Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHigh-Throughput Study of Antisolvents on the Stability of Multicomponent Metal Halide Perovskites through Robotics-Based Synthesis and Machine Learning Approaches
285
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Antisolvent crystallization methods are frequently used to fabricate high-quality perovskite thin films, to produce sizable single crystals, and to synthesize nanoparticles at room temperature. However, a systematic exploration of the effect of specific antisolvents on the intrinsic stability of multicomponent metal halide perovskites has yet to be demonstrated. Here, we develop a high-throughput experimental workflow that incorporates chemical robotic synthesis, automated characterization, and machine learning techniques to explore how the choice of antisolvent affects the intrinsic stability of binary perovskite systems in ambient conditions over time. Different combinations of the endmembers, MAPbI3, MAPbBr3, FAPbI3, FAPbBr3, CsPbI3, and CsPbBr3, are used to synthesize 15 combinatorial libraries, each with 96 unique combinations. In total, roughly 1100 different compositions are synthesized. Each library is fabricated twice using two different antisolvents: toluene and chloroform. Once synthesized, photoluminescence spectroscopy is automatically performed every 5 minutes for approximately 6 hours. Non-negative Matrix Factorization (NMF) is then utilized to map the time- and compositional-dependent optoelectronic properties. Through the utilization of this workflow for each library, we demonstrate that the selection of antisolvent is critical to the stability of metal halide perovskites in ambient conditions. We explore possible dynamical processes, such as halide segregation, responsible for either the stability or eventual degradation as caused by the choice of antisolvent. Overall, this high-throughput study demonstrates the vital role that antisolvents play in the synthesis of high-quality multicomponent metal halide perovskite systems.
rate research
Read More
Metal halide perovskites (MHPs) have become a promising candidate in a myriad of applications, such as light-emitting diodes, solar cells, lasing, photodetectors, photocatalysis, transistors, etc. This is related to the synergy of their excellent features, including high photoluminescence quantum yields, narrow and tunable emission, long charge carrier lifetimes, broad absorption spectrum along with high extinction absorptions coefficients, among others. However, the main bottleneck is the poor stability of the MHPs under ambient conditions. This is imposing severe restrictions with respect to their industrialized applications and commercialization. In this context, metal oxide (MOx) coatings have recently emerged as an efficient strategy towards overcoming the stabilities issues as well as retain the excellent properties of the MHPs, and therefore facilitate the development of the related devices stabilities and performances.This review provides a summary of the recent progress on synthetic methods, enhanced features, the techniques to assess the MHPs-MOxcomposites, and applications of the [email protected], novel approaches to fabricate the composites and new applications of the composites are also reported in this review for the first time. This is rounded by a critical outlook about the current MHPs stability issues and the further direction to ensure a bright future of MHPs@MOx
Lead halide perovskites are a remarkable class of materials that have emerged over the past decade as being suitable for application in a broad range of devices, such as solar cells, light-emitting diodes, lasers, transistors, and memory devices, among others. While they are often solution-processed semiconductors deposited at low temperatures, perovskites exhibit properties one would only expect from highly pure inorganic crystals that are grown at high temperatures. This unique phenomenon has resulted in fast-paced progress toward record device performance; unfortunately, the basic science behind the remarkable nature of these materials is still not well understood. This review assesses the current understanding of the photoluminescence (PL) properties of metal halide perovskite materials and highlights key areas that require further research. Furthermore, the need to standardize the methods for characterization of PL in order to improve comparability, reliability and reproducibility of results is emphasized.
In recent years, metal halide perovskites have generated tremendous interest for optoelectronic applications and their underlying fundamental properties. Due to the large electron-phonon coupling characteristic of soft lattices, self-trapping phenomena are expected to dominate hybrid perovskite photoexcitation dynamics. Yet, while the photogeneration of small polarons was proven in low dimensional perovskites, the nature of polaron excitations in technologically relevant 3D perovskites, and their influence on charge carrier transport, remain elusive. In this study, we used a combination of first principle calculations and advanced spectroscopy techniques spanning the entire optical frequency range to pin down polaron features in 3D metal halide perovskites. Mid-infrared photoinduced absorption shows the photogeneration of states associated to low energy intragap electronic transitions with lifetime up to the ms time scale, and vibrational mode renormalization in both frequency and amplitude. Density functional theory supports the assignment of the spectroscopic features to large polarons leading to new intra gap transitions, hardening of phonon mode frequency, and renormalization of the oscillator strength. Theory provides quantitative estimates of the charge carrier masses and mobilities increase upon polaron formation, confirming experimental results. Overall, this work contributes to complete the scenario of elementary photoexcitations in metal halide perovskites and highlights the importance of polaronic transport in perovskite-based optoelectronic devices.
2D Ruddlesden Popper perovskites have been extensively studied for their exceptional optical and electronic characteristics while only a few studies have shed light on their mechanical properties. The existing literature mainly discusses the mechanical strength of single crystal perovskites, however a systematic study towards structure tunability of 2D perovskite thin films is still missing. In this study, we report the effect of number of inorganic layers `n on elastic modulus of Butylammonium based 2D, quasi-2D perovskites and 3D perovskite using nanoindentation technique. The experimental results have also been substantiated using first principle density functional theory calculations. Understanding the mechanical behaviour of 2D Ruddlesden Popper perovskites thin films in comparison with conventional 3D perovskite offers intriguing insights into the atomic layer dependent properties and paves the path for next generation mechanically durable novel devices.
Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
