Immobilization of gold nanoclusters inside porous electrospun fibers for selective detection of Cu(II): A strategic approach to shielding pristine performance

Here, a distinct demonstration of highly sensitive and selective detection of copper (Cu2+) in a vastly porous cellulose acetate fibers (pCAF) has been carried out using dithiothreitol capped gold nanocluster (DTT.AuNC) as fluorescent probe. A careful optimization of all potential factors affecting the performance of the probe for effective detection of Cu2+ were studied and the resultant sensor strip exhibiting unique features including high stability, retained parent fluorescence nature and reproducibility. The visual colorimetric detection of Cu2+ in water, presenting the selective sensing performance towards Cu2+ ions over Zn2+, Cd2+ and Hg2+ under UV light in naked eye, contrast to other metal ions that didn't significantly produce such a change. The comparative sensing performance of DTT.AuNC@pCAF, keeping the nonporous CA fiber (DTT.AuNC@nCAF) as a support matrix has been demonstrated. The resulting weak response of DTT.AuNC@nCAF denotes the lack of ligand protection leading to the poor coordination ability with Cu2+. The determined detection limit (50 ppb) is far lower than the maximum level of Cu2+ in drinking water (1.3 ppm) set by U.S. Environmental Protection Agency (EPA). An interesting find from this study has been the specific oxidation nature between Cu2+ and DTT.AuNC, offering solid evidence for selective sensors

High spatial resolution laser cavity extinction and laser-induced incandescence in low-soot-producing flames

Abstract Accurate measurement techniques for in situ determination of soot are necessary to understand and monitor the process of soot particle production. One of these techniques is line-of-sight extinction, which is a fast, low-cost and quantitative method to investigate the soot volume fraction in flames. However, the extinction-based technique suffers from relatively high measurement uncertainty due to low signal-to-noise ratio, as the single-pass attenuation of the laser beam intensity is often insufficient. Multi-pass techniques can increase the sensitivity, but may suffer from low spatial resolution. To overcome this problem, we have developed a high spatial resolution laser cavity extinction technique to measure the soot volume fraction from low-soot-producing flames. A laser beam cavity is realised by placing two partially reflective concave mirrors on either side of the laminar diffusion flame under investigation. This configuration makes the beam convergent inside the cavity, allowing a spatial resolution within 200 μm, whilst increasing the absorption by an order of magnitude. Three different hydrocarbon fuels are tested: methane, propane and ethylene. The measurements of soot distribution across the flame show good agreement with results using laser-induced incandescence (LII) in the range from around 20 ppb to 15 ppm.B. Tian is funded through a fellowship provided by China Scholarship Council. Y. Gao and S. Balusamy are funded through a grant from EPSRC EP/K02924X/1 and EP/G035784/1, respectively.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s00340-015-6156-

Toxicity of lanthanum oxide (La2O3) nanoparticles in aquatic environments

This study demonstrates the acute toxicity of lanthanum oxide nanoparticles (La2O3 NP) on two sentinel aquatic species, fresh-water microalgae Chlorella sp. and the crustacean Daphnia magna. The morphology, size and charge of the nanoparticles were systematically studied. The algal growth inhibition assay confirmed absence of toxic effects of La2O3 NP on Chlorella sp., even at higher concentration (1000 mg L-1) after 72 h exposure. Similarly, no significant toxic effects were observed on D. magna at concentrations of 250 mg L-1 or less, and considerable toxic effects were noted in higher concentrations (effective concentration [EC50] 500 mg L-1; lethal dose [LD50] 1000 mg L-1). In addition, attachment of La2O3 NP on aquatic species was demonstrated using microscopy analysis. This study proved to be beneficial in understanding acute toxicity in order to provide environmental protection as part of risk assessment strategies. © The Royal Society of Chemistry 2015

Ultrasensitive electrospun fluorescent nanofibrous membrane for rapid visual colorimetric detection of H2O2

We report herein a flexible fluorescent nanofibrous membrane (FNFM) prepared by decorating the gold nanocluster (AuNC) on electrospun polysulfone nanofibrous membrane for rapid visual colorimetric detection of H2O2. The provision of AuNC coupled to NFM has proven to be advantageous for facile and quick visualization of the obtained results, permitting instant, selective, and on-site detection. We strongly suggest that the fast response time is ascribed to the enhanced probabilities of interaction with AuNC located at the surface of NF. It has been observed that the color change from red to blue is dependent on the concentration, which is exclusively selective for hydrogen peroxide. The detection limit has been found to be 500 nM using confocal laser scanning microscope (CLSM), visually recognizable with good accuracy and stability. A systematic comparison was performed between the sensing performance of FNFM and AuNC solution. The underlying sensing mechanism is demonstrated using UV spectra, transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). The corresponding disappearance of the characteristic emissions of gold nanoclusters and the emergence of a localized surface plasmon resonance (LSPR) band, stressing this unique characteristic of gold nanoparticles. Hence, it is evident that the conversion of nanoparticles from nanoclusters has taken place in the presence of H2O2. Our work here has paved a new path for the detection of bioanalytes, highlighting the merits of rapid readout, sensitivity, and user-friendliness. © 2015 Springer-Verlag Berlin Heidelberg

Grain boundary engineering in electrospun ZnO nanostructures as promising photocatalysts

Electrospun ZnO nanofibers (ZNF) have received increased attention as photocatalysts owing to their potential for incredible performance. However, uncertainty still exists in determining the correlation between grain boundaries (GBs) and photocatalytic activity. Therefore, effective thought has been put into engineering the GBs to convert ZNF into a promising photocatalyst. Herein, the obtained electrospun ZnO structures are composed of nanograins, which are connected to each other in an ordered manner. In-depth studies have revealed that the growth of nanograins severely altered the morphology of ZNF and GB areas at higher annealing temperatures ranging from 500 °C to 1000 °C. Based on the morphological features and their structural evolution, the obtained structures are named as ZnO nanofibers-1 (ZNF-1, 500 °C), ZnO hollow tubes (ZHT, 600 °C), ZnO nanofibers-2 (ZNF-2, 700 °C), ZnO bamboo structured fibers (ZBF, 800 °C), ZnO segmented fibers (ZSF, 900 °C) and ZnO nanoparticles (ZNP, 1000 °C). A strong correlation between the inherent emission features of ZNF and their peak positions have been detected with the GB. The comparative degradation efficiency of methylene blue (MB) has been studied and the results showed that the ZNF-1 with highly stacked nanograins containing rich grain boundaries demonstrated ∼6 times higher efficiency than other structures. In addition, it has been shown to have a strong effect towards the degradation of Rhodamine B (Rh B) and 4-nitro-phenol (4-NP). A critical parameter for improving the photocatalytic activity is found to be the GB mediated defects, which are proposed to be oxygen/zinc vacancies at nanograin fusion interfaces, while supposedly maintaining its fibrous structure, wherein no relationship has been drawn implying the direct domination of morphology, surface area and defect. © 2016 The Royal Society of Chemistry

Nanograined surface shell wall controlled ZnO-ZnS core-shell nanofibers and their shell wall thickness dependent visible photocatalytic properties

The core-shell form of ZnO-ZnS based heterostructural nanofibers (NF) has received increased attention for use as a photocatalyst owing to its potential for outstanding performance under visible irradiation. One viable strategy to realize the efficient separation of photoinduced charge carriers in order to improve catalytic efficiency is to design core-shell nanostructures. But the shell wall thickness plays a vital role in effective carrier separation and lowering the recombination rate. A one dimensional (1D) form of shell wall controlled ZnO-ZnS core-shell nanofibers has been successfully prepared via electrospinning followed by a sulfidation process. The ZnS shell wall thickness can be adjusted from 5 to 50 nm with a variation in the sulfidation reaction time between 30 min and 540 min. The results indicate that the surfaces of the ZnO nanofibers were converted to a ZnS shell layer via the sulfidation process, inducing visible absorption behavior. Photoluminescence (PL) spectral analysis indicated that the introduction of a ZnS shell layer improved electron and hole separation efficiency. A strong correlation between effective charge separation and the shell wall thickness aids the catalytic behavior of the nanofiber network and improves its visible responsive nature. The comparative degradation efficiency toward methylene blue (MB) has been studied and the results showed that the ZnO-ZnS nanofibers with a shell wall thickness of ∼20 nm have 9 times higher efficiency than pristine ZnO nanofibers, which was attributed to effective charge separation and the visible response of the heterostructural nanofibers. In addition, they have been shown to have a strong effect on the degradation of Rhodamine B (Rh B) and 4-nitrophenol (4-NP), with promising reusable catalytic efficiency. The shell layer upgraded the nanofiber by acting as a protective layer, thus avoiding the photo-corrosion of ZnO during the catalytic process. A credible mechanism for the charge transfer process and a mechanism for photocatalysis supported by trapping experiments in the ZnO-ZnS heterostructural system for the degradation of an aqueous solution of MB are also explicated. Trapping experiments indicate that h+ and OH are the main active species in the ZnO-ZnS heterostructural catalyst, which do not effectively contribute in a bare ZnO catalytic system. Our work also highlights the stability and recyclability of the core-shell nanostructure photocatalyst and supports its potential for environmental applications. We thus anticipate that our results show broad potential in the photocatalysis domain for the design of a visible light functional and reusable core-shell nanostructured photocatalyst. © 2017 The Royal Society of Chemistry

Ultrasensitive electrospun fluorescent nanofibrous membrane for rapid visual colorimetric detection of H2O2

We report herein a flexible fluorescent nanofibrous membrane (FNFM) prepared by decorating the gold nanocluster (AuNC) on electrospun polysulfone nanofibrous membrane for rapid visual colorimetric detection of H2O2. The provision of AuNC coupled to NFM has proven to be advantageous for facile and quick visualization of the obtained results, permitting instant, selective, and on-site detection. We strongly suggest that the fast response time is ascribed to the enhanced probabilities of interaction with AuNC located at the surface of NF. It has been observed that the color change from red to blue is dependent on the concentration, which is exclusively selective for hydrogen peroxide. The detection limit has been found to be 500 nM using confocal laser scanning microscope (CLSM), visually recognizable with good accuracy and stability. A systematic comparison was performed between the sensing performance of FNFM and AuNC solution. The underlying sensing mechanism is demonstrated using UV spectra, transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). The corresponding disappearance of the characteristic emissions of gold nanoclusters and the emergence of a localized surface plasmon resonance (LSPR) band, stressing this unique characteristic of gold nanoparticles. Hence, it is evident that the conversion of nanoparticles from nanoclusters has taken place in the presence of H2O2. Our work here has paved a new path for the detection of bioanalytes, highlighting the merits of rapid readout, sensitivity, and user-friendliness. © 2015 Springer-Verlag Berlin Heidelberg

Optimized machine learning model for air quality index prediction in major cities in India

Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems

In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients’ medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier’s error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL’s effectiveness and efficiency in identifying diseases is evaluated and compared