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Caden Myers

Publications and source records attributed to Caden Myers.

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Local Structure and Dynamics of Three-Dimensional Covalent Organic Frameworks

Resolving and controlling the local dynamical properties of covalent organic frameworks (COFs) remains a central challenge, particularly when assembled from large, flexible building units. Here, we combine synchrotron X-ray pair distribution function (PDF) analyses with machine learning-accelerated molecular dynamics (MD) simulations to resolve the local structure and dynamics of two three-dimensional imine-linked COFs, COF-682 {[(DHP)(TAM)]$_{imine}$}, assembled from 6,13-dihydropentacene (DHP) and tetrakis(4-aminophenyl)methane (TAM), and COF-612 {[(HBC-LA$_{12}$)(HAPT)$_2$]$_{imine}$}, assembled from nanographene dodecabenzaldehyde hexakis{[3,5-bis($p$-formylphenyl)-4,6-dimethoxyphenyl]}hexabenzocoronene (HBC-LA$_{12}$) and 2,3,6,7,14,15-hexa(4-aminophenyl)triptycene (HAPT). Validated against the experimental PDFs through ensemble-averaged calculations, the simulations show that the exposed $π$-surface and V-shaped geometry of the DHP linker endow COF-682 with enhanced local flexibility through face-to-face and offset $π$-stacking interactions differing in both their average interplanar separation and their ring-plane tilt angle. In contrast, the extended nanographene linker rigidifies COF-612 by maintaining the planarity of its fused cores, while the linker pendant aryl rings equip both COFs with enhanced librational ability. The simulations further provide quantitative measures of the translational and reorientational mobility of the linkers, revealing how local COF dynamics may be tuned by balancing non-covalent interactions and different degrees of aromatic rigidity. The PDF-MD experimental-computational approach holds promise as a general method beyond conventional crystallography to gain insights into the local properties of COFs with the aim of directing their dynamic function.

cond-mat.mtrl-sci

diffpy.morph: Python tools for model independent comparisons between sets of 1D functions

diffpy$.$morph addresses a need to gain scientific insights from 1D scientific spectra in model independent ways. A powerful approach for this is to take differences between pairs of spectra and look for meaningful changes that might indicate underlying chemical, structural, or other modifications. The challenge is that the difference curve may contain uninteresting differences such as experimental inconsistencies and benign physical changes such as the effects of thermal expansion. diffpy$.$morph allows researchers to apply simple transformations, or "morphs", to one of the datasets to remove the unwanted differences revealing, when they are present, non-trivial differences. diffpy$.$morph is an open-source Python package available on the Python Package Index and conda-forge. Here, we describe its functionality and apply it to solve a range of experimental challenges on diffraction and PDF data from x-rays and neutrons, though we note that it may be applied to any 1D function in principle.

physics.comp-ph