LiCoO_(2) Epitaxial Film Enabling Precise Analysis of Interfacial Degradations

Interfacial structure evolution and degradation are critical to the electrochemical performance of LiCoO_(2)(LCO),the most widely studied and used cathode material in lithium ion batteries.To understand such processes requires precise and quantitative measurements.Herein,we use well-defined epitaxial LCO thin films to reveal the interfacial degradation mechanisms.Through our systematical investigations,we find that surface corrosion is significant after forming the surface phase transition layer,and the cathode electrolyte interphase(CEI)has a double layer structure,an inorganic inner layer containing CoO,LiF,LiOH/Li_(2)O and Li_(x)PF_(y)O_(2),and an outmost layer containing Li2CO_(3) and organic carbonaceous components.Furthermore,surface cracks are found to be pronounced due to mechanical failures and chemical etching.This work demonstrates a model material to realize the precise measurements of LCO interfacial degradations,which deepens our understanding on the interfacial degradation mechanisms.

Modulation of the electronic states of perovskite SrCrO3 thin films through protonation via low-energy hydrogen plasma implantation approaches

Hydrogenation of transition metal oxides offers a powerful platform to tailor physical functionalities as well as for potential applications in modern electronic technologies.An ideal nondestructive and efficient hydrogen incorporation approach is important for the realistic technological applications.We demonstrate the proton injection on SrCro3 thin films via an efficient low-energy hydrogen plasma implantation experiments,without destroying the original lattice framework.Hydrogen ions accumu-late largely at the interfacial regions with amorphous character which extend about one-third of the total thickness.The Hx.SrCro3(HSCO)thin films appear like exfoliated layers which however retain the fully strained state with distorted perovskite structure.Proton doping induces the change of Cr oxidation state from Cr^4+to Cr^3+in HSCO thin films and a transition from metallic to insulat-ing phase.Our investigations suggest an attractive platform in manipulating the electronic phases in proton-based approaches and may offer a potential peeling off strategy for nanoscale devices through low-energy hydrogen plasma implantation approaches.

Electron energy levels determining cathode electrolyte interphase formation

Cathode electrolyte interphase(CEI)has a significant impact on the performance of rechargeable batteries and is gaining increasing attention.Understanding the fundamental and detailed CEI formation mechanism is of critical importance for battery chemistry.Herein,a diverse of characterization tools are utilized to comprehensively analyze the composition of the CEI layer as well as its formation mechanism by LiCoO_(2)(LCO)cathode.We reveal that CEI is mainly composed of the reduction products of electrolyte and it only parasitizes the degraded LCO surface which has transformed into a disordered spinel structure due to oxygen loss and lithium depletion.Based on the energy diagram and the chemical potential analysis,the CEI formation process has been well explained,and the proposed CEI formation mechanism is further experimentally validated.This work highlights that the CEI formation process is nearly identical to that of the anode-electrolyte-interphase,both of which are generated due to the electrolyte directly in contact with the low chemical potential electrode material.This work can deepen and refresh our understanding of CEI.

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems.However,the present paradigm involves time-intensive manual analysis that is inherently biased,error-prone,and unable to accommodate the large volumes of data produced by modern instrumentation.While more automated approaches have been proposed,many are not robust to a high variety of data,and do not generalize well to diverse microstructural features and material systems.Here,we present a flexible,semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems:(1)epitaxial heterostructures of SrTiO_(3)/Ge,(2)La_(0.8)Sr_(0.2)FeO_(3) thin films,and(3)MoO_(3) nanoparticles.We demonstrate that the few-shot learning method is more robust against noise,more reconfigurable,and requires less data than conventional image analysis methods.This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.