Nanoparticles (NPs) may behave like atoms or molecules in the self-assembly into artificial solids with stimuli-responsive properties. However, the functionality engineering of nanoparticle-assembled solids is still far behind the aesthetic approaches for molecules, with a major problem arising from the lack of atomic-precision in the NPs, which leads to incoherence in superlattices. Here we exploit coherent superlattices (or supercrystals) that are assembled from atomically precise AuS(SR) NPs (core dia. = 1.6 nm, SR = thiolate) for controlling the charge transport properties with atomic-level structural insights. The resolved interparticle ligand packing in AuS(SR)-assembled solids reveals the mechanism behind the thermally-induced sharp transition in charge transport through the macroscopic crystal. Specifically, the response to temperature induces the conformational change to the R groups of surface ligands, as revealed by variable temperature X-ray crystallography with atomic resolution. Overall, this approach leads to an atomic-level correlation between the interparticle structure and a bi-stability functionality of self-assembled supercrystals, and the strategy may enable control over such materials with other novel functionalities.
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http://dx.doi.org/10.1039/d3sc02753h | DOI Listing |
Nat Commun
January 2025
State Key Laboratory of Precision Spectroscopy, East China Normal University, Shanghai, China.
Partial wave analysis is key to interpretation of the photoionization of atoms and molecules on the attosecond timescale. Here we propose a heterodyne analysis approach, based on the delay-resolved anisotropy parameters to reveal the role played by high-order partial waves during photoionization. This extends the Reconstruction of Attosecond Beating By Interference of Two-photon Transitions technique into the few-photon regime.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Laboratoire De Physique de l'École Normale Supérieure, ENS, PSL, CNRS, Sorbonne Université, Université de Paris, 24 rue Lhomond, 75005 Paris, France.
Electric quadrupole traps are a leading technology for suspending charged objects ranging in size from single protons to atomic and molecular ions, and even to nano- and micron-sized bodies. If the levitated objects' charge distribution contains multipoles, the time-dependent trapping fields can significantly impact its rotational motion. Here, we experimentally observe the transition from librational motion to a regime where a microparticle rotates in sync with the trap drive.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Shenyang 110819, China.
Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan.
Background: The growing number of AD patients is a public concern all over the world. During the decade, anti-amyloid beta-proteins (Aβ) monoclonal antibodies for AD patients have been developed. Among the immunotherapeutic agents, lecanemab is an anti-Aβ monoclonal antibody that binds to Aβ protofibrils (Aβ PFs), which is an intermediate molecule in Aβ species.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Electron density is a fundamental quantity that can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a real-space grid have been proposed, the demand for a number of high-order basis functions or grid points is enormous. In this work, we propose an efficient grid-point sampling strategy that combines targeted sampling favoring a large density and a screening of grid points associated with linearly independent atomic features.
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