PASCAL:包晶自动旋涂组装线加快了三卤化物包晶合金的成分筛选速度

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-05-22 DOI:10.1039/D4DD00075G
Deniz N. Cakan, Rishi E. Kumar, Eric Oberholtz, Moses Kodur, Jack R. Palmer, Apoorva Gupta, Ken Kaushal, Hendrik M. Vossler and David P. Fenning
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引用次数: 0

摘要

我们介绍了包光体自动旋涂装配线(PASCAL),这是一个用于旋涂薄膜沉积和表征的材料加速平台,特别适用于卤化物包光体。我们首先展示了通过控制工艺参数提高的包光体薄膜制造一致性,这些参数的影响在自动化实验框架下得到了独特的体现。接下来,我们报告了为提高串联太阳能电池应用中包晶石吸收剂的耐久性而进行的自动成分工程活动。我们筛选的成分跨越了三阳离子、三卤化物成分空间,即 MAxFA0.78Cs0.22-xPb(I0.8-y-zBryClz)3。数据驱动聚类确定了该空间中有关耐久性和开路电压的四个特征行为,每个样品的数据都是在 PASCAL 表征线中自动获取的。最后,通过在高通量数据集上训练的回归模型,确定了耐光和耐高温暴露的薄膜成分。本文详述的方法、硬件和数据突出表明,自动化平台是加速鉴定和发现新型薄膜材料的一个机会,并证明了 PASCAL 专门用于自动化溶液处理光电薄膜研究的功效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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PASCAL: the perovskite automated spin coat assembly line accelerates composition screening in triple-halide perovskite alloys†

The Perovskite Automated Spin Coat Assembly Line – PASCAL – is introduced as a materials acceleration platform for the deposition and characterization of spin-coated thin films, with specific application to halide perovskites. We first demonstrate improved consistency of perovskite film fabrication by controlling process parameters, the influence of which is uniquely exposed under the automated experimental framework. Next, we report on an automated campaign of composition engineering to improve the durability of perovskite absorbers for tandem solar cell applications. We screen compositions spanning the triple-cation, triple-halide composition space, MAxFA0.78Cs0.22−xPb(I0.8−yzBryClz)3. Data-driven clustering identifies four characteristic behaviors within this space regarding figures of merit for durability and open-circuit voltage, with data from each sample acquired automatically in PASCAL characterization line. Finally, a film composition durable to light and elevated temperature exposure is identified via a regression model trained on the high-throughput dataset. The approach, hardware, and data detailed herein highlight automated platforms as an opportunity to accelerate the identification and discovery of novel thin film materials and demonstrates the efficacy of PASCAL specifically for automation of solution-processed optoelectronic thin film research.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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