Strong Interlayer Transition in Few-Layer InSe/PdSe2 van der Waals Heterostructure for Near-Infrared Photodetection

Near infrared (NIR) photodetectors based on 2D materials are widely studied for their potential application in next generation sensing, thermal imaging, and optical communication. Construction of van der Waals (vdWs) heterostructure provides a tremendous degree of freedom to combine and extend the features of 2D materials, opening up new functionalities on photonic and optoelectronic devices. Herein, a type-II InSe/PdSe₂ vdWs heterostructure with strong interlayer transition for NIR photodetection is demonstrated. Strong interlayer transition between InSe and PdSe₂ is predicted via density functional theory calculation and confirmed by photoluminance spectroscopy and Kelvin probe force microscopy. The heterostructure exhibits highly sensitive photodetection in NIR region up to 1650 nm. The photoresponsivity, detectivity, and external quantum efficiency at this wavelength respectively reaches up to 58.8 A W⁻¹, 1 × 10¹⁰ Jones, and 4660%. The results suggest that the construction of vdWs heterostructure with strong interlayer transition is a promising strategy for infrared photodetection, and this work paves the way to developing high-performance optoelectronic devices based on 2D vdWs heterostructures.     

Unconstrained Face Detection of Multiple Humans Present in the Video

Accurate and fast localization of randomly deployed sensor nodes is needed for many applications in wireless sensor networks. Localization also benefits in recognizing the geographically area where an event took place. There is no meaning of any event information without the knowledge of its location coordinates. DV-Hop is one of the main range free localization technique, which estimates the position of nodes using distance vector. Particle swarm optimization is suitable for the localization issues because of its fast computing speed and high precision. To further reduce the positioning error, the traditional DV-Hop localization algorithm based on single objective optimization algorithm is converted into a multi objective optimization algorithm. In our proposed scheme, we have considered six different single objective functions and three different multi objective functions. In this paper, a multi objective particle swarm optimization based DV-Hop localization is proposed in 3-dimensional wireless sensor networks. The proposed functions has been evaluated on the basis of computation time, average localization error and localization error variance. The simulation results show that our proposed multi objective function performs better as compared to traditional single objective function.

     

Catalytic Growth of Structured Carbon via the Decomposition of Chlorobenzene over Ni/SiO2

The synthesis of highly ordered carbonaceous materials, including carbon nanofibers, has been the subject of a disparate and burgeoning literature over the past decade. The growth of carbon nanofibers by an atypical catalytic route, the decomposition of chlorobenzene over (10%w/w) Ni/SiO₂, is considered in this paper. The reaction of chlorobenzene with hydrogen in the temperature range 550–700 °C also generated benzene via hydrodechlorination and a volatile component that results from catalytic hydrocracking/hydrogenolysis, The characteristics of the carbonaceous product are illustrated through a combination of high resolution transmission electron microscopy (HRTEM) and temperature programmed oxidation (TPO). The response of carbon yield and structural order to varying reaction time (up to 4 h on-stream) and temperature are presented and discussed. Under identical reaction conditions, the chlorobenzene feed delivered appreciably higher carbon yields than that recorded for the decomposition of benzene while the carbon growth in the former case was significantly more ordered. These findings are discussed in terms of Cl/catalyst interaction(s) and metal site restructuring.     

Beneficial role of “sol–gel” synthesized mesoscale silica networks on the performance of liquid silicone elastomer

This piece of contribution highlights the profound effect of unique mesoscale morphology of tailor made nanosilica assembly (SS‐Silica), synthesized by sol–gel route, on the mechanical and dynamic rheological properties of platinum catalyzed addition‐cured silicone elastomers. While commercial colloidal nanosilica (CS Silica) is used as the control nanofiller representing particulate morphology, the tailor‐made SS‐Silica having highly percolated network structure offers 10‐fold increase in storage modulus of the uncured reactive PDMS precursor nanocomposite with stable dynamic rheological behavior and more than 180% enhancement in tensile strength of resulting liquid silicone rubber (LSR) produced on curing, as compared to colloidal silica of commercial origin. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40125.     

Preliminary investigations on alkali leaching kinetics of red sediment ilmenite slag

The present paper deals with soda ash roasting of red sediment ilmenite (47.03% TiO₂) and leaching of obtained titanium rich slag with hydrochloric acid for preparation of synthetic rutile. The experimental conditions used for roasting are Na₂CO₃ to ilmenite ratio of 1: 1 at 1,223 K for 4 h. This soda ash slag product is subjected to hydrochloric acid leaching to remove the iron content. The optimum conditions for leaching achieved are 6M hydrochloric acid at 398 K for 2.5 h (10/1 liquid/solid mass ratio) at 100 rpm. Shrinking core model is found to be fit for the experimental results. The apparent activation energy is 37.9 kJ/mol. This process of soda ash roasting is one of the best processes for preparation of high purity synthetic rutile assaying about 97.21% TiO₂.     

Targeting DNA G‐Quadruplexes with Helical Small Molecules

We previously identified quinoline‐based oligoamide helical foldamers and a trimeric macrocycle as selective ligands of DNA quadruplexes. Their helical structures might permit targeting of the backbone loops and grooves of G‐quadruplexes instead of the G‐tetrads. Given the vast array of morphologies G‐quadruplex structures can adopt, this might be a way to achieve sequence selective binding. Here, we describe the design and synthesis of molecules based on macrocyclic and helically folded oligoamides. We tested their ability to interact with the human telomeric G‐quadruplex and an array of promoter G‐quadruplexes by using FRET melting assay and single‐molecule FRET. Our results show that they constitute very potent ligands—comparable to the best so far reported. Their modes of interaction differ from those of traditional tetrad binders, thus opening avenues for the development of molecules specific for certain G‐quadruplex conformations.     

A novel electro‐organic synthesis of aniline‐based copolymers at platinum electrodes

A novel, efficient, and ecocompatible electro copolymerization of anilines at platinum electrode using acetonitrile as non‐aqueous solvent and lithiumperchlorate as supporting electrolyte was carried and described here. The progress and mechanistic study of copolymerization was done using cyclic voltammetry. The reaction proceeded smoothly and quantitatively at ambient temperature. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Arithmetic Optimization with Ensemble Deep Transfer Learning BasedMelanoma Classification

Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC model focuses on identification and classification of melanoma from dermoscopic images. To accomplish this, IAOEDTT-MC model applies image preprocessing at the initial stage in which Gabor Filtering (GF) technique is utilized. In addition, U-Net segmentation approach is employed to segment the lesion regions in dermoscopic images. Besides, an ensemble of DL models including ResNet50 and ElasticNet models is applied in this study. Moreover, AO algorithm with Gated Recurrent Unit (GRU) method is utilized for identification and classification of melanoma. The proposed IAOEDTT-MC method was experimentally validated with the help of benchmark datasets and the proposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.     

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.