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

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

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-

Laser diagnostics of pulverized coal combustion in O<inf>2</inf>/N<inf>2</inf> and O<inf>2</inf>/CO<inf>2</inf> conditions: velocity and scalar field measurements

Optical diagnostic techniques are applied to a 21 kW laboratory-scale pulverized coal–methane burner to map the reaction zone during combustion, in mixtures with varying fractions of O_2, N_2 and CO_2. Simultaneous Mie scatter and OH planar laser-induced fluorescence (PLIF) measurements have been carried out to study the effect of the oxidizer/diluent concentrations as well as the coal-loading rate. The spatial distribution of soot is captured using laser-induced incandescence (LII). Additionally, velocity profiles at selected axial locations are measured using the pairwise two-dimensional laser Doppler velocimetry technique. The OH PLIF images capture the reaction zones of pilot methane–air flames and the variation of the coal flame structure under various O_2/CO_2 compositions. Coal particles devolatilize immediately upon crossing the flame interface, so that the Mie scatter signal almost vanishes. Increasing coal-loading rates leads to higher reaction rates and shorter flames. LII measurements show that soot is formed primarily in the wake of remaining coal particles in the product regions. Finally, differences in the mean and RMS velocity field are explained by the combined action of thermal expansion and the changes in particle diameter between reacting and non-reacting flows.This work is supported by EPSRC research Grant (#EP/G062153/1). Yi Gao is supported by EPSRC Grant (#EP/K02924X/1), S. Lowe acknowledges support through an EPSRC DTA grant, M. Kamal is supported by University of Engineering and Technology Peshawar (Pakistan), and Bo Tian acknowledges support from CSC.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s00348-015-1965-

Machine Learning-Based System for Automated Presentation Generation from CSV Data

Effective presentation slides are crucial for conveying information efficiently, yet existing tools lack content analysis capabilities. This paper introduces a content-based PowerPoint presentation generator, aiming to address this gap. By leveraging automated techniques, slides are generated from text documents, ensuring original concepts are effectively communicated. Unstructured data poses challenges for organizations, impacting productivity and profitability. While traditional methods fall short, AI-based approaches offer promise. This systematic literature review (SLR) explores AI methods for extracting data from unstructured details. Findings reveal limitations in existing methods, particularly in handling complex document layouts. Moreover, publicly available datasets are task-specific and of low quality, highlighting the need for comprehensive datasets reflecting real-world scenarios. The SLR underscores the potential of Artificial-based approaches for information extraction but emphasizes the challenges in processing diverse document layouts. The proposed is a framework for constructing high-quality datasets and advocating for closer collaboration between businesses and researchers to address unstructured data challenges effectivel

A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT

The proliferation of IoT services has spurred a surge in network attacks, heightening cybersecurity concerns. Essential to network defense, intrusion detection and prevention systems (IDPSs) identify malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, infiltration, and Heartbleed. This study focuses on leveraging unsupervised learning for training detection models to counter these threats effectively. The proposed method utilizes basic autoencoders (bAEs) for dimensionality reduction and encompasses a three-stage detection model: one-class support vector machine (OCSVM) and deep autoencoder (dAE) attack detection, complemented by density-based spatial clustering of applications with noise (DBSCAN) for attack clustering. Accurately delineated clusters aid in mapping attack tactics. The MITRE ATT&amp;CK framework establishes a “Cyber Threat Repository”, cataloging attacks and tactics, enabling immediate response based on priority. Leveraging preprocessed and unlabeled normal network traffic data, this approach enables the identification of novel attacks while mitigating the impact of imbalanced training data on model performance. The autoencoder method utilizes reconstruction error, OCSVM employs a kernel function to establish a hyperplane for anomaly detection, while DBSCAN employs a density-based approach to identify clusters, manage noise, accommodate diverse shapes, automatically determining cluster count, ensuring scalability, and minimizing false positives and false negatives. Evaluated on standard datasets such as CIC-IDS2017 and CSECIC-IDS2018, the proposed model outperforms existing state of art methods. Our approach achieves accuracies exceeding 98% for the two datasets, thus confirming its efficacy and effectiveness for application in efficient intrusion detection systems

Detecting the Possession of Harmful Weapons by Humans Through Surveillance System

Security Surveillance is a very tedious and time-consuming job. The system is to automate the task of analyzing video surveillance and alert systems. We will analyze the video feed in real-time and identify abnormal activities like gun and knife detection. There is much research going on in the industry about video surveillance, among them. The role of CCTV video has been overgrown, and CCTV cameras are placed all over the place for surveillance and security. The user gets notified for detecting the objects. It is crucial to proper surveillance for the safety and security of people and their assets. The libraries which have been used for detecting the object are TensorFlow, OpenCV, etc. The Convolutional Neural Network (CNN) is a deep learning algorithm that can take in an input image, assign importance to various aspects and objects in the image and be able to differentiate one from the other. The typical applications of deep surveillance are theft identification, violence detection, and detection of the chances of explosion

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

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