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Physics-constrained inverse estimation of irradiation-induced strain in He–H ion-implanted 4H-SiC using nanoindentation and finite element modeling
Nanoindentation is widely used to evaluate the mechanical properties of irradiated materials; however, its potential for quantifying irradiation-induced subsurface strain remains underexplored. In this work, an integrated experimental–numerical framework based on a physics-constrained inverse modeling approach is employed to estimate the magnitude of a depth-dependent irradiation-induced strain distribution in single-crystal 4H-SiC following sequential He and H ion implantation. The approach combines depth-sensing nanoindentation, finite element modeling (FEM), and a simplex-based inverse optimization routine to calibrate a physically motivated eigenstrain profile derived from ion-damage simulations. The strain field is assumed to follow a lognormal distribution consistent with independently determined damage profiles (stopping and range of ions in matter) and is implemented in the FEM model through a depth-dependent thermal expansion formulation. By minimizing the squared error between simulated and experimental force–displacement curves, the peak tensile strain is estimated to be ∼0.91%, accompanied by an effective Young's modulus of 310 GPa and a yield strength of 16.4 GPa. Independent validation by nano-beam precession electron diffraction confirms good agreement between the reconstructed and experimentally measured out-of-plane strain profiles in both magnitude and spatial distribution. The results demonstrate that nanoindentation, when combined with physics-based inverse modeling, can provide a practical tool for quantifying irradiation-induced strain and residual stress in nuclear ceramics. This methodology offers a complementary approach to diffraction-based techniques for assessing subsurface damage in ion-irradiated materials relevant to advanced nuclear systems.
Spin Edelstein effect in irradiated topological insulator thin films
We investigate the low-temperature spin Edelstein effect in topological insulator thin films subjected to circularly polarized light. Using a high-frequency Floquet formalism, we derive an effective Hamiltonian that captures light-induced renormalization of the mass gap and Fermi velocity. Based on this model, we compute the spin Edelstein susceptibilities using the Kubo linear-response theory, accounting for both intraband and interband contributions. Our results reveal that optical driving enables strong tunability of spin polarization, with critical behavior emerging at the Floquet-engineered band structure where the effective Fermi velocity vanishes. We further demonstrate that surface asymmetry, hybridization strength, and interband relaxation processes play crucial roles in shaping the spin responses. These findings suggest promising routes for dynamically controlling spin accumulation in topological materials via light, offering new opportunities for spintronic applications.
Precipitation of Y-rich nano-oxides in FeCr alloy: An ion beam synthesis study
Y-rich nano-oxide precipitates were successfully produced by ion beam synthesis in high purity Fe–10%Cr. After annealing at 1100 °C of Y and O ion-implanted FeCr, a very high density (>1023 m−3) of small nano-oxides that are fairly uniform in size is achieved. A careful analysis of the structure and composition of these precipitates using HRTEM, STEM-HAADF (scanning transmission electron microscopy coupled with high-angle annular dark-field), and STEM-EDX (scanning transmission electron microscopy coupled with energy dispersive x-ray spectroscopy) demonstrates characteristics that are similar to those of the nano-oxides in non-Ti-doped oxide dispersion strengthened (ODS) steels. Both cubic and monoclinic Y2O3 precipitates are identified, and a Cr-rich shell has begun to form around them. In contrast to precipitates with a cubic structure, those with a monoclinic structure are observed to exhibit large distortions. Comparison of the precipitate characteristics with those from a previous study involving sequential Y, Ti, and O ion implantation into the same material indicates that the additional presence of Ti substantially reduces the size of the precipitates, consistent with observations in conventionally produced ODS steels, and stabilizes the cubic structure of yttria at the expense of the monoclinic structure. The present ion beam synthesis investigation demonstrates that the mechanism of Ti-stimulated precipitate refinement is not related to the specific precipitating structure because Ti addition promotes smaller Y–Ti precipitates as compared to pure Y2O3 ones even when these Y–Ti oxide phases have crystallographic structure and lattice parameters matching those of pure cubic yttria.
Reversing chiral selectivity in optical trapping via Babinet complementary metasurfaces
We systematically investigate the chiral trapping mechanism of Babinet complementary plasmonic metasurfaces. The original structure and its complementary one exhibit naturally opposite chiral selectivity, which can be dynamically flipped by modulating the polarization angle of the incident light. The results reveal a competitive mechanism between the electromagnetic trapping potential and the chiral trapping potential. At a critical parameter, the two trapping potentials exactly cancel, leaving chiral nanoparticles completely free. Moreover, the maximum total trapping potential shifts from the fully chiral extrema (κ = ±1) to an intrinsically preferred optimal chirality parameter (κopt ≈ ±0.7). Our results advance the understanding of chiral optical trapping and provide a theoretical foundation for chiral sorting using a single geometry and its complement.
Repulsive magnetic field-enhanced laser-induced plasma-assisted ablation for high-efficiency and high-quality sapphire microfabrication
Nanosecond laser microfabrication of sapphire remains challenging due to its broadband optical transparency and pronounced thermal effects. In this paper, a repulsive magnetic field-enhanced laser-induced plasma ablation (LIPAA) technique is proposed to improve processing efficiency and fabrication accuracy. The processing quality of sapphire microstructures is investigated at different magnetic induction intensities. The repulsive magnetic field effectively suppresses the horizontal expansion of plasma plume, forming deeper and narrower ablation craters. At the optimal magnetic induction intensity of 400 mT, the sapphire microfabrication achieves a 4.6-fold increase in materials’ removal rate and a 72% reduction in sidewall roughness compared to the LIPAA without magnetic field assistance. This improvement is attributed to the reduction of Larmor radius under the magnetic confinement, which concentrates the plasma into a smaller ablation area. A uniform microcolumn array is successfully fabricated on sapphire, demonstrating its potentials in optical device microstructures.
First-principles study of infrared, Raman, piezoelectric, and elastic properties of Mg–IV–N2 (IV = Si, Ge, Sn)
Mg–IV–N2 compounds with IV = Si, Ge, and Sn are ultra-wide bandgap semiconductors with various potential electronic and optoelectronic applications. They share the Pna21 space group crystal structure. Here, we present density functional perturbation theory calculations of the vibrational modes of these materials. We focus on the vibrational modes at the zone center to establish the relation between vibrational modes and their corresponding point-group symmetries, which determine the Raman and infrared spectra but also report the full Brillouin zone phonon dispersions and density of states. We also determine the piezoelectric tensor and the elastic compliance tensor.
Physics-informed deep learning for predicting optical properties of Nb–Ta anodic oxide films from spectroscopic ellipsometry
The prediction of optical properties for anodic oxide films from spectroscopic ellipsometry data constitutes a typical inverse problem. Traditional iterative fitting methods suffer from high computational cost and strong dependence on the model structure. This study develops a physics-informed deep learning framework that addresses this challenge through two complementary modeling scenarios. At its core is a Fresnel-constrained neural network, which predicts optical constants (n, k) and ellipsometric parameters [tan(Ψ), cos(Δ)] using the two inputs of base metal composition and anodizing voltage. A hybrid loss function involving the Fresnel equations is employed to guarantee physical consistency with optical reflection rules, thus avoiding non-physical solutions. In addition, two complementary data-optimization strategies were introduced: wavelength selection and model-based data augmentation. The wavelength-selection strategy compresses the spectral input by retaining the most informative 200–350 nm region, while Gaussian-noise-enhanced synthetic samples are generated to expand the training dataset and reduce the risk of surrogate-model bias propagation. The integrated model exhibits good prediction accuracy, with test-set RMSE values of 0.090 for n and 0.038 for k. Notably, the model maintains its training and prediction efficiency even as the volume of data increases. These results show that embedding fundamental optical laws into a deep learning structure yields a robust and efficient framework. Together with targeted data-optimization strategies, this framework offers a promising avenue for the high-throughput inverse design and characterization of complex functional oxide systems.
How fatigue frequency and load level govern grain growth kinetics in indentation and micro-bending beam fatigue in confined volumes
Nanocrystalline metals offer exceptional strength but are prone to microstructural instability under cyclic loading. Fatigue-induced grain growth, in particular, limits their long-term reliability. This work examines how fatigue frequency, load ratio, and volume confinement govern microstructural evolution in nanocrystalline nickel across different loading regimes. Bulk indentation fatigue produces predominantly homogeneous grain coarsening confined to the plastically deformed zone, whereas micromechanical bending fatigue promotes localized grain growth along fatigue cracks and ahead of crack tips. Increasing fatigue frequency in indentation fatigue further alters the deformation mode, shifting the response from pileup-dominated plasticity toward material flow and grain alignment. These results identify fatigue frequency, load level, and confinement as key boundary conditions controlling fatigue- and strain-induced grain growth in nanocrystalline metals.
Data-driven automated identification of optimal feature-representative images in infrared thermography using statistical and morphological descriptors
Infrared thermography (IRT) is a widely used non-destructive testing technique for detecting structural features, such as subsurface. Most IRT data post-processing methodologies result in the generation of image sequences in which defect visibility varies strongly across time, frequency, or coefficient/index domains, making the selection of defect-representative images a non-trivial and critical task. Conventional evaluation metrics, such as the signal-to-noise ratio or the Tanimoto criterion, often rely on prior knowledge of defect location or defect-free reference regions, which limits their applicability for automated and unsupervised analysis. In this work, a data-driven methodology is proposed to identify images within IRT datasets that are most likely to contain and represent features, especially anomalies and defects, without requiring any prior information about their spatial position. The investigation focuses on three complementary descriptors. First, the Homogeneity Index of Mixture quantifies statistical heterogeneity through deviations of local intensity distributions from a global reference distribution. The second descriptor is a Representative Elementary Area derived from a Minkowski-functional-based adaptation of the Representative Elementary Volume concept to two-dimensional images. Building upon these approaches, a third descriptor is introduced: a geometrical-topological Total Variation Energy index based on two-dimensional Minkowski functionals, designed to enhance sensitivity to localized anomalies. The proposed framework is validated experimentally using pulse-heated IRT data acquired from a carbon fiber-reinforced polymer plate containing six artificial defects at depths between 0.135 and 0.810 mm and is further supported by one-dimensional N-layer thermal model simulations. The results demonstrate that the proposed descriptors enable robust, unbiased ranking of image sequences and provide a reliable basis for automated defect-oriented image selection in IRT.
Calibration method of driving energy for vaporizing foil actuator welding and its application
In this study, a driving energy calibration method applicable to the vaporizing foil actuator (VFA) was proposed and applied to the process of vaporizing foil actuator welding (VFAW). This method is based on a technical approach combining high-speed photography with finite element simulation: the corresponding relationship between the driving velocity and actual driving energy was established via simulation, and then the actual driving energy was derived by inversion, according to the driving velocity measured by high-speed photography. Based on the established VFA driving energy model, the enhancement effect of the water medium on the driving capacity of VFA was quantitatively revealed for the first time, with an average improvement of approximately 61.5%. Furthermore, it was analyzed that about 50% of this gain is attributed to the “medium effect” of water. Finally, the calibrated driving energy was applied to the full-process numerical modeling of VFAW, enabling the morphology prediction of the welding interface in standoff-free VFAW. The results show that the interface morphology, wave characteristics, and weld width obtained from the simulation are in good agreement with the observations by scanning electron microscopy, which verifies the reliability of the established full-process simulation model. The proposed energy calibration method can offer theoretical guidance for VFAW process optimization and interface morphology prediction.
Energy-sensing kinase OsSnRK1b and its regulator OsCTK1 promote chilling-induced stomatal closure and cold tolerance
Decoding non-faradaic signal transduction in organic electrochemical transistor based biosensors
The Plasmodium heme detoxification protein functions in mitochondrial protein synthesis
Abstract Malaria blood-stage parasites digest ~80% of host cell hemoglobin within a degradative vacuole, releasing heme that is detoxified by sequestration into hemozoin crystals. Although essential for survival and a validated drug target, the mechanisms of heme biomineralization remain unclear. Here, we study the parasite’s Heme Detoxification Protein (HDP), previously proposed to mediate hemozoin formation, using genetic, microscopic, bioenergetic, and proteomic approaches. Endogenous tagging reveals that HDP localizes to the mitochondrion, not the digestive vacuole. HDP inactivation has no effect on heme biomineralization, but causes mitochondrial depolarization, proguanil hypersensitivity, and developmental arrest, which is rescued by bypassing respiratory-chain-dependent pyrimidine biosynthesis. HDP knockout abolishes mitochondrial electron flow due to loss of complexes III and IV, consistent with impaired mitochondrial protein synthesis. Integration of structural modelling with quantitative proteomics places HDP within the mitoribosomal large subunit. Here, we show that HDP is essential for mitochondrial function and does not contribute to hemozoin formation.
Enabling molecular signaling with temperature and ionic-strength independence or programmable dependence
Abstract The equilibrium constants of chemical reactions fundamentally depend on temperature, posing challenges for living systems. However, many conformer organisms do not maintain a stable internal temperature. This raises the question: can molecular signaling pathways inherently resist temperature susceptibility? Molecular commutation is a recently discovered, fundamentally distinct mechanism of biological information processing and storage within reversible association/dissociation reactions. Here, we show that molecular commutation enables complex signaling systems that are independent of temperature and ionic strength and, even more generally, programmably dependent on these parameters. Using examples of various DNA logic gates, receptor-activator networks, and systems with complex input–output relationships (e.g., computed as algebraic functions), we demonstrate computationally that introducing compensatory reactions in these networks can render their signaling independent of temperature and ionic strength. We experimentally validate such independence for a case of a YES-logic gate. Finally, we computationally demonstrate networks with outputs that follow predefined functional forms of temperature and ionic strength (e.g., sin(T), where T is temperature). The presented intrinsic capabilities of affinity-based networks provide a remarkable homeostasis and signaling control mechanism that may be used by biological systems of arbitrarily high complexity.
Engineering N-site isomerism in donor-acceptor covalent organic frameworks for efficient Fenton-like water purification
Abstract Covalent organic frameworks (COFs) have emerged as promising candidates for singlet oxygen ( 1 O 2 ) generation via peroxymonosulfate (PMS) activation, yet their structure-property-activity relationships remain poorly understood. Herein, three constitutionally isomeric donor-acceptor COFs (TF-22Bpy, TA-22Bpy and TA-33Bpy) were constructed via N-site isomeric engineering, which involved precisely modulation of the imine and pyridine nitrogen positions within the skeleton. This systematically structural engineering was undertaken to unravel the fundamental effects of regioisomerism on both the electronic structure and subsequent Fenton-like catalytic activity. Among the isomers, TA-33Bpy showed the best catalytic activity for PMS activation, exhibiting an observed rate constant ( k obs ) of 0.165 min −1 . This value is substantially higher than those of TF-22Bpy (0.013 min −1 ) and TA-22Bpy (0.052 min −1 ) by factors of 12.7 and 3.2, respectively. Mechanistic investigations indicate that direct PMS-COF interaction induces charge polarization within the donor-acceptor framework, generating localized electron-deficient and electron-enriched domains that promote the coupled redox steps required for selective ¹O₂ generation. This work identifies PMS-triggered charge polarization as a key determinant of PMS activation and offers a design principle for high-performance COF catalysts for water purification.