Size-Dependent Grain-Boundary Structure with Improved Conductive and Mechanical Stabilities in Sub-10-nm Gold Crystals

Deciphering the Crystallographic Effect in Radially Architectured Polycrystalline Layered Cathode Materials for Lithium-Ion Batteries

Layered oxide cathode materials with primary-secondary architecture face challenges of inhomogeneous Li+ diffusion and chemomechanical degradation due to misorientations between equiaxed primary particles. Although a radial architecture, featuring elongated grains, is widely believed to enhance diffusion, it does not address the root cause of chemomechanical failure-crystallographic misorientation. The impact of crystallography on the electrochemical performance of radially architectured secondary particles, compared to conventional designs, remains poorly understood. Here, by combining transmission Kikuchi diffraction with multimodal characterization, we decipher the crucial role of crystallography in the performance and stability of polycrystalline high-Ni layered oxide cathode materials. Contrary to the conventional belief that a preferential texture induced by the radial architecture is the key to performance enhancement, we uncover that the radial architecture primarily alters the misorientation distribution by introducing substantially increased low-angle grain boundaries and twin boundaries that significantly mitigate chemomechanical cracking and phase degradation. This crystallographic refinement facilitates enhanced Li+ diffusion between primary particles, ultimately boosting the rate capability and long-term stability of the cathodes. By quantitatively uncovering the crystallographic influence on performance, this work provides a new avenue for optimizing Li+ diffusion kinetics and chemomechanical resilience in polycrystalline cathode materials through crystallographic engineering.

Wafer-Scale Nanoprinting of 3D Interconnects beyond Cu

Cloud operations and services, as well as many other modern computing tasks, require hardware that is run by very densely packed integrated circuits (ICs) and heterogenous ICs. The performance of these ICs is determined by the stability and properties of the interconnects between the semiconductor devices and ICs. Although some ICs with 3D interconnects are commercially available, there has been limited progress on 3D printing utilizing emerging nanomaterials. Moreover, laying out reliable 3D metal interconnects in ICs with the appropriate electrical and physical properties remains challenging. Here, we propose high-throughput 3D interconnection with nanoscale precision by leveraging lines of forces. We successfully nanoprinted multiscale and multilevel Au, Ir, and Ru 3D interconnects on the wafer scale in non-vacuum conditions using a pulsed electric field. The ON phase of the pulsed field initiates in situ printing of nanoparticle (NP) deposition into interconnects, whereas the OFF phase allows the gas flow to evenly distribute the NPs over an entire wafer. Characterization of the 3D interconnects confirms their excellent uniformity, electrical properties, and free-form geometries, far exceeding those of any 3D-printed interconnects. Importantly, their measured resistances approach the theoretical values calculated here. The results demonstrate that 3D nanoprinting can be used to fabricate thinner and faster interconnects, which can enhance the performance of dense ICs; therefore, 3D nanoprinting can complement lithography and resolve the challenges encountered in the fabrication of critical device features.

Stress-Driven Grain Boundary Structural Transition in Diamond by Machine Learning Potential

Understanding the structural dynamics of carbon grain boundaries, particularly in diamond, is essential for advancing next-generation device applications. Carbon's diverse allotropes, driven by its versatile chemical bonding, hold immense potential, yet analyzing these boundaries is challenging due to the limitations of experimental techniques and the computational demands of ab initio molecular dynamics simulations. In this study, a machine learning-based molecular dynamics potential, rigorously trained on ab initio data, that accurately predicts structural transitions in incoherent twin boundaries within diamond is introduced. This potential reveals the atomic-scale mechanisms driving these transitions and identifies an 80% reduction in interfacial thermal conductance during the grain boundary transition. These findings provide deep insights into the complex behavior of diamond grain boundaries, uncovering a novel mechanism that regulates thermal properties and paving the way for enhanced thermal management in diamond-based technologies.

Resolving electrochemically triggered topological defect dynamics and structural degradation in layered oxides

Understanding topological defects-controlled structural degradation of layered oxides-a key cathode material for high-performance lithium-ion batteries-plays a critical role in developing next-generation cathode materials. Here, by constructing a nanobattery in an electron microscope enabling atomic-scale monitoring of electrochemcial reactions, we captured the electrochemically driven atomistic dynamics and evolution of dislocations-a most important topological defect in material. We deciphered how dislocations nucleate, move, and annihilate within layered cathodes at the atomic scale. Specifically, we found two types of dislocation configurations, i.e., single dislocations and dislocation dipoles. Both pure dislocation glide/climb and mixed motions were captured, and the dislocation glide and climb velocities were first experimentally measured. Moreover, dislocation activity-mediated structural degradation such as crack nucleation, phase transformation, and lattice reorientation was unraveled. Our work provides deep insights into the atomistic dynamics of electrochemically driven dislocation activities in layered oxides.

Deep-Learning Aided Atomic-Scale Phase Segmentation toward Diagnosing Complex Oxide Cathodes for Lithium-Ion Batteries

Phase transformation─a universal phenomenon in materials─plays a key role in determining their properties. Resolving complex phase domains in materials is critical to fostering a new fundamental understanding that facilitates new material development. So far, although conventional classification strategies such as order-parameter methods have been developed to distinguish remarkably disparate phases, highly accurate and efficient phase segmentation for material systems composed of multiphases remains unavailable. Here, by coupling hard-attention-enhanced U-Net network and geometry simulation with atomic-resolution transmission electron microscopy, we successfully developed a deep-learning tool enabling automated atom-by-atom phase segmentation of intertwined phase domains in technologically important cathode materials for lithium-ion batteries. The new strategy outperforms traditional methods and quantitatively elucidates the correlation between the multiple phases formed during battery operation. Our work demonstrates how deep learning can be employed to foster an in-depth understanding of phase transformation-related key issues in complex materials.

Tension-Induced Cavitation in Li-Metal Stripping

Designing stable Li metal and supporting solid structures (SSS) is of fundamental importance in rechargeable Li-metal batteries. Yet, the stripping kinetics of Li metal and its mechanical effect on the supporting solids (including solid electrolyte interface) remain mysterious to date. Here, through nanoscale in situ observations of a solid-state Li-metal battery in an electron microscope, two distinct cavitation-mediated Li stripping modes controlled by the ratio of the SSS thickness (t) to the Li deposit's radius (r) are discovered. A quantitative criterion is established to understand the damage tolerance of SSS on the Li-metal stripping pathways. For mechanically unstable SSS (t/r < 0.21), the stripping proceeds via tension-induced multisite cavitation accompanied by severe SSS buckling and necking, ultimately leading to Li "trapping" or "dead Li" formation; for mechanically stable SSS (t/r > 0.21), the Li metal undergoes nearly planar stripping from the root via single cavitation, showing negligible buckling. This work proves the existence of an electronically conductive precursor film coated on the interior of solid electrolytes that however can be mechanically damaged, and it is of potential importance to the design of delicate Li-metal supporting structures to high-performance solid-state Li-metal batteries.

Resolving complex intralayer transition motifs in high-Ni-content layered cathode materials for lithium-ion batteries

High-Ni-content layered materials are promising cathodes for next-generation lithium-ion batteries. However, investigating the atomic configurations of the delithiation-induced complex phase boundaries and their transitions remains challenging. Here, by using deep-learning-aided super-resolution electron microscopy, we resolve the intralayer transition motifs at complex phase boundaries in high-Ni cathodes. We reveal that an O3 → O1 transformation driven by delithiation leads to the formation of two types of O1-O3 interface, the continuous- and abrupt-transition interfaces. The interfacial misfit is accommodated by a continuous shear-transition zone and an abrupt structural unit, respectively. Atomic-scale simulations show that uneven in-plane Li⁺ distribution contributes to the formation of both types of interface, and the abrupt transition is energetically more favourable in a delithiated state where O1 is dominant, or when there is an uneven in-plane Li⁺ distribution in a delithiated O3 lattice. Moreover, a twin-like motif that introduces structural units analogous to the abrupt-type O1-O3 interface is also uncovered. The structural transition motifs resolved in this study provide further understanding of shear-induced phase transformations and phase boundaries in high-Ni layered cathodes.

Higher Damping Capacities in Gradient Nanograined Metals

The capability of damping mechanical energy in polycrystalline metals depends on the activities of defects such as dislocation and grain boundary (GB). However, operating defects has the opposite effect on strength and damping capacity. In the quest for high damping metals, maintaining the level of strength is desirable in practice. In this work, gradient nanograined structure is considered as a candidate for high-damping metals. The atomistic simulations show that the gradient nanograined models exhibit enhanced damping capacities compared with the homogeneous counterparts. The property can be attributed to the long-range order of GB orientations in gradient grains, where shear stresses facilitate GB sliding. Combined with the extraordinary mechanical properties, the gradient structure achieves a strength-ductility-damping synergy. The results provide promising solutions to the conflicts between mechanical properties and damping capacity in polycrystalline metals.

High Reversible Strain in Nanotwinned Metals

Development of bulk metals exhibiting large reversible strain is of great interest, owing to their potential applications in flexible electronic devices. Bulk metals with nanometer-scale twins have demonstrated high strength, good ductility, and promising electrical conductivity. Here, ultrahigh reversible strain as high as ∼7.8% was observed in bent twin lamellae with 1-2 nm thickness in nanotwinned metals, where the maximum reversible strain increases with the reduction in twin lamella thickness. This high reversible strain is attributed to the suppression of dislocation nucleation, including both hard mode dislocations in the bent twin lamellae, while soft mode dislocations along twin boundaries have insignificant contribution. In situ transmission electron microscopy experiments show that higher recoverability was achieved in twinned Au nanorods compared with twin-free ones with similar aspect ratios and diameters during bending deformation, which demonstrates that the introduction of thin twin lamellae also significantly improves the shape recoverability of Au nanorods. This result introduces a novel pathway for developing bulk metals with the capability for large reversible strain.

Super-compression of large electron microscopy time series by deep compressive sensing learning

The development of ultrafast detectors for electron microscopy (EM) opens a new door to exploring dynamics of nanomaterials; however, it raises grand challenges for big data processing and storage. Here, we combine deep learning and temporal compressive sensing (TCS) to propose a novel EM big data compression strategy. Specifically, TCS is employed to compress sequential EM images into a single compressed measurement; an end-to-end deep learning network is leveraged to reconstruct the original images. Owing to the significantly improved compression efficiency and built-in denoising capability of the deep learning framework over conventional JPEG compression, compressed videos with a compression ratio of up to 30 can be reconstructed with high fidelity. Using this approach, considerable encoding power, memory, and transmission bandwidth can be saved, allowing it to be deployed to existing detectors. We anticipate the proposed technique will have far-reaching applications in edge computing for EM and other imaging techniques.

Metallic nanocrystals with low angle grain boundary for controllable plastic reversibility

Advanced nanodevices require reliable nanocomponents where mechanically-induced irreversible structural damage should be largely prevented. However, a practical methodology to improve the plastic reversibility of nanosized metals remains challenging. Here, we propose a grain boundary (GB) engineering protocol to realize controllable plastic reversibility in metallic nanocrystals. Both in situ nanomechanical testing and atomistic simulations demonstrate that custom-designed low-angle GBs with controlled misorientation can endow metallic bicrystals with endurable cyclic deformability via GB migration. Such fully reversible plasticity is predominantly governed by the conservative motion of Shockley partial dislocation pairs, which fundamentally suppress damage accumulation and preserve the structural stability. This reversible deformation is retained in a broad class of face-centred cubic metals with low stacking fault energies when tuning the GB structure, external geometry and loading conditions over a wide range. These findings shed light on practical advances in promoting cyclic deformability of metallic nanomaterials.

Improving the reversible plastic deformability and damage tolerance of nanosized metals remains challenging. Here, the authors custom-design low angle grain boundaries in metallic bicrystals to achieve controllable plastic reversibility via fully conservative grain boundary migration.

In situ atomic-scale observation of grain size and twin thickness effect limit in twin-structural nanocrystalline platinum

Twin-thickness-controlled plastic deformation mechanisms are well understood for submicron-sized twin-structural polycrystalline metals. However, for twin-structural nanocrystalline metals where both the grain size and twin thickness reach the nanometre scale, how these metals accommodate plastic deformation remains unclear. Here, we report an integrated grain size and twin thickness effect on the deformation mode of twin-structural nanocrystalline platinum. Above a ∼10 nm grain size, there is a critical value of twin thickness at which the full dislocation intersecting with the twin plane switches to a deformation mode that results in a partial dislocation parallel to the twin planes. This critical twin thickness value varies from ∼6 to 10 nm and is grain size-dependent. For grain sizes between ∼10 to 6 nm, only partial dislocation parallel to twin planes is observed. When the grain size falls below 6 nm, the plasticity switches to grain boundary-mediated plasticity, in contrast with previous studies, suggesting that the plasticity in twin-structural nanocrystalline metals is governed by partial dislocation activities.

The deformation mechanisms of micron-sized twinned metals are well-understood, but it is not so for twinned nanocrystalline metals. Here, the authors use high resolution microscopy to image the deformation of nanocrystalline twinned platinum and show that grain boundary behaviors dominate plasticity below 6 nm.

Atomistic and dynamic structural characterizations in low-dimensional materials: recent applications of in situ transmission electron microscopy

In situ transmission electron microscopy has achieved remarkable advances for atomic-scale dynamic analysis in low-dimensional materials and become an indispensable tool in view of linking a material's microstructure to its properties and performance. Here, accompanied with some cutting-edge researches worldwide, we briefly review our recent progress in dynamic atomistic characterization of low-dimensional materials under external mechanical stress, thermal excitations and electrical field. The electron beam irradiation effects in metals and metal oxides are also discussed. We conclude by discussing the likely future developments in this area.

Size Dependence of Grain Boundary Migration in Metals under Mechanical Loading

The greatly increased grain boundary (GB) mobility in nanograined metals under mechanical loading is distinguished from that in their coarse-grained counterparts. The feature leads to softening of nanograined materials and deviation of strength from the classical Hall-Petch relationship. In this Letter, grain size dependences of GB migration in nanograined Ag, Cu, and Ni under tension were investigated quantitatively in a wide size range. As grain size decreases from submicron, GB migration intensifies and then diminishes below a critical grain size. The GB migration peaks at about 80, 75, and 38 nm in Ag, Cu, and Ni, respectively. The suppression of GB migration below a critical size can be attributed to GB relaxation during sample processing or by postthermal annealing. With relaxed GBs the governing deformation mechanism of nanograins shifts from GB migration to formation of through-grain twins or stacking faults. GB relaxation, analogous to GB segregation, offers a novel approach to stabilizing nanograined materials under mechanical loading.