Electrodeposition of a dendrite-free 3D Al anode for improving cycling of an aluminum-graphite battery

Aluminum-metal batteries show great potential as next-generation energy storage due to their abundant resources and intrinsic safety.However,the crucial limitations of metallic Al anodes,such as dendrite and corrosion problems in conventional aluminum-metal batteries,remain challenging and elusive.Here,we report a novel electrodeposition strategy to prepare an optimized 3D Al anode on carbon cloth with an uniform deposition morphology,low local current density,and mitigatory volume change.The symmetrical cells with the 3D Al anode show superior stable cycling(>450 h)and low-voltage hysteresis(~170 mV)at 0.5 mA cm^(−2).High reversibility(~99.7%)is achieved for the Al plating/stripping.The graphite||Al‐4/CC full batteries show a long lifespan of 800 cycles with 54 mAh g^(−1) capacity at a high current density of 1000 mA g^(−1),benefiting from the high capacitive-controlled distribution.This study proposes a novel strategy to design 3D Al anodes for metallic-Al-based batteries by eliminating the problems of planar Al anodes and realizing the potential applications of aluminum-graphite batteries.

Photogrammetry and Deep Learning

Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN.

Tuning Superhydrophobic Materials with Negative Surface Energy Domains

Hydrophobic/superhydrophobic materials with intrinsic water repellence are highly desirable in engineering fields including antiicing in aerocrafts,antidrag and anticorrosion in ships,and antifog and self-cleaning in optical lenses,screen,mirrors,and windows.However,superhydrophobic material should have small surface energy(SE)and a micro/nanosurface structure which can reduce solid-liquid contact significantly.The low SE is generally found in organic materials with inferior mechanical properties that is undesirable in engineering.Intriguingly,previous theoretical calculations have predicted a negative SE forθ-alumina(θ-Al_(2)O_(3)),which inspires us to use it as a superhydrophobic material.Here,we report the experimental evidence of the small/negative SE ofθ-Al_(2)O_(3) and aθ-Al_(2)O_(3)-based superhydrophobic coating prepared by one-step scalable plasma arcing oxidation.The superhydrophobic coating has complete ceramic and desired micro/nanostructure and therefore exhibits excellent aging resistance,wear resistance,corrosion resistance,high-temperature tolerance,and burning resistance.Owing to the rarity of the small/negative SE in inorganic materials,the concept to reduce SE byθ-Al_(2)O_(3) may foster a blowout to develop robust superhydrophobicity by complete inorganic materials.

Toward a unified theoretical framework for photogrammetry

The objective of photogrammetry is to extract information from imagery.With the increasing interaction of sensing and computing technologies,the fundamentals of photogrammetry have undergone an evolutionary change in the past several decades.Numerous theoretical progresses and practical applications have been reported from traditionally different but related multiple disciplines,including computer vision,photogrammetry,computer graphics,pattern recognition,remote sensing and machine learning.This has gradually extended the boundary of traditional photogrammetry in both theory and practice.This paper introduces a new,holistic theoretical framework to describe various photogrammetric tasks and solutions.Under this framework,photogrammetry is generally regarded as a reversed imaging process formulated as a unified optimization problem.Depending on the variables to be determined through optimization,photogrammetric tasks are mostly divided into image space tasks,image-object space tasks and object space tasks,each being a special case of the general formulation.This paper presents representative solution approaches for each task.With this effort,we intend to advocate an imminent and necessary paradigm change in both research and learning of photogrammetry.