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At least 91 records · Page 5

Realizing tunable Fermi level in SnTe by defect control

The tuning of the Fermi level in tin telluride, a topological crystalline insulator, is essential for accessing its unique surface states and optimizing its electronic properties for applications such as spintronics and quantum computing. In this study, we demonstrate that the Fermi level in tin telluride can be effectively modulated by controlling the tin concentration during chemical vapor deposition synthesis. By introducing tin-rich conditions, we observed a blue shift in the x-ray photoelectron spectroscopy core-level peaks of both tin and tellurium, indicating an upward shift in the Fermi level. Further, this shift is corroborated by a decrease in work function values measured via ultraviolet photoelectron spectroscopy, confirming the suppression of Sn vacancies. Our findings provide a low-cost, scalable method to achieve tunable Fermi levels in tin telluride, offering a significant advancement in the development of materials with tailored electronic properties for next-generation technological applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND

Grain engineering for efficient near-infrared perovskite light-emitting diodes

Metal halide perovskites show promise for next-generation light-emitting diodes, particularly in the near-infrared range, where they outperform organic and quantum-dot counterparts. However, they still fall short of costly III-V semiconductor devices, which achieve external quantum efficiencies above 30% with high brightness. Among several factors, controlling grain growth and nanoscale morphology is crucial for further enhancing device performance. This study presents a grain engineering methodology that combines solvent engineering and heterostructure construction to improve light outcoupling efficiency and defect passivation. Solvent engineering enables precise control over grain size and distribution, increasing light outcoupling to ~40%. Constructing 2D/3D heterostructures with a conjugated cation reduces defect densities and accelerates radiative recombination. The resulting near-infrared perovskite light-emitting diodes achieve a peak external quantum efficiency of 31.4% and demonstrate a maximum brightness of 929 W sr -1 m -2 . These findings indicate that perovskite light-emitting diodes have potential as cost-effective, high-performance near-infrared light sources for practical applications.

42 ENGINEERING

Highly Crystalline and Porous Borocarbonitrides as Metal‐Free Catalysts for Boosted N‐Heterocycle Dehydrogenation

Safe and efficient hydrogen storage is pivotal for enabling a clean hydrogen economy. Liquid organic hydrogen carriers (LOHCs) offer a practical solution, but their deployment is hindered by the lack of highly active and economical dehydrogenation catalysts. In this work, we report a metal‐free catalyst design that overcomes the long‐standing trade‐off between crystallinity and surface area in two‐dimensional frameworks for highly efficient dehydrogenation of LOHCs. A flux‐assisted reconstruction strategy transforms amorphous borocarbonitrides (AM‐BCN) into highly crystalline, defect‐rich BCN nanosheets (C‐BCN) with large surface area and accessible porosity, as confirmed by complementary spectroscopic, x‐ray, and neutron analyses. C‐BCN catalyzes the acceptor‐less dehydrogenation of aza‐fused LOHCs with quantitative hydrogen release under mild conditions, outperforming AM‐BCN and previously reported metal‐free scaffolds. Mechanistic insights from x‐ray, neutron scattering, and theoretical calculations identify open C‐B‐N and N‐B‐N defect motifs as the primary active sites. This work establishes a generalizable strategy to engineer crystalline, porous, defect‐rich two‐dimensional lattices and demonstrates a highly active metal‐free platform for LOHC dehydrogenation with high‐purity H 2 generation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

High‐Temperature Single‐Photon Emission From Covalently Functionalized van der Waals Heterostructures

Two-dimensional (2D) transition metal dichalcogenides (TMDs) such as tungsten diselenide (WSe 2 ) are attractive nanomaterials for quantum information applications due to single-photon emission (SPE) from intrinsic atomic defects. Defect and strain engineering techniques have been developed to produce high purity, deterministically placed SPE in WSe 2 . However, a major challenge in the application of these techniques is the low temperature required to observe defect-bound TMD exciton emission, typically limiting SPE to T < 30 K. SPE at higher temperatures either loses purity or requires integration into complex devices such as optical cavities. Here, 2D heterostructure engineering and molecular functionalization are combined to achieve high purity (>90%) SPE in strained WSe 2 persisting to over T = 90 K. Covalent diazonium functionalization of graphite in layered WSe 2 /graphite heterostructures maintains high purity up to T = 90 K and single-photon source integrity up to T = 115 K. This method preserves the best qualities of SPE from WSe 2 while increasing working temperature to more than three times the typical range. This work demonstrates the versatility of surface functionalization and heterostructure design to synergistically improve the properties of quantum emission and offers new insights into the phenomenon of SPE from 2D materials.

2D materials

Mesoscale atomic engineering in a crystal lattice

Controlling individual atoms using lasers, ion traps and scanning probe tips has transformed our understanding of matter and enabled breakthroughs in quantum science. Extending this control into three-dimensional (3D) solids and across mesoscopic scales, however, remains a foundational challenge. Electron irradiation in electron microscopes is known to induce atomic displacements, and atomic manipulation has been proposed and demonstrated. Yet repeated and deterministic control has remained elusive. Here, in this study, we demonstrate deterministic atomic engineering in a 3D crystal, creating ordered arrangements of more than 40,000 user-defined defects within minutes across a 150 nm × 100 nm × 13 nm volume. By steering individual Cr atoms in the magnetic semiconductor CrSBr into selected interstitial sites using an electron beam directed with sub-20-pm-scale accuracy, we create vacancy–interstitial complexes. The resulting impurity array forms a mesoscale crystal embedded within the host lattice, a new form of engineered artificial matter that remains stable at room temperature and outside the microscope. By tracking Cr atom displacements, we identify conditions under which the defect structures are predictable. Our calculations suggest that these defects form correlated impurity states with intra-defect optical transitions and inter-defect kinetic and Coulomb interactions. This establishes a generalizable platform for atomic defect engineering at mesoscopic, and potentially macroscopic, scales, opening opportunities for scalable quantum technologies, including deterministic colour-centre placement, quantum simulation of many-body lattice models and atomic-scale manufacturing.

74 ATOMIC AND MOLECULAR PHYSICS

Probing the role of local tunnel variations in early-stage lithiation of α-MnO₂ nanowires via in situ TEM

Understanding lithium-ion transport in tunnel-structured manganese oxides is essential for designing high-performance lithium-ion battery electrode materials. Here, we elucidate the early-stage lithiation mechanism of potassium-stabilized α-MnO 2 nanowires using in situ transmission electron microscopy (TEM) coupled with electron energy-loss spectroscopy (EELS), high-resolution TEM (HRTEM), and geometric phase analysis (GPA). Real-time TEM imaging reveals clear volume expansion at the reaction front, while EELS analysis uncovers lithium-ion diffusion far beyond this region, where no visible expansion is observed, indicating fast, defect-assisted transport. GPA and HRTEM analyses show that localized tensile and compressive strain fields, originating from pre-existing local tunnel structural variations, persist after lithiation. The tensile-strained regions enable lithium-ion insertion with minimal lattice distortion, offering additional free volume that facilitates rapid lithium-ion accommodation ahead of the structural transformation. Our results demonstrate a local tunnel variation-mediated fast diffusion pathway that precedes bulk reaction, underscoring the critical role of local strain in enabling early-stage lithium transport. Given the structural versatility of MnO 2 and its ability to accommodate diverse atomic arrangements beyond the well-known tunnel phases (β-, γ-, δ-, λ-, R-phases), our findings highlight the importance of understanding and engineering local structural environments. This work provides fundamental insights into the interplay between defects, strain, and ion dynamics, and presents defect engineering as a promising approach to enhance both rate performance and structural stability in manganese-based cathodes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND

Anomalous thermal transport in Eshelby twisted van der Waals nanowires

Dislocations in van der Waals (vdW) layered nanomaterials induce strain and structural changes that substantially impact thermal transport. Understanding these effects could enable the manipulation of dislocations for improved thermoelectric and optoelectronic applications, but experimental insights remain limited. Here, in this study, we use synthetic Eshelby twisted vdW GeS nanowires (NWs) with single screw dislocations as a model system to explore the interplay between dislocation-induced structural modifications and lattice thermal conductivity. Our measurements reveal a monoclinic structure stabilized by the dislocation, leading to a substantial drop in thermal conductivity for larger-diameter NWs (70% at room temperature), supported by first-principles calculations. Interestingly, we also find an anomalous enhancement of thermal conductivity with decreasing diameter in twisted NWs, contrary to typical trends in non-twisted GeS NWs. This is attributed to increased conductivity near the NW cores due to compressive strain around the central dislocations, and aligns with a density-functional-theory-informed core–shell model. Our results highlight the critical role of dislocations in thermal conduction, providing fundamental insights for defect and strain engineering in advanced thermal applications.

36 MATERIALS SCIENCE

Amorphous boron nitride: synthesis, properties and device application

Amorphous boron nitride (a-BN) exhibits remarkable electrical, optical, and chemical properties, alongside robust mechanical stability, making it a compelling material for advanced applications in nanoelectronics and photonics. This review comprehensively examines the unique characteristics of a-BN, emphasizing its electrical and optical attributes, state-of-the-art synthesis techniques, and device applications. Key advancements in low-temperature growth methods for a-BN are highlighted, offering insights into their potential for integration into scalable, CMOS-compatible platforms. Additionally, the review discusses the emerging role of a-BN as a dielectric material in electronic and photonic devices, serving as substrates, encapsulation layers, and gate insulators. Finally, perspectives on future challenges, including defect control, interface engineering, and scalability, are presented, providing a roadmap for realizing the full potential of a-BN in next-generation device technologies.

36 MATERIALS SCIENCE

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

Anisotropic Response of Defect Bound States to the Magnetic Field in Epitaxial FeSn Films

Crystal defects, whether intrinsic or engineered, drive many fundamental phenomena and novel functionalities of quantum materials. Here, we report symmetry-breaking phenomena induced by Sn vacancy defects on the surface of epitaxial Kagome antiferromagnetic FeSn films using low-temperature scanning tunneling microscopy and spectroscopy. Near the single Sn vacancy, anisotropic quasiparticle interference patterns are observed in the differential conductance dI/dV maps, breaking the 6-fold rotational symmetry of the Kagome layer. Furthermore, the Sn vacancy defects induce bound states that exhibit anomalous Zeeman shift under an out-of-plane magnetic field, where the energy of the bound states moves linearly toward higher energy independent of the direction of the magnetic field. Under an in-plane magnetic field, the shift of the bound state energy also shows a 2-fold oscillating behavior as a function of the azimuth angle. These findings demonstrate defectenabled new functionalities in Kagome antiferromagnets for potential applications in nanoscale spintronic devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC

Atomic-scale visualization of defect-induced localized vibrations in GaN

Phonon engineering is crucial for thermal management in GaN-based power devices, where phonon-defect interactions limit performance. However, detecting nanoscale phonon transport constrained by III-nitride defects is challenging due to limited spatial resolution. Here, we used advanced scanning transmission electron microscopy and electron energy loss spectroscopy to examine vibrational modes in a prismatic stacking fault in GaN. By comparing experimental results with ab initio calculations, we identified three types of defect-derived modes: localized defect modes, a confined bulk mode, and a fully extended mode. Additionally, the PSF exhibits a smaller phonon energy gap and lower acoustic sound speeds than defect-free GaN, suggesting reduced thermal conductivity. Our study elucidates the vibrational behavior of a GaN defect via advanced characterization methods and highlights properties that may affect thermal behavior.

36 MATERIALS SCIENCE

Mechanochemical topological defects in an active nematic

We propose a reaction-diffusion system that converts topological information of an active nematic into chemical signals. We show that a curvature-activated reaction dipole is sufficient for creating a system that dynamically senses topology by producing a concentration field possessing local extrema coinciding with ±$\frac{1}{2}$ defects. The enabling term is analogous to polarization charge density seen in dielectric materials. We demonstrate the ability of this system to identify defects in both passive and active nematics. Our results illustrate that a relatively simple feedback scheme, expressed as a system of partial differential equations, is capable of producing chemical signals in response to inherently nonlocal structures in anisotropic media. Here, we posit that such coarse-grained systems can help generate testable hypotheses for regulated processes in biological systems, such as morphogenesis, and motivate the creation of bio-inspired materials that utilize dynamic coupling between nematic structure and biochemistry.

42 ENGINEERING

Impact of threading dislocations on the V-defect assisted lateral carrier injection and recombination in InGaN quantum well LEDs

The nonuniform hole distribution between InGaN quantum wells (QWs) of light emitting diodes (LEDs) has a negative impact on LED efficiency. The uniformity can be increased by using lateral hole injection through sidewalls of V-defects, which form at threading dislocations. However, the inherent coupling between the V-defects and dislocations might affect efficiency of the hole injection and nonradiative recombination. In this work, we have tested the possible impact of the dislocations on the injection and recombination by means of scanning near-field electroluminescence and photoluminescence spectroscopy on single green-emitting InGaN QW LEDs containing large (∼0.5 μm) V-defects. The measurements have not provided any evidence of a lower hole injection efficiency or enhanced nonradiative recombination at the dislocations located at the V-defect facets or their apexes. This shows that large V-defects are excellent volumetric injectors for long wavelength InGaN LEDs. Furthermore, it was established that V-defects are preferential hole injectors even in single quantum well devices. Compared to vertical injection, the V-defect injection allows lowering the operating voltage, which should contribute to an enhanced wall plug efficiency.

42 ENGINEERING

Modifications in the charge trap landscape in Hf 0.5 Zr 0.5 O 2 as a function of oxygen vacancy concentration observed with photoemission electron microscopy

Oxygen vacancies in Hf x Zr (1-x) O 2 (HZO) both contribute to stabilization of the ferroelectric orthorhombic phase and promote leakage pathways that limit the endurance of devices based on the material. Here, for this reason, the defect states of oxygen vacancies were investigated using photoemission electron microscopy (PEEM) and photoluminescence spectroscopy (PL), as their concentration was varied via ex situ laser exposure. Following a controlled oxygen vacancy reduction via visible (2.54 eV) laser dosing of HZO, deep-ultraviolet (DUV, 5.82 eV) PEEM was used to spatially probe the resulting mid-gap defect states and work function. Work function was found to increase monotonically with the laser-induced reduction in oxygen vacancy concentration culminating in a total increase near 70 meV. The change implies a Fermi level shift toward the valence band as the total available electron-filled charge states are reduced with the removal of oxygen vacancies. A reduction in charge states is corroborated by the observed lessening of both photoemission and photoluminescence intensities after laser dosing. The deduced position of the Fermi level is within a band of near-conduction band defect states produced by oxygen vacancies that are linked to endurance limiting leakage currents. Together, these results directly identify the primary role of oxygen vacancies on the defect states in HZO while demonstrating that laser exposure can be used for their modification.

42 ENGINEERING

Defect Chemistry in High‐Voltage Cathode Materials for Lithium‐Ion Batteries

High-voltage cathodes (HVCs) have emerged as a paramount role for the next-generation high-energy-density lithium-ion batteries (LIBs). However, the pursuit of HVCs comes with inherent challenges related to defective structures, which significantly impact the electrochemical performance of LIBs. The current obstacle lies in the lack of a comprehensive understanding of defects and their precise effects. This perspective aims to provide insights into defect chemistry for governing HVCs. The classifications, formation mechanisms, and evolution of defects are outlined to explore the intricate relationship between defects and electrochemical behavior. The pressing need for cutting-edge characterization techniques that comprehensively investigate defects across various temporal and spatial scales is emphasized. Building on these fundamental understandings, engineering strategies such as composition tailoring, morphology design, interface modification, and structural control to mitigate or utilize defects are thoroughly discussed for enhanced HVCs performance. Furthermore, these insights are expected to provide vital guidelines for developing high-performance HVCs for next-generation high-energy lithium-ion batteries.

cross-scale characterization

Tailoring MoS 2 for Small-Molecule Electroreduction: The Role of Metal Doping and Heterostructures

The electrification of chemical transformations central to sustainable fuel production and waste valorization, such as overall water splitting (OWS), hydrogen evolution reaction (HER), and electrochemical reduction of CO 2 (CO 2 R), presents a powerful opportunity to advance carbon-neutral energy technologies. Transition metal dichalcogenides (TMDs), particularly MoS 2 , have emerged as promising electrocatalyst candidates, owing to their abundance, tunable active sites, and defect-rich structures. This review highlights recent progress in leveraging metal doping and heterostructure engineering of MoS 2 to enhance the electrocatalytic activity and selectivity. By compiling insights from experimental studies and density functional theory (DFT) predictions, we examine how defect creation, electronic structure modification, and interface design contribute to improved charge transport and catalytic efficiency. Particular emphasis is placed on rational design principles, synthetic strategies, and operando characterization methods that provide a pathway to understanding and optimizing MoS 2 -based materials. We also discuss the challenges of stability, mechanistic ambiguity, and scaling while outlining opportunities to bridge theory and experiment. Collectively, this review underscores how defect and heterostructure engineering of MoS 2 can accelerate the development of efficient, sustainable electrocatalysts for both fuel generation and waste-to-value generation.

CO2 reduction

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY