1,4-Diazabicyclo [2.2.2] Octane Functionalized Mesoporous Silica SBA-15 (SBA-15@DABCO): a Novel Highly Selective Adsorbent for Selective Separation/Preconcentration of Cr(VI) from Environmental Water Samples

Silicon - In this research, the potential application of 1,4-Diazabicyclo[2.2.2]octane (DABCO) functionalized mesoporous SBA-15 (SBA-15@DABCO) was evaluated as a new and efficient adsorbent for the...     

Novel Linear Piezo-resistive Auxetic Meta-Sensors with Low Young's Modulus by a Core–Shell Conceptual Design and Electromechanical Modelling

Production of piezo-resistive auxetic sensors is usually carried out through mixing and coating methods. Although these methods are beneficial, Young's modulus of mixed sensors becomes high because of using a high percentage of sensing elements while the durability of coated sensors gets low due to the separation of sensing elements from the sensor surface. This article presents a new core–shell metamaterial model to address the mentioned problems. The shell and the core are produced of polydimethylsiloxane (PDMS) rubber and a mixture of PDMS/graphite powders (73.45 wt% graphite powders), respectively. A finite element model is developed via COMSOL software to predict the electromechanical behaviors of the created sensor and verified by an experimental study. Scanning electron microscope imaging is conducted to detect the separations of the graphite particles. The main important feature of this meta-sensor is to possess a linear sensitivity due to having zero Poisson's ratio. The advantage of this method is that Young's modulus of the sensor does not decrease (unlike the mixing method), and the sensor-coated particles do not separate from the sensor after a while (unlike the coating method). The introduced model has advantages that promote potential applications such as using sensory gloves to detect, for instance, human hand movements.     

Experimental study on the effect of molecular weight and chemical composition distribution on the mechanical response of high-density polyethylene

This article aims to appraise the effect of microstructure comprising molecular weight distribution and chemical composition distribution on the mechanical properties of high-density polyethylene (HDPE). HDPE resins were synthesized using several titanium–magnesium-supported Ziegler–Natta catalysts in the industrial gas phase reactor under the same polymerization condition. Gel permeation chromatography and crystallization elution fractionation (CEF) were conducted on the resins to characterize the molecular weight and comonomer distribution. Crystallization, thermal and rheological behavior were evaluated following differential scanning calorimetry, polarization light microscopy, and rheometric mechanical spectrometry. The resins with higher soluble fraction in trichlorobenzene below 80°C (highly branched low molecular weight chains) exhibited longer crystallization time based on the crystallization kinetic obtained from the Avrami model. Rheological determination of the molecular weight between entanglements (M_e) and the average lamella thickness based on the Gibbs–Thomson equation revealed that the entanglement density and impact strength decreased, and the average lamella thickness increased with an increase in the ratio of CEF eluted fraction below 80°C to the crystallizable fraction in the range of 80–90°C.     

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.     

Circuit breakers in HVDC systems: state-of-the-art review and future trends

High voltage direct current (HVDC) systems are efcient solutions for the integration of large-scale renewable energy sources with the main power grids. The rapid development of the HVDC grid has resulted in a growing interest in DC circuit breakers (DCCBs). A fast and reliable circuit breaker is a necessary requirement in the development of large scale HVDC grids. This paper provides a comprehensive review and survey of the HVDC CBs and discusses potential research directions. Operational principles and the main features of various DCCBs are described and their merits and shortcomings are also highlighted.     

Monitoring the fluidized bed reactor for polyethylene polymerization based on fibre optic acoustic emission sensor

This paper presents applications of a new class of fibre optic acoustic emission (FOAE) sensor to monitor the operation of fluidized bed reactors used in polyethylene production. Specifically, the sensor was implemented to detect undesired, abnormal phenomena related to particle agglomeration, wall sheeting, fine overflow, and variations in the superficial gas velocity. The experiments were executed using a fluidized bed cold model setup for polyethylene powders with average particle sizes of 250, 1000, and 2000 μm. The results demonstrated that the presence of agglomerated particles in the fluidized bed reactor increases the kurtosis of the acoustic emission (AE) signal. Furthermore, the overflow of small particles can be detected by mounting the FOAE sensor below the gas distributor plate and monitoring an increase in the root mean square (RMS) of the AE signal. The AE signal RMS increased with the rising superficial gas velocity. Besides, forming a sheet layer on the reactor wall decreased the AE signal RMS. The proposed sensor's main benefits are its simple design, rapid response to abnormal conditions in the fluidized bed reactor, immunity against electromagnetic noise, high-temperature resistance, and safety in hazardous areas.