Quantifying the Likelihood of Structural Models through a Dynamically Enhanced Powder X-Ray Diffraction Protocol

Computational Protocol for the Spectral Assignment of NMR Resonances in Covalent Organic Frameworks

Solid-state nuclear magnetic resonance spectroscopy is routinely used in the field of covalent organic frameworks to elucidate or confirm the structure of the synthesized samples and to understand dynamic phenomena. Typically this involves the interpretation and simulation of the spectra through the assumption of symmetry elements of the building units, hinging on the correct assignment of each line shape. To avoid misinterpretation resulting from library-based assignment without a theoretical basis incorporating the impact of the framework, this work proposes a first-principles computational protocol for the assignment of experimental spectra, which exploits the symmetry of the underlying building blocks for computational feasibility. In this way, this protocol accommodates the validation of previous experimental assignments and can serve to complement new NMR measurements.

OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking

While free energy surfaces are the crux of our understanding of many chemical and biological processes, their accuracy is generally unknown. Moreover, many developments to improve their accuracy are often complicated, limiting their general use. Luckily, several tools and guidelines are already in place to identify these shortcomings, but they are typically lacking in flexibility or fail to systematically determine how to improve the accuracy of the free energy calculation. To overcome these limitations, this work introduces OGRe, a Python package for optimal grid refinement in an arbitrary number of dimensions. OGRe is based on three metrics that gauge the confinement, consistency, and overlap of each simulation in a series of umbrella sampling (US) simulations, an enhanced sampling technique ubiquitously adopted to construct free energy surfaces for hindered processes. As these three metrics are fundamentally linked to the accuracy of the weighted histogram analysis method adopted to generate free energy surfaces from US simulations, they facilitate the systematic construction of accurate free energy profiles, where each metric is driven by a specific umbrella parameter. This allows for the derivation of a consistent and optimal collection of umbrellas for each simulation, largely independent of the initial values, thereby dramatically increasing the ease-of-use toward accurate free energy surfaces. As such, OGRe is particularly suited to determine complex free energy surfaces with large activation barriers and shallow minima, which underpin many physical and chemical transformations and hence to further our fundamental understanding of these processes.

Single-Atom Catalysis in Organic Synthesis

Single-atom catalysts hold the potential to significantly impact the chemical sector, pushing the boundaries of catalysis in new, uncharted directions. These materials, featuring isolated metal species ligated on solid supports, can exist in many coordination environments, all of which have shown important functions in specific transformations. Their emergence has also provided exciting opportunities for mimicking metalloenzymes and bridging the gap between homogeneous and heterogeneous catalysis. This Review outlines the impressive progress made in recent years regarding the use of single-atom catalysts in organic synthesis. We also illustrate potential knowledge gaps in the search for more sustainable, earth-abundant single-atom catalysts for synthetic applications.

Engineering of Phenylpyridine- and Bipyridine-Based Covalent Organic Frameworks for Photocatalytic Tandem Aerobic Oxidation/Povarov Cyclization

Covalent organic frameworks (COFs) are emerging as a new class of photoactive organic semiconductors, which possess crystalline ordered structures and high surface areas. COFs can be tailor-made toward specific (photocatalytic) applications, and the size and position of their band gaps can be tuned by the choice of building blocks and linkages. However, many types of building blocks are still unexplored as photocatalytic moieties and the scope of reactions photocatalyzed by COFs remains quite limited. In this work, we report the synthesis and application of two bipyridine- or phenylpyridine-based COFs: TpBpyCOF and TpPpyCOF. Due to their good photocatalytic properties, both materials were applied as metal-free photocatalysts for the tandem aerobic oxidation/Povarov cyclization and α-oxidation of N-aryl glycine derivatives, with the bipyridine-based TpBpyCOF exhibiting the highest activity. By expanding the range of reactions that can be photocatalyzed by COFs, this work paves the way toward the more widespread application of COFs as metal-free heterogeneous photocatalysts as a convenient alternative for commonly used homogeneous (metal-based) photocatalysts.

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al₂(OH)₂TCPP) [H₂TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.

Quantifying the Likelihood of Structural Models through a Dynamically Enhanced Powder X-Ray Diffraction Protocol

Structurally characterizing new materials is tremendously challenging, especially when single crystal structures are hardly available which is often the case for covalent organic frameworks. Yet, knowledge of the atomic structure is key to establish structure-function relations and enable functional material design. Herein, a new protocol is proposed to unambiguously predict the structure of poorly crystalline materials through a likelihood ordering based on the X-ray diffraction (XRD) pattern. Key of the procedure is the broad set of structures generated from a limited number of building blocks and topologies, which is submitted to operando structural characterization. The dynamic averaging in the latter accounts for the operando conditions and inherent temporal character of experimental measurements, yielding unparalleled agreement with experimental powder XRD patterns. The proposed concept can hence unquestionably identify the structure of experimentally synthesized materials, a crucial step to design next generation functional materials.