Search for: All records

Abstract This study presents the Br‐rich in situ synthesis of blue‐emitting 2D CsPbBr₃nanoplatelets (NPLs) with various Br/Pb ratios using ZnBr₂as a Br precursor to enhance Br ion adsorption significantly. This leads to effective passivation of surface defects, particularly Pb−Br bonds, by increasing the positive charge density around Pb atoms, thus creating a stable bonding environment and reducing defect formation. Consequently, the photoluminescence quantum yield (PLQY) improves from 31.15% for a Br/Pb ratio of 2 to 87.2% for a ratio of 6. NPLs with a Br/Pb ratio of 6 also exhibit longer lifetimes (16.69 ns) and slower bleach recovery dynamics, indicating fewer non‐radiative recombination pathways and effective exciton dynamics. Additionally, NPLs with the Br/Pb ratio of 6 demonstrated better thermal stability, with an activation energy of 124.3 meV, indicating stronger exciton binding. These NPLs also exhibited enhanced stability, with UV tolerance at 43.9% and water resistance at 23.8%, making them suitable for displays and lighting. Furthermore, Br‐passivated CsPbBr₃NPLs are used as blue emitters in prototype white LEDs, achieving a wide color gamut, 126.6% of the National Television Standards Committee and 94.5% of Rec. 2020, demonstrating their potential for high‐quality lighting and advanced display technologies. 

Abstract Among promising applications of metal‐halide perovskite, the most research progress is made for perovskite solar cells (PSCs). Data from myriads of research work enables leveraging machine learning (ML) to significantly expedite material and device optimization as well as potentially design novel configurations. This paper represents one of the first efforts in providing open‐source ML tools developed utilizing the Perovskite Database Project (PDP), the most comprehensive open‐source PSC database to date with over 43 000 entries from published literature. Three ML model architectures with short‐circuit current density (J_sc) as a target are trained exploiting the PDP. Using the XGBoost architecture, a root mean squared error (RMSE) of 3.58 , R²of 0.35 and a mean absolute percentage error (MAPE) of 9.49% are achieved. This performance is comparable to results reported in literature, and through further investigation can likely be improved. To overcome challenges with manual database creation, an open‐source data cleaning pipeline is created for PDP data. Through the creation of these tools, which have been published on GitHub, this research aims to make ML available to aid the design for PSC while showing the already promising performance achieved. The tools can be adapted for other applications, such as perovskite light‐emitting diodes (PeLEDs), if a sufficient database is available. 

We report a photolithography‐based technology for patterning quantum dot color converters for micro‐LED displays. A patterning resolution of ~1 µm is achieved. The method can be applied to any color converter materials. Integration of perovskite quantum dots and CdSe/ZnS quantum dots is demonstrated to show the versatility of the technology.