Synthesis and characterization of antimicrobial colloidal polyanilines.

The potential application of colloidal polyaniline (PANI) as an antimicrobial is limited by challenges related to solubility in common organic solvents, scalability, and antimicrobial potency. To address these limitations, we introduced a functionalized PANI (fPANI) with carboxyl groups through the polymerisation of aniline and 3-aminobenzoic acid in a 1:1 molar ratio. fPANI is more soluble than PANI which was determined using a qualitative study. We further enhanced the solubility and antimicrobial activity of fPANI by incorporating Ag nanoparticles onto the synthesized fPANI colloid via direct addition of 10 mM AgNO3. The improved solubility can be attributed to an approximately 3-fold reduction in size of particles. Mean particle sizes are measured at 1322 nm for fPANI colloid and 473 nm for fPANI-Ag colloid, showing a high dispersion and deagglomeration effect from Ag nanoparticles. Antimicrobial tests demonstrated that fPANI-Ag colloids exhibited superior potency against Gram-positive Staphylococcus aureus, Gram-negative Escherichia coli, and Bacteriophage PhiX 174 when compared to fPANI alone. The minimum bactericidal concentration (MBC) and minimum virucidal concentration (MVC) values were halved for fPANI-Ag compared to fPANI colloid and attributed to the combination of Ag nanoparticles with the fPANI polymer. The antimicrobial fPANI-Ag colloid presented in this study shows promising results, and further exploration into scale-up can be pursued for potential biomedical applications.

ThoraxNet: a 3D U-Net based two-stage framework for OAR segmentation on thoracic CT images.

An important phase of radiation treatment planning is the accurate contouring of the organs at risk (OAR), which is necessary for the dose distribution calculation. The manual contouring approach currently used in clinical practice is tedious, time-consuming, and prone to inter and intra-observer variation. Therefore, a deep learning-based auto contouring tool can solve these issues by accurately delineating OARs on the computed tomography (CT) images. This paper proposes a two-stage deep learning-based segmentation model with an attention mechanism that automatically delineates OARs in thoracic CT images. After preprocessing the input CT volume, a 3D U-Net architecture will locate each organ to generate cropped images for the segmentation network. Next, two differently configured U-Net-based networks will perform the segmentation of large organs-left lung, right lung, heart, and small organs-esophagus and spinal cord, respectively. A post-processing step integrates all the individually-segmented organs to generate the final result. The suggested model outperformed the state-of-the-art approaches in terms of dice similarity coefficient (DSC) values for the lungs and the heart. It is worth mentioning that the proposed model achieved a dice score of 0.941, which is 1.1% higher than the best previous dice score, in the case of the heart, an important organ in the human body. Moreover, the clinical acceptance of the results is verified using dosimetric analysis. To delineate all five organs on a CT scan of size [Formula: see text], our model takes only 8.61 s. The proposed open-source automatic contouring tool can generate accurate contours in minimal time, consequently speeding up the treatment time and reducing the treatment cost.

Visible light photodegradation of organic dyes using electrochemically synthesized MoO3/ZnO.

In this study, flake-like MoO3-ZnO composite was prepared using a simple and robust electrochemical setup. The composite was characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, elemental analysis, X-ray photoelectron spectroscopy, thermogravimetric analysis, photoluminescence, zeta potential analysis, and electrochemical impedance study. The modified ZnO shows a remarkable catalytic activity towards the photodegradation of three potentially hazardous dyes, malachite green, crystal violet, and methylene blue. More than 95% of both malachite green and crystal violet degraded within 140 min under visible light irradiation. Scavenger studies reveal that OH· radicals produced by the photo-separated charges on MoO3-ZnO are responsible for the degradation of all three dyes. The photoactive charge carriers show less recombination rate as evidenced by the photoluminescence spectrum due to the interparticle charge migration process. This work suggests a new versatile procedure for the synthesis of MoO3-ZnO composites and establishes its photocatalytic efficacy under visible light with three common pollutant dyes found in wastewater.

Electrochemical synthesis, photodegradation and antibacterial properties of PEG capped zinc oxide nanoparticles.

The effect of surfactant and dopant on the properties of zinc oxide nanoparticles were studied by preparing polyethylene glycol (PEG) capped ZnO and tungsten doped PEG capped ZnO nanoparticles via the electrochemical method. These nanoparticles were characterized using X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Ultraviolet Diffuse Reflection Spectroscopy (UV-DRS), Scanning Electron Microscopy (SEM) and Electron Dispersive Analysis of X Rays (EDAX). The photocatalytic degradation of malachite green dye using these nanoparticles was studied under visible light. The effects of various reaction parameters like dye concentration, catalyst concentration, pH and time were studied to optimize the photodegradation reaction. Reusability of these nanoparticles was studied and no significant change was observed in the degradation efficiency of PEG capped ZnO till the fourth cycle, while there was a gradual decrease in the degradation efficiency of tungsten doped PEG capped ZnO. Langmuir- Hinshelwood kinetic model well describes the photodegradation capacity and the degradation of malachite green follows pseudo-first order kinetics.Photocatalytic studies reveal that PEG capping increases the degradation properties of ZnO while tungsten doping decreases the extent of PEG capping and has a detrimental effect on the degradation properties of ZnO. The prepared nanoparticles exhibit significant antibacterial properties against gram-positive Bacillus cereus and gram-negative Escherichia coli bacterial strains by agar well diffusion method.