Communications Chemistry最新文献

Deuterated alcohols are valuable synthetic targets due to their roles in pharmaceuticals, materials, and mechanistic studies. Conventional homogeneous strategies for their synthesis, while effective, often require expensive ligands and offer limited catalyst recovery. Heterogeneous catalysis, by contrast, provides a robust and recyclable alternative with enhanced scalability. Recent advances in supported metal nanoparticles and single-atom catalysts (SACs) have enabled high-efficiency and site-selective deuteration of alcohols. This Perspective presents heterogeneous catalytic systems as evolving into scalable and efficient platforms for deuterated alcohol synthesis, opening new directions for sustainable isotope incorporation.

In pursuit of the reductive conversion of NO in organic synthesis, this study presents the direct diazotization of indoles using 2-methoxyethyl nitrite (MOE-ONO). Diazo compounds are invaluable intermediates in organic synthesis, serving as carbene precursors for constructing diverse carbon frameworks. However, traditional C-H diazotization methods often require azide compounds or excessive amounts of acid/base, posing significant environmental and safety challenges. Herein, we introduce a sustainable and efficient diazotization protocol that overcomes these problems. The use of 2-methoxyethyl nitrite (MOE-ONO), a stable and highly reactive NO donor developed by our group, in combination with TEMPO and a catalytic amount of Sc(OTf)₃, played a significant role in the direct diazotization of 2-substituted indoles. MOE-ONO showed higher reactivity and selectivity than conventional NO donors, even including tert-butyl nitrite or NaNO₂/AcOH. Since MOE-ONO is synthesized from NO gas, oxygen, and 2-methoxyethanol, this diazotization serves as an effective utilization of NO. Furthermore, the diazotization proceeds efficiently even in water, which is important for designing clean chemical processes. The synthesized 3-diazoindoles exhibit broad reactivity, such as Grignard reaction at the C2-position and rhodium-catalyzed cyclopropanation at the C3-position. Such reactions can be practical solutions for synthesizing highly functionalized indoles and indolines, which are important for the design and discovery of pharmacologically active compounds.

Remote boronate rearrangement of boronic acids to C═N bonds is a valuable in synthetic chemistry. Conventional approaches are constrained by the need to pre-install specialized directing groups onto the starting materials. Here, we report a lactam-driven dynamic directing strategy, achieving 1,5- and 1,4-boronate rearrangements. The strategy circumvents the need for substrate pre-activation procedures, successfully overcoming a challenge in the functionalization of inactive C = N bonds to N-alkyl anilines and 3-aryl quinoxalinones. Comprehensive mechanistic investigations unveil three transformative insights: (i) Lactam leverages boron activation to C = N bonds through tetracoordinate boron species; (ii) the 1,5-boronate rearrangement to N-alkyl anilines is favored via an eight-membered boronate complex, as supported by density functional theory (DFT) studies; (iii) a catalyst-free 1,4-boronate rearrangement pathway operates through HFIP-stabilized tetracoordinate boron intermediates. This lactam-enabled boronate rearrangements offers a methodology with transformative potential.

Structural characterization of powder materials, including those synthesized by mechanochemical methods, remains challenging due to the lack of single crystals suitable for X-ray diffraction. Microcrystal-Electron Diffraction (MicroED) enables structure determination from sub-micrometer crystallites but faces limitations, particularly in locating hydrogen atoms and distinguishing light atoms (C, N, O). We present a general workflow that integrates MicroED with high-resolution mass spectrometry, database mining, solution and solid-state NMR, and DFT-D/GIPAW calculations to resolve atomic structures of complex powders, even with unknown composition. The approach is demonstrated on a pyridoxine-N-acetyl-L-cysteine salt, a mechanochemically synthesized adduct for which large single crystals could not be obtained, and on N-formyl-methionyl-leucyl-phenylalanine (fMLF), a bacterial chemoattractant peptide. This strategy enables comprehensive structure resolution, including identification of molecular components, crystal packing, atom assignments and hydrogen positions. Its modularity and scalability make it suitable for a wide range of powder materials, e.g., pigments, pharmaceutical compounds, etc., especially when conventional crystallography fails.

While it has been long supposed that asteroids played a role in the delivery of important prebiotic compounds to early Earth, the exact nature of the interactions between asteroidal material and metabolism remains largely unquantified. Pristine material from asteroid sample-return missions provides an unprecedented opportunity to evaluate the potential for the asteroids' chemistry to support the origin and persistence of life. Here we use metabolic network expansion to computationally test the viability of contrasted biochemical networks, including a group of acetogens and methanogens representing primitive metabolisms, on the known chemistry of three asteroids (Itokawa, Ryugu, Bennu) and two meteorites (Murchison, Murray). The chemistry of Murchison and Bennu appears to support the potential viability of the acetogenic and methanogenic metabolisms. In contrast, Murray, Ryugu and Itokawa samples lack critical substrates, particularly adenine and D-ribose needed for ATP production, suggesting that carbonaceous bodies vary in their compositional capacity to support the acetogenic and methanogenic metabolisms. This highlights the astrobiological relevance of asteroids rich in carbon, nitrogen, and phosphate such as Bennu, and hints at the habitability, past or present, of their parent bodies.

Chiral organic materials show great promise in optoelectronics, sensing and catalysis. Among those, macrocycles are of great interest due to their preorganisation and potential amplification of chiroptical properties. Understanding the effects of different sources of chirality on the resulting chiroptical properties of these molecules is key to unlocking tailored chiral materials. To this end, we have synthesised a family of bis-perylene diimide-based macrocycles containing multiple sources of chirality, specifically point chirality in the linker, helical chirality in the perylene diimide and supramolecular chirality in the macrocyclic dimer. We found a dominant effect from the helical chirality of the perylene diimide on the chiroptical properties, including the induction of chirality in an achiral guest molecule, which opens up new possibilities for hybrid chiroptical materials.

Pure shift NMR spectroscopy has found extensive applications in exploring the structure, function, and interactions of molecules in an ultrahigh-resolution manner. However, time-consuming data acquisition resulting from additional time dimension for pure shift evolution impedes its further applications. In this study, a general and robust AI-assisted NMR methodology combining non-uniform chunk sampling with physics-informed deep learning (DL) reconstruction is proposed for fast implementation of pure shift NMR spectroscopy. The proposed DL protocol enables the suppression on sparsely sampling artifacts, faithful recovery of weak signals, as well as high-fidelity reconstruction on peak intensities, thus implementing accelerated pure shift NMR while maintaining spectral quality. The well-trained model shows broad applicability across one-dimensional, two-dimensional, even multi-dimensional pure shift NMR. In addition, ablation experiments are further performed to provide mechanistic insights into deep learning reconstruction on sparse sampled pure shift NMR spectra. Moreover, its application potentials have been further demonstrated through in-situ monitoring of 1-butanol electrooxidation on Pt/C and PtRu/C catalysts. As a result, this study establishes a robust AI-assisted NMR framework for disentangling molecular structure and dynamics information for complex sample systems with high temporal and spectral resolution, and could find wide applications across multiple chemistry disciplines.

The ability to conjugate multiple molecules to a protein is of great interest for pharmaceutical and vaccine development, especially if this can be achieved in a one-pot procedure. Multicomponent reactions are powerful procedures that allow the assembly of complex constructs incorporating at least three molecular fragments, but many of them use amino and carboxylic groups that are too abundant in proteins. Herein, we introduce the use of the Passerini 3-component reaction with isocyanoproteins for the assembly of multivalent protein (glyco)conjugates. Proteins were tagged with isocyanide handles and next derivatized to investigate the efficacy and limitations of the Passerini bioconjugation. The multicomponent conjugation enabled the simultaneous functionalization of proteins with two biologically relevant molecules such as carbohydrate antigens, lipids, and polymers. The efficient display of various antigens in a unimolecular multivalent construct is a notable result that paves the way towards new applications in preventive vaccines and therapeutics.

Highly-charged intrinsically disordered proteins (IDPs) underpin biomolecular condensate formation through liquid-liquid phase separation, yet the influence of charge sequences on the dynamics within the condensate phase remains poorly understood. Using extensive molecular dynamics simulations with explicit hydrodynamics and electrostatics, we study the dynamics in IDP condensates across different length and time scales, by systematically varying the charge sequences of the constituent IDPs. Contrary to the expectation that long-range interactions are heavily screened in dense semidilute polymer solutions, we find hydrodynamics and electrostatics significantly influence the dynamics in IDP condensates and their effects are strongly coupled to the charge sequence of the constituent IDPs. For condensates of low to intermediate-κ IDPs, where κ is a measure of the charge blockiness of the charge sequence, we find hydrodynamics dominates the dynamics up to the length scale of the chain and beyond. On the sub-chain level, segmental relaxation is highly coupled to intra-chain electrostatic correlations due to local charge patterns, where sections with more charge-balanced blocks have faster relaxation. Furthermore, the viscosity in IDP condensates is significantly length-scale-dependent, with condensates of high-κ IDPs exhibiting large difference between microscopic and macroscopic viscosity. Such length-scale-dependent viscosity may be the key to understanding the experimentally observed extremely fast molecule-level dynamics in biocondensates of highly-charged IDPs. Our findings highlight the intricate relationship between charge sequences, hydrodynamics, and electrostatics in shaping the dynamics in IDP condensates at different length and time scales.

While photocatalysis has emerged as a transformative tool in modern synthesis, AI-assisted reaction prediction faces significant challenges due to data limitations. We present PhotoCatDB - a curated, open-source database containing 26.7 K photocatalytic reactions with detailed mechanistic annotations, including 9.2 K multicomponent transformations. Leveraging this resource alongside 100 million molecular data points, we developed PhotoCat, a Transformer-based platform that achieves unprecedented accuracy in photocatalytic reaction prediction (82.6%), retrosynthesis (77.1%), and condition recommendation (88.5%). The platform's capabilities were experimentally validated through the discovery of four novel photocatalytic reactions with yields up to 75.3%. This integrated approach establishes a new paradigm for data-driven innovation in photocatalysis, bridging computational prediction with experimental validation to accelerate discovery in sustainable chemistry.