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Many interesting target guest molecules have low symmetry, yet most methods for synthesising hosts result in highly symmetrical capsules. Methods of generating lower symmetry pores are thus required to maximise the binding affinity in host-guest complexes. Herein, we use mixtures of tetraaldehyde building blocks with cyclohexanediamine to access low-symmetry imine cages. Whether a low-energy cage is isolated can be correctly predicted from the thermodynamic preference observed in computational models. The stability of the observed structures depends on the geometrical match of the aldehyde building blocks. One bent aldehyde stands out as unable to assemble into high-symmetry cages-and the same aldehyde generates low-symmetry socially self-sorted cages when combined with a linear aldehyde. We exploit this finding to synthesise a family of low-symmetry cages containing heteroatoms, illustrating that pores of varying geometries and surface chemistries may be reliably accessed through computational prediction and self-sorting.
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http://dx.doi.org/10.1002/anie.202007571 | DOI Listing |
J Colloid Interface Sci
September 2025
Department of Chemistry, State Key Laboratory of Porous Materials for Separation and Conversion, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, and iChEM, Fudan University, Shanghai 200438, China. Electronic address:
We present a coordination-inspired strategy for assembling binary nanocrystal superlattices (BNSLs) using CdSe nanotetrapods as symmetry-encoding building blocks. Exploiting their intrinsic tetrahedral geometry, which mimics the sp hybridization of carbon atoms in a diamond lattice, we encode spatially defined binding sites that guide regioselective coassembly with spherical nanocrystals. By tuning the size ratio between components, we achieve both three-dimensional and two-dimensional BNSLs with long-range structural order.
View Article and Find Full Text PDFSci Robot
September 2025
University of Chicago, Chicago, IL 60637, USA.
Reading fluency is a vital building block for developing literacy, yet the best way to practice fluency-reading aloud-can cause anxiety severe enough to inhibit literacy development in ways that can have an adverse effect on students through adulthood. One promising intervention to mitigate oral reading anxiety is to have children read aloud to a robot. Although observations in prior work have suggested that people likely feel more comfortable in the presence of a robot instead of a human, few studies have empirically demonstrated that people feel less anxious performing in front of a robot compared with a human or used objective physiological indicators to identify decreased anxiety.
View Article and Find Full Text PDFInt J Dev Biol
September 2025
Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA.
How the dorsal thalamus of amniotes (reptiles, birds, and mammals) is organized remains an important but incompletely answered question. Identification of meaningful subdivisions would greatly aid in its understanding. Because the dorsal thalamus is more simply organized during development, studies have examined this structure during embryogenesis.
View Article and Find Full Text PDFAcc Chem Res
September 2025
Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada.
ConspectusHydroaminoalkylation, the catalytic addition of amines to alkenes, has evolved as a powerful tool in modern synthetic chemistry, offering an atom-economic and green approach to the construction of C-C bonds. This reaction enables the direct amine functionalization of alkenes and alkynes without the need for protecting groups, directing groups, or prefunctionalization, thereby eliminating stoichiometric waste and minimizing synthetic steps. Over the past two decades, significant advances in catalyst development and mechanistic understanding have expanded the scope of hydroaminoalkylation, allowing for control over regio-, diastereo-, and enantioselectivity.
View Article and Find Full Text PDFiScience
September 2025
School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.
Deep learning has rapidly emerged as a promising toolkit for protein optimization, yet its success remains limited, particularly in the realm of activity. Moreover, most algorithms lack rigorous iterative evaluation, a crucial aspect of protein engineering exemplified by classical directed evolution. This study introduces DeepDE, a robust iterative deep learning-guided algorithm leveraging triple mutants as building blocks and a compact library of ∼1,000 mutants for training.
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