VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

Background radioactivity level estimation and passive shield optimization using adjoint Monte Carlo method.

The current study proposes a procedure to estimate the activity concentration of natural radionuclides and to optimize passive shielding solutions for HPGe detectors using adjoint Monte Carlo (MC) simulation technique of Geant4 for the first time. The background spectrum is acquired for 1.56 × 106 s using an HPGe detector model (GC3020), set inside a shielding solution, during 2021-2022 to estimate the activity concentration of natural radionuclides inside the shielding. While, a background spectrum for 65,000 s is acquired with shielding removed to estimate the concentration of natural radionuclides in the building materials of the laboratory. The detector design used in the simulations is validated by comparing computed and measured Full Energy Peak Efficiency (FEPE) for point sources 241Am, 152Eu, 137Cs, 133Ba, and 60Co. Adjoint MC simulations are used to compute the activity concentration of natural radionuclides assuming an isotropic distribution. The activity concentration of 40K, 226Ra and 232Th in the building material is found to be 524 ± 140, 83 ± 20 and 65 ± 18 Bqkg-1, respectively. The computed values are found in good agreement with the published data. The natural radioactivity levels of 40K, 226Ra and 232Th measured in lead shielding are 155.7 ± 0.1 mBqkg-1, 24 ± 13 mBqkg-1 and 33 ± 17 mBqkg-1 respectively. The radiological risks arising due to natural radioactivity is assessed by calculating radium equivalent activity (Raeq), indoor radiation hazard index (Hin) and annual effective dose equivalent. All the radiological parameters are found below their permissible limits and building materials may be considered radiologically safe. The optimal lead shield thickness for the detector is determined to be 12 cm, resulting in reduction of background signal by two orders of magnitude compared to an unshielded detector. The adjoint MC simulations in Geant4 are 103-104 times more rapid as compared to normal simulations for shield optimization of HPGe detectors and therefore, are identified as viable computing solution to calculate the activity of the background radiation.

Classification of Pneumonia via a Hybrid ZFNet-Quantum Neural Network Using a Chest X-ray Dataset.

INTRODUCTION: Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep neural network (QDNN). Therefore, we introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting pneumonia using 5863 X-ray scans with binary cases. METHODS: The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10-6 and six layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs. RESULTS: The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy gained by the convolutional neural network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method. CONCLUSION: This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the speed and accuracy of the network in the clinical and healthcare sectors.

Does trade policy uncertainty hurt renewable energy-related sustainable development goals in China?

Trade policy uncertainty might hamper trade flow, including the trade of green and renewable energy technologies. Therefore, this study aims to examine the asymmetric effects of trade policy uncertainty (TPU) on renewable energy consumption (REC) in China. To calculate the short- and long-term relationships between REC, TPU, national income, carbon footprints, and financial development, we used the nonlinear QARDL technique. The estimates reveal that an upsurge in TPU hurts REC in the short and long run. Conversely, a stable trade policy or a reduction in TPU increases REC in the long run. In the short run, a fall in TPU exerts no influence on REC. The findings further imply that various factors, including GDP, CO2 emissions, and financial development, contribute to long-term improvements in REC in China, both in the short and long run.

In vitro exploration of Acinetobacter strain (SG-5) for antioxidative potential and phytohormone biosynthesis in maize (Zea mays L.) cultivars differing in cadmium tolerance.

Cadmium (Cd) poses serious threats to plant growth and development, whereas the use of plant growth-promoting rhizobacteria (PGPR) has emerged a promising approach to diminish Cd retention in crops. A pot experiment was conducted to evaluate the effect of Cd tolerant strain Acinetobacter sp. SG-5 on growth, phytohormonal response, and Cd uptake of two maize cultivars (3062 and 31P41) under various Cd stress levels (0, 5, 12, 18, 26, and 30 µM CdCl2). The results revealed that CdCl2 treatment significantly suppressed the seed germination and growth together with higher Cd retention in maize cultivars in a dose-dependent and cultivar-specific manner with pronounced negative effect in 31P41. However, SG-5 strain exerted positive impact by up-regulating seed germination traits, plant biomass, photosynthetic pigments, enzymatic and non-enzymatic antioxidants, endogenous hormone level indole-3-acetic acid (IAA), abscisic acid (ABA), and sustained optimal nutrient's levels in both cultivars but predominantly in Cd-sensitive one (31P41). Further, Cd-resistant PGPR decreased the formation of reactive oxygen species in terms of malondialdehyde (MDA) and hydrogen peroxide (H2O2) verified through 3, 3'-diaminobenzidine (DAB) and nitroblue tetrazolium (NBT) analysis in conjunction with reduced Cd uptake and translocation in maize root and shoots in comparison to controls, advocating its sufficiency for bacterial-assisted Cd bioremediation. In conclusion, both SG-5 inoculated cultivars exhibited maximum Cd tolerance but substantial Cd tolerance was acquired by Cd susceptible cultivar-31P41 than Cd-tolerant one (3062). Current work recommended SG-5 strain as a promising candidate for plant growth promotion and bacterial-assisted phytomanagement of metal-polluted agricultural soils.

Response of various histological types of locally advanced rectal cancer to neoadjuvant multimodality therapy.

Objectives: To determine the response of various histological types of locally advanced rectal cancer to neoadjuvant multimodality therapy. METHODS: The non-randomised, quasi-experimental retrospective cohort study was conducted at the Combined Military Hospital, Rawalpindi, Pakistan, and comprised data of patients treated between January 1, 2020, to September 30, 2021. The data retrieved related to histologically proven and locally advanced rectal cancer patients aged 18-70 years receiving neoadjuvant chemoradiotherapy. Radiotherapy dose was 45 gray to pelvis with a boost to gross tumour of 5.4 gray in 3 fractions by using volumetric arc therapy concurrently with capecitabine 625mg/m² daily. A magnetic resonance imaging scan of pelvis with contrast was done at 5-10 weeks before surgery. Histological response to neoadjuvant treatment of various histological types was evaluated using the Rectal Cancer Regression Grade. Data was analysed using SPSS 22. RESULTS: Of the 182 patients evaluated, 108(59.34%) were included; 64(59.3%) males and 44(40.7%) females. The overall mean age was 45.4±5.2 years. Regression status was grade 1 in 24(22%) patients, grade 2 in 43(40%) and grade 3 in 41(38%) (p=0.074). There were 12(11.11%) patients with signet ring cell and 10(83.3%) showed pathological tumour regression. There were 17(15.74%) patients with mucinous variant, and 12(70.5%) had tumour regression. There were 79(73.15%) patients with adenocarcinoma, and 59(74.6%) of them showed tumour regression. . CONCLUSIONS: There was less tumour regression in mucinous and signet ring cell variants of adenocarcinoma. Modification and intensification of neoadjuvant therapy may be required in such histologies.

Surviving chlorinated waters: bleaching sensitivity and persistence of free-living amoebae.

Recent advancements in membrane technologies and disinfection methods have enhanced drinking water quality significantly. However, microorganisms, including free-living amoebae (FLA), persist and pose potential threats to humans. FLA are linked to severe neuro-ophthalmic infections and serve as hosts of pathogenic bacteria. This study examined FLA presence in chlorinated and ultrafiltration drinking water and evaluated chlorine's disinfectant. Of 115 water samples, 21 tested positive for Acanthamoeba sp., Allovahlkampfia sp., and Vermamoeba vermiformis, originating from chlorinated sources. FLA trophozoites withstand temperatures up to 37 °C, while the cysts tolerate heat shocks of 60-70 °C. Trophozoites are susceptible to 5 mg L-1 chlorine, but cysts remain viable at concentrations up to 10 mg L-1. FLAs' survival in chlorinated waters is attributed to high cyst tolerance and lower residual chlorine concentrations. These findings highlight the need for ultrafiltration or enhanced chlorination protocols to ensure safer drinking water.

An Integrated Framework for Analysis and Prediction of Impact of Single Nucleotide Polymorphism Associated with Human Diseases.

Single nucleotide polymorphisms are most common type of genetic variation in human genome. Analyzing genetic variants can help us better understand the genetic basis of diseases and develop predictive models which are useful to identify individuals who are at increased risk for certain diseases. Several SNP analysis tools have already been developed. For running these tools, the user needs to collect data from various databases. Secondly, often researchers have to use multiple variant analysis tools for cross validating their results and increase confidence in their findings. Extracting data from multiple databases and running multiple tools at a time, increases complexity and time required for analysis. There are some web-based tools that integrate multiple genetic variant databases and provide variant annotations for a few tools. These approaches have some limitations such as retrieving annotation information, filtering common pathogenic variants. The proposed web-based tool, namely IPSNP: An Integrated Platform for Predicting Impact of SNPs is written in Django which is a python-based framework. It uses RESTful API of MyVariant.info to extract annotation information of variants associated with a given gene, rsID, HGVS format variants specified in a VCF file for 29 tools. The results are in the form of a CSV file of predictions (1) derived from the consensus decision, (2) a file having annotations for the variants associated with the given gene, (3) a file showing variants declared as pathogenic commonly by the selected tools, and (4) a CSV file containing chromosome coordinates based on GRCh37 and GRCh38 genome assemblies, rsIDs and proteomic data, so that users may use tools of their choice and avoiding manual parameter collection for each tool. IPSNP is a valuable resource for researchers and clinicians and it can help to save time and effort in discovering the novel disease-associated variants and the development of personalized treatments.

Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection.

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Acute toxicity appraisal of Saussurea heteromalla extract and development of its chemotherapeutic herbal dosage form.

Ethnopharmacological relevance of Saussurea species for anti-cancer compounds instigated us to develop chemotherapeutic herbal tablets. This study was an ongoing part of our previous research based on the scientific evaluation of Saussurea heteromalla (S. heteromalla) for anti-cancer lead compounds. In the current study, S. heteromalla herbal tablets (500 /800 mg) were designed and evaluated for anti-cancer activity. Arctigenin was found as a bioactive lead molecule with anti-cancer potential for cervical cancer. The in vitro results on the HeLa cell line supported the ethnopharmacological relevance and traditional utilization of S. heteromalla and provided the scientific basis for the management of cervical cancer as proclaimed by traditional practitioners in China. LD50 of the crude extract was established trough oral acute toxicity profiling in mice, wherein the minimum lethal dose was noticed as higher than 1000 mg/kg body weight orally. Chromatographic fingerprint analysis ensured the identity and consistency of S. heteromalla in herbal tablets in terms of standardization of the herbal drug. About 99.15% of the drug (S. heteromalla crude extract) was recovered in herbal tablets (RSD: 0.45%). In vitro drug release profile was found to be more than 87% within 1 h, which was also correlated with different mathematical kinetic models of drug release (r2 = 0.992), indicating that drug release from matrix tablets into the blood is constant throughout the delivery. The dosage form was found stable after an accelerated stability parameters study which may be used for anti-cervical cancer therapy in the future, if it qualifies successful preclinical investigation parameters.

Serological and epidemiological investigation of Mycobacterium avium subspecies paratuberculosis in bovines in Pakistan.

OBJECTIVE: This study aimed to investigate the prevalence of paratuberculosis in cattle and buffaloes at twelve public dairy farms in Punjab, Pakistan. METHODS: A total of 2,181 more than two-year-old animals (1,242 cattle and 939 buffaloes) were tested by avian tuberculin, i.e., killed purified protein derivative of Mycobacterium avium paratuberculosis and indirect enzyme linked immunosorbent assay (ELISA). Blood and fecal samples were collected from tuberculin positive animals. These samples were further processed by indirect ELISA. The data were analyzed using frequency analysis and logistic analysis procedures. RESULTS: The prevalence of paratuberculosis at public dairy farms was 3.8%, as determined by tuberculin+ELISA test. It varied from 0.71% to 13.5% with a 100% herd prevalence. Multivariate logistic regression analysis revealed that species, milk production, total animals, total small ruminants, and total buffaloes were significantly associated with the occurrence of paratuberculosis. Odd ratio analysis revealed that with a one-kilogram increase in body weight, there will be a 0.006% increase in disease occurrence. With the increase in one animal in small ruminants and buffaloes, there will be 0.008% and 0.42% greater chances of developing paratuberculosis, respectively. Bivariate logistic regression analysis of cattle and buffaloes revealed that farm number, age, and total number of cattle were significantly associated with the occurrence of paratuberculosis. A one-month increase in lactation length increases the chance of tuberculosis by 0.004%; similarly, a one-liter increase in milk production increases the chance of disease by 10%. With each additional buffalo in the herd, there will be a 0.007% greater chance for the occurrence of paratuberculosis. CONCLUSION: This study concluded that tuberculin testing can be used in conjunction with ELISA to screen animals for paratuberculosis in countries with scarce resources, such as Pakistan. The efficacy of disease diagnosis can be improved by combining multiple tests.

Evaluation of a novel composite of expanded polystyrene with rGO and SEBS-g-MA.

This study displays the effect of reduced graphene oxide (rGO) nanofiller and polystyrene-b-poly(ethylene-ran-butylene)-b-polystyrene-grafted maleic anhydride (SEBS-g-MA) on the optical, thermal, and mechanical features of expanded polystyrene (EPS). First, the thin films of pristine EPS and composites were prepared using solution cast method. The prepared films were subjected to fourier-transform infrared (FTIR), SEM, UV-visible spectrophotometer, thermogravimetric analysis/differential scanning calorimetry, and universal testing machine for structural, morphological, optical, thermal, and mechanical characterizations. Optical study revealed a significant increase in refractive index and absorption of composites than EPS. Indirect band-gap energy of EPS (~4.08 eV) was reduced to ~1.61 eV for rGO composite and ~ 2.23 eV for composite composed of rGO and SEBS-g-MA. Thermal analyses presented improvement in characterization temperatures such as T10, T50, Tp, Tm, and Tg of composites, which ultimately lead to the thermal stability of prepared composites than pristine EPS. Stress-strain curves displayed higher yield strength (46.62 MPa), Young's modulus (96.29 MPa), and strain at break (0.54%) for EPS+rGO composite than pure EPS having stress at break (1.01 MPa), Young's modulus (12.44 MPa), and strain at break (0.08%). Moreover, ductility with relatively higher strain at break (0.61%) and lower Young's modulus (79.32 MPa) and yield strength (32.98 MPa) was noticed in EPS+rGO+SEBS-g-MA composite than EPS+rGO composite film. Morphological analysis revealed a change in globular morphology of EPS and inhomogeneous dispersion of rGO in EPS to homogeneously dispersed rGO in EPS matrix without globules owing to the addition of SEBS-g-MA. The increase in compatibility of EPS and rGO due to SEBS-g-MA was also observed in FTIR spectra. RESEARCH HIGHLIGHTS: Here, the solution casting approach was used to create the composite film of EPS and rGO with globules of various sizes. After adding SEBS-g-MA, the shape altered to globular free films exhibiting homogenous dispersion of rGO in EPS matrix. An optical investigation showed that composite materials had a significantly higher refractive index and absorption than EPS. The optical, thermal, and mechanical investigations suggest that the produced composites may be a great substitute for virgin EPS, allowing for a wider range of applications.

Predictors of quitting smoking behavior: evidence from Pakistan.

This study attempts to identify factors that significantly encourage the cessation of smoking in the context of Pakistan. The study distributes a modified questionnaire among 421 respondents (current as well as former smokers) in the capital city of Pakistan, Islamabad. The binary regression method was employed to data for analyzing predictors of making quit attempts and successful smoking cessation. The result indicates that respondents having strong intentions to quit, high socioeconomic status, low nicotine dependency, and past quit attempts, and those having no-smoking friends, are more likely to quit cigarette smoking successfully. On the other hand, factors like social pressure to quit smoking, religious information against smoking, intention to quit smoking, and public regulation on smoking are more likely to encourage smokers to make quit attempts. The study calls for community and school-wide smoking cessation campaigns involving officials, peers and parents, religious leaders, and other influential individuals to inform people about the dangers of smoking. In addition, religious leaders should be encouraged to issue rulings against smoking especially during "Friday Prayer." Furthermore, the government should pronounce more strict and comprehensive regulations on smoking by properly monitoring its implementation to encourage cessation of cigarette smoking.

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

Microalgal and activated sludge processing for biodegradation of textile dyes.

The textile industry contributes substantially to water pollution. To investigate bioremediation of dye-containing wastewater, the decolorization and biotransformation of three textile azo dyes, Red HE8B, Reactive Green 27, and Acid Blue 29, were considered using an integrated remediation approach involving the microalga Chlamydomonas mexicana and activated sludge (ACS). At a 5 mg L-1 dye concentration, using C. mexicana and ACS alone, decolorization percentages of 39%-64% and 52%-54%, respectively, were obtained. In comparison, decolorization percentages of 75%-79% were obtained using a consortium of C. mexicana and ACS. The same trend was observed for the decolorization of dyes at higher concentrations, but the potential for decolorization was low. The toxic azo dyes adversely affect the growth of microalgae and at high concentration 50 mg L-1 the growth rate inhibited to 50-60% as compared to the control. The natural textile wastewater was also treated with the same pattern and got promising results of decolorization (90%). Moreover, the removal of BOD (82%), COD (72%), TN (64%), and TP (63%) was observed with the consortium. The HPLC and GC-MS confirm dye biotransformation, revealing the emergence of new peaks and the generation of multiple metabolites with more superficial structures, such as N-hydroxy-aniline, naphthalene-1-ol, and sodium hydroxy naphthalene. This analysis demonstrates the potential of the C. mexicana and ACS consortium for efficient, eco-friendly bioremediation of textile azo dyes.

Assessment of bioremediation potential of Calotropis procera and Nerium oleander for sustainable management of vehicular released metals in roadside soils.

Land transportation is a major source of heavy metal contamination along the roadside, posing significant risks to human health through inhalation, oral ingestion, and dermal contact. Therefore, this study has been designed to determine the concentrations of vehicular released heavy metals (Cd, Pb, Ni, and Cu) in roadside soil and leaves of two commonly growing native plant species (Calotropis procera and Nerium oleander).Two busy roads i.e., Lahore-Okara road (N-5) and Okara-Faisalabad roads (OFR) in Punjab, Pakistan, were selected for the study. The data were collected from five sites along each road during four seasons. Control samples were collected ~ 50 m away from road. The metal content i.e. lead (Pb), cadmium (Cd) nickel (Ni) and copper (Cu) were determined in the plant leaves and soil by using Atomic Absorption Spectrophotometer (AAS). Significantly high amount of all studied heavy metals were observed in soil and plant leaves along both roads in contrast to control ones. The mean concentration of metals in soil ranged as Cd (2.20-6.83 mg/kg), Pb (4.53-15.29 mg/kg), Ni (29.78-101.26 mg/kg), and Cu (61.68-138.46 mg/kg) and in plant leaves Cd (0.093-0.53 mg/kg), Pb (4.31-16.34 mg/kg), Ni (4.13-16.34 mg/kg) and Cu (2.98-32.74 mg/kg). Among roads, higher metal contamination was noted along N-5 road. Significant temporal variations were also noted in metal contamination along both roads. The order of metal contamination in soil and plant leaves in different seasons was summer > autumn > spring > winter. Furthermore, the metal accumulation potential of Calotropis procera was higher than that of Nerium oleander. Therefore, for sustainable management of metal contamination, the plantation of Calotropis procera is recommended along roadsides.

Engineering Nitrogen-Doped Carbon Quantum Dots: Tailoring Optical and Chemical Properties through Selection of Nitrogen Precursors.

The process of N-doping is frequently employed to enhance the properties of carbon quantum dots. However, the precise requirements for nitrogen precursors in producing high-quality N-doped carbon quantum dots (NCQDs) remain undefined. This research systematically examines the influence of various nitrogen dopants on the morphology, optical features, and band structure of NCQDs. The dots are synthesized using an efficient, eco- friendly, and rapid continuous hydrothermal flow technique. This method offers unparalleled control over synthesis and doping, while also eliminating convention-related issues. Citric acid is used as the carbon source, and urea, trizma base, beta-alanine, L-arginine, and EDTA are used as nitrogen sources. Notably, urea and trizma produced NCQDs with excitation-independent fluorescence, high quantum yields (up to 40%), and uniform dots with narrow particle size distributions. Density functional theory (DFT) and time-dependent DFT modelling established that defects and substituents within the graphitic structure have a more significant impact on the NCQDs' electronic structure than nitrogen-containing functional groups. Importantly, for the first time, this work demonstrates that the conventional approach of modelling single-layer structures is insufficient, but two layers suffice for replicating experimental data. This study, therefore, provides essential guidance on the selection of nitrogen precursors for NCQD customization for diverse applications.

Silica-Based Superhydrophobic and Superoleophilic Cotton Fabric with Enhanced Self-Cleaning Properties for Oil-Water Separation and Methylene Blue Degradation.

Superhydrophobic textiles with multifunctional characteristics are highly desired and have attracted tremendous research attention. This research employs a simple dip-coating method to obtain a fluorine-free silica-based superhydrophobic and superoleophilic cotton fabric. Pristine cotton fabric is coated with SiO2 nanoparticles and octadecylamine. SiO2 nanoparticles are anchored on the cotton fabric to increase surface roughness, and octadecyl amine lowers the surface energy, turning the hydrophilic cotton fabric into superhydrophobic. The designed cotton fabric exhibits a water contact angle of 159° and a sliding angle of 7°. The prepared cotton fabric is characterized by attenuated total reflectance-fourier transform infrared spectroscopy, X-ray diffraction, atomic force microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. In addition, the coated fabric reveals excellent features, including mechanical and chemical stability, superhydrophobicity, superoleophilicity, and the self-cleaning ability. SiO2 nanoparticles and octadecylamine-coated cotton fabric demonstrate exceptional oil-water separation and wastewater remediation performance by degrading the methylene blue solution up to 89% under visible light. The oil-water separation ability is tested against five different oils with more than 90% separation efficiency. This strategy has the advantages of low-cost precursors, a simple and scalable coating method, enhanced superhydrophobicity and superoleophilicity, self-cleaning ability, efficient oil-water separation, and exceptional wastewater remediation performance.

Efficient performance of InP and InP/ZnS quantum dots for photocatalytic degradation of toxic aquatic pollutants.

In recent years, the growing concern over the presence of toxic aquatic pollutants has prompted intensive research into effective and environmentally friendly remediation methods. Photocatalysis using semiconductor quantum dots (QDs) has developed as a promising technology for pollutant degradation. Among various QD materials, indium phosphide (InP) and its hybrid with zinc sulfide (ZnS) have gained considerable attention due to their unique optical and photocatalytic properties. Herein, InP and InP/ZnS QDs were employed for the removal of dyes (crystal violet, and congo red), polyaromatic hydrocarbons (pyrene, naphthalene, and phenanthrene), and pesticides (deltamethrin) in the presence of visible light. The degradation efficiencies of crystal violet (CV) and congo red (CR) were 74.54% and 88.12% with InP, and 84.53% and 91.78% with InP/ZnS, respectively, within 50 min of reaction. The InP/ZnS showed efficient performance for the removal of polyaromatic hydrocarbons (PAHs). For example, the removal percentage for naphthalene, phenanthrene, and pyrene was 99.8%, 99.6%, and 88.97% after the photocatalytic reaction. However, the removal percentage of InP/ZnS for pesticide deltamethrin was 90.2% after 90 min light irradiation. Additionally, advanced characterization techniques including UV-visible spectrophotometer (UV-Vis), photoluminescence (PL), X-ray diffractometer (XRD), energy-dispersive spectrometer (EDS) elemental mapping, transmission electron microscopy (TEM), and thermogravimetric analysis (TGA) were used to analyze the crystal structure, morphology, and purity of the fabricated materials in detail. The particle size results obtained from TEM are in the range of 2.28-4.60 nm. Both materials (InP and InP/ZnS) exhibited a spherical morphology, displaying distinct lattice fringes. XRD results of InP depicted lattice planes (111), (220), and (311) in good agreement with cubic geometry. Furthermore, the addition of dopants was discovered to enhance the thermal stability of the fabricated material. In addition, QDs exhibited efficacy in the breakdown of PAHs. The analysis of their fragmentation suggests that the primary mechanism for PAHs degradation is the phthalic acid pathway.