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Ion irradiation of semiconductors has emerged as a promising approach for fabricating self-organized nanosystems with high atomic precision, despite often being accompanied by undesirable phenomena. Exploring the mechanisms underlying structural transformations is crucial for assessing nanostructure array types under complex irradiation environments. By quantitatively calculating the thermodynamically driven processes and analyzing the impact of intrinsic structural parameters, distinct structural transformations in response to intense electronic excitation are systematically investigated in gallium antimonide (GaSb) and gallium arsenide (GaAs) systems. In high-energy regimes, the nanofibers layer of GaSb exhibits intriguing structural discrepancy, characterized by partial nanofibers with coherent boundaries, interspersed nanopores accompanied by antisite defects and Ga precipitates, distinguishing to a series of discontinuous latent tracks that emerged within cylindrical trajectories in GaAs. Furthermore, significant diffusion behaviors of the nanohillocks are discovered in GaAs, with higher average roughness than GaSb, driven by the gradient stress distribution influenced by the free-surface effects. The deposition energy for melting phase formation, Gibbs free energy, and Ga diffusion coefficients contribute to the distinctive structural features, evidencing relatively stable morphological configurations and higher irradiation resistance in GaAs. Consequently, special optoelectronic properties associated with structural discrepancies facilitate the design and optimization of material functionalities by irradiation technologies.

Currently, the spectra-based physical models and deep learning methods are frequently used to detect wildfires from remote sensing data. However, physical algorithms mainly rely on radiative transfer processes, which limit their effectiveness in detecting small and weak fires. On the other hand, deep learning methods usually lack mechanism constraints, thus generally resulting in false alarms of bright surfaces. It is promising to combine the advantages of them and correspondingly reduce the inherent error of a single algorithm. To this end, in this paper, both the local contextual and the global index method based on physical mechanisms are optimized, simultaneously, a new U-Net model is also establish to accurately detect fires. Moreover, YOLO v5 is incorporated for the first time to extract and remove the false alarms of objects with high exposure. Based on the above series of novel works, a self-adaptive fusing algorithm is finally proposed. Our results reveal that: (1) Short-wave infrared band of about 2.15 μm is crucial in fire detection for data with moderate-to-high resolutions. Taking Landsat 8 as an example, the band combinations of 7, 6, 2(SWIR + VI), 7, 6, 5(SWIR + NIR), and 7, 5, 3(SWIR + VI + NIR) show reasonable accuracy, with recall rate of greater than 81 %. The thermal infrared band can be used to assist in detecting the general location of the fire and serve as alternative choice in extreme cases. (2) The optimized physical algorithm can reduce false alarms and predict more accurate fire positions. (3) It is very effective to introduce the YOLO v5 framework to remove false alarms with high exposure in urban and suburban regions. (4) The proposed self-adaptive fusion algorithm integrates the advantages of various schemes, proving its better performance in terms of robustness, stability and generality compared to any single method. Even in extreme situations such as the Gobi Desert, thin cloud edges, and mountain shadow areas, the fusion algorithm still works well. The generality tests based on Sentinel-2A, WorldView-3, and SPOT-4 reveal the potential applicability of the newly proposed fusing algorithm, especially for data with fine spatial and spectral resolutions.

The development of prediction model on irradiation embitterment (PMIE) of reactor pressure vessel (RPV) is an important method for nuclear reactor long term operation. Based on the physical mechanism of RPV irradiation embrittlement, a preliminary model is determined and the critical threshold of Cu content of 0.072% is obtained according to this preliminary model. Then a prediction model named PMIE-2020 for low Cu RPV steels is developed. At last the residual, standard deviation and predicted values and test values distribution analysis are given. Simultaneously, a comparison between PMIE-2020 and other prediction model and irradiation data is provided. Results indicate that the predicted results of PMIE-2020 has no tendency with influence factors such as neutron fluence, flux, irradiation temperature, chemical elements Cu, P, Mn, Ni, Si. The residual standard deviation is 10.76 °C, which is lower than present prediction model. The distribution between predicted values of PMIE-2020 and test values are located the area near the 45° line. These results prove that the PMIE-2020 have high accuracy on irradiation embrittlement prediction.

The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R2 increases from 0.6 to 0.79), particularly for polluted episodes (PM2.5 concentration > 55 µg/m3) with pronounced regional transport to be captured by GNN_LSTM model. The inclusion of the AOD feature further enhances the model performance in predicting PM2.5 over the sites where the AOD can inform additional aloft PM2.5 pollution features related to regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of the target area) features for long-term PM2.5 forecast is demonstrated by the increased performance in predicting PM2.5 in the target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the “source”-receptor relationship, as impacts from distanced sites associated with regional transport grow along with the forecasting time (from 0% to 38% in 72 h) following the wind flow. Such results suggest the great potential of GNN_LSTM in long-term air quality forecasting and air pollution prevention.

Microplastics (MPs) pose significant risks to aquatic life and human health. Conventional water treatment is ineffective in removing MPs, demanding alternative technologies. Biochar exhibits a potential for removing MPs through adsorption and filtration. The efficiency of biochar derived from macadamia (Macadamia Integrifolia) nutshells on MP removal from contaminated water was assessed in fixed-bed column tests at environmentally relevant MP concentrations in upward flowing regime. Fragmental polyethylene MPs (50-100 and 100-300 μm) were tested on the effects of the operating conditions, the relative MP-biochar size ratios (0.05-0.14 and 0.13-0.36 for small and large MPs), and biofilm formation on their retention in the biochar bed. The interactions between MPs and biochar are apparently electrostatically repulsive. Small biochar demonstrated >78% removal of the MPs at flow rates of 2.78 × 10 -5 , 2.78 × 10 -4 , or 1.39 × 10 -3  m/s. Increasing the MP influent concentrations significantly increased the MP removal by the filter. The lower flow rates increase the MP removal with both MP influent concentrations and MP sizes, showing a maximum of 96% removal of small MP. The removal of large MPs by biochar filters (i.e. MP-biochar size ratio: 0.13-0.36) is significantly different when the highest flow rate is used. This difference moderates as the flow rates and MP size decline. Biofilm formation at its early stage altered the porous characteristics and surface morphology of the biochar and enhanced the MP removal. Overall, this study provides insights into the application of biochar filters in tertiary wastewater treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier Inc. All rights reserved.)

Localized CO2 laser repairing of surface damage on fused silica optics has been successfully applied in high-power laser system in the field of controllable nuclear fusion. In order to accurately predict the surface topography evolution and to reveal the intrinsic physical mechanism during the process of laser mitigation, experiments of localized CO2 laser mitigation were firstly carried out to analyze the features of mitigated craters under different laser powers. Then a multi-physics coupled mathematical model was developed based on the fluid control equation, heat and mass transfer equation and material phase transition kinetics to investigate the thermodynamic and kinetic behaviors of laser interaction with silica. The model considered the effects of Marangoni convection, gravity, capillary force and vaporization recoil pressure, as well as the nonlinear variation of physical parameters of silica material with respect to temperature. The results showed that with the increase of laser power, the material ablation and the appearance of raised rim occurred simultaneously. The depth of the mitigated crater increased sharply when the threshold for material ablation was attained, while the lateral dimension increased linearly. The vaporization recoil pressure was found to be the dominant factor for the formation of Gaussian crater with the raised rim feature. The capillary force caused the material at the edge of the molten pool to have a tendency to reflow after laser shutting down, but it was too small to change the surface topography. This work could significantly contribute to the understanding of laser mitigation process, which laid the foundation for the accurate prediction and evaluation of surface quality of CO2 laser repaired fused silica surface.

Here, we developed one kind of novel TiO2 ceramics with colossal dielectric constant by chemical modifications (Bi3+ and Sb5+), and discussed the physical origin for giant dielectric constant. Effects of Bi and/or Sb on their microstructure, dielectric properties as well as its frequency and temperature stability were studied in detail. It was found that their dielectric properties are strongly sensitive to (Bi,Sb) contents, and colossal dielectric permittivity (CP) (104∼105) together with low dielectric loss (∼5.7%) can be obtained in a wide composition range. In addition, all the ceramics possessed good frequency (102∼106 Hz) and temperature (−150–200 °C) stability of dielectric properties. In addition, the defects caused by the Bi volatilization may be the reason for higher dielectric properties of (Bi0.5Sb0.5)xTi1−xO2 ceramics with respect to (A0.5Sb0.5)xTi1−xO2 (A = In, Pr, Dy, Sm, Gd, Yb, Ga, Al, Fe or Sc). According to the results of complex impedance and XPS, the electron-pinned defect-dipoles may be suitable to explain the CP phenomenon, and oxygen vacancies-induced by Bi3+&Sb5+ substitution for Ti4+ should be responsible for conduction mechanism. We believe that this profound investigation can benefit the development of TiO2 ceramics as a CP material.

In this work, based on density functional theory (DFT) and wave function analysis, the properties of absorption spectrum, electronic circular dichroism (ECD) spectrum and Raman spectrum of infinitene (monomer and dimer) with double helical structure are theoretically studied. The electronic excitation properties of infinitene were investigated based on the visualization method charge density difference (CDD) and transition density matrix (TDM). It is found that there is obvious intermolecular charge transfer behavior in the dimer. The electromagnetic interaction mechanism of the chirality of infinitene is explained by decomposing transition electric\magnetic dipole moments (TEDMs\TMDMs). The response of Raman spectra to excitation light of different wavelengths was calculated. Then, the electron delocalization degree and magnetic response intensity of infinitene were studied based on the magnetically induced current under external magnetic field. The interaction of infinitene with the external environment was studied by electrostatic and van der Waals potentials, and it was shown that non-polar or low-polar molecules are more inclined to be adsorbed at the groove position of infinitene. Finally, the mechanism of intermolecular interactions in dimer was investigated based on independent gradient model based on Hirshfeld partition (IMGH), Atoms-In-Molecules (AIM), and energy decomposition analysis based on forcefield (EDA-FF). And revealed that the stacking in the dimer is dominated by dispersive interactions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)