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DOE OSTI2022

X-ray photoelectron spectroscopy of morpholinium ionic liquids: impact of a long alkyl side substituent on the cation–anion interactions

In this study, X-ray photoelectron spectroscopy is used to analyse nine morpholinium ionic liquids, which are of great interest in green chemistry because of their low toxicity and high recyclability. Here, the effect of the alkyl chain length on the aliphatic C 1s binding energy and the impact of the anion basicity on the cationic N 1s and O 1s binding energies are investigated. It is concluded that by changing the basicity of the anion, there is a more notable change in the electronic environment of the oxygen centre. The impact of a long alkyl side substituent on the cation–anion interactions is also discussed. It is observed that there is an intense charge shielding effect of the alkyl side chain in the cases of octyl and dodecyl substituents, which is reflected in the reduced Br 3d 5/2 binding energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-driven approach combining active learning and high-throughput experimentation to discover, control, and understand the formation of phases with different dimensionalities in the morpholinium (morph) lead iodide system. Using a robot-assisted workflow, we synthesized and characterized two novel MHP derivatives that have distinct optical properties: a one-dimensional (1D) morphPbI 3 phase ([C 4 H 10 NO][PbI 3 ]) and a two-dimensional (2D) (morph) 2 PbI 4 phase ([C 4 H 10 NO] 2 [PbI 4 ]). To efficiently acquire the data needed to construct a machine learning (ML) model of the reaction conditions where the 1D and 2D phases are formed, data acquisition was guided by a diverse-mini-batch-sampling active learning algorithm, using prediction confidence as a stopping criterion. Querying the ML model uncovered the reaction parameters that have the most significant effects on dimensionality control. Based on these insights, we discuss possible reaction schemes that may selectively promote the formation of morph-Pb-I phases with different dimensionalities. The data-driven approach presented here, including the use of additives to manipulate dimensionality, will be valuable for controlling the crystallization of a range of materials over large reaction-composition spaces.

36 MATERIALS SCIENCE↗