Journal of the Korean Physical Society最新文献

Achieving highly conductive graphene films requires the elimination of pores formed during the thermal reduction of graphene oxide (GO). Conventional methods such as hydraulic pressing often struggle to remove these pores effectively, especially in sub-micron large area films for uniform high pressure. In this study, we introduce a thermal expansion-assisted hot pressing (TEHP) technique that leverages the differential thermal expansion between graphite and tungsten to achieve pore-free, highly conductive graphene films. Here we heat the GO film sandwiched between graphite (high thermal expansion coefficient) and tungsten (low thermal expansion coefficient) to 1800 °C where pressures of 13–48 MPa are estimated. The TEHP resulted in graphene films with a smooth, metallic surface, free of macropores. Raman spectroscopy and electron microscopy analyses confirmed the enhanced crystallinity and compactness of the films. The electrical conductivity of the hot-pressed graphene films shows a threefold improvement over normally annealed films. This scalable method offers a viable pathway for producing high-performance graphene films for advanced applications.

The purpose of this study is to design a predictive model for a daily quality assurance (QA) system that remains unaffected by specific patterns in correlated time series data. All data were sampled from the measured output factor at specific times over a 5-year period during the daily QA process for a 6 MV photon beam of the Varian linear accelerator (LINAC) system. Before constructing predictive structures, an autocorrelation function (ACF) analysis was conducted to verify the correlation of the given time series data. This study determined the optimal configuration for the autoregressive integrated moving average (ARIMA) and nonlinear autoregressive (NAR) neural network models for prediction. Additionally, it utilized correlated time series data to evaluate its impact on the predictive capability. We then compared the actual QA values to those predicted by the selected ARIMA and NAR models for the sampled daily output. Our findings suggest that while the ARIMA model offers a quick and relatively easy approach without requiring complex computational methods, the NAR model outperforms ARIMA, especially in the context of correlated time series data, demonstrating its real clinical utility as a prediction model. This result reveals that correlations are frequently observed in daily QA data. We concluded that these correlations can substantially influence the accuracy of machine behavior predicted based on historical observations. Consequently, analyzing specific patterns and correlated data is imperative for designing predictive structures.

This paper presents two novel classification techniques, the customized template matching classifier (CTMC) and the dynamic template matching classifier (DTMC), which aim to significantly enhance the performance of the minimum distance classifier (MDC). CTMC tailors a feature subspace specifically for MDC, leveraging a set of effective templates that capture the distinguishing characteristics of each class. This customized feature space ensures accurate representation and enhanced distinguishability between classes, thereby improving MDC’s classification accuracy. DTMC, on the other hand, builds upon the CTMC approach by introducing a dynamic template optimization process. Inspired by semi-supervised learning techniques, DTMC utilizes unlabeled data to enrich class information and iteratively update the templates in the feature space. This dynamic optimization process allows DTMC to adapt to variations in the data, further enhancing the classification performance of MDC. Our key contributions include: (1) introducing the concept of a customized feature space tailored for MDC, demonstrating its effectiveness in improving classifier performance; (2) presenting CTMC and DTMC as comprehensive classification systems that seamlessly integrate feature extraction and classification, outperforming traditional loosely coupled approaches; and (3) incorporating a reliability mechanism to assess the classification of test samples, enabling the selection of high-reliability samples to update class templates, effectively addressing the issue of limited labeled data and further boosting the overall performance of the classification system.

Surface-enhanced Raman scattering (SERS)–fluorescence bimodal composites have attracted great interest for biomedical applications. Herein, we report the characterization of Au-decorated CaTiO₃:Eu³⁺ nanocomposites (CaTiO₃:Eu³⁺@Au NCs) using SERS and fluorescence properties. X-ray diffraction (XRD) of the CaTiO₃:Eu³⁺@Au NCs shows orthorhombic phases, which are in good agreement with the standard CaTiO₃ XRD peaks. The photoluminescence (PL) spectra show a strong visible red emission at 614 nm (⁵D₀ → ⁷F₂) with an excitation wavelength of 380 nm. The PL-emission intensity of the CaTiO₃:Eu³⁺@Au NCs increased more than that of the CaTiO₃:Eu³⁺ powder. The maximum PL-emission intensity of the CaTiO₃:Eu³⁺@Au NCs was obtained at an Au doping concentration of 0.8 mol/L; however, the intensity at higher Au doping concentrations (> 0.8 mol/L) was lower. This behavior is consistent with the SERS results. These results suggest that varying the Au nanoparticle concentration can control the PL-emission intensity and SERS of the CaTiO3:Eu³⁺@Au NCs. Therefore, CaTiO₃:Eu³⁺@Au NCs may be useful for multiplex detection and bioimaging.

In this study, we calculate the dc resistivity of the half-filled disordered Hubbard model near the Mott and Anderson metal–insulator transitions. We employ the standard Kubo formula with typical medium dynamical mean field theory to perform our calculations. Our investigation explores the effects of random potential, on-site Coulomb interaction, and temperature on the dc resistivity within the model. In addition, we highlight and discuss the distinct resistivity behaviors observed near the Mott and Anderson transitions.

Love numbers h, k, and l are convenient parameters to assess the deformation of the Earth or other planet due to external perturbing forces. While the elastic Love numbers are used for tidal deformation, the correct amount of permanent tidal deformation can be assessed using the fluid Love numbers. Accurate values of the elastic Love numbers of Earth have been repeatedly attained for several Earth models, and also the fluid Love numbers h_f and k_f of the Earth were recently reported. In this short note, new evaluation of the Earth’s fluid Love number as well as secular Love number, both originally defined by Munk and MacDonald, is presented using updated values of the Earth’s physical property model and with minute enhancement in the formulation; ({(h}_{s}, {k}_{s}) =(1.9437, 0.9437)) by their 1st approach and ({(h}_{f}, {k}_{f}) =(1.9337, 0.9337)) by their 2nd approach. Through numerical calculations with gradual stepwise reduction in rigidity, the three fluid Love numbers of the Earth were determined as: (h_f, k_f, l_f) = (1.935, 0.935, 1.07) for PREM and (h_f, k_f, l_f) = (1.937, 0.937, 1.07) for ak135-F Earth models. In addition, the fluid Love numbers of other spherical harmonic degrees 0, 3, 4, and 5 were estimated. The permanent tide of the Earth ellipsoid due to the Moon and the Sun has been re-calculated: the vertical displacement of the permanent tide is 0.1924 m at the equator and (- 0.3801) m at the pole, while the horizontal displacement of the permanent tide is found to be 0.317 m at mid-latitude.

Diffusion-weighted imaging (DWI) is one of the most sensitive techniques to noise among magnetic resonance imaging (MRI) techniques. As the b-value used to acquire the DWI image increases, an image in which the difference in diffusion is emphasized can be obtained. However, DWI images with increased b-values inevitably have a major drawback in that noise is amplified. Thus, in this study, a dictionary learning (DL)-based denoising algorithm was modeled and applied to DWI images. The designed algorithm was modeled as a DL-based algorithm using the expected patch log likelihood. The DWI images were obtained by adjusting the b-value from 400 to 400 intervals. When the proposed DL-based denoising algorithm was applied to DWI, we confirmed that the contrast-to-noise ratio and coefficient of variation were improved by approximately 4.26 and 5.22 times, respectively, compared with noisy images. In conclusion, we expect that the proposed DL-based denoising algorithm will be highly efficient in acquiring DWI images using a high b-value, which is useful for observing acute cerebral infarction and microvascular disease.

Recently Li doping is employed to control the properties of bioceramic biphasic calcium phosphate (BCP), which consists of (beta -hbox {Ca}_{3}(hbox {PO}_4)_2) ((beta)-TCP) and hydroxyapatite(HAp). We investigated the microscopic properties of Li in BCP through first-principles electronic structure calculations. The stable microscopic structure of Li-driven state is identified by comparing the formation enthalpies of Li-driven structures: interstitial ((hbox {Li}_{int})) and substitutional ((hbox {Li}_{Ca})). It is found that the (hbox {Li}_{int}) is more stable than the substitutional state (hbox {Li}_{Ca}). The (hbox {Li}_{int}) atom is strongly coupled to three or four O atoms, which stabilizes the interstitial state. The (hbox {Li}_{int}) is calculated to be relatively more stable in (beta)-TCP than in HAp, because the electronic level of (hbox {Li}_{int}) is much lower in (beta)-TCP than in HAp.

Numerous studies have focused on analyzing and enhancing the performance of ozone generation systems to achieve higher ozone concentrations with lower energy consumption. This paper presents an experimental comparative study between a multi-tube DBD ozone generator with a parallel tube configuration and a simple DBD ozone generator with the same discharge volume, where the energy efficiency and produced ozone concentration were analyzed. Subsequently, the multi-tube ozone generator was employed for the wastewater treatment of the Moulay Slissen sewage-treatment plant in Sidi Bel Abbes. The results demonstrated that the new multi-tube ozone generator exhibited significantly improved performance in terms of energy efficiency and produced ozone concentration levels compared to the simple DBD generator. Furthermore, through the analyses conducted on the wastewater samples before and after treatment, the advanced generator proved its efficacy in improving the physicochemical and bacteriological quality of wastewater.