finding could contribute to further understanding the catalytic mechanism of bimetallic plasmonic photothermal catalysis.

A deep learning model for generating fundus autofluorescence images from color fundus photography

Background:Fundus Autofluorescence(FAF)is a valuable imaging technique used to assess metabolic alterations in the retinal pigment epithelium(RPE)associated with various age-related and disease-related changes.The practical uses of FAF are ever-growing.This study aimed to evaluate the effectiveness of a generative deep learning(DL)model in translating color fundus(CF)images into synthetic FAF images and explore its potential for enhancing screening of age-related macular degeneration(AMD).Methods:A generative adversarial network(GAN)model was trained on pairs of CF and FAF images to generate synthetic FAF images.The quality of synthesized FAF images was assessed objectively by common generation metrics.Additionally,the clinical effectiveness of the generated FAF images in AMD classification was evaluated by measuring the area under the curve(AUC),using the LabelMe dataset.Results:A total of 8410 FAF images from 2586 patients were analyzed.The synthesized FAF images exhibited an impressive objectively assessed quality,achieving a multi-scale structural similarity index(MS-SSIM)of 0.67.When evaluated on the LabelMe dataset,the combination of generated FAF images and CF images resulted in a noteworthy improvement in AMD classification accuracy,with the AUC increasing from 0.931 to 0.968.Conclusions:This study presents the first attempt to use a generative deep learning model to create authentic and high-quality FAF images from CF images.The incorporation of the translated FAF images on top of CF images improved the accuracy of AMD classification.Overall,this study presents a promising approach to enhance largescale AMD screening.