A. Shakil

Bio: A. Shakil is an academic researcher from University of Liverpool. The author has an hindex of 1, co-authored 1 publications receiving 94 citations.

High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis

Abstract: Supramolecular synthesis is a powerful strategy for assembling complex molecules, but to do this by targeted design is challenging. This is because multicomponent assembly reactions have the potential to form a wide variety of products. High-throughput screening can explore a broad synthetic space, but this is inefficient and inelegant when applied blindly. Here we fuse computation with robotic synthesis to create a hybrid discovery workflow for discovering new organic cage molecules, and by extension, other supramolecular systems. A total of 78 precursor combinations were investigated by computation and experiment, leading to 33 cages that were formed cleanly in one-pot syntheses. Comparison of calculations with experimental outcomes across this broad library shows that computation has the power to focus experiments, for example by identifying linkers that are less likely to be reliable for cage formation. Screening also led to the unplanned discovery of a new cage topology-doubly bridged, triply interlocked cage catenanes.

Modular and predictable assembly of porous organic molecular crystals

Abstract: Nanoporous molecular frameworks are important in applications such as separation, storage and catalysis. Empirical rules exist for their assembly but it is still challenging to place and segregate functionality in three-dimensional porous solids in a predictable way. Indeed, recent studies of mixed crystalline frameworks suggest a preference for the statistical distribution of functionalities throughout the pores rather than, for example, the functional group localization found in the reactive sites of enzymes. This is a potential limitation for ‘one-pot’ chemical syntheses of porous frameworks from simple starting materials. An alternative strategy is to prepare porous solids from synthetically preorganized molecular pores. In principle, functional organic pore modules could be covalently prefabricated and then assembled to produce materials with specific properties. However, this vision of mix-and-match assembly is far from being realized, not least because of the challenge in reliably predicting three-dimensional structures for molecular crystals, which lack the strong directional bonding found in networks. Here we show that highly porous crystalline solids can be produced by mixing different organic cage modules that self-assemble by means of chiral recognition. The structures of the resulting materials can be predicted computationally, allowing in silico materials design strategies. The constituent pore modules are synthesized in high yields on gram scales in a one-step reaction. Assembly of the porous co-crystals is as simple as combining the modules in solution and removing the solvent. In some cases, the chiral recognition between modules can be exploited to produce porous organic nanoparticles. We show that the method is valid for four different cage modules and can in principle be generalized in a computationally predictable manner based on a lock-and-key assembly between modules.

Chemistry of Soft Porous Crystals: Structural Dynamics and Gas Adsorption Properties.

Abstract: In this Minireview, we discuss the fundamental chemistry of soft porous crystals (SPCs) by characterizing their common structural features and the resulting structural softness and transitions. In particular, we focus on the recently emerging properties based on metastable transitions and those arising from local dynamics. By comparing the resulting adsorption properties to those of commonly applied rigid adsorbents, we highlight the potential of SPCs to revolutionize adsorption-based technologies, considering our current understanding of the thermodynamic and kinetic aspects. We provide brief outlines for the experimental and computational characterization of such phenomena and offer an outlook toward next-generation SPCs likely to be discovered in the next decade.

The chemistry of multi-component and hierarchical framework compounds

Abstract: Multi-component hierarchically porous materials are an emerging class of materials with tailored compositions, tunable distribution and sophisticated applications. An increasing demand for multifunctionalities and hierarchical structures has resulted in extensive studies on multi-component hierarchical metal–organic frameworks and other open framework compounds. This review article focuses on recent advances in multi-component and hierarchical framework materials, covering the design and synthetic strategies of these architectures, their characterization, and the latest applications. Multivariate MOFs prepared under various synthetic conditions (one-pot or post-synthetic) and their building block distributions are introduced and summarized. This is followed by a short review of characterization techniques including solid-state NMR and photothermal induced resonance, and their potential applications in gas storage, separation, heterogeneous catalysis, guest delivery, and luminescence. Furthermore, guided by the same design principles, the synthesis and applications of multi-component hierarchical covalent-organic frameworks, metal–organic cages and porous organic cages are introduced and discussed. Together, this review is expected to provide a library of multi-component hierarchically porous compounds, which could also guide the state-of-the-art design and discovery of future porous materials with unprecedented tunability, synergism and precision.

Next-Generation Experimentation with Self-Driving Laboratories

Abstract: The ever-growing demand for advanced functional materials requires disruption of conventional approaches to experimentation and acceleration of the discovery process. State-of-the-art approaches to scientific discovery are inherently slow, capital intensive, and have arguably reached a plateau. Significant advances are possible when rethinking and redesigning the traditional experimentation process. Self-driving laboratories promise to substantially accelerate the discovery process by augmenting automated experimentation platforms with artificial intelligence (AI). AI methods actively search for promising experimental procedures by hypothesizing about their outcomes based on previous experiments. This feedback loop is crucial to reduce the number of experiments needed for discovery. Supplying automated platforms with AI enables self-driving laboratories to fully embrace the vision of autonomous experimentation.