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1.
Commun Chem ; 7(1): 4, 2024 Jan 03.
Artículo en Inglés | MEDLINE | ID: mdl-38172567

RESUMEN

In situ structures of Platinum (Pt) nanoparticles (NPs) can be determined with graphene liquid cell transmission electron microscopy. Atomic-scale three-dimensional structural information about their physiochemical properties in solution is critical for understanding their chemical function. We here analyze eight atomic-resolution maps of small (<3 nm) colloidal Pt NPs. Their structures are composed of an ordered crystalline core surrounded by surface atoms with comparatively high mobility. 3D reconstructions calculated from cumulative doses of 8500 and 17,000 electrons/pixel, respectively, are characterized in terms of loss of atomic densities and atomic displacements. Less than 5% of the total number of atoms are lost due to dissolution or knock-on damage in five of the structures analyzed, whereas 10-16% are lost in the remaining three. Less than 5% of the atomic positions are displaced due to the increased electron irradiation in all structures. The surface dynamics will play a critical role in the diverse catalytic function of Pt NPs and must be considered in efforts to model Pt NP function computationally.

3.
Sci Rep ; 13(1): 1814, 2023 Feb 01.
Artículo en Inglés | MEDLINE | ID: mdl-36725868

RESUMEN

Determining the 3D atomic structures of multi-element nanoparticles in their native liquid environment is crucial to understanding their physicochemical properties. Graphene liquid cell (GLC) TEM offers a platform to directly investigate nanoparticles in their solution phase. Moreover, exploiting high-resolution TEM images of single rotating nanoparticles in GLCs, 3D atomic structures of nanoparticles are reconstructed by a method called "Brownian one-particle reconstruction". We here introduce a 3D atomic structure determination method for multi-element nanoparticle systems. The method, which is based on low-pass filtration and initial 3D model generation customized for different types of multi-element systems, enables reconstruction of high-resolution 3D Coulomb density maps for ordered and disordered multi-element systems and classification of the heteroatom type. Using high-resolution image datasets obtained from TEM simulations of PbSe, CdSe, and FePt nanoparticles that are structurally relaxed with first-principles calculations in the graphene liquid cell, we show that the types and positions of the constituent atoms are precisely determined with root mean square displacement values less than 24 pm. Our study suggests that it is possible to investigate the 3D atomic structures of synthesized multi-element nanoparticles in liquid phase.

4.
Biomed Opt Express ; 12(12): 7717-7731, 2021 Dec 01.
Artículo en Inglés | MEDLINE | ID: mdl-35003862

RESUMEN

The performance of structured illumination microscopy (SIM) systems depends on the computational method used to process the raw data. In this paper, we present a regularized three-dimensional (3D) model-based (MB) restoration method with positivity constraint (PC) for 3D processing of data from 3D-SIM (or 3-beam interference SIM), in which the structured illumination pattern varies laterally and axially. The proposed 3D-MBPC method introduces positivity in the solution through the reconstruction of an auxiliary function using a conjugate-gradient method that minimizes the mean squared error between the data and the 3D imaging model. The 3D-MBPC method provides axial super resolution, which is not the same as improved optical sectioning demonstrated with model-based approaches based on the 2D-SIM (or 2-beam interference SIM) imaging model, for either 2D or 3D processing of a single plane from a 3D-SIM dataset. Results obtained with our 3D-MBPC method show improved 3D resolution over what is achieved by the standard generalized Wiener filter method, the first known method that performs 3D processing of 3D-SIM data. Noisy simulation results quantify the achieved 3D resolution, which is shown to match theoretical predictions. Experimental verification of the 3D-MBPC method with biological data demonstrates successful application to data volumes of different sizes.

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