The beginning of iron corrosion - high-resolution visualization with 3D electron tomography

Understanding the genesis and early-phase reactions of iron corrosion is essential for the early detection, mitigation and prevention of metal degradation. In this work, high-resolution 3D tomography of metallic iron oxidation was acquired using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM). In particular, dendritic capillaries (<0.5 nm) were observed during the initial oxidation of fresh nanoscale zero-valent iron due to the differential oxygen diffusion and iron atoms migration. This observation led to the proposal of a nanoscale "pothole" model for early-phase corrosion, wherein hollowing out of the metal nanoparticle and formation of nanovoids beneath the iron/oxide interface through Kirkendall effect. Coalescence of the nanocapillaries results in the ultimate collapse of metal structure and/or functional failure. Using nanoscale zero-valent iron as a research model, this work provides unprecedented insights into the nano- and atomic-scale mechanisms of iron oxidation, paving the way for advanced detection and prevention strategies for iron corrosion.

Recent Advances in High-Entropy Layered Oxide Cathode Materials for Alkali Metal-Ion Batteries

Since the electrochemical de/intercalation behavior is first detected in 1980, layered oxides have become the most promising cathode material for alkali metal-ion batteries (Li+/Na+/K+; AMIBs) owing to their facile synthesis and excellent theoretical capacities. However, the inherent drawbacks of unstable structural evolution and sluggish diffusion kinetics deteriorate their electrochemical performance, limiting further large-scale applications. To solve these issues, the novel and promising strategy of high entropy has been widely applied to layered oxide cathodes for AMIBs in recent years. Through multielement synergy and entropy stabilization effects, high-entropy layered oxides (HELOs) can achieve adjustable activity and enhanced stability. Herein, the basic concepts, design principles, and synthesis methods of HELO cathodes are introduced systematically. Notably, it explores in detail the improvements of the high-entropy strategy on the limitations of layered oxides, highlighting the latest advances in high-entropy layered cathode materials in the field of AMIBs. In addition, it introduces advanced characterization and theoretical calculations for HELOs and proposes potential future research directions and optimization strategies, providing inspiration for researchers to develop advanced HELO cathode materials in the areas of energy storage and conversion.

Obtaining 3D Atomic Reconstructions from Electron Microscopy Images Using a Bayesian Genetic Algorithm: Possibilities, Insights, and Limitations

The Bayesian genetic algorithm (BGA) is a powerful tool to reconstruct the 3D structure of mono-atomic single-crystalline metallic nanoparticles imaged using annular dark field scanning transmission electron microscopy. The number of atoms in a projected atomic column in the image is used as input to obtain an accurate and atomically precise reconstruction of the nanoparticle, taking prior knowledge and the finite precision of atom counting into account. However, as the number of parameters required to describe a nanoparticle with atomic detail rises quickly with the size of the studied particle, the computational costs of the BGA rise to prohibitively expensive levels. In this study, we investigate these computational costs and propose methods and control parameters for efficient application of the algorithm to nanoparticles of at least up to 10 nm in size.

3D reconstruction of a million atoms by multiple-section local-orbital tomography

Three-dimensional (3D) structural analysis is an important field in physical and biological sciences. There exist two groups of electron microscopy methods that are capable of providing 3D structural information of an object, i.e., electron tomography and depth sectioning. Electron tomography is capable of resolving atoms in all three dimensions, but the accuracy in atomic positions is low and the object size that can be reconstructed is limited. Depth sectioning methods give high positional accuracy in the imaging plane, but the spatial resolution in the third dimension is low. In this work, electron tomography and depth sectioning are combined to form a method called multiple-section local-orbital tomography, or nLOT in short. The nLOT method provides high spatial resolution and high positional accuracy in all three dimensions. The object size that can be reconstructed is extended to a million atoms. The present method establishes a foundation for the widespread application of atomic electron tomography.

Quantitative 3D structural analysis of small colloidal assemblies under native conditions by liquid-cell fast electron tomography

Electron tomography has become a commonly used tool to investigate the three-dimensional (3D) structure of nanomaterials, including colloidal nanoparticle assemblies. However, electron microscopy is typically done under high-vacuum conditions, requiring sample preparation for assemblies obtained by wet colloid chemistry methods. This involves solvent evaporation and deposition on a solid support, which consistently alters the nanoparticle organization. Here, we suggest using electron tomography to study nanoparticle assemblies in their original colloidal liquid environment. To address the challenges related to electron tomography in liquid, we devise a method that combines fast data acquisition in a commercial liquid-cell with a dedicated alignment and reconstruction workflow. We present the advantages of this methodology in accurately characterizing two different systems. 3D reconstructions of assemblies comprising polystyrene-capped Au nanoparticles encapsulated in polymeric shells reveal less compact and more distorted configurations for experiments performed in a liquid medium compared to their dried counterparts. A similar expansion can be observed in quantitative analysis of the surface-to-surface distances of self-assembled Au nanorods in water rather than in a vacuum, in agreement with bulk measurements. This study, therefore, emphasizes the importance of developing high-resolution characterization tools that preserve the native environment of colloidal nanostructures.

3D imaging photocatalytically degraded micro- and nanoplastics

Microplastics (MPs) and nanoplastics have been an emerging global concern, with hazardous effects on plant, animal, and human health. Their small size makes it easier for them to spread to various ecosystems and enter the food chain; they are already widely found in aqueous environments and within aquatic life, and have even been found within humans. Much research has gone into understanding micro-/nanoplastic sources and environmental fate, but less work has been done to understand their degradation. Photocatalytic degradation is a promising green technique that uses visible or ultraviolet light in combination with photocatalyst to degrade plastic particles. While complete degradation, reducing plastics to small molecules, is often the goal, partial degradation is more common. We examined microscale polyethylene (PE) (125-150µm in diameter) and nanoscale polystyrene (PS) (∼300 nm in diameter) spheres both before and after degradation using multiple imaging techniques, especially electron tomography in addition to conventional electron microscopy. Electron tomography is able to image the 3D exterior and interior of the nanoplastics, enabling us to observe within aggregates and inside degraded spheres, where we found potentially open interior structures after degradation. These structures may result from differences in degradation and aggregation behavior between the different plastic types, with our work finding that PE MPs typically cracked into sharp fragments, while PS nanoplastics often fragmented into smoother, more curved shapes. These and other differences, along with interior and 3D surface images, provide new details on how the structure and aggregation of PE MPs and PS nanoplastics changes when degraded, which could influence how the resulting worn particles are collected or treated further.

Controlling Phase in Colloidal Synthesis

A fundamental precept of chemistry is that properties are manifestations of the elements present and their arrangement in space. Controlling the arrangement of atoms in nanocrystals is not well understood in nanocrystal synthesis, especially in the transition metal chalcogenides and pnictides, which have rich phase spaces. This Perspective will cover some of the recent advances and current challenges. The perspective includes introductions to challenges particular to chalcogenide and pnictide chemistry, the often-convoluted roles of bond dissociation energies and mechanisms by which precursors break down, using very organized methods to map the synthetic phase space, a discussion of polytype control, and challenges in characterization, especially for solving novel structures on the nanoscale and time-resolved studies.

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 .

Abnormal Silicon Etching Behaviors in Nanometer-Sized Channels

Modern semiconductor fabrication is challenged by difficulties in overcoming physical and chemical constraints. A major challenge is the wet etching of dummy gate silicon, which involves the removal of materials inside confined spaces of a few nanometers. These chemical processes are significantly different in the nanoscale and bulk. Previously, electrical double-layer formation, bubble entrapment, poor wettability, and insoluble intermediate precipitation have been proposed. However, the exact suppression mechanisms remain unclear due to the lack of direct observation methods. Herein, we investigate limiting factors for the etching kinetics of silicon with tetramethylammonium hydroxide at the nanoscale by using liquid-phase transmission electron microscopy, three-dimensional electron tomography, and first-principles calculations. We reveal suppressed chemical reactions, unstripping phenomena, and stochastic etching behaviors that have never been observed on a macroscopic scale. We expect that solutions can be suggested from this comprehensive insight into the scale-dependent limiting factors of fabrication.

Precise Surface Profiling at the Nanoscale Enabled by Deep Learning

Surface topography, or height profile, is a critical property for various micro- and nanostructured materials and devices, as well as biological systems. At the nanoscale, atomic force microscopy (AFM) is the tool of choice for surface profiling due to its capability to noninvasively map the topography of almost all types of samples. However, this method suffers from one drawback: the convolution of the nanoprobe's shape in the height profile of the samples, which is especially severe for sharp protrusion features. Here, we report a deep learning (DL) approach to overcome this limit. Adopting an image-to-image translation methodology, we use data sets of tip-convoluted and deconvoluted image pairs to train an encoder-decoder based deep convolutional neural network. The trained network successfully removes the tip convolution from AFM topographic images of various nanocorrugated surfaces and recovers the true, precise 3D height profiles of these samples.

Revealing the 3D Structure of Block Copolymers with Electron Microscopy: Current Status and Future Directions

Block copolymers (BCPs) are considered model systems for understanding and utilizing self-assembly in soft matter. Their tunable nanometric structure and composition enable comprehensive studies of self-assembly processes as well as make them relevant materials in diverse applications. A key step in developing and controlling BCP nanostructures is a full understanding of their three-dimensional (3D) structure and how this structure is affected by the BCP chemistry, confinement, boundary conditions, and the self-assembly evolution and dynamics. Electron microscopy (EM) is a leading method in BCP 3D characterization owing to its high resolution in imaging nanosized structures. Here we discuss the two main 3D EM methods: namely, transmission EM tomography and slice and view scanning EM tomography. We present each method's principles, examine their strengths and weaknesses, and discuss ways researchers have devised to overcome some of the challenges in BCP 3D characterization with EM- from specimen preparation to imaging radiation-sensitive materials. Importantly, we review current and new cutting-edge EM methods such as direct electron detectors, energy dispersive X-ray spectroscopy of soft matter, high temporal rate imaging, and single-particle analysis that have great potential for expanding the BCP understanding through EM in the future.

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.

Accelerating Quantum Materials Development with Advances in Transmission Electron Microscopy

Quantum materials are driving a technology revolution in sensing, communication, and computing, while simultaneously testing many core theories of the past century. Materials such as topological insulators, complex oxides, superconductors, quantum dots, color center-hosting semiconductors, and other types of strongly correlated materials can exhibit exotic properties such as edge conductivity, multiferroicity, magnetoresistance, superconductivity, single photon emission, and optical-spin locking. These emergent properties arise and depend strongly on the material's detailed atomic-scale structure, including atomic defects, dopants, and lattice stacking. In this review, we describe how progress in the field of electron microscopy (EM), including in situ and in operando EM, can accelerate advances in quantum materials and quantum excitations. We begin by describing fundamental EM principles and operation modes. We then discuss various EM methods such as (i) EM spectroscopies, including electron energy loss spectroscopy (EELS), cathodoluminescence (CL), and electron energy gain spectroscopy (EEGS); (ii) four-dimensional scanning transmission electron microscopy (4D-STEM); (iii) dynamic and ultrafast EM (UEM); (iv) complementary ultrafast spectroscopies (UED, XFEL); and (v) atomic electron tomography (AET). We describe how these methods could inform structure-function relations in quantum materials down to the picometer scale and femtosecond time resolution, and how they enable precision positioning of atomic defects and high-resolution manipulation of quantum materials. For each method, we also describe existing limitations to solve open quantum mechanical questions, and how they might be addressed to accelerate progress. Among numerous notable results, our review highlights how EM is enabling identification of the 3D structure of quantum defects; measuring reversible and metastable dynamics of quantum excitations; mapping exciton states and single photon emission; measuring nanoscale thermal transport and coupled excitation dynamics; and measuring the internal electric field and charge density distribution of quantum heterointerfaces- all at the quantum materials' intrinsic atomic and near atomic-length scale. We conclude by describing open challenges for the future, including achieving stable sample holders for ultralow temperature (below 10K) atomic-scale spatial resolution, stable spectrometers that enable meV energy resolution, and high-resolution, dynamic mapping of magnetic and spin fields. With atomic manipulation and ultrafast characterization enabled by EM, quantum materials will be poised to integrate into many of the sustainable and energy-efficient technologies needed for the 21st century.

Simultaneous multimaterial operando tomography of electrochemical devices

The performance of electrochemical energy devices, such as fuel cells and batteries, is dictated by intricate physiochemical processes within. To better understand and rationally engineer these processes, we need robust operando characterization tools that detect and distinguish multiple interacting components/interfaces in high contrast. Here, we uniquely combine dual-modality tomography (simultaneous neutron and x-ray tomography) and advanced image processing (iterative reconstruction and metal artifact reduction) for high-contrast multimaterial imaging, with signal and contrast enhancements of up to 10 and 48 times, respectively, compared to conventional single-modality imaging. Targeted development and application of these methods to electrochemical devices allow us to resolve operando distributions of six interacting fuel cell components (including void space) with the highest reported pairwise contrast for simultaneous yet decoupled spatiotemporal characterization of component morphology and hydration. Such high-contrast tomography ushers in key gold standards for operando electrochemical characterization, with broader applicability to numerous multimaterial systems.

Advanced techniques in automated high-resolution scanning transmission electron microscopy

Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its versatility, however, experimental workflows currently rely heavily on experienced human operators and can only acquire data from small regions of a sample at a time. Here, we demonstrate a flexible pipeline-based system for high-throughput acquisition of atomic-resolution structural data using an all-piezo sample stage applied to large-scale imaging of nanoparticles and multimodal data acquisition. The system is available as part of the user program of the Molecular Foundry at Lawrence Berkeley National Laboratory.

Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts

Accurate 3D nanometrology of catalysts with small nanometer-sized particles of light 3d or 4d metals supported on high-atomic-number oxides is crucial for understanding their functionality. However, performing quantitative 3D electron tomography analysis on systems involving metals like Pd, Ru, or Rh supported on heavy oxides (e.g., CeO2) poses significant challenges. The low atomic number (Z) of the metal complicates discrimination, especially for very small nanoparticles (1-3 nm). Conventional reconstruction methods successful for catalysts with 5d metals (e.g., Au, Pt, or Ir) fail to detect 4d metal particles in electron tomography reconstructions, as their contrasts cannot be effectively separated from those of the underlying support crystallites. To address this complex 3D characterization challenge, we have developed a full deep learning (DL) pipeline that combines multiple neural networks, each one optimized for a specific image-processing task. In particular, single-image super-resolution (SR) techniques are used to intelligently denoise and enhance the quality of the tomographic tilt series. U-net generative adversarial network algorithms are employed for image restoration and correcting alignment-related artifacts in the tilt series. Finally, semantic segmentation, utilizing a U-net-based convolutional neural network, splits the 3D volumes into their components (metal and support). This approach enables the visualization of subnanometer-sized 4d metal particles and allows for the quantitative extraction of catalytically relevant structural information, such as particle size, sphericity, and truncation, from compressed sensing electron tomography volume reconstructions. We demonstrate the potential of this approach by characterizing nanoparticles of a metal widely used in catalysis, Pd (Z = 46), supported on CeO2, a very high density (7.22 g/cm3) oxide involving a quite high-atomic-number element, Ce (Z = 58).

Three-dimensional reconstruction of Y-IrNi rhombic dodecahedron nanoframe by STEM/EDS tomography

The structural analysis of nanocrystals via transmission electron microscopy (TEM) is a valuable technique for the material science field. Recently, two-dimensional images by scanning TEM (STEM) and energy-dispersive X-ray spectroscopy (EDS) have successfully extended to three-dimensional (3D) imaging by tomography. However, despite improving TEM instruments and measurement techniques, detector shadowing, the missing-wedge problem, X-ray absorption effects, etc., significant challenges still remain; therefore, the various required corrections should be considered and applied when performing quantitative tomography. Nonetheless, this 3D reconstruction technique can facilitate active site analysis and the development of nanocatalyst systems, such as water electrolysis and fuel cell. Herein, we present a 3D reconstruction technique to obtain tomograms of IrNi rhombic dodecahedral nanoframes (IrNi-RFs) from STEM and EDS images by applying simultaneous iterative reconstruction technique and total variation minimization algorithms. From characterizing the morphology and spatial chemical composition of the Ir and Ni atoms in the nanoframes, we were able to infer the origin of the physical and catalytic durability of IrNi-RFs. Also, by calculating the surface area and volume of the 3D reconstructed model, we were able to quantify the Ir-to-Ni composition ratio and compare it to the EDS measurement result.

Applications of Transmission Electron Microscopy in Phase Engineering of Nanomaterials

Phase engineering of nanomaterials (PEN) is an emerging field that aims to tailor the physicochemical properties of nanomaterials by precisely manipulating their crystal phases. To advance PEN effectively, it is vital to possess the capability of characterizing the structures and compositions of nanomaterials with precision. Transmission electron microscopy (TEM) is a versatile tool that combines reciprocal-space diffraction, real-space imaging, and spectroscopic techniques, allowing for comprehensive characterization with exceptional resolution in the domains of time, space, momentum, and, increasingly, even energy. In this Review, we first introduce the fundamental mechanisms behind various TEM-related techniques, along with their respective application scopes and limitations. Subsequently, we review notable applications of TEM in PEN research, including applications in fields such as metallic nanostructures, carbon allotropes, low-dimensional materials, and nanoporous materials. Specifically, we underscore its efficacy in phase identification, composition and chemical state analysis, in situ observations of phase evolution, as well as the challenges encountered when dealing with beam-sensitive materials. Furthermore, we discuss the potential generation of artifacts during TEM imaging, particularly in scanning modes, and propose methods to minimize their occurrence. Finally, we offer our insights into the present state and future trends of this field, discussing emerging technologies including four-dimensional scanning TEM, three-dimensional atomic-resolution imaging, and electron microscopy automation while highlighting the significance and feasibility of these advancements.

Lattice-Strain Engineering for Heterogenous Electrocatalytic Oxygen Evolution Reaction

The energy efficiency of metal-air batteries and water-splitting techniques is severely constrained by multiple electronic transfers in the heterogenous oxygen evolution reaction (OER), and the high overpotential induced by the sluggish kinetics has become an uppermost scientific challenge. Numerous attempts are devoted to enabling high activity, selectivity, and stability via tailoring the surface physicochemical properties of nanocatalysts. Lattice-strain engineering as a cutting-edge method for tuning the electronic and geometric configuration of metal sites plays a pivotal role in regulating the interaction of catalytic surfaces with adsorbate molecules. By defining the d-band center as a descriptor of the structure-activity relationship, the individual contribution of strain effects within state-of-the-art electrocatalysts can be systematically elucidated in the OER optimization mechanism. In this review, the fundamentals of the OER and the advancements of strain-catalysts are showcased and the innovative trigger strategies are enumerated, with particular emphasis on the feedback mechanism between the precise regulation of lattice-strain and optimal activity. Subsequently, the modulation of electrocatalysts with various attributes is categorized and the impediments encountered in the practicalization of strained effect are discussed, ending with an outlook on future research directions for this burgeoning field.

Strain and crystallographic identification of the helically concaved gap surfaces of chiral nanoparticles

Identifying the three-dimensional (3D) crystal plane and strain-field distributions of nanocrystals is essential for optical, catalytic, and electronic applications. However, it remains a challenge to image concave surfaces of nanoparticles. Here, we develop a methodology for visualizing the 3D information of chiral gold nanoparticles ≈ 200 nm in size with concave gap structures by Bragg coherent X-ray diffraction imaging. The distribution of the high-Miller-index planes constituting the concave chiral gap is precisely determined. The highly strained region adjacent to the chiral gaps is resolved, which was correlated to the 432-symmetric morphology of the nanoparticles and its corresponding plasmonic properties are numerically predicted from the atomically defined structures. This approach can serve as a comprehensive characterization platform for visualizing the 3D crystallographic and strain distributions of nanoparticles with a few hundred nanometers, especially for applications where structural complexity and local heterogeneity are major determinants, as exemplified in plasmonics.

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.

Probing the atomically diffuse interfaces in Pd@Pt core-shell nanoparticles in three dimensions

Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is found to be atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle's morphology or crystallographic texture. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds, which is confirmed by atomic images of Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.

Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles

Heterogeneous bimetallic catalysts have broad applications in industrial processes, but achieving a fundamental understanding on the nature of the active sites in bimetallic catalysts at the atomic and molecular level is very challenging due to the structural complexity of the bimetallic catalysts. Comparing the structural features and the catalytic performances of different bimetallic entities will favor the formation of a unified understanding of the structure-reactivity relationships in heterogeneous bimetallic catalysts and thereby facilitate the upgrading of the current bimetallic catalysts. In this review, we will discuss the geometric and electronic structures of three representative types of bimetallic catalysts (bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles) and then summarize the synthesis methodologies and characterization techniques for different bimetallic entities, with emphasis on the recent progress made in the past decade. The catalytic applications of supported bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles for a series of important reactions are discussed. Finally, we will discuss the future research directions of catalysis based on supported bimetallic catalysts and, more generally, the prospective developments of heterogeneous catalysis in both fundamental research and practical applications.

Photodarkening, Photobrightening, and the Role of Color Centers in Emerging Applications of Lanthanide-Based Upconverting Nanomaterials

Upconverting nanoparticles (UCNPs) compose a class of luminescent materials that utilize the unique wavelength-converting properties of lanthanide (Ln) ions for light-harvesting applications, photonics technologies, and biological imaging and sensing experiments. Recent advances in UCNP design have shed light on the properties of local color centers, both intrinsic and controllably induced, within these materials and their potential influence on UCNP photophysics. In this review, we describe fundamental studies of color centers in Ln-based materials, including research into their origins and their roles in observed photodarkening and photobrightening mechanisms. We place particular focus on the new functionalities that are enabled by harnessing the properties of color centers within Ln-doped nanocrystals, illustrated through applications in afterglow-based bioimaging, X-ray detection, all-inorganic nanocrystal photoswitching, and fully rewritable optical patterning and memory.

Accurate real space iterative reconstruction (RESIRE) algorithm for tomography

Tomography has made a revolutionary impact on the physical, biological and medical sciences. The mathematical foundation of tomography is to reconstruct a three-dimensional (3D) object from a set of two-dimensional (2D) projections. As the number of projections that can be measured from a sample is usually limited by the tolerable radiation dose and/or the geometric constraint on the tilt range, a main challenge in tomography is to achieve the best possible 3D reconstruction from a limited number of projections with noise. Over the years, a number of tomographic reconstruction methods have been developed including direct inversion, real-space, and Fourier-based iterative algorithms. Here, we report the development of a real-space iterative reconstruction (RESIRE) algorithm for accurate tomographic reconstruction. RESIRE iterates between the update of a reconstructed 3D object and the measured projections using a forward and back projection step. The forward projection step is implemented by the Fourier slice theorem or the Radon transform, and the back projection step by a linear transformation. Our numerical and experimental results demonstrate that RESIRE performs more accurate 3D reconstructions than other existing tomographic algorithms, when there are a limited number of projections with noise. Furthermore, RESIRE can be used to reconstruct the 3D structure of extended objects as demonstrated by the determination of the 3D atomic structure of an amorphous Ta thin film. We expect that RESIRE can be widely employed in the tomography applications in different fields. Finally, to make the method accessible to the general user community, the MATLAB source code of RESIRE and all the simulated and experimental data are available at  https://zenodo.org/record/7273314 .

Revealing 3D Ripple Structure and Its Dynamics in Freestanding Monolayer MoSe2 by Single-Frame 2D Atomic Image Reconstruction

An atomic-scale ripple structure has been revealed by electron tomography based on sequential projected atomic-resolution images, but it requires harsh imaging conditions with negligible structure evolution of the imaged samples. Here, we demonstrate that the ripple structure in monolayer MoSe₂ can be facilely reconstructed from a single-frame scanning transmission electron microscopy (STEM) image collected at designated collection angles. The intensity and shape of each Se₂ atomic column in the single-frame projected STEM image are synergistically combined to precisely map the slight misalignments of two Se atoms induced by rippling, which is then converted to three-dimensional (3D) ripple distortions. The dynamics of 3D ripple deformation can thus be directly visualized at the atomic scale by sequential STEM imaging. In addition, the reconstructed images provide the first opportunity for directly testing the validity of the classical theory of thermal fluctuations. Our method paves the way for a 3D reconstruction of a dynamical process in two-dimensional materials with a reasonable temporal resolution.

Tuning Mass Transport in Electrocatalysis Down to Sub-5 nm through Nanoscale Grade Separation

Nano and single-atom catalysis open new possibilities of producing green hydrogen (H₂ ) by water electrolysis. However, for the hydrogen evolution reaction (HER) which occurs at a characteristic reaction rate proportional to the potential, the fast generation of H₂ nanobubbles at atomic-scale interfaces often leads to the blockage of active sites. Herein, a nanoscale grade-separation strategy is proposed to tackle mass-transport problem by utilizing ordered three-dimensional (3d) interconnected sub-5 nm pores. The results reveal that 3d criss-crossing mesopores with grade separation allow efficient diffusion of H₂ bubbles along the interconnected channels. After the support of ultrafine ruthenium (Ru), the 3d mesopores are on a superior level to two-dimensional system at maximizing the catalyst performance and the obtained Ru catalyst outperforms most of the other HER catalysts. This work provides a potential route to fine-tuning few-nanometer mass transport during water electrolysis.

Deep-Learning Electron Diffractive Imaging

We report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained CNNs to recover the phase images from electron diffraction patterns of twisted hexagonal boron nitride, monolayer graphene, and a gold nanoparticle with comparable quality to those reconstructed by a conventional ptychographic algorithm. Fourier ring correlation between the CNN and ptychographic images indicates the achievement of a resolution in the range of 0.70 and 0.55 Å. We further develop CNNs to recover the probe function from the experimental data. The ability to replace iterative algorithms with CNNs and perform real-time atomic imaging from coherent diffraction patterns is expected to find applications in the physical and biological sciences.

High-Resolution Electron Tomography of Ultrathin Boerdijk-Coxeter-Bernal Nanowire Enabled by Superthin Metal Surface Coating

The rapid advancement of transmission electron microscopy has resulted in revolutions in a variety of fields, including physics, chemistry, and materials science. With single-atom resolution, 3D information of each atom in nanoparticles is revealed, while 4D electron tomography is shown to capture the atomic structural kinetics in metal nanoparticles after phase transformation. Quantitative measurements of physical and chemical properties such as chemical coordination, defects, dislocation, and local strain have been made. However, due to the incompatibility of high dose rate with other ultrathin morphologies, such as nanowires, atomic electron tomography has been primarily limited to quasi-spherical nanoparticles. Herein, the 3D atomic structure of a complex core-shell nanowire composed of an ultrathin Boerdijk-Coxeter-Bernal (BCB) core nanowire and a noble metal thin layer shell deposited on the BCB nanowire surface is discovered. Furthermore, it is demonstrated that a new superthin noble metal layer deposition on an ultrathin BCB nanowire could mitigate electron beam damage using an in situ transmission electron microscope and atomic resolution electron tomography. The colloidal coating method developed for electron tomography can be broadly applied to protect the ultrathin nanomaterials from electron beam damage, benefiting both the advanced material characterizations and enabling fundamental in situ mechanistic studies.

Precisely Picking Nanoparticles by a "Nano-Scalpel" for 360° Electron Tomography

Electron tomography (ET) has gained increasing attention for the 3D characterization of nanoparticles. However, the missing wedge problem due to a limited tilt angle range is still the main challenge for accurate reconstruction in most experimental TEM setups. Advanced algorithms could in-paint or compensate to some extent the missing wedge artifacts, but cannot recover the missing structural information completely. 360° ET provides an option to solve this problem by tilting a needle-shaped specimen over the full tilt range and thus filling the missing information. However, sample preparation especially for fine powders to perform full-range ET is still challenging, thus limiting its application. In this work, we propose a new universal sample preparation method that enables the transfer of selected individual nanoparticle or a few separated nanoparticles by cutting a piece of carbon film supporting the specimen particles and mounting them onto the full-range tomography holder tip with the help of an easily prepared sharp tungsten tip. This method is demonstrated by 360° ET of Pt@TiO2 hollow cage catalyst showing high quality reconstruction without missing wedge.

High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery

High-entropy nanoparticles have become a rapidly growing area of research in recent years. Because of their multielemental compositions and unique high-entropy mixing states (i.e., solid-solution) that can lead to tunable activity and enhanced stability, these nanoparticles have received notable attention for catalyst design and exploration. However, this strong potential is also accompanied by grand challenges originating from their vast compositional space and complex atomic structure, which hinder comprehensive exploration and fundamental understanding. Through a multidisciplinary view of synthesis, characterization, catalytic applications, high-throughput screening, and data-driven materials discovery, this review is dedicated to discussing the important progress of high-entropy nanoparticles and unveiling the critical needs for their future development for catalysis, energy, and sustainability applications.

Recent Progress on Revealing 3D Structure of Electrocatalysts Using Advanced 3D Electron Tomography: A Mini Review

Electrocatalysis plays a key role in clean energy innovation. In order to design more efficient, durable and selective electrocatalysts, a thorough understanding of the unique link between 3D structures and properties is essential yet challenging. Advanced 3D electron tomography offers an effective approach to reveal 3D structures by transmission electron microscopy. This mini-review summarizes recent progress on revealing 3D structures of electrocatalysts using 3D electron tomography. 3D electron tomography at nanoscale and atomic scale are discussed, respectively, where morphology, composition, porous structure, surface crystallography and atomic distribution can be revealed and correlated to the performance of electrocatalysts. (Quasi) in-situ 3D electron tomography is further discussed with particular focus on its impact on electrocatalysts' durability investigation and post-treatment. Finally, perspectives on future developments of 3D electron tomography for eletrocatalysis is discussed.

Direct Observation of Three-Dimensional Atomic Structure of Twinned Metallic Nanoparticles and Their Catalytic Properties

We determined a full 3D atomic structure of a dumbbell-shaped Pt nanoparticle formed by a coalescence of two nanoclusters using deep learning assisted atomic electron tomography. Formation of a double twin boundary was clearly observed at the interface, while substantial anisotropy and disorder were also found throughout the nanodumbbell. This suggests that the diffusion of interfacial atoms mainly governed the coalescence process, but other dynamic processes such as surface restructuring and plastic deformation were also involved. A full 3D strain tensor was clearly mapped, which allows direct calculation of the oxygen reduction reaction activity at the surface. Strong tensile strain was found at the protruded region of the nanodumbbell, which results in an improved catalytic activity on {100} facets. This work provides important clues regarding the coalescence mechanism and the relation between the atomic structure and catalytic property at the single-atom level.

Structural Analysis of Molecular Materials Using the Pair Distribution Function

This is a review of atomic pair distribution function (PDF) analysis as applied to the study of molecular materials. The PDF method is a powerful approach to study short- and intermediate-range order in materials on the nanoscale. It may be obtained from total scattering measurements using X-rays, neutrons, or electrons, and it provides structural details when defects, disorder, or structural ambiguities obscure their elucidation directly in reciprocal space. While its uses in the study of inorganic crystals, glasses, and nanomaterials have been recently highlighted, significant progress has also been made in its application to molecular materials such as carbons, pharmaceuticals, polymers, liquids, coordination compounds, composites, and more. Here, an overview of applications toward a wide variety of molecular compounds (organic and inorganic) and systems with molecular components is presented. We then present pedagogical descriptions and tips for further implementation. Successful utilization of the method requires an interdisciplinary consolidation of material preparation, high quality scattering experimentation, data processing, model formulation, and attentive scrutiny of the results. It is hoped that this article will provide a useful reference to practitioners for PDF applications in a wide realm of molecular sciences, and help new practitioners to get started with this technique.

Three-dimensional atomic packing in amorphous solids with liquid-like structure

Liquids and solids are two fundamental states of matter. However, our understanding of their three-dimensional atomic structure is mostly based on physical models. Here we use atomic electron tomography to experimentally determine the three-dimensional atomic positions of monatomic amorphous solids, namely a Ta thin film and two Pd nanoparticles. We observe that pentagonal bipyramids are the most abundant atomic motifs in these amorphous materials. Instead of forming icosahedra, the majority of pentagonal bipyramids arrange into pentagonal bipyramid networks with medium-range order. Molecular dynamics simulations further reveal that pentagonal bipyramid networks are prevalent in monatomic metallic liquids, which rapidly grow in size and form more icosahedra during the quench from the liquid to the glass state. These results expand our understanding of the atomic structures of amorphous solids and will encourage future studies on amorphous-crystalline phase and glass transitions in non-crystalline materials with three-dimensional atomic resolution.

Revisiting the factors influencing the magnetic resonance contrast of Gd2O3 nanoparticles

Gadolinium oxide nanoparticles (GONs) have the potential to be one of the best candidates for the contrast agents of magnetic resonance imaging. Even though the influence of parameters on the relaxation has been substantially demonstrated, the variation of the r ₁ of GONs with a similar structure and surface chemistry implied our limited understanding. We herein synthesized GONs with adjustable size, shape, and crystallinity, modified them with a series of molecules with different acidities, and recorded their r ₁ values and imaging contrast. Our results showed that the isoelectric point could be regarded as an indicator of the relaxation covering the influence of both surface modification and size, which highlighted the impact of protons dissociated from the contrast agents. We further showed that the nanoparticles with lower crystallinity possess higher relaxivity, and this phenomenon manifested significantly under a low field. Our work clarified that the longitudinal relaxivity of Gd₂O₃ nanoparticles is sensitively dependent on the numbers of H⁺ generated from the surface and in the environment, which may shed light on developing high-performance nanoparticulate T ₁ contrast agents.

Recent Advances and Prospects in Colloidal Nanomaterials

Colloidal nanomaterials of metals, metal oxides, and metal chalcogenides have attracted great attention in the past decade owing to their potential applications in optoelectronics, catalysis, and energy conversion. Introduction of various synthetic routes has resulted in diverse colloidal nanostructured materials with well-controlled size, shape, and composition, enabling the systematic study of their intriguing physicochemical, optoelectronic, and chemical properties. Furthermore, developments in the instrumentation have offered valuable insights into the nucleation and growth mechanism of these nanomaterials, which are crucial in designing prospective materials with desired properties. In this perspective, recent advances in the colloidal synthesis and mechanism studies of nanomaterials of metal chalcogenides, metals, and metal oxides are discussed. In addition, challenges in the characterization and future direction of the colloidal nanomaterials are provided.

Making the Most of your Electrons: Challenges and Opportunities in Characterizing Hybrid Interfaces with STEM

Inspired by the unique architectures composed of hard and soft materials in natural and biological systems, synthetic hybrid structures and associated soft-hard interfaces have recently evoked significant interest. Soft matter is typically dominated by fluctuations even at room temperature, while hard matter (which often serves as the substrate or anchor for the soft component) is governed by rigid mechanical behavior. This dichotomy offers considerable opportunities to leverage the disparate properties offered by these components across a wide spectrum spanning from basic science to engineering insights with significant technological overtones. Such hybrid structures, which include polymer nanocomposites, DNA functionalized nanoparticle superlattices and metal organic frameworks to name a few, have delivered promising insights into the areas of catalysis, environmental remediation, optoelectronics, medicine, and beyond. The interfacial structure between these hard and soft phases exists across a variety of length scales and often strongly influence the functionality of hybrid systems. While scanning/transmission electron microscopy (S/TEM) has proven to be a valuable tool for acquiring intricate molecular and nanoscale details of these interfaces, the unusual nature of hybrid composites presents a suite of challenges that make assessing or establishing the classical structure-property relationships especially difficult. These include challenges associated with preparing electron-transparent samples and obtaining sufficient contrast to resolve the interface between dissimilar materials given the dose sensitivity of soft materials. We discuss each of these challenges and supplement a review of recent developments in the field with additional experimental investigations and simulations to present solutions for attaining a nano or atomic-level understanding of these interfaces. These solutions present a host of opportunities for investigating and understanding the role interfaces play in this unique class of functional materials.

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning

Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.

Defect engineering of oxide perovskites for catalysis and energy storage: synthesis of chemistry and materials science

Oxide perovskites have emerged as an important class of materials with important applications in many technological areas, particularly thermocatalysis, electrocatalysis, photocatalysis, and energy storage. However, their implementation faces numerous challenges that are familiar to the chemist and materials scientist. The present work surveys the state-of-the-art by integrating these two viewpoints, focusing on the critical role that defect engineering plays in the design, fabrication, modification, and application of these materials. An extensive review of experimental and simulation studies of the synthesis and performance of oxide perovskites and devices containing these materials is coupled with exposition of the fundamental and applied aspects of defect equilibria. The aim of this approach is to elucidate how these issues can be integrated in order to shed light on the interpretation of the data and what trajectories are suggested by them. This critical examination has revealed a number of areas in which the review can provide a greater understanding. These include considerations of (1) the nature and formation of solid solutions, (2) site filling and stoichiometry, (3) the rationale for the design of defective oxide perovskites, and (4) the complex mechanisms of charge compensation and charge transfer. The review concludes with some proposed strategies to address the challenges in the future development of oxide perovskites and their applications.

Capturing 3D atomic defects and phonon localization at the 2D heterostructure interface

The three-dimensional (3D) local atomic structures and crystal defects at the interfaces of heterostructures control their electronic, magnetic, optical, catalytic, and topological quantum properties but have thus far eluded any direct experimental determination. Here, we use atomic electron tomography to determine the 3D local atomic positions at the interface of a MoS2-WSe2 heterojunction with picometer precision and correlate 3D atomic defects with localized vibrational properties at the epitaxial interface. We observe point defects, bond distortion, and atomic-scale ripples and measure the full 3D strain tensor at the heterointerface. By using the experimental 3D atomic coordinates as direct input to first-principles calculations, we reveal new phonon modes localized at the interface, which are corroborated by spatially resolved electron energy-loss spectroscopy. We expect that this work will pave the way for correlating structure-property relationships of a wide range of heterostructure interfaces at the single-atom level.

Lattice distortion of CaF2 nanocrystals for shortening their 19F longitude relaxation time

As a promising ¹⁹F MRI tracer, the relatively slow lattice-spin relaxation of CaF₂ nanocrystals leads to an unacceptable scanning time in MR imaging, hampering their application. We herein controlled the size and lattice distortion of CaF₂ nanocrystals and showed that the shortened interplanar spacing pronouncedly sped up the longitude relaxation.

Limited-angle computed tomography with deep image and physics priors

Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. One major challenge in some applications, such as in electron or x-ray tomography systems, is that the projections cannot be gathered over all the angles due to the sample holder setup or shape of the sample. This results in an ill-posed problem called the limited angle reconstruction problem. Typical image reconstruction in this setup leads to distortion and artifacts, thereby hindering a quantitative evaluation of the results. To address this challenge, we use a generative model to effectively constrain the solution of a physics-based approach. Our approach is self-training that can iteratively learn the nonlinear mapping from partial projections to the scanned object. Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training. We also complement our approach with total-variation regularization to handle high-frequency noise in reconstructions and implement a solver based on alternating direction method of multipliers. We present numerical results for various degrees of missing angle range and noise levels, which demonstrate the effectiveness of the proposed approach.

Advances and Applications of Atomic-Resolution Scanning Transmission Electron Microscopy

Although scanning transmission electron microscopy (STEM) images of individual heavy atoms were reported 50 years ago, the applications of atomic-resolution STEM imaging became wide spread only after the practical realization of aberration correctors on field-emission STEM/TEM instruments to form sub-Ångstrom electron probes. The innovative designs and advances of electron optical systems, the fundamental understanding of electron–specimen interaction processes, and the advances in detector technology all played a major role in achieving the goal of atomic-resolution STEM imaging of practical materials. It is clear that tremendous advances in computer technology and electronics, image acquisition and processing algorithms, image simulations, and precision machining synergistically made atomic-resolution STEM imaging routinely accessible. It is anticipated that further hardware/software development is needed to achieve three-dimensional atomic-resolution STEM imaging with single-atom chemical sensitivity, even for electron-beam-sensitive materials. Artificial intelligence, machine learning, and big-data science are expected to significantly enhance the impact of STEM and associated techniques on many research fields such as materials science and engineering, quantum and nanoscale science, physics and chemistry, and biology and medicine. This review focuses on advances of STEM imaging from the invention of the field-emission electron gun to the realization of aberration-corrected and monochromated atomic-resolution STEM and its broad applications.

Probing atom dynamics of excited Co-Mo-S nanocrystals in 3D

Advances in electron microscopy have enabled visualizations of the three-dimensional (3D) atom arrangements in nano-scale objects. The observations are, however, prone to electron-beam-induced object alterations, so tracking of single atoms in space and time becomes key to unravel inherent structures and properties. Here, we introduce an analytical approach to quantitatively account for atom dynamics in 3D atomic-resolution imaging. The approach is showcased for a Co-Mo-S nanocrystal by analysis of time-resolved in-line holograms achieving ~1.5 Å resolution in 3D. The analysis reveals a decay of phase image contrast towards the nanocrystal edges and meta-stable edge motifs with crystallographic dependence. These findings are explained by beam-stimulated vibrations that exceed Debye-Waller factors and cause chemical transformations at catalytically relevant edges. This ability to simultaneously probe atom vibrations and displacements enables a recovery of the pristine Co-Mo-S structure and establishes, in turn, a foundation to understand heterogeneous chemical functionality of nanostructures, surfaces and molecules.

The authors introduce an analytical approach for quantitative analysis of 3D atom dynamics during electron microscopy. They image a Co-Mo-S nanocrystal with 1.5 Å resolution, and observe chemical transformations caused by beam-stimulated vibrations.

NUDIM: A non-uniform fast Fourier transform based dual-space constraint iterative reconstruction method in biological electron tomography

Electron tomography, a powerful imaging tool for studying 3D structures of macromolecular assemblies, always suffers from imperfect reconstruction with limited resolution due to the intrinsic low signal-to-noise ratio (SNR) and inaccessibility to certain tilt angles induced by radiation damage or mechanical limitation. In order to compensate for such insufficient data with low SNR and further improve imaging resolution, prior knowledge constraints about the objects in both real space and reciprocal space are thus exploited during tomographic reconstruction. However, direct Fast Fourier transform (FFT) between real space and reciprocal space remains extraordinarily challenging owing to their inconsistent grid sampling modes, e.g. regular and uniform grid sampling in real space whereas radial or polar grid sampling in reciprocal space. In order to solve such problem, a technique of non-uniform fast Fourier transform (NFFT) has been developed to transform efficiently between non-uniformly sampled grids in real and reciprocal space with sufficient accuracy. In this work, a Non-Uniform fast Fourier transform based Dual-space constraint Iterative reconstruction Method (NUDIM) applicable to biological electron tomography is proposed with a combination of basic concepts from equally sloped tomography (EST) and NFFT based reconstruction. In NUDIM, the use of NFFT can circumvent such grid sampling inconsistency and thus alleviate the stringent equally-sloped sampling requirement in EST reconstruction, while the dual-space constraint iterative procedure can dramatically enhance reconstruction quality. In comparison with conventional reconstruction methods, NUDIM is numerically and experimentally demonstrated to produce superior reconstruction quality with higher contrast, less noise and reduced missing wedge artifacts. More importantly, it is also capable of retrieving part of missing information from a limited number of projections.