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In the Ghost Neighborhoods of Columbus project, we are developing and applying machine learning (ML) and geographic information science (GIS) methods to extract data from historical Sanborn fire insurance maps and build 3D urban models of how neighborhoods looked in the past. We are focusing on historically Black neighborhoods in Columbus that have been altered by urban highway construction, urban renewal and redlining practices. We are working with neighborhood members and community partners to identify neighborhoods for investigation, model use cases, design features and delivery modes. We are also collecting stories, memories and photos with the intent of using the 3D urban models as platforms for storytelling about lived experiences in these lost places. In this paper, we describe our approaches to 3D model development, model delivery and community engagement. We identify and discuss issues and bottlenecks we discovered, and strategies for resolving these problems. 

Two-dimensional transition metal carbides and nitrides (MXenes) are a class of materials that have drawn substantial attention for their diverse application, particularly in the field of energy storage. These materials are commonly derived from layered ternary solids, MAX phases, through etching processes. Efforts have been made to expand the scope of MXene synthesis beyond these top-down approaches to include bottom-up synthesis. Here we demonstrate a general direct synthetic route for scalable and atom-economic synthesis of various MXenes using different organohalide compounds as precursors for both carbon and surface termination groups. By reacting organochlorides (such as C2Cl4, C2Cl6 and CH2Cl2) or organobromides (CBr4 and CH2Br2) with transition metals, we synthesized a variety of MXenes (Ti2CCl2, Ti2CBr2, Zr2CCl2, Zr2CBr2 and Nb2CCl2), including a Nb2CBr2 MXene phase not accessed by other routes. The use of molecular precursors enables precise control of their reactivity, which allows the direct synthesis of MXene nanostructures. We demonstrate that nanometre-scale MXenes show higher surface reactivity compared with MXenes with micrometre-size flakes. 

The concept that proteins are selected to fold into a well-defined native state has been effectively addressed within the framework of energy landscapes, underpinning the recent successes of structure prediction tools like AlphaFold. The amyloid fold, however, does not represent a unique minimum for a given single sequence. While the cross-βhydrogen-bonding pattern is common to all amyloids, other aspects of amyloid fiber structures are sensitive not only to the sequence of the aggregating peptides but also to the experimental conditions. This polymorphic nature of amyloid structures challenges structure predictions. In this paper, we use AI to explore the landscape of possible amyloid protofilament structures composed of a single stack of peptides aligned in a parallel, in-register manner. This perspective enables a practical method for predicting protofilament structures of arbitrary sequences: RibbonFold. RibbonFold is adapted from AlphaFold2, incorporating parallel in-register constraints within AlphaFold2’s template module, along with an appropriate polymorphism loss function to address the structural diversity of folds. RibbonFold outperforms AlphaFold2/3 on independent test sets, achieving a mean TM-score of 0.5. RibbonFold proves well-suited to study the polymorphic landscapes of widely studied sequences with documented polymorphisms. The resulting landscapes capture these observed polymorphisms effectively. We show that while well-known amyloid-forming sequences exhibit a limited number of plausible polymorphs on their “solubility” landscape, randomly shuffled sequences with the same composition appear to be negatively selected in terms of their relative solubility. RibbonFold is a valuable framework for structurally characterizing amyloid polymorphism landscapes. 

Hybrid organic–inorganic MXenes (h-MXenes) are a novel class of two-dimensional materials with tuneable structural and chemical properties, making them promising candidates for electronic and structural applications as well as platforms for exploring novel physical phenomena [1]. In this study, we employ aberration-corrected scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy (EELS) at both room and cryogenic temperatures to investigate the formation and behaviour of ripplocations—localized ripple-like distortions—in h-MXenes. Our findings reveal that ripplocations are abundant in these materials and can be induced in situ with high precision under controlled electron beam exposure. 

Inorganic–organic hybrid MXenes (h‐MXenes) are a family of 2D transition metal carbides and nitrides functionalized with alkylimido and alkylamido surface groups. Using cryogenic and room temperature scanning transmission electron microscopy (STEM) and electron energy‐loss spectroscopy (EELS), it is shown that ripplocations, a form of a fundamental defect in 2D and layered structures, are abundant in this family of materials. Furthermore, detailed studies of electron probe sample interactions, focusing on structural deformations caused by the electron beam are presented. The findings indicate that at cryogenic temperatures (≈100 K) and below a specific dose threshold, the structure of h‐MXenes remains largely intact. However, exceeding this threshold leads to electron beam‐induced deformation through ripplocations. Interestingly, the deformation behavior, required dose, and resultant structure are highly dependent on temperature. At 100 K, it is demonstrated that the electron beam can induce ripplocations in situ with a high degree of precision.