Metal-free carbon-based anode for electrochemical degradation of tetracycline and metronidazole in wastewater.

Degradation of antibiotics through electrocatalytic oxidation has recently been comprehended as a promising strategy in wastewater treatment. Herein, nitrogen and sulphur doped graphene oxide (N,S-rGO) nanosheets were synthesized and employed as metal-free anodic material for electrochemical degradation of antibiotics, viz. metronidazole (MNZ) and tetracycline (TC).

Fabrication of bioinspired keratin/sodium alginate based biopolymeric mat loaded with herbal drug and green synthesized zinc oxide nanoparticles as a dual drug antimicrobial wound dressing.

Dual drug antibacterial wound dressings with biological materials possess crucial wound healing characteristics including biocompatibility, non-toxicity, degradability, mechanical strength and antibacterial properties. The study focusses on fabricating keratin (K)‑sodium alginate (A) based wound dressings by loading green synthesized zinc oxide nanoparticles (ZnO NPs) using C. roseus (leaf extract) and M.

Development of chitosan-based cerium and titanium oxide loaded polycaprolactone for cutaneous wound healing and antibacterial applications.

Cerium dioxide (CeO) based nanomaterials have emerged as promising dermal equivalents, promoting fibroblast infiltration and tissues regeneration. To enhance the antibacterial and wound healing activity, herein chitosan (CS)-CeO combined nano titanium dioxide (TiO) complex loaded polycaprolactone (PCL) nanohybrid (CS-CeO/TiO/PCL) scaffolds were prepared through casting method. The nanohybrid scaffolds' physiochemical, morphological, mechanical, and biological properties were evaluated using advanced analytical techniques.

An Overview of the Additive Manufacturing of Bast Fiber-Reinforced Composites and Envisaging Advancements Using the Patent Landscape.

Natural fiber composites attract attention owing to their environmentally friendly attributes. Many techniques, including fiber treatment, coatings, and fiber orientations, are used to improve the strength of natural fiber-reinforced composites. Still, the strength needs to be improved as expected.

Conversion of seaweed waste to biochar for the removal of heavy metal ions from aqueous solution: A sustainable method to address eutrophication problem in water bodies.

The present study investigated the sustainable approach for wastewater treatment using waste algal blooms. The current study investigated the removal of toxic metals namely chromium (Cr), nickel (Ni), and zinc (Zn) from aqueous solutions in batch and column studies using biochar produced by the marine algae Ulva reticulata. SEM/EDX, FTIR, and XRD were used to examine the adsorbents' properties and stability.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used.

Correction: Recent trends in carbon nanotube (CNT)-based biosensors for the fast and sensitive detection of human viruses: a critical review.

[This corrects the article DOI: 10.1039/D2NA00236A.].

Exploring Modified Rice Straw Biochar as a Sustainable Solution for Simultaneous Cr(VI) and Pb(II) Removal from Wastewater: Characterization, Mechanism Insights, and Application Feasibility.

This study aimed to investigate the efficacy of a rice straw biosorbent in batch adsorption for the removal of chromium (Cr(VI)) and lead (Pb(II)) heavy-metal ions from wastewater. The biosorbent was chemically synthesized and activated by using concentrated sulfuric acid. The produced biosorbent was then characterized by using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) analyses, which provided insights into surface morphology and functional groups.

Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer.

Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI).

Impact of air pollutants on climate change and prediction of air quality index using machine learning models.

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022.

Assessment of biodegradation of lignocellulosic fiber-based composites - A systematic review.

Lignocellulosic fiber-reinforced polymer composites are the most extensively used modern-day materials with low density and better specific strength specifically developed to render better physical, mechanical, and thermal properties. Synthetic fiber-reinforced composites face some serious issues like low biodegradability, non-environmentally friendly, and low disposability. Lignocellulosic or natural fiber-reinforced composites, which are developed from various plant-based fibers and animal-based fibers are considered potential substitutes for synthetic fiber composites because they are characterized by lightweight, better biodegradability, and are available at low cost.

Impact of climate change and anthropogenic activities on aquatic ecosystem - A review.

All living things depend on their natural environment, either directly or indirectly, for their high quality of life, growth, nutrition, and development. Due to the fast emissions of greenhouse gases (GHGs), the Earth's climate system is being negatively impacted by global warming. Stresses caused by climate change, such as rising and hotter seas, increased droughts and floods, and acrid waters, threaten the world's most populated areas and aquatic ecosystems.

Correction: Induced UV photon sensing properties in narrow bandgap CdTe quantum dots through controlling hot electron dynamics.

Correction for 'Induced UV photon sensing properties in narrow bandgap CdTe quantum dots through controlling hot electron dynamics' by Thankappan Thrupthika , , 2023, https://doi.org/10.1039/d3cp02424e.

An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification.

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images.

Photocatalytic degradation of fluoroquinolone antibiotics using chitosan biopolymer functionalized copper oxide nanoparticles prepared by facile sonochemical method.

Photocatalytic degradation is an excellent method for removing pharmaceutical residues due to their simplicity, ecological benignity, high efficiency, and exceptional stability. Herein, we demonstrate the sonochemically synthesised chitosan biopolymer functionalized copper oxide nanoparticles as an efficient photocatalyst for the degradation of fluoroquinolone-based antibiotics. The X-ray diffraction Rietveld refinement revealed the formation of single-phase copper oxide (CuO) with a monoclinic structure.

Gelatin/polyethylene glycol-loaded magnesium hydroxide nanocomposite to attenuate acetylcholinesterase, neurotoxicity, and activation of GPR55 protein in rat models of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease marked by mitochondrial dysfunction, amyloid-β (Aβ) aggregation, and neuronal cell loss. G-protein-coupled receptor 55 (GPR55) has been used as a promising target for insulin receptors in diabetes therapy, but GPR55's role in AD is still unidentified. Gelatin (GE) and polyethylene glycol (PEG) polymeric hydrogels are commonly used in the drug delivery system.

Induced UV photon sensing properties in narrow bandgap CdTe quantum dots through controlling hot electron dynamics.

Mn-doped CdTe (Mn-CdTe) quantum dot (QD) as well as quantum dot solid (QD solid) nanostructures are formed and the established structures are confirmed through HR-TEM analysis. The dynamics of charge carriers in both doped & undoped QD and QD solid structures were investigated by transient absorption (TA) spectroscopy. A slow band edge bleach recovery is obtained for Mn-doped CdTe QD and CdTe QD solid systems at room temperature.

A study on managing plastic waste to tackle the worldwide plastic contamination and environmental remediation.

Over the past 50 years, the emergence of plastic waste as one of the most urgent environmental problems in the world has given rise to several proposals to address the rising levels of contaminants associated with plastic debris. Worldwide plastic production has increased significantly over the last 70 years, reaching a record high of 359 million tonnes in 2020. China is currently the world's largest plastic producer, with a share of 17.

Implementation and Efficient Analysis of Preprocessing Techniques in Deep Learning for Image Classification.

Background: Deep learning models have recently been preferred to perform certain image-processing tasks. Recently, with the increasing radiation, heat, and poor lighting conditions, the raw image samples may contain noisy and ambiguous information.

Objective: To process these images, the deep learning model requires a large number of data samples to learn the missing information from other clear data samples.

Optimal transfer learning based nutrient deficiency classification model in ridge gourd (Luffa acutangula).

The efficient detection of nutrient deficiency and proper fertilizer for that deficiency becomes the critical challenges various farmers face. The family Cucurbitaceae includes members cultivated globally as a source of indigenous medicines, food, and fiber. Luffa acutangula (L.

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance.

Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment.

Drug Repositioning Using Computer-aided Drug Design (CADD).

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs.

Microfluidic paper-based device coupled with 3D printed imaging box for colorimetric detection in resource-limited settings.

Rapid and effective methods for the detection of analytes such as water contaminants, food adulterants and biomolecules are essential for the protection of public health and environmental protection. Most of the currently established analytical techniques need sophisticated equipment, centralized testing facilities, costly operations, and trained personnel. Such limitations make them inaccessible to the general populace, particularly in regions with limited resources.

Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

Introduction: Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images.

Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings.

Earthquakes are cataclysmic events that can harm structures and human existence. The estimation of seismic damage to buildings remains a challenging task due to several environmental uncertainties. The damage grade categorization of a building takes a significant amount of time and work.