Accurate prediction of ion-induced energy deposition in polymeric materials is essential for space radiation shielding and advanced radiotherapy applications. This study proposes a generalizable machine learning framework for predicting Bragg peak characteristics using Monte Carlo simulation. Energy deposition profiles were generated via SRIM/TRIM and Geant4 for ions with atomic numbers Z = 1–10 at energies between 70 and 150 MeV. Two key targets were defined: Bragg peak position (MM150) and maximum ionization intensity (IONIZ150). For SRIM/TRIM data, the Poly-2 method achieved near-ideal accuracy for MM150 (R2 = 0.9999), while persistence performed best for IONIZ150 (R2 = 0.9991). However, performance degraded in Geant4 data, where MM150 errors increased (for Poly-2) and IONIZ150 showed higher variability (for sMAPE). The proposed machine learning framework, evaluated using LOPO validation, achieved consistently high generalization performance. In SRIM/TRIM, R2 reached 0.9942 ± 0.0178 (MM150) and 0.9951 ± 0.0144 (IONIZ150), with sMAPE values of 5.11% and 3.76%, respectively. In Geant4, similar robustness was observed, with R2 up to 0.9942 and sMAPE reduced to 4.41% for IONIZ150. These results demonstrate that simulation-informed machine learning enables accurate, robust, generalizable prediction of Bragg peak behavior in polymeric materials, offering a scalable framework for radiation interaction modeling and material design.
Antibiotics are widely used in the treatment of humans and animals. Since the body does not fully absorb it, a significant portion reaches environmental systems and wastewater treatment plants. Antibiotics can be removed from the body at low or high levels through various treatment methods. Generally, conventional systems are insufficient for effectively removing antibiotics, resulting in the discharge waters containing specific amounts of antibiotics. On the other hand, advanced treatment methods can achieve high antibiotic removal efficiency. To mitigate water scarcity, the reuse of reclaimed water for agricultural purposes has become increasingly important. Reclaimed water is used directly or indirectly for irrigation purposes. However, antibiotic residues in reclaimed water can accumulate in soil and then be transferred to plant roots, stems, leaves, and fruits. The physicochemical properties of antibiotics affect their transport in environmental systems. Antibiotics may tend to remain in the root zone or be translocated to the fruit. Consuming fruits with antibiotic residues poses various health risks to humans and other contaminated with antibiotic residues poses various health risks to humans and other organisms. This study summarizes the presence of antibiotics in environmental systems, evaluates the removal performance of physical, chemical, and biological treatment systems for antibiotics, and examines the mechanisms by which antibiotics are transferred to plants through irrigation with reclaimed water.
In this study, a biodegradable and sustainable piezoelectric–triboelectric hybrid nanogenerator (HENG) was designed and fabricated using cellulose nanofibrils (CNFs) as a nucleating agent and phycocyanin (PC), an algae-derived protein from Spirulina platensis, as the tribo-positive layer paired with poly(vinylidene fluoride) (PVDF) films. The investigation was conducted in two parts to investigate the effect of CNF incorporation into different layers. In the first part, CNFs were incorporated into the PVDF layer, whereas in second part incorporated into the PC layer. Electromechanical performance was characterized under periodic contact–separation motion. Even in the absence of nanofillers, the PVDF–PC pair exhibited efficient electromechanical behavior, generating an open-circuit voltage (Voc) of 84 V and a short-circuit current (Isc) of 87 µA. Upon CNF incorporation, the output was significantly enhanced. The highest performance was observed when CNFs were added to the PVDF layer (at 20 wt.
This paper investigates the change in seismic demands of a reinforced concrete (RC) building retrofitted by seismic isolation technique, due to change in superstructure characteristics. First, an RC building that possesses the general characteristics of fixed-base building stock in Türkiye is identified. Then, it is retrofitted by means of Lead Rubber Bearings (LRBs) and subjected to bidirectional ground motion excitations through nonlinear response history analyses (NRHA). Concrete compressive strength, dimensions of both beams and columns, and first story height are the parameters considered in the analyses. Two different numerical models are established by considering both linear and nonlinear element definitions for the superstructure. In either case, LRBs are represented by deteriorating hysteretic force-displacement relations that consider the effect of heating in the lead core during cyclic motion. Results indicate that increasing the beam dimensions is the most effective approach in retrofitting of the analyzed structural model to reduce inter-story drift ratios (ISDRs). An increase in beam dimensions led to an approximately 30
The escalating accumulation of synthetic polymer waste underscores the urgent need for sustainable biodegradation strategies. Among microbial enzymes, cutinases have recently gained prominence for their ability to hydrolyze aliphatic polyesters such as polycaprolactone (PCL). This study reports the isolation and characterization of PCL-degrading bacteria from plastic-contaminated sites in the Bursa region of Turkey, expanding the known ecological distribution of cutinase activity microorganisms. A total of 82 bacterial isolates were obtained, of which 14 exhibited hydrolytic activity on Tween 20, Tween 80, and PCL-containing media, indicative of extracellular esterase/cutinase activity. Three isolates—Peribacillus sp. M.2.2, Peribacillus sp. K.2.2, and Stutzerimonas sp. G.K.5.1—showed the most prominent PCL degradation zones and were selected for further analysis based on 16 S rRNA gene sequencing. Among these isolates, Stutzerimonas sp. G.K.5.1 achieved a degradation efficiency of 10.75
