Publications

This work aimed to investigate the degradation performance of natural cellulose acetate (CA) membranes filled with ZnO nanostructures. Photocatalytic degradation of reactive toxic dye methylene blue (MB) was studied as a model reaction using UV light. A CA membrane was previously casted and fabricated through the phase inversion processes and laboratory-synthesized ZnO microparticles as filler. The prepared membrane was characterized for pore size, ultrafiltration (UF) performance, porosity, morphology using scanning electron micrographs (SEM), water contact angle and catalytic degradation of MB. The prepared membrane shows a significant amount of photocatalytic oxidation under UV. The photocatalytic results under UV-light radiation in CA filled with ZnO nanoparticles (CA/ZnO) demonstrated faster and more efficient MB degradation, resulting in more than 30% of initial concentration. The results also revealed how the CA/ZnO combination effectively improves the membrane's photocatalytic activity toward methylene blue (MB), showing that the degradation process of dye solutions to UV light is chemically and physically stable and cost-effective. This photocatalytic activity toward MB of the cellulose acetate membranes has the potential to make these membranes serious competitors for removing textile dye and other pollutants from aqueous solutions. Hence, polymer-ZnO composite membranes were considered a valuable and attractive topic in membrane technology.

Novel steroidal (6R)‐spiro‐1,3,4‐thiadiazoline derivatives were synthesized by the cyclization of steroidal thiosemicarbazones with acetic anhydride, screened in vitro against antibacterial activity using disc‐diffusion method and the minimum inhibitory concentration. The results showed that steroidal thiadiazoline derivatives exhibited better antibacterial activity than the steroidal thiosemicarbazone derivatives. Chloro and acetoxy substituents on the 3β‐position of the steroidal thiadiazoline ring increased the antibacterial activity. Among all the compounds, compound 7 and 8 were found better inhibitors of both types of bacteria (Gram‐positive and Gram‐negative) as compared to the respective drug amoxicillin. All the synthesized compounds were well characterized by spectroscopic methods such as IR, 1 H‐NMR, 13 C‐NMR mass, and elemental analysis and their stereochemistry was also discussed.

In the title compound, C13H8Cl3NO4S, the aromatic rings are oriented at a dihedral angle of 68.94 (1)° and the mol-ecule adopts a V-shape. An intra-molecular N-H⋯O inter-action generates a six-membered S(6) ring motif. In the crystal, pairs of O-H⋯O hydrogen bonds involving the carb-oxy group link the mol-ecules into inversion dimers with an R 2 (2)(8) motif. N-H⋯O and non-classical C-H⋯O inter-actions connect the mol-ecules, forming sheets propagating in (100).

In the title compound, C(17)H(14)O(2), the indan-1-one system is almost planar (r.m.s. deviation = 0.007 Å) and the benzene ring is twisted out of its plane by 8.15 (6)°. The conformation about the C=C double bond [1.348 (2) Å] is E. Helical supra-molecular chains along [010] feature in the crystal packing; these are sustained by C-H⋯O hydrogen bonds and π-π inter-actions between translationally related indan-1-one systems [centroid-centroid distance = 3.7970 (10) Å].

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

The title compound 2 is obtained as bright yellow metastable crystals, which on mechanical grinding change to the dark red stable form; unlike piezochromic compounds, the reverse colour change does not occur; X-ray crystallographic analyses shows that the yellow form has a folded structure while the red form has a twisted structure.

Deep reinforcement learning (DRL) has been applied to the routing, modulation, and spectrum assignment (RMSA) in elastic optical networks (EON), enabling the learning of RMSA policies through interaction between a DRL agent and the EON environment. Existing approaches aim to make decisions that fulfill spectrum utilization requirements. For the quality of transmission (QoT) assurance, they rely on distance-dependent modulation selection. However, other factors such as physical impairments, spectrum fragmentation, and traffic dynamics can impact QoT. In this work, we introduce DeepRMSA-QoT (deep reinforcement learning for QoT-focused routing, modulation, and spectrum assignment), a technique using the DRL framework to explore efficient QoT-aware RMSA policies for EONs. We propose a state representation that includes QoT-level information. Additionally, two reward functions are developed alongside the basic one, incorporating the QoT information. Considering QoT during the development of state representation and reward functions can guide the agent to actions that meet the QoT requirements and minimize exploration blindness when performing RMSA. This ultimately leads to more efficient learning of improved policies. Furthermore, a QoT unit is designed to guarantee the QoT and to facilitate the implementation of proposed reward functions. Extensive simulation results demonstrate that our proposed approach outperforms the existing approaches. Our approach can identify lightpath feasibility based on QoT requirements, select feasible lightpaths to meet QoT of demands, and avoid infeasible ones. In addition, our approach achieves a lower blocking rate while provisioning high bit-rates with dynamic traffic demands in different topologies.