Dielectric metasurfaces,composed of planar arrays of subwavelength dielectric structures that collectively mimic the operation of conventional bulk optical elements,have revolutionized the field of optics by their pot...Dielectric metasurfaces,composed of planar arrays of subwavelength dielectric structures that collectively mimic the operation of conventional bulk optical elements,have revolutionized the field of optics by their potential in constructing high-efficiency and multi-functional optoelectronic systems on chip.The performance of a dielectric metasurface is largely determined by its constituent material,which is highly desired to have a high refractive index,low optical loss and wide bandgap,and at the same time,be fabrication friendly.Here,we present a new material platform based on tantalum pentoxide(Ta2O5)for implementing high-performance dielectric metasurface optics over the ultraviolet and visible spectral region.This wide-bandgap dielectric,exhibiting a high refractive index exceeding 2.1 and negligible extinction coefficient across a broad spectrum,can be easily deposited over large areas with good quality using straightforward physical vapor deposition,and patterned into high-aspect-ratio subwavelength nanostructures through commonly-available fluorine-gas-based reactive ion etching.We implement a series of highefficiency ultraviolet and visible metasurfaces with representative light-field modulation functionalities including polarization-independent high-numerical-aperture lensing,spin-selective hologram projection,and vivid structural color generation,and the devices exhibit operational efficiencies up to 80%.Our work overcomes limitations faced by scalability of commonly-employed metasurface dielectrics and their operation into the visible and ultraviolet spectral range,and provides a novel route towards realization of high-performance,robust and foundry-manufacturable metasurface optics.展开更多
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging...High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry.Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries,particularly in optimizing electrodes and electrolytes.This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems,which bridges the gap between artificial intelligence and electrochemistry.We also examine the potential challenges and opportunities associated with different deep learning approaches,tailoring them to specific battery requirements.Ultimately,we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology.Furthermore,we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering.展开更多
基金the National Institute of Standards and Technology(NIST)Physical Measurement Laboratory,Award No.70NANB14H209,through the University of Maryland.O.K.was supported by an appointment to the Intelligence Community Postdoctoral Research Fellowship Program at NIST administered by Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the U.S.Department of Energy and the Office of the Director of National Intelligence(ODNI).
文摘Dielectric metasurfaces,composed of planar arrays of subwavelength dielectric structures that collectively mimic the operation of conventional bulk optical elements,have revolutionized the field of optics by their potential in constructing high-efficiency and multi-functional optoelectronic systems on chip.The performance of a dielectric metasurface is largely determined by its constituent material,which is highly desired to have a high refractive index,low optical loss and wide bandgap,and at the same time,be fabrication friendly.Here,we present a new material platform based on tantalum pentoxide(Ta2O5)for implementing high-performance dielectric metasurface optics over the ultraviolet and visible spectral region.This wide-bandgap dielectric,exhibiting a high refractive index exceeding 2.1 and negligible extinction coefficient across a broad spectrum,can be easily deposited over large areas with good quality using straightforward physical vapor deposition,and patterned into high-aspect-ratio subwavelength nanostructures through commonly-available fluorine-gas-based reactive ion etching.We implement a series of highefficiency ultraviolet and visible metasurfaces with representative light-field modulation functionalities including polarization-independent high-numerical-aperture lensing,spin-selective hologram projection,and vivid structural color generation,and the devices exhibit operational efficiencies up to 80%.Our work overcomes limitations faced by scalability of commonly-employed metasurface dielectrics and their operation into the visible and ultraviolet spectral range,and provides a novel route towards realization of high-performance,robust and foundry-manufacturable metasurface optics.
文摘High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions,which,in turn,promote a sustainable future.However,the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry.Recent advancements have demonstrated the potential of deep learning techniques in efficiently designing batteries,particularly in optimizing electrodes and electrolytes.This review provides comprehensive concepts and principles of deep learning and its application in solving battery-related electrochemical problems,which bridges the gap between artificial intelligence and electrochemistry.We also examine the potential challenges and opportunities associated with different deep learning approaches,tailoring them to specific battery requirements.Ultimately,we aim to inspire future advancements in both fundamental scientific understanding and practical engineering in the field of battery technology.Furthermore,we highlight the potential challenges and opportunities for different deep learning methods according to the specific battery demand to inspire future advancement in fundamental science and practical engineering.
