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A central challenge in developing quantum computers and long-range quantum networks is the distribution of entanglement across many individually controllable qubits. Colour centres in diamond have emerged as leading solid-state 'artificial atom' qubits because they enable on-demand remote entanglement, coherent control of over ten ancillae qubits with minute-long coherence times and memory-enhanced quantum communication. A critical next step is to integrate large numbers of artificial atoms with photonic architectures to enable large-scale quantum information processing systems. So far, these efforts have been stymied by qubit inhomogeneities, low device yield and complex device requirements. Here we introduce a process for the high-yield heterogeneous integration of 'quantum microchiplets'-diamond waveguide arrays containing highly coherent colour centres-on a photonic integrated circuit (PIC). We use this process to realize a 128-channel, defect-free array of germanium-vacancy and silicon-vacancy colour centres in an aluminium nitride PIC. Photoluminescence spectroscopy reveals long-term, stable and narrow average optical linewidths of 54 megahertz (146 megahertz) for germanium-vacancy (silicon-vacancy) emitters, close to the lifetime-limited linewidth of 32 megahertz (93 megahertz). We show that inhomogeneities of individual colour centre optical transitions can be compensated in situ by integrated tuning over 50 gigahertz without linewidth degradation. The ability to assemble large numbers of nearly indistinguishable and tunable artificial atoms into phase-stable PICs marks a key step towards multiplexed quantum repeaters and general-purpose quantum processors.
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http://dx.doi.org/10.1038/s41586-020-2441-3 | DOI Listing |
J Am Chem Soc
September 2025
Department of Chemistry, National Taiwan University, Taipei 106319, Taiwan.
The exclusive formation of artificial multicomponent assemblies remains a significant challenge, in contrast to the well-established organization observed in natural systems, due to intrinsic entropic constraints. To overcome this limitation, recent efforts have been focused on developing precision self-assembly strategies for the rational construction of such architectures. Here, we construct an ideal complementary pair of 2,2':6',2″-terpyridine (tpy)-based ligands by fine-tuning the substituent bulkiness, which enables the quantitative formation of robust nested cages through efficient dynamic heteroleptic complexation with multivalent coordination.
View Article and Find Full Text PDFBiomater Res
September 2025
Laboratory of Medical Imaging, The First People's Hospital of Zhenjiang, Zhenjiang 212001, P. R. China.
Mesoporous metal nanomaterials (MMNs) have gained interest in biomedicine for their unique properties, but their potential is limited by the predominance of spherical shapes and the neglect of morphological effects on biological activity, which hinders the reasonable evaluation of morphology-dependent enzyme-like activities and biological behaviors and its further biomedical applications. It is therefore imperative to find an effective and facile method to design and prepare MMNs with novel, well-defined morphologies. Herein, we fabricated 3 mesoporous platinum nanoenzymes including sphere, rod, and bipyramid topologies [Au@mesoPt sphere, Au@mesoPt rod, and Au@mesoPt bipyramid nanoparticles (NPs), respectively] via a facile atomic layer deposition method using gold NPs (Au NPs) as the templated cores and Pluronic F127 as a structure-directing agent.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang, District 1, Ho Chi Minh City 700000, Vietnam.
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity and constrained model generalization due to the expensive and time-consuming task of acquiring labeled data and (2) inadequate initial node and edge features that fail to incorporate comprehensive chemical domain knowledge, notably orbital information. To address these limitations, we introduce a Knowledge-Guided Graph (KGG) framework employing self-supervised learning to pretrain models using orbital-level features in order to mitigate reliance on extensive labeled data sets.
View Article and Find Full Text PDFChem Rev
September 2025
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Achieving precise control of materials synthesis is a cornerstone of modern manufacturing, driving efficiency, functionality, and device innovation. This review examines the roles of transmission electron microscopy (TEM) and neutron scattering (NS) in advancing our understanding of these processes. TEM offers atomic-scale insights into nucleation, growth, and phase transitions, while NS provides an analysis of reaction pathways, phase evolution, and structural transformations over broader length scales.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
September 2025
CFisUC, Department of Physics, University of Coimbra, 3004-516, Coimbra, Portugal.
With the goal of manipulating (bio)chemical processes, photoswitches emerge as important assets in molecular nanotechnology. To guide synthetic strategies toward increasingly more efficient systems, conformational dynamics studies performed with atomic rigor are in demand, particularly if this information can be extracted with control over the size of a perturbing solvation layer. Here, we use jet-cooled rotational spectroscopy and quantum chemistry calculations to unravel the structure and micro-hydration dynamics of a prototype photoswitch.
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