Journal of the Korean Physical Society最新文献

Power-dependent time-integrated/time-resolved photoluminescence (PL) measurements of the quantum ring (QR) structures were performed under 4 K. As excitation power is increased, the exciton and biexciton states were confirmed by linearly and superlinearly increasing power factors, respectively. Notably, the power-dependent blue shift showed fine structure splitting owing to the diverse sizes of the excited states. These different excited states could be distinguished by measuring the power-dependent decay times. Asymmetric electron–hole exchange interactions, attributed to the different sizes of energy states and different oscillator strengths between the ground and excited exciton states, were analyzed using time-resolved PL techniques. The various excited states were resolved based on their exponential decay rates.

This study examines the potential of defect-engineered pure SnO₂ nanoparticles as efficient photocatalysts for wastewater treatment under sunlight irradiation. Two distinct SnO₂ nanosystems are synthesized through a facile and cost-effective sol–gel approach. Defects are deliberately introduced into one of the samples by altering the annealing process. After the formation of SnO₂ gel, this sample undergoes rapid cooling between two consecutive annealing stages of 2 h and 4 h at 100 ℃ under vacuum conditions respectively. In contrast, the other sample is subjected to conventional annealing in a hot air environment at 400 ℃ for 24 h. The structural and optical properties of these samples are meticulously characterized using X-ray diffraction, UV–visible spectroscopy, TEM, FTIR, BET and EPR respectively. Subsequently, the photocatalytic performance of both SnO₂ nanoparticle samples is evaluated by degrading a methyl orange solution, which serves as a model contaminant for textile industry wastewater under sunlight exposure. The defect-engineered SnO₂ sample demonstrates a remarkable improvement in photocatalytic efficiency, degrading 82.51% of methyl orange with a rate constant of 0.02886 min⁻¹ under 60 min of sunlight irradiation. In comparison, the standard SnO₂ sample degrades only 29.71% of methyl orange, with a significantly lower rate constant of 0.00575 min⁻¹ under the same irradiation conditions. This improvement is attributed to the increased number of defect sites (oxygen vacancy), surface area (59.877 m²/gm) and narrow optical bandgap (2.85 eV), which enhance light absorption and generate more active excitons, thus accelerating the degradation process. The novelty of this research lies in the successful enhancement of photocatalytic performance without the need for doping or composite formation, making it a cost-effective and scalable approach for wastewater treatment applications.

Advanced logic gates and transistor technologies play a crucial role in the design of high-speed computing systems. In this paper, a novel 3-dimensional fin-shaped reconfigurable transistor is presented, which exhibits identical behavior in both n-enhancement mode and p-enhancement mode operations. The key highlight of this reconfigurable transistor lies in its ability to integrate XNOR, NOT, and AND gates within a single device. Unlike conventional reconfigurable transistors, the proposed device incorporates a dual-doped n⁺/p⁺ source and a Schottky drain region. Notably, this device only requires a control gate, while the drain electrode serves the dual purpose of being the output and program gate. The findings demonstrate remarkable performance characteristics for the n-enhancement mode and p-enhancement mode operations. Specifically, the on-state current is measured to be 3.68 µA and 2.85 µA, with corresponding on/off current ratios of 11.25 × 10⁸ and 1.23 × 10⁸, respectively. Moreover, the device achieves a subthreshold swing of 61 mV/dec and 63 mV/dec for the n-enhancement mode and p-enhancement mode, respectively. This innovative design highlights the potential of utilizing a single FinFET reconfigurable transistor to design complex logic gates, demonstrating a significant advancement in integrated circuit technology towards enhanced efficiency and versatility.

In this work, we explored the mass transfer characteristics of a non-Newtonian Williamson nanofluid flow resulting from a stretched sheet in response to various environmental factors such as chemical reactions, activation energy, non-Darcy porous medium, slip velocity, and viscous dissipation. The primary circumstance under study is one in which a Williamson nanofluid’s viscosity and thermal conductivity differ with temperature. An algorithmic solution to the anticipated problem is presented using the BVP4C method. Consequently, several plots have been generated to illustrate how different physical attributes that emerge in the issues impact concentration, temperature, and velocity profiles. It was found that the slip velocity assumption, magnetic field, activation energy, and the viscous dissipation phenomenon influenced the heat and mass transfer processes. Some key outcomes are: the slip velocity parameter and viscosity parameter reduce the velocity field; the slip velocity parameter and porosity parameter increase the temperature field; activation energy improves the concentration field; and chemical reaction decreases the concentration. There is a strong qualitative agreement between theoretical and numerical results.

In this paper, we study an open quantum system consisting of a qubit coupled to a harmonic oscillator subject to two-photon relaxation and demonstrate that such a system can be utilized to construct a cat qubit capable of passive error correction. To this end, we first show that the steady state of the qubit-oscillator system, described by the open quantum Rabi model with two-photon relaxation, undergoes a super-radiant phase transition that breaks the strong symmetry of the Lindblad master equation. In the strong symmetry-broken phase, we show that a cat qubit can be stabilized in the steady state by tuning the qubit-oscillator coupling strength and demonstrate that passive error correction can be realized against errors due to fluctuations in the system frequencies. Our study deepens the understanding of dissipative phases in a qubit-oscillator system with strong symmetry and paves the way to utilize them for passive error correction.

The testing of radioactive waste drums is a fundamental aspect of nuclear waste disposal. To meet the need for rapid classification and measurement of radioactive wastes, we proposed a detection method based on array scintillation detectors to realize chromatographic gamma-scanning of casks of nuclear wastes. This method has been successfully developed and applied in the form of an array NaI(Tl) detector radioactive waste cask measurement system. The measurement system was divided into four main sections: (1) the radioactive source, (2) the mechanical and control system, (3) the detector system, and (4) the data processing system. The detector system comprises seven NaI(Tl) detectors, taking into account the detector layout, detection efficiency, and shielded room (to reduce the background of the environment). The results of the performance tests on the measurement system indicated that the detection limit was 6191.75 s⁻¹, the energy resolution was 7.698% at 661.7 keV (¹³⁷Cs), and the minimum detectable activity was 0.201 µCi for ¹³⁷Cs and 0.143 µCi for ⁶⁰Co. Finally, transmission measurements and image reconstruction of the three different media in the drum using a ¹³⁷Cs radioactive source could effectively reconstruct the spatial location distribution of the media in the drum.

Activities observed in a neural system, especially superficial cortical layers, often show cascades of bursts, called neuronal avalanches, and the distribution of them obeys a power law. Some hypotheses have been suggested for the principle underlying the phenomenon. One of the suggestions is that such a power-law activity distribution emerges as a specific form of the stationary solution of the Markov process, rather than the criticality, and some special characteristics in the spiking dynamics are the important ones causing the phenomenon. Various properties of activity distributions in all-to-all connection structures have been well explained based on the theory, but there is a lack of studies on the activities in a more cortical-like structure. Motivated by the point, the properties of activities in a two-dimensional structure are investigated in this paper. The firing statistics observed in the cortex may not be properly implemented if an incorrect reduction model is used. In this paper, presented is one of the minimal models to reproduce the experimental observations in simulations.

Under the condition of weak signal of photon-counting lidar and strong noise of solar background, the signal is completely submerged by noise, resulting in the detection of multiple peaks through photon-counting entropy. Consequently, the distinction between signal and noise may become difficult, causing the significant fluctuation in ranging error. To address this issue, we propose the lidar denoising algorithm based on an improved correlation parameter of ensemble empirical mode decomposition, including the coarse denoising stage and recognition stage. In the coarse denoising stage, the method of ensemble empirical mode decomposition is primarily used for extracting and eliminating the noise components from the signal. To identify noise components, we propose an improved correlation parameter based on the combination of first-order linearity and second-order nonlinearity fitting using the least squares algorithm. In the recognition stage, the photon-counting entropy is further utilized for anti-noise and identifying the target signal. According to the simulation and experimental analysis, the ranging error of our proposed method are less than 5 and 30 cm, respectively. When compared with the denoising algorithm of photon-counting entropy, the average ranging accuracy is enhanced by 74.69% and 74.42%, respectively. Meanwhile, in comparison to other algorithms, it also possesses superior capabilities.

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.