Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods

Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming

Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria for strength, stiffness, and permeability. High workability and consistency are essential attributes for BPC because it is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting the slump of BPC. In addition, prediction models offer valuable tools to estimate various strength parameters, enabling adjustments to BPC mixing designs to optimize project construction, leading to cost and time savings. Therefore, this study explores the multi-expression programming (MEP) technique to predict the key characteristics of BPC, such as slump, compressive strength (fc), and elastic modulus (Ec). In the present study, 158, 169, and 111 data points were collected from the experimental studies for the slump, fc, and Ec, respectively. The dataset was divided into three sets: 70% for training, 15% for testing, and another 15% for model validation. The MEP models exhibited excellent accuracy with a correlation coefficient (R) of 0.9999 for slump, 0.9831 for fc, and 0.9300 for Ec. Furthermore, the comparative analysis between MEP models and conventional linear and non-linear regression models revealed remarkable precision in the predictions of the proposed MEP models, surpassing the accuracy of traditional regression methods. SHapley Additive exPlanation analysis indicated that water, cement, and bentonite exert significant influence on slump, with water having the greatest impact on compressive strength, while curing time and cement exhibit a higher influence on elastic modulus. In summary, the application of machine learning algorithms offers the capability to deliver prompt and precise early estimates of BPC properties, thus optimizing the efficiency of construction and design processes.

Development of biochar/HDPE composites and characterization of the effects of carbon loadings on the electromagnetic shielding properties

The aim of this research is to develop high carbon-yielding biochar from pinewood, coffee husk, sugarcane bagasse, and maize cob and to characterize the biochar/HDPE composites for electromagnetic (EM) shielding application. During the biochar/HDPE composites fabrication, slow pyrolysis and compression molding manufacturing were used. The enhanced properties characterizations were conducted by using thermogravimetric analysis (TGA), scanning electron microscopy (SEM), differential thermal analysis (DTA), Fourier transform spectrometry (FTIR), Brunauer-Emmet-Teller (BET) analysis, digital multi-meter, and proximity analysis. The results of biochar pyrolysis showed the maximum carbon yield of 74.6 %, 68.9 %, 68.4 %, and 40 % for pine wood, maize cob, sugarcane bagasse, and coffee husk respectively. The BET analysis showed the maximum specific surface area (734.5 m2/g), pore volume (0.2364 cm3/g), and pore radius (9.897 Å) from the pine wood biochar. The biochar loading analysis results showed that the 30 % and 40 % pine wood biochar significantly enhanced the electrical conductivity, thermal conductivity, thermal stability, crystallinity, and EM shielding effectiveness (SE) of the biochar/HDPE composites. In particular, the biochar/HDPE composite with 30 wt% pine wood biochar showed the highest thermal conductivity of 2.219 W/mK, and the 40 wt% pine wood biochar/HDPE composite achieved the highest electrical conductivity of 4.67 × 10-7 S/cm and EM SE of 44.03 dB at 2.1 GHz.

Printed graphene and its composite with copper for electromagnetic interference shielding applications

Advances in mobile electronics and telecommunication systems along with 5G technologies have been escalating the electromagnetic interference (EMI) problem in recent years. Graphene-based material systems such as pristine graphene, graphene-polymer composites and other graphene-containing candidates have been shown to provide adequate EMI shielding performance. Besides achieving the needed shielding effectiveness (SE), the method of applying the candidate shielding material onto the object in need of protection is of enormous importance due to considerations of ease of application, reduced logistics and infrastructure, rapid prototyping and throughput, versatility to handle both rigid and flexible substrates and cost. Printing readily meets all these criteria and here we demonstrate plasma jet printing of thin films of graphene and its composite with copper to meet the EMI shielding needs. SE over 30 dB is achieved, which represents blocking over 99.9% of the incoming radiation. Graphene and its composite with copper yield higher green index compared to pure copper shields, implying reduced reflection of incoming electromagnetic waves to help reduce secondary pollution.

Artificial Neural Networks Based Optimization Techniques: A Review

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

Gamma Irradiation and the Radiation Shielding Characteristics: For the Lead Oxide Doped the Crosslinked Polystyrene-b-Polyethyleneglycol Block Copolymers and the Polystyrene-b-Polyethyleneglycol-Boron Nitride Nanocomposites

This work aimed to research the efficiency of gamma irradiation and shielding characteristics on the lead oxide (PbO) doped the crosslinked polystyrene-b-polyethyleneglycol (PS-b-PEG) block copolymers and polystyrene-b-polyethyleneglycol-boron nitride (PS-b-PEG-BN) nanocomposites materials. The crosslinked PS-b-PEG block copolymers and PS-b-PEG-BN nanocomposites mixed with different percentage rates of PbO were used to research gamma-ray shielding characteristics. The synthesis of the copolymer was done by emulsion polymerization methods. The characterization and morphological analyses of irradiated samples were explored handling with the Nuclear Magnetic Resonance (NMR), Fourier Transform Infrared Spectroscopy (FTIR), Gel Permeation Chromatography (GPC), Thermogravimetric Analysis (TGA), and Scanning Electron Microscope (SEM) methods. The gamma-rays that were emitted from the E 152u source were observed with a High Purity Germanium (HPGe) detector system and examined with a GammaVision computer program. Our samples, including the different percentage rates of the PS-b-PEG (1000, 1500, 10,000), BN, and PbO, were irradiated in various gamma-ray photon energy regions (from 121.78 keV to 1408.01 keV). Then, Linear-Mass Attenuation Coefficients (LACs-MACs), Half-Tenth Value Layer (HVL), Mean Free Path (MFP), and Radiation Protection Efficiency (RPE) values of the samples were calculated. Via crosschecking the acquired data from samples with and without PbO and BN, it was observed that, if the different percentage rates by weight nano-powder of PbO and BN are added in the polymer mixture, it can be used as a convenient shielding material against gamma rays.