Interface defects repair of core/shell quantum dots through halide ion penetration.

The interface defects of core-shell colloidal quantum dots (QDs) affect their optoelectronic properties and charge transport characteristics. However, the limited available strategies pose challenges in the comprehensive control of these interface defects. Herein, we introduce a versatile strategy that effectively addresses both surface and interface defects in QDs through simple post-synthesis treatment.

Fluorine Passivation Inhibits "Particle Talking" Behaviors under Thermal and Electrical Conditions of Pure Blue Mixed Halide Perovskite Nanocrystals.

Owing to outstanding optoelectronic properties, lead halide perovskite nanocrystals (PNCs) are considered promising emitters for next-generation displays. However, the development of pure blue (460-470 nm) perovskite nanocrystal light-emitting diodes (PNC-LEDs), which correspond to the requirements of Rec. 2020 standard, lag far behind that of their green and red counterparts.

Silica-Encapsulated Perovskite Nanocrystals for X-ray-Activated Singlet Oxygen Production and Radiotherapy Application.

Multicomponent systems consisting of lead halide perovskite nanocrystals (CsPbX-NCs, X = Br, I) grown inside mesoporous silica nanospheres (NSs) with selectively sealed pores combine intense scintillation and strong interaction with ionizing radiation of CsPbX NCs with the chemical robustness in aqueous environment of silica particles, offering potentially promising candidates for enhanced radiotherapy and radio-imaging strategies. We demonstrate that CsPbX NCs boost the generation of singlet oxygen species (O) in water under X-ray irradiation and that the encapsulation into sealed SiO NSs guarantees perfect preservation of the inner NCs after prolonged storage in harsh conditions. We find that the O production is triggered by the electromagnetic shower released by the CsPbX NCs with a striking correlation with the halide composition (I > I Br > Br).

A Novel Strategy to Enhance the Photostability of InP/ZnSe/ZnS Quantum Dots with Zr Doping.

Plentiful research of InP semiconductor quantum dots (QDs) has been launched over the past few decades for their excellent photoluminescence properties and environmentally friendly characteristics in various applications. However, InP QDs show inferior photostability because they are extremely sensitive to the ambient environment. In this study, we propose a novel method to enhance the photostability of InP/ZnSe/ZnS QDs by doping zirconium into the ZnS layer.

Stable Lead-Free Tin Halide Perovskite with Operational Stability >1200 h by Suppressing Tin(II) Oxidation.

Sn-based perovskites are the most promising alternative materials for Pb-based perovskites to address the toxicity problem of lead. However, the development of Sn -based perovskites has been hindered by their extreme instability. Here, we synthesized efficient and stable lead-free Cs SnBr perovskite by using SnF as tin source instead of easily oxidized SnBr .

High-Efficiency Semitransparent Light-Emitting Diodes with Perovskite Nanocrystals.

Artificial intelligence offers new opportunities for translucent displays. However, achieving translucent light-emitting diodes (LEDs) with high efficiency and high color purity remains a challenge. Here, we propose a strategy of using an alkali metal/inert metal (calcium/silver) bilayer metal electrode as a top electrode and perovskite nanocrystals as an emitter layer in the device structure, which allows us to not only fabricate excellent opaque LEDs but also manufacture highly efficient semitransparent LEDs with high color purity, total brightness (over 7000 cd m), total external quantum efficiency (over 12%), and 56% transmittance around 520 nm.

Extracting automata from neural networks using active learning.

Deep learning is one of the most advanced forms of machine learning. Most modern deep learning models are based on an artificial neural network, and benchmarking studies reveal that neural networks have produced results comparable to and in some cases superior to human experts. However, the generated neural networks are typically regarded as incomprehensible black-box models, which not only limits their applications, but also hinders testing and verifying.