Search Results (3,686)

Surface and sub-surface atomic configurations are critical for catalysis as they host the active sites governing electrochemical processes. This study employs density functional theory (DFT) calculations and Monte Carlo simulations combined with the cluster-expansion approach to investigate atom distribution and Pt segregation in Pt-Fe alloys across varying Pt/Fe ratios. Our simulations reveal a strong tendency for Pt atoms to segregate to the surface layer while Fe atoms enrich the sub-surface region. Crucially, the calculations predict the stability of a periodic Pt₂Fe alloy surface model, characterized by specific defect structures, at low platinum content and low annealing temperatures. Electronic structure analysis indicates that forming this Pt₂Fe surface alloy lowers the d-band center of Pt atoms, weakening CO adsorption and thereby enhancing resistance to CO poisoning. Although defect-induced strains can modulate the d-band center, crystal orbital Hamilton population (COHP) analysis confirms that such strains generally strengthen Pt-CO interactions. Therefore, the theoretical design of Pt₂Fe alloy surfaces and controlling defect density are predicted to be effective strategies for enhancing catalyst resistance to CO poisoning. This work highlights the advantages of periodic Pt₂Fe surface models for anti-CO poisoning and provides computational guidance for designing efficient Pt-based electrocatalysts. Full article

Affecting over 50% of stroke patients, dysphagia is still challenging to diagnose and manage due to its complex multifactorial nature and can be the result of disruptions in the coordination of cortical and subcortical neural activity as reflected in electroencephalographic (EEG) signal patterns. Sample Entropy (SampEn), a signal complexity or predictability measure, could serve as a tool to identify any abnormalities associated with dysphagia. The present study aimed to identify quantitative dysphagia biomarkers using SampEn from EEG recordings in post-stroke patients. Sample entropy was calculated in the theta, alpha, and beta bands of EEG recordings in a repetitive swallowing task performed by three groups: 22 stroke patients without dysphagia (controls), 36 stroke patients with dysphagia, and 21 healthy age-matched individuals. Post-stroke patients, both with and without dysphagia, exhibited significant differences in SampEn compared to healthy subjects in the alpha and theta bands, suggesting widespread alterations in brain dynamics. These changes likely reflect impairments in sensorimotor integration and cognitive control mechanisms essential for effective swallowing. A significant cluster was identified in the left parietal region during swallowing in the beta band, where dysphagic patients showed higher entropy compared to healthy individuals and controls. This finding suggests altered neural dynamics in a region crucial for sensorimotor integration, potentially reflecting disrupted cortical coordination associated with dysphagia. The precise quantification of these neurophysiological alterations offers a robust and objective biomarker for diagnosing neurogenic dysphagia and monitoring therapeutic interventions by means of EEG, a non-invasive and cost-efficient technique. Full article

Optically pumped magnetometer OPM-MEG has the potential to replace the traditional low-temperature superconducting quantum interference device SQUID-MEG. Coherence analysis can be used to evaluate the functional connectivity and reflect the information transfer process between brain regions. In this paper, a finger tapping movement paradigm based on auditory cues was used to measure the functional signals of the brain using OPM-MEG, and the coherence between the primary motor cortex (M1) and the primary motor area (PM) was calculated and analyzed. The results demonstrated that the coherence of the three frequency bands of Alpha (8–13 Hz), Beta (13–30 Hz), and low Gamma (30–45 Hz) and the selected reference signal showed roughly the same position, the coherence strength and coherence range decreased from Alpha to low Gamma, and the coherence coefficient changed with time. It was inferred that the change in coherence indicated different neural patterns in the contralateral motor cortex, and these neural patterns also changed with time, thus reflecting the changes in the connection between different functional areas in the time-frequency domain. In summary, OPM-MEG has the ability to measure brain coherence during finger movements and can characterize connectivity between brain regions. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

The quantification of the short-range order (SRO) of glassy materials has remained an open challenge over the years. In particular, in borate glasses, this task is further complicated by the change in the B coordination number from 3 to 4 and by the formation of superstructural units. Nevertheless, in two recent articles from our group, the SRO structure of bismuth (xBi₂O₃-(1-x)B₂O₃) and zinc (xZnO-(1-x)B₂O₃) borate glasses was completely resolved by two independent methods. The first one, for Bi-borates, involved the analysis of infrared absorption coefficient spectra into Gaussian component bands, whereas the second one, for Zn-borates, involved the application of the short-range order configuration model (SROC), an extension of the well-known lever rule. In this article, we extend the application of the SROC model in bismuth borate glasses into the range where Bi cations were found to act predominantly as modifiers, i.e., 0.20 ≤ x ≤ 0.40. Our extension results in a modification of the originally proposed SROC model by adding an additional node and by defining the prerequisites for any augmented version of the model. The molar fractions of the borate units for the calculated SRO structure, in a continuous way throughout the range investigated, are in excellent agreement with the existing literature data. Moreover, the research highlights how the onset of disproportionation reactions between borate units can be handled in the framework of the introduced augmented short-range order configuration model, ASROC. Full article

To enhance the survivability of armed helicopters in high-threat environments, integrated infrared (IR) suppressors are increasingly adopted to reduce thermal signatures. However, such integration significantly alters the exhaust flow field, which may in turn affect both the infrared and acoustic characteristics of the helicopter. This study investigates the aerodynamic, infrared, and acoustic impacts of an integrated IR suppressor through the comparative analysis of two helicopter configurations: a conventional design and a design equipped with an integrated IR suppressor. Full-scale models are used to analyze flow field and IR radiation characteristics, while scaled models are employed for aeroacoustic simulations. The results show that although the integrated IR suppressor increases flow resistance and reduces entrainment performance within the exhaust mixing duct, it significantly improves the thermal dissipation efficiency of the exhaust plume. The infrared radiation analysis reveals that the integrated suppressor effectively reduces radiation intensity in both the 3~5 μm and 8~14 μm bands, especially under cruise conditions where the exhaust is more efficiently cooled by ambient airflow. Equivalent radiation temperatures calculated along principal axes confirm lower IR signatures for the integrated configuration. Preliminary acoustic analyses suggest that the slit-type nozzle and integrated suppressor layout may also offer potential benefits in jet noise reduction. Overall, the integrated IR suppressor provides a clear advantage in lowering the infrared observability of armed helicopters, with acceptable aerodynamic and acoustic trade-offs. These findings offer valuable guidance for the future development of low-observable helicopter platforms. Full article

Van der Waals (vdW) heterostructures, typically composed of two-dimensional (2D) atomic layers, have attracted significant attention over the past few decades. Their performance is closely dependent on their composition and interlayer interactions. In this study, we constructed four types of 2D hexagonal BP monolayer (h-BP)/borophosphene vdW heterostructures with different stacking orders: (i) B-B stacking, (ii) P-P stacking, (iii) moire-I, and (iv) moire-II. Their structural stability and their electronic and optical properties were explored by using first-principles calculations. The results show that h-BP/borophosphene heterostructures can maintain their configurations with good structural stability and minimal lattice mismatch. All vdW heterostructures exhibit semiconducting characteristics, and their band gaps are highly dependent on interlayer stacking orders. Due to the regular atomic arrangement and enhanced interlayer dipole interactions, the B-B stacking bilayer opens a relatively large band gap of 0.157 eV, while the moire-II bilayer exhibits a very small band gap of 0.045 eV because of its irregular atom arrangements. By calculating the complex dielectric function, optical absorption spectra of B-B and P-P stacking bilayers were discussed. This study suggests that h-BP/borophosphene heterostructures have desirable optical properties, broadening the potential applications of the constituent monolayers. Full article

This article aims to utilize a microelectromechanical system (MEMS) to modulate coupling behavior of silicon nitride (Si₃N₄) waveguides to perform an optical switch based on a directional coupling (DC) mechanism. There are two states of the switch. First state, a Si₃N₄ wire is initially positioned up suspended in the air. In the second state, this wire will be moved down to be placed between two arms of the DC waveguides, changing the coupling behavior to achieve bar and cross states of the optical switch function. In the future, the MEMS will be used to move this wire down. In this work, we present simulations of the two static states to optimize the DC structure parameters. Based on the simulated results, the device size is 8.8 μm × 55 μm. The insertion loss is calculated to be approximately 0.24 dB and 0.33 dB, the extinction ratio is approximately 24.70 dB and 25.46 dB, and the crosstalk is approximately −24.60 dB and −25.56 dB, respectively. In the C band of optical communication, the insertion loss ranges from 0.18 dB to 0.47 dB. As such, this device will exhibit excellent optical switch performance and provide advantages in many integrated optics-related optical systems applications. Furthermore, it can be used in optical communications, data centers, LiDAR, and so on, enhancing important reference value for such applications. Full article

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

The fabrication and application of single-site heterogeneous reaction centers are new frontiers in chemistry. Single-site heterogeneous reaction centers are analogous to metal centers in enzymes and transition-metal complexes: they are charged and decorated with ligands and would exhibit superior reactivity and selectivity in chemical conversion. Such high reactivity would also result in significant response, such as a band gap or resistance change, to approaching molecules, which can be used for sensing applications. As a proof of concept, the electronic structure and reaction pathways with NO and NO₂ of Au(I) fragments dispersed on phosphorene (Pene) were investigated with first-principle-based calculations. Atomic-deposited Au atoms on Pene (Au₁-Pene) have hybridized Au states in the bulk band gap of Pene and a decreased band gap of 0.14 eV and would aggregate into clusters. Passivation of the Au hybrid states with -OH and -CH₃ forms thermodynamically plausible HO-Au₁-Pene and H₃C-Au₁-Pene and restores the band gap to that of bulk Pene. Inspired by this, HO-Au₁-Pene and H₃C-Au₁-Pene were examined for detection of NO and NO₂ that would react with -OH and -CH₃, and the resulting decrease of band gap back to that of Au₁-Pene would be measurable. HO-Au₁-Pene and H₃C-Au₁-Pene are highly sensitive to NO and NO₂, and their calculated theoretical sensitivities are all 99.99%. The reaction of NO₂ with HO-Au₁-Pene is endothermic, making the dissociation of product HNO₃ more plausible, while the barriers for the reaction of CH₃-Au₁-Pene with NO and NO₂ are too high for spontaneous detection. Therefore, HO-Au₁-Pene is not eligible for NO₂ sensing and CH₃-Au₁-Pene is not eligible for NO and NO₂ sensing. The calculated energy barrier for the reaction of HO-Au-Pene with NO is 0.36 eV, and the reaction is about thermal neutral, suggesting HO-Au-Pene is highly sensitive for NO sensing and the reaction for NO detection is spontaneous. This work highlights the potential superior sensing performance of transition-metal fragments and their potential for next-generation sensing applications. Full article

Inspired by the active site of methane monooxygenase, we designed a Cu₂O cluster anchored in the six-membered nitrogen cavity of a C₂N monolayer (Cu₂O@C₂N) as a stable and efficient enzyme-like catalyst. Density functional theory (DFT) calculations reveal that the bridged Cu-O-Cu structure within C₂N exhibits strong electronic coupling, which is favorable for methanol formation. Two competing mechanisms—the concerted and radical-rebound pathways—were systematically investigated, with the former being energetically preferred due to lower energy barriers and more stable intermediate states. Furthermore, strain engineering was employed to tune the geometric and electronic structure of the Cu-O-Cu site. Biaxial strain modulates the Cu-O-Cu bond angle, adsorption properties, and d-band center alignment, thereby selectively enhancing the concerted pathway. A volcano-like trend was observed between the applied strain and the methanol formation barrier, with 1% tensile strain yielding the overall energy barrier to methanol formation (ΔG_overall) as low as 1.31 eV. N₂O effectively regenerated the active site and demonstrated strain-responsive kinetics. The electronic descriptor Δε (ε_d − ε_p) captured the structure–activity relationship, confirming the role of strain in regulating catalytic performance. This work highlights the synergy between geometric confinement and mechanical modulation, offering a rational design strategy for advanced C1 activation catalysts. Full article

Since the definitions of the two-dimensional (2D) optical absorption coefficient and photoconductivity are independent of the thickness of 2D materials, they are more suitable than the dielectric function to describe the optical properties of 2D materials. Based on the many-body GW method and the Bethe–Salpeter equation, we calculated the quasiparticle electronic structure, optical absorbance, and complex photoconductivity of 2D InSe from a single layer (1L) to three layers (3L). The calculation results show that the energy difference between the direct and indirect band gaps in 1L, 2L, and 3L InSe is so small that strain can readily tune its electronic structure. The 2D optical absorbance results calculated taking into account exciton effects show that light absorption increases rapidly near the band gap. Strain modulation of 1L InSe shows that it transforms from an indirect bandgap semiconductor to a direct bandgap semiconductor in the biaxial compressive strain range of −1.66 to −3.60%. The biaxial compressive strain causes a slight blueshift in the energy positions of the first and second absorption peaks in monolayer InSe while inducing a measurable redshift in the energy positions of the third and fourth absorption peaks. Full article

The one-dimensional (1D) Sb(III)-based organic–inorganic hybrid perovskite (AImd)₂¹_∞[SbI₅] (AImd = 1-allylimidazolium) crystallizes in the orthorhombic, centrosymmetric space group Pnma. The structure consists of corner-sharing [SbI₆] octahedra forming 1D chains separated by allylimidazolium cations. Void analysis through Mercury CSD software confirmed a densely packed lattice with a calculated void volume of 1.1%. Integrated quantum theory of atoms in molecules (QTAIM) and non-covalent interactions index (NCI) analyses showed that C–H···I interactions between the cations and the ¹_∞[SbI₅]²⁻ network predominantly stabilize the supramolecular assembly followed by N–H···I hydrogen bonds. The calculated growth morphology (GM) model fits very well to the experimental morphology. UV–Vis diffuse reflectance spectroscopy allowed us to determine the optical band gap to 3.15 eV. Density functional theory (DFT) calculations employing the B3LYP, CAM-B3LYP, and PBE0 functionals were benchmarked against experimental data. CAM-B3LYP best reproduced Sb–I bond lengths, while PBE0 more accurately captured the HOMO–LUMO gap and the associated electronic descriptors. These results support the assignment of an inorganic-to-organic [Sb–I] → π* charge-transfer excitation, and clarify how structural dimensionality and cation identity shape the material’s optoelectronic properties. Full article

As a key insulating material in power equipment, epoxy resins (EP) are often limited in practical applications due to space charge accumulation and mechanical degradation. This study systematically investigates the effects of SiO₂ nanoparticle doping on the electrical and mechanical properties of SiO₂/EP composites through molecular dynamics simulations and first-principles calculations. The results demonstrate that SiO₂ doping enhances the mechanical properties of EP, with notable improvements in Young’s modulus, bulk modulus, and shear modulus, while maintaining excellent thermal stability across different temperatures. Further investigations reveal that SiO₂ doping effectively modulates the interfacial charge behavior between EP and metals (Cu/Fe) by introducing shallow defect states and reconstructing interfacial dipoles. Density of states analysis indicates the formation of localized defect states at the interface in doped systems, which dominate the defect-assisted hopping mechanism for charge transport and suppress space charge accumulation. Potential distribution calculations show that doping reduces the average potential of EP (1 eV for Cu layer and 1.09 eV for Fe layer) while simultaneously influencing the potential distribution near the polymer–metal interface, thereby optimizing the interfacial charge injection barrier. Specifically, the hole barrier at the maximum valence band (VBM) after doping significantly increased, rising from the initial values of 0.448 eV (Cu interface) and 0.349 eV (Fe interface) to 104.02% and 209.46%, respectively. These findings provide a theoretical foundation for designing high-performance epoxy-based composites with both enhanced mechanical properties and controllable interfacial charge behavior. Full article

As renewable energy technologies advance, identifying efficient photocatalytic materials for water splitting to produce hydrogen has become an important research focus in materials science. This study presents a multi-task regression model (MTRM) designed to predict the conduction band minimum (CBM), valence band maximum (VBM), and solar-to-hydrogen efficiency (STH) of inorganic materials. Utilizing crystallographic and band gap data from over 15,000 materials in the SNUMAT database, machine-learning methods are applied to predict CBM and VBM, which are subsequently used as additional features to estimate STH. A deep neural network framework with a multi-branch, multi-task regression structure is employed to address the issue of error propagation in traditional cascading models by enabling feature sharing and joint optimization of the tasks. The calculated results show that, while traditional tree-based models perform well in single-task predictions, MTRM achieves superior performance in the multi-task setting, particularly for STH prediction, with an MSE of 0.0001 and an R² of 0.8265, significantly outperforming cascading approaches. This research provides a new approach to predicting photocatalytic material performance and demonstrates the potential of multi-task learning in materials science. Full article