Solving the atomic structure of metallic clusters is fundamental to understanding their optical, electronic, and chemical properties. Herein we present the structure of the largest aqueous gold cluster, Au146(p-MBA)57 (p-MBA: para-mercaptobenzoic acid), solved by electron diffraction (MicroED) to subatomic resolution (0.85 Å) and by X-ray diffraction at atomic resolution (1.3 Å). The 146 gold atoms may be decomposed into two constituent sets consisting of 119 core and 27 peripheral atoms. The core atoms are organized in a twinned FCC structure whereas the surface gold atoms follow a C2 rotational symmetry about an axis bisecting the twinning plane. The protective layer of 57 p-MBAs fully encloses the cluster and comprises bridging, monomeric, and dimeric staple motifs. Au146(p-MBA)57 is the largest cluster observed exhibiting a bulk-like FCC structure as well as the smallest gold particle exhibiting a stacking fault.

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Due to the exploratory nature of computational notebook development, a notebook can be extensively evolved even though it is small, potentially incurring substantial technical debt. Indeed, in interview studies notebook authors have attested to performing on-going tidying and big cleanups. However, many notebook authors are not trained as software developers, and environments like JupyterLab possess few features to aid notebook maintenance. As software refactoring is traditionally a critical tool for reducing technical debt, we sought to better understand the unique and growing ecology of computational notebooks by investigating the refactoring of public Jupyter notebooks. We randomly selected 15,000 Jupyter notebooks hosted on GitHub and studied 200 with meaningful commit histories. We found that notebook authors do refactor, favoring a few basic classic refactorings as well as those involving the notebook cell construct. Those with a computing background refactored differently than others, but not more so. Exploration-focused notebooks had a unique refactoring profile compared to more exposition-focused notebooks. Authors more often refactored their code as they went along, rather than deferring maintenance to big cleanups. These findings point to refactoring being intrinsic to notebook development.

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