Accurate real space iterative reconstruction (RESIRE) algorithm for tomography

Unveiling Atomic-Scaled Local Chemical Order of High-Entropy Intermetallic Catalyst for Alkyl-Substitution-Dependent Alkyne Semihydrogenation

High-entropy intermetallic (HEI) nanocrystals, composed of multiple elements with an ordered structure, are of immense interest in heterogeneous catalysis due to their unique geometric and electronic structures and the cocktail effect. Despite tremendous efforts dedicated to regulating the metal composition and structures with advanced synthetic methodologies to improve the performance, the surface structure, and local chemical order of HEI and their correlation with activity at the atomic level remain obscure yet challenging. Herein, by determining the three-dimensional (3D) atomic structure of quinary PdFeCoNiCu (PdM) HEI using atomic-resolution electron tomography, we reveal that the local chemical order of HEI regulates the surface electronic structures, which further mediates the alkyl-substitution-dependent alkyne semihydrogenation. The 3D structures of HEI PdM nanocrystals feature an ordered (intermetallic) core enclosed by a disordered (solid-solution) shell rather than an ordered surface. The lattice mismatch between the core and shell results in apparent near-surface distortion. The chemical order of the intermetallic core increases with annealing temperature, driving the electron redistribution between Pd and M at the surface, but the surface geometrical (chemically disordered) configurations and compositions are essentially unchanged. We investigate the catalytic performance of HEI PdM with different local chemical orders toward semihydrogenation across a broad range of alkynes, finding that the electron density of surface Pd and the hindrance effect of alkyl substitutions on alkynes are two key factors regulating selective semihydrogenation. We anticipate that these findings on surface atomic structure will clarify the controversy regarding the geometric and/or electronic effects of HEI catalysts and inspire future studies on tuning local chemical order and surface engineering toward enhanced catalysts.

Seeing the Supracolloidal Assemblies in 3D: Unraveling High-Resolution Structures Using Electron Tomography

Transmission electron microscopy (TEM) imaging has revolutionized modern materials science, nanotechnology, and structural biology. Its ability to provide information about materials' structure, composition, and properties at atomic-level resolution has enabled groundbreaking discoveries and the development of innovative materials with precision and accuracy. Electron tomography, single particle reconstruction, and microcrystal electron diffraction techniques have paved the way for the three-dimensional (3D) reconstruction of biological samples, synthetic materials, and hybrid nanostructures at near atomic-level resolution. TEM tomography using a series of two-dimensional (2D) projections has been used extensively in biological science, but in recent years it has become an important method in synthetic nanomaterials and soft matter research. TEM tomography offers unprecedented morphological details of 3D objects, internal structures, packing patterns, growth mechanisms, and self-assembly pathways of self-assembled colloidal systems. It complements other analytical tools, including small-angle X-ray scattering, and provides valuable data for computational simulations for predictive design and reverse engineering of nanomaterials with the desired structure and properties. In this perspective, I will discuss the importance of TEM tomography in the structural understanding and engineering of self-assembled nanostructures with specific emphasis on colloidal capsids, composite cages, biohybrid superlattices with complex geometries, polymer assemblies, and self-assembled protein-based superstructures.

Real space iterative reconstruction for vector tomography (RESIRE-V)

Tomography has had an important impact on the physical, biological, and medical sciences. To date, most tomographic applications have been focused on 3D scalar reconstructions. However, in some crucial applications, vector tomography is required to reconstruct 3D vector fields such as the electric and magnetic fields. Over the years, several vector tomography methods have been developed. Here, we present the mathematical foundation and algorithmic implementation of REal Space Iterative REconstruction for Vector tomography, termed RESIRE-V. RESIRE-V uses multiple tilt series of projections and iterates between the projections and a 3D reconstruction. Each iteration consists of a forward step using the Radon transform and a backward step using its transpose, then updates the object via gradient descent. Incorporating with a 3D support constraint, the algorithm iteratively minimizes an error metric, defined as the difference between the measured and calculated projections. The algorithm can also be used to refine the tilt angles and further improve the 3D reconstruction. To validate RESIRE-V, we first apply it to a simulated data set of the 3D magnetization vector field, consisting of two orthogonal tilt series, each with a missing wedge. Our quantitative analysis shows that the three components of the reconstructed magnetization vector field agree well with the ground-truth counterparts. We then use RESIRE-V to reconstruct the 3D magnetization vector field of a ferromagnetic meta-lattice consisting of three tilt series. Our 3D vector reconstruction reveals the existence of topological magnetic defects with positive and negative charges. We expect that RESIRE-V can be incorporated into different imaging modalities as a general vector tomography method. To make the algorithm accessible to a broad user community, we have made our RESIRE-V MATLAB source codes and the data freely available at https://github.com/minhpham0309/RESIRE-V .

Three-dimensional atomic structure and local chemical order of medium- and high-entropy nanoalloys

Medium- and high-entropy alloys (M/HEAs) mix several principal elements with near-equiatomic composition and represent a model-shift strategy for designing previously unknown materials in metallurgy1-8, catalysis9-14 and other fields15-18. One of the core hypotheses of M/HEAs is lattice distortion5,19,20, which has been investigated by different numerical and experimental techniques21-26. However, determining the three-dimensional (3D) lattice distortion in M/HEAs remains a challenge. Moreover, the presumed random elemental mixing in M/HEAs has been questioned by X-ray and neutron studies27, atomistic simulations28-30, energy dispersive spectroscopy31,32 and electron diffraction33,34, which suggest the existence of local chemical order in M/HEAs. However, direct experimental observation of the 3D local chemical order has been difficult because energy dispersive spectroscopy integrates the composition of atomic columns along the zone axes7,32,34 and diffuse electron reflections may originate from planar defects instead of local chemical order35. Here we determine the 3D atomic positions of M/HEA nanoparticles using atomic electron tomography36 and quantitatively characterize the local lattice distortion, strain tensor, twin boundaries, dislocation cores and chemical short-range order (CSRO). We find that the high-entropy alloys have larger local lattice distortion and more heterogeneous strain than the medium-entropy alloys and that strain is correlated to CSRO. We also observe CSRO-mediated twinning in the medium-entropy alloys, that is, twinning occurs in energetically unfavoured CSRO regions but not in energetically favoured CSRO ones, which represents, to our knowledge, the first experimental observation of correlating local chemical order with structural defects in any material. We expect that this work will not only expand our fundamental understanding of this important class of materials but also provide the foundation for tailoring M/HEA properties through engineering lattice distortion and local chemical order.

Nanoscale Three-Dimensional Network Structure of a Mesoporous Particle Unveiled via Adaptive Multidistance Coherent X-ray Tomography

Mesoporous nanoparticles provide rich platforms to devise functional materials by customizing the three-dimensional (3D) structures of nanopores. With the pore network as a key tuning parameter, the noninvasive and quantitative characterization of these 3D structures is crucial for the rational design of functional materials. This has prompted researchers to develop versatile nanoprobes with a high penetration power to inspect various specimens sized a few micrometers at nanoscale 3D resolutions. Here, with adaptive phase retrievals on independent data sets with different sampling frequencies, we introduce multidistance coherent X-ray tomography as a noninvasive and quantitative nanoprobe to realize high-resolution 3D imaging of micrometer-sized specimens. The 3D density distribution of an entire mesoporous silica nanoparticle was obtained at 13 nm 3D resolution for quantitative physical and morphological analyses of its 3D pore structure. The morphological features of the whole 3D pore network and pore connectivity were examined to gain insight into the potential functions of the particles. The proposed multidistance tomographic imaging scheme with quantitative structural analyses is expected to advance studies of functional materials by facilitating their structure-based rational design.

Maximum Lyapunov exponent-based multiple chaotic slime mold algorithm for real-world optimization

Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers from issues of missing the optimal solution or collapsing to local optimum when facing complicated problems. To conquer these drawbacks, we consider adding a novel multi-chaotic local operator to the bio-shock feedback mechanism of SMA to compensate for the lack of exploration of the local solution space with the help of the perturbation nature of the chaotic operator. Based on this, we propose an improved algorithm, namely MCSMA, by investigating how to improve the probabilistic selection of chaotic operators based on the maximum Lyapunov exponent (MLE), an inherent property of chaotic maps. We implement the comparison between MCSMA with other state-of-the-art methods on IEEE Congress on Evolution Computation (CEC) i.e., CEC2017 benchmark test suits and CEC2011 practical problems to demonstrate its potency and perform dendritic neuron model training to test the robustness of MCSMA on classification problems. Finally, the parameters' sensitivities of MCSMA, the utilization of the solution space, and the effectiveness of the MLE are adequately discussed.

In Situ and Emerging Transmission Electron Microscopy for Catalysis Research

Catalysts are the primary facilitator in many dynamic processes. Therefore, a thorough understanding of these processes has vast implications for a myriad of energy systems. The scanning/transmission electron microscope (S/TEM) is a powerful tool not only for atomic-scale characterization but also in situ catalytic experimentation. Techniques such as liquid and gas phase electron microscopy allow the observation of catalysts in an environment conducive to catalytic reactions. Correlated algorithms can greatly improve microscopy data processing and expand multidimensional data handling. Furthermore, new techniques including 4D-STEM, atomic electron tomography, cryogenic electron microscopy, and monochromated electron energy loss spectroscopy (EELS) push the boundaries of our comprehension of catalyst behavior. In this review, we discuss the existing and emergent techniques for observing catalysts using S/TEM. Challenges and opportunities highlighted aim to inspire and accelerate the use of electron microscopy to further investigate the complex interplay of catalytic systems.