Halide Perovskite Light‐Emitting Diode Technologies

Funder: European Research Council; Id: http://dx.doi.org/10.13039/501100000781Abstract: Halide perovskites have attracted considerable attention in next‐generation solid‐state lighting and displays owing to their outstanding optoelectronic properties. Over the past few years, perovskite light‐emitting diodes (LEDs) have achieved high external quantum efficiencies of >20% with active layers showing photoluminescence quantum efficiencies close to unity. This paper reviews the historical breakthroughs and recent advancements in perovskite LEDs with near‐infrared, red, green, and blue emission colors. Critical challenges, including device stability and material toxicity, are discussed. Finally, an outlook on white emission and lasing applications based on perovskite materials is presented

Stable Hexylphosphonate-Capped Blue-Emitting Quantum-Confined CsPbBr3 Nanoplatelets.

Quantum-confined CsPbBr3 nanoplatelets (NPLs) are extremely promising for use in low-cost blue light-emitting diodes, but their tendency to coalesce in both solution and film form, particularly under operating device conditions with injected charge-carriers, is hindering their adoption. We show that employing a short hexyl-phosphonate ligand (C6H15O3P) in a heat-up colloidal approach for pure, blue-emitting quantum-confined CsPbBr3 NPLs significantly suppresses these coalescence phenomena compared to particles capped with the typical oleyammonium ligands. The phosphonate-passivated NPL thin films exhibit photoluminescence quantum yields of ∼40% at 450 nm with exceptional ambient and thermal stability. The color purity is preserved even under continuous photoexcitation of carriers equivalent to LED current densities of ∼3.5 A/cm2. 13C, 133Cs, and 31P solid-state MAS NMR reveal the presence of phosphonate on the surface. Density functional theory calculations suggest that the enhanced stability is due to the stronger binding affinity of the phosphonate ligand compared to the ammonium ligand.J. S. and S.D.S. acknowledge the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (HYPERION, grant agreement number 756962). S.D.S acknowledges funding from the Royal Society and Tata Group (UF150033). R.H.F. and Y.L. acknowledge sup-port from the Simons Foundation (grant 601946). M.A. and D.K. acknowledges funding from the European Union’s Hori-zon 2020 research and innovation programme under the Ma-rie Skłodowska-Curie (grant agreement number 841386 and 841136, respectively). K.J. acknowledges funding from the Royal Society (RGFR1180002). K.F. acknowledges a George and Lilian Schiff Studentship, Winton Studentship, the Engineer-ing and Physical Sciences Research Council (EPSRC) student-ship, Cambridge Trust Scholarship, and Robert Gardiner Scholarship. C. P. G. acknowledges the European Research Council (ERC) under the European Union’s Horizon 2020 re-search and innovation program (835073) and the Royal Society for a Research Professorship (RP\R1\180147). The authors acknowledge the EPSRC for funding (EP/R023980/1)

Fast A‐site cation cross‐exchange at room temperature: single‐to double‐ and triple‐cation halide perovskite nanocrystals

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGWe report here fast A-site cation cross-exchange between APbX3 perovskite nanocrystals (NCs) made of different A-cations (Cs (cesium), FA (formamidinium), and MA (methylammonium)) at room temperature. Surprisingly, the A-cation cross-exchange proceeds as fast as the halide (X=Cl, Br, or I) exchange with the help of free A-oleate complexes present in the freshly prepared colloidal perovskite NC solutions. This enabled the preparation of double (MACs, MAFA, CsFA)- and triple (MACsFA)-cation perovskite NCs with an optical band gap that is finely tunable by their A-site composition. The optical spectroscopy together with structural analysis using XRD and atomically resolved high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and integrated differential phase contrast (iDPC) STEM indicates the homogeneous distribution of different cations in the mixed perovskite NC lattice. Unlike halide ions, the A-cations do not phase-segregate under light illumination.Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 | Ref. PID2020-117371RA-I00Xunta de Galicia https://doi.org/10.13039/501100010801 | Ref. ED431F2021/05HORIZON EUROPE European Research Council https://doi.org/10.13039/100019180 | Ref. ERC-CoG-2019 815128European Commission https://doi.org/10.13039/501100000780 | Ref. 731019Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 | Ref. EP/R023980/1Royal Society https://doi.org/10.13039/50110000028

Robust cross-linked Na3V2(PO4)2F3 full sodium-ion batteries

Sodium-ion batteries (SIBs) have rapidly risen to the forefront of energy storage systems as a promising supplementary for Lithium-ion batteries (LIBs). Na3V2(PO4)2F3 (NVPF) as a common cathode of SIBs, features the merits of high operating voltage, small volume change and favorable specific energy density. However, it suffers from poor cycling stability and rate performance induced by its low intrinsic conductivity. Herein, we propose an ingenious strategy targeting superior SIBs through cross-linked NVPF with multi-dimensional nanocarbon frameworks composed of amorphous carbon and carbon nanotubes (NVPF@C@CNTs). This rational design ensures favorable particle size for shortened sodium ion transmission pathway as well as improved electronic transfer network, thus leading to enhanced charge transfer kinetics and superior cycling stability. Benefited from this unique structure, significantly improved electrochemical properties are obtained, including high specific capacity (126.9 mAh g−1 at 1 C, 1 C = 128 mA g−1) and remarkably improved long-term cycling stability with 93.9% capacity retention after 1000 cycles at 20 C. The energy density of 286.8 Wh kg−1 can be reached for full cells with hard carbon as anode (NVPF@C@CNTs//HC). Additionally, the electrochemical performance of the full cell at high temperature is also investigated (95.3 mAh g−1 after 100 cycles at 1 C at 50 oC). Such nanoscale dual-carbon networks engineering and thorough discussion of ion diffusion kinetics might make contributions to accelerating the process of phosphate cathodes in SIBs for large-scale energy storages

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts

Understanding and optimizing perovskite optoelectronic devices with multi-dimensional imaging techniques

This thesis explores the application of multi-dimensional optical imaging techniques in understanding and optimizing perovskite-based optoelectronics. Chapters 1 and 2 give the motivation behind this work and background to perovskite optoelectronics and machine learning. Chapter 3 introduces the main experimental techniques. The debated passivation strategies on perovskite solar cells (PSCs) are studied in Chapter 4 through quantitative hyperspectral imaging. Specifically, alkali metal passivation imposes distinct effects on the optical and structural properties of the devices based on different transport layers. It is shown that the formation of secondary phases, either due to the additives in the perovskite precursor or in the transport layer, leads to an increase in nonradiative recombination and local open-circuit voltage loss. This provides important guidance to the development of passivation techniques toward efficient and stable PSCs. Chapter 5 expands the capability of the latest hyperspectral microscopy technique by developing a machine-learning-based image processing algorithm that is suitable for scientific research. The proposed algorithm achieves state-of-the-art denoising performances compared to recent ML models and conventional handcrafted algorithms. It is able to strengthen signals from unknown samples under low illumination conditions by exploiting spectral information and adopting a self-learning approach. This enables fast and low-dose measurements for emerging semiconductor materials with poor stability. Chapter 6 characterizes perovskite materials for a wide range of applications using the imaging platform developed in Chapter 5. The degradation of mixed halide perovskite light-emitting diodes (LEDs) is tracked through in-situ PL and in-operando electroluminescence mapping. We reveal that lateral ion migration under device operation leads to the growth of chloride-rich defective regions that emit poorly. This is the first time lateral halide migration is observed in perovskite LEDs due to locally-varying electric fields. Finally, Chapter 7 summarizes all the findings and discusses future research directions. The research presented in this thesis, with approaches across the multidisciplinary scientific fields of physics, material science, and machine learning, paves the way for computer vision-accelerated development of emerging technologies towards commercialization and scale-up

Evaluation of Global Descriptor Methods for Appearance-Based Visual Place Recognition

Visual place recognition (VPR) is considered among the most challenging problems due to the extreme variations in appearance and viewpoint. Essentially, appearance-based VPR can be considered as an image retrieval task, thus the key is to accurately and efficiently describe the images. Recently, global descriptor methods have attracted substantial attention from the VPR community, which has contributed to numerous important outcomes. Despite the growing number of global descriptors presented, little attention has been paid to the comparison and evaluation of these methods and so it remains difficult for researchers to disentangle the factors that led to better performance. This study provided comprehensive insight into global descriptors from a practical application perspective. We present a systematic evaluation that integrates 15 commonly used global descriptors, 6 benchmark datasets, and 5 evaluation metrics, and subsequently extended this evaluation to discuss the key factors impacting the matching performance and computational efficiency. We also report practical suggestions for constructing promising CNN descriptors, based on the experimental conclusions. Our analysis reveals both advantages and limitations of three different types of global descriptors, including handcrafted features-based ones, off-the-shelf CNN-based ones, and customized CNN-based ones. Finally, we evaluate the practicality of reported global descriptors to mediate the trade-offs between matching performance and computational efficiency

Defect Healing of MAPbI3 Perovskite Single Crystal Surface by Benzylamine

Controlling the surface traps in metal halide perovskites (MHPs) is essential for device performance, stability, and commercialization. Here, a facile approach is introduced to passivate the methylammonium lead iodide (MAPbI3) perovskite single crystal (PSC) surface defects by benzylamine (BA) ligand treatment, and the natural crystallographic (100) facets surface of PSC is chosen as the research platform to provide a deeper understanding of the passivation process. The confocal photoluminescence (PL) results show that the pristine three-dimensional (3D) MAPbI3 PSC surface with a symmetric emission spectrum is normally converted to a pure two-dimensional (2D) BA2PbI4, and also forms a quasi-2D Ruddlesden–Popper perovskite (RPP) BA2MAn−1PbnI3n+1 (n = 2, 3, 4, … ∞) after BA exchange with cation defects. The blue shift in the PL peak, as well as the extended exciton lifetimes of time-resolved photoluminescence (TRPL), indicate the realization of surface defect passivation. Additionally, changes in surface morphology are also investigated. The reaction starts with the formation of small, layered crystallites over the surface; as time elapses, the layered crystallites spread and merge in contact with each other, eventually resulting in smooth features. Our findings present a simple approach for MAPbI3 PSC surface defect passivation, which aims to advance MHP optimization processes toward practical perovskite device applications

Defect Healing of MAPbI₃ Perovskite Single Crystal Surface by Benzylamine

Controlling the surface traps in metal halide perovskites (MHPs) is essential for device performance, stability, and commercialization. Here, a facile approach is introduced to passivate the methylammonium lead iodide (MAPbI3) perovskite single crystal (PSC) surface defects by benzylamine (BA) ligand treatment, and the natural crystallographic (100) facets surface of PSC is chosen as the research platform to provide a deeper understanding of the passivation process. The confocal photoluminescence (PL) results show that the pristine three-dimensional (3D) MAPbI3 PSC surface with a symmetric emission spectrum is normally converted to a pure two-dimensional (2D) BA2PbI4, and also forms a quasi-2D Ruddlesden–Popper perovskite (RPP) BA2MAn−1PbnI3n+1 (n = 2, 3, 4, … ∞) after BA exchange with cation defects. The blue shift in the PL peak, as well as the extended exciton lifetimes of time-resolved photoluminescence (TRPL), indicate the realization of surface defect passivation. Additionally, changes in surface morphology are also investigated. The reaction starts with the formation of small, layered crystallites over the surface; as time elapses, the layered crystallites spread and merge in contact with each other, eventually resulting in smooth features. Our findings present a simple approach for MAPbI3 PSC surface defect passivation, which aims to advance MHP optimization processes toward practical perovskite device applications