A new light-responsive resistive random-access memory device containing hydrogen-bonded complexes.

Acknowledgements: TSV acknowledges financial support from the Ministry of Higher Education of Malaysia through the Fundamental Research Grant Scheme [FP079-2018A]. AR acknowledges Ministerio de Economía y Competitividad (MINECO) for his PhD grant BES-2015-071235, under the project MAT2014-55205-P. VMA acknowledges the University Malaya for the grant RF004B-2018. AMF would like to thank the Royal Academy of Engineering, U.K., for the grant NRCP1516/4/61 (Newton Research Collaboration Programme), the University of Aberdeen, for the award of the grant SF10192, the Carnegie Trust for the Universities of Scotland, for the Research Incentive Grant RIG008586, the Royal Society and Specac Ltd., for the Research Grant RGS\R1\201397, and the Royal Society of Chemistry for the award of a mobility grant (M19-0000). AMF and TSV further acknowledge University Malaya for travelling support.Peer reviewedPostprin

The significance of gtf genes in caries expression: A rapid identification of Streptococcus mutans from dental plaque of child patients

Aim: Rapid phylogenetic and functional gene (gtfB) identification of S. mutans from the dental plaque derived from children. Material and Methods: Dental plaque collected from fifteen patients of age group 7-12 underwent centrifugation followed by genomic DNA extraction for S. mutans. Genomic DNA was processed with S. mutans specific primers in suitable PCR condtions for phylogenetic and functional gene (gtfB) identification. The yield and results were confirmed by agarose gel electrophoresis. Results: 1% agarose gel electrophoresis depicts the positive PCR amplification at 1,485 bp when compared with standard 1 kbp indicating the presence of S. mutans in the test sample. Another PCR reaction was set using gtfB primers specific for S. mutans for functional gene identification. 1.2% agarose gel electrophoresis was done and a positive amplication was observed at 192 bp when compared to 100 bp standards. Conclusion: With the advancement in molecular biology techniques, PCR based identification and quantification of the bacterial load can be done within hours using species-specific primers and DNA probes. Thus, this technique may reduce the laboratory time spend in conventional culture methods, reduces the possibility of colony identification errors and is more sensitive to culture techniques

Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements’ presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors

Preparation of Hybrid Chitosan/Silica Composites Via Ionotropic Gelation and Its Electrochemical Impedance Studies

Hybrid materials are inimitable conjugates of organic/inorganic structures having synergetic behaviour and extraordinary properties. Therefore, hybrid chitosan/silica composite was synthesized by using sodium tripolyphosphate via ionotropic gelation technique. The obtained intercalation of hybrid chitosan/silica composite was characterized using Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analysis. The surface morphology of the composite was studied through field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM). The EDX analysis was performed to find the wt.% of the present elements in the composite that showed the increment of O element up to 47.50 wt. % and the presence of Si (27.61 wt. %) revealed that the silica is present within the chitosan matrix, which demonstrates successful hybridization of chitosan/silica. Furthermore, the obtained hybrid chitosan/silica was incorporated within the epoxy matrix and electrochemical studies such as electrochemical impedance spectroscopy (EIS) was employed for 60 days. The experimental results revealed that the presence of hybrid chitosan/silica composite up to 0.8 wt. % resulted in a profound outcome as a considerable active reinforcing agent for corrosion prevention application with the highest coating resistance, (1.97 ± 0.01) × 1011 Ω and lowest breakpoint frequency, 51 × 10-3 Hz after 60 days. © 2020 Elsevier B.V

Empowering Cybersecurity Using Enhanced Rat Swarm Optimization With Deep Stack-Based Ensemble Learning Approach

Cybersecurity is a vital technology and measures intended to protect networks, computers, information, and programs from threats and illegal access, modification, or damage. A security model covers a network and a computer safety method. Each system has antivirus software, firewalls, and an intrusion detection system (IDS). IDS helps in discovering and identifying illegal system behavior such as usage, copying, alteration, and damage. By estimating network traffic anomalies and patterns, deep learning (DL) models can enhance the detection abilities of IDS when compared to traditional rule-based methods. These models learn complex representations from data, authorizing them to recognize subtle and developing attack patterns. Techniques like recurrent neural network (RNN) and convolutional neural network (CNN) can be applied to progress consecutive or spatial features in network data, correspondingly. This manuscript empowers Cybersecurity by utilizing an Enhanced Rat Swarm Optimizer with a Deep Stack-Based Ensemble Learning (ERSO-DSEL) model. The ERSO-DSEL approach leverages feature selection (FS) with EL strategies to boost cybersecurity. In the ERSO-DSEL system, Z-score normalization is employed to measure the input data. Besides, an improved equilibrium optimizer (IEO) based FS approach is applied to choose a set of features. For cyberattack recognition, the ERSO-DSBEL approach uses the DSEL approach comprising three models namely deep neural network (DNN), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM). Furthermore, the hyperparameter selection of these DL models takes place using the ERSO system. The solution result of the ERSO-DSBEL model is executed on a benchmark IDS database. A wide-contrast study reported that the ERSO-DSBEL model accomplishes an enhanced accuracy outcome of 99.67% over other models of cybersecurity