This article presents a study of the fundamental properties of β-CsYX3 (Y = Ca, Cd, Ge, Pb, Sn, Sr; X = I, Br, Cl) perovskite materials using machine learning methods. Six algorithms, such as linear regression, Ridge regression, robust regression, k-nearest neighbors regression, ElasticNet, and Lasso regression, are used to predict the bandgap, Debye temperature, and unit cell volume. Based on MAE, MSE, and R2 metrics, it was determined that linear regression yields the best results for a number of the properties under investigation. However, analysis of the RMSE revealed that, for certain properties, lower RMSE values were achieved using robust regression and Ridge regression. Thus, model performance depends on both the target property and the chosen evaluation metric. Residual analysis also demonstrated relatively stable prediction behaviour across all models considered. Overall, the results confirm the potential of machine learning methods for the accelerated screening and discovery of perovskite materials with favourable electronic and structural characteristics. The predicted values for band gap, Debye temperature, and unit cell volume serve as useful descriptors for the preliminary assessment and comparison of candidate materials for optoelectronic applications.
This study investigates the effects of cold work (with deformation levels of 10%, 16%, and 20%) and dissolved oxygen (DO) concentration on the stress corrosion cracking (SCC) growth behavior of 316L stainless steel in high-temperature, high-pressure water. It is found that sustained intergranular crack growth requires a minimum cold-work deformation of 16% or a DO concentration of 50 ppb. Under conditions of 10% cold work and low DO, only short, predominantly transgranular cracks were observed, with a growth rate (≈1.6 × 10-12 m/s) more than an order of magnitude lower than that under any other tested condition. Once intergranular cracks are initiated, the average crack growth rates across all conditions fall within a relatively narrow range (6 × 10-11 to 3 × 10-10 m/s); further increases in cold-work deformation or DO concentration result in, at most, a roughly fivefold increase in the crack growth rate. Consequently, for 316L stainless steel, limiting cold-work deformation is an effective mitigation strategy—provided that the DO concentration is maintained at a low level. However, fluctuations in DO concentration on the order of tens of ppb are sufficient to trigger intergranular cracking, even in materials with only slight deformation. This paper discusses the synergistic effects of cold work and DO on the SCC growth behavior of 316L stainless steel, as well as the underlying micro-mechanisms.
Reliable prediction of soil and structural deformation is important not only for safety but also for resource-efficient underground construction. Excessively conservative constitutive modeling may lead to unnecessary use of retaining, bracing, and cementitious ground-improvement materials, whereas underestimated deformation can result in infrastructure damage and resource-intensive remediation. In this study, parameters for the Modified Cam-Clay (MCC), Hardening Soil (HS), and Hardening Soil with small-strain stiffness (HSS) models were determined for silty clay sampled at the investigated site in Nanning, China, using laboratory tests on undisturbed samples, in situ shear-wave velocity measurements, and empirical correlations. The three parameter sets were then implemented in PLAXIS 2D to simulate a representative excavation section above existing twin metro tunnels, and the calculated responses were compared with field monitoring data. The MCC and HS models reproduced the overall deformation patterns of the retaining system and tunnels but systematically overpredicted tunnel heave. The HSS model provided closer predictions of tunnel vertical displacement and central-column rebound than the MCC and HS models at the final excavation stage, although the agreement varied with monitoring location and excavation stage. For tunnel sidewall displacement, however, the HSS model tended to underestimate deformation near the central column, whereas the HS model showed better local agreement. These findings suggest that accounting for small-strain stiffness and adopting site-specific constitutive parameters may help reduce overprediction of excavation-induced rebound in the investigated case. The field-evaluated parameter sets provide a modeling basis for safer excavation design and may support more rational use of retaining, bracing, and ground-improvement measures, although the associated material and environmental benefits were not directly quantified in this study.
The rapid development of nanoscience and nanotechnology has unlocked new possibilities in biomedicine, particularly in diagnostics, imaging, and targeted therapies. Among various nanomaterials, metallic nanoparticles, especially those based on copper, have attracted growing interest due to their unique physicochemical properties, ease of synthesis, and broad-spectrum antimicrobial activity. In this study, we report the synthesis and characterization of copper-based nanoparticles with significant antifungal activity against Candida spp., a genus responsible for a wide range of fungal infections, including life-threatening invasive candidiasis. Two distinct synthetic approaches were employed: a green, plant-mediated method to produce bimetallic Cu@Au nanoparticles with novel morphologies, and conventional synthesis routes for metallic Cu2O and Au nanoparticles. All nanoparticles were evaluated under the same experimental conditions to assess their antifungal efficacy against Candida albicans and Candida krusei, the latter being intrinsically resistant to fluconazole. In addition to their antifungal activity and moderate cytocompatibility at the effective concentrations, the Au@Cu nanoparticles exhibited intrinsic peroxidase-like catalytic activity, as demonstrated by colorimetric assays, UV–Vis spectroscopy, and steady-state kinetic analysis. The enzyme-like catalytic behavior suggests that oxidative catalytic processes may contribute to their antifungal mechanism, complementing the well-known antimicrobial effects of copper ions. Overall, our findings demonstrate that Au@Cu nanoparticles represent multifunctional nanomaterials with promising potential for antifungal and nanozyme-based biomedical applications, while highlighting the importance of sustainable synthesis strategies in the development of next-generation nanomedicines.
Redispersible polymer powder (RDP) and defoamers are widely used to tune the workability and hardened response of cementitious mortars, but favorable pre-freeze indicators do not necessarily translate into freeze–thaw durability. This study examined a polypropylene-fiber-reinforced joint-filling mortar for prefabricated pavement bases using reference (REF), RDP-modified (RDP), defoamer-modified (DF), and combined RDP–defoamer (RDP–DF) formulations, with a commercial grout as an external benchmark. The primary comparison was between RDP and RDP–DF: both mixtures contained 280 kg m-3 mixing water and differed by the incorporation of 0.15% defoamer by binder mass. Pre-freeze mechanical, transport, and volume-stability responses were characterized, followed by longitudinal freeze–thaw tracking using mass loss and relative dynamic elastic modulus (RDEM) and by residual mechanical evaluation. Relative to RDP, RDP–DF reduced water absorption and 28-day drying shrinkage by 46.0% and 50.0%, while secant modulus increased by approximately 23%. Flexural and compressive strengths decreased by 28.8% and 25.4%, and permeability did not improve. After 15 freeze–thaw cycles, mass loss increased from approximately 0% to 5.48% and RDEM decreased from 83.94% to 64.59%. The median first observed cycle at which mass loss reached or exceeded 5% decreased from 50 to 15 cycles. In an independent 15-cycle batch, post-freeze–thaw compressive strength was 23.8 MPa for RDP and 17.2 MPa for RDP–DF, a difference of −27.7%. For similar RDP-modified joint-filling mortars, the results support combining pre-freeze properties with deterioration trajectories and residual mechanical capacity when assessing freeze–thaw durability.
