CO2 capture by benzene-based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

In this research, porous benzene-based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO₂ capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi-empirical quadratic model for adsorption capacity was presented based on RSM-central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO₂ uptake capacity was obtained around 167 (mg/g).     

Cu Current Collector with Binder-Free Lithiophilic Nanowire Coating for High Energy Density Lithium Metal Batteries

Despite significant efforts to fabricate high energy density (ED) lithium (Li) metal anodes, problems such as dendrite formation and the need for excess Li (leading to low N/P ratios) have hampered Li metal battery (LMB) development. Here, the use of germanium (Ge) nanowires (NWs) directly grown on copper (Cu) substrates (Cu-Ge) to induce lithiophilicity and subsequently guide Li ions for uniform Li metal deposition/stripping during electrochemical cycling is reported. The NW morphology along with the formation of the Li₁₅Ge₄ phase promotes uniform Li-ion flux and fast charge kinetic, resulting in the Cu-Ge substrate demonstrating low nucleation overpotentials of 10 mV (four times lower than planar Cu) and high Columbic efficiency (CE) efficiency during Li plating/stripping. Within a full-cell configuration, the Cu-Ge@Li – NMC cell delivered a 63.6% weight reduction at the anode level compared to a standard graphite-based anode, with impressive capacity retention and average CE of over 86.5% and 99.2% respectively. The Cu-Ge anodes are also paired with high specific capacity sulfur (S) cathodes, further demonstrating the benefits of developing surface-modified lithiophilic Cu current collectors, which can easily be integrated at the industrial scale.     

Machine learning modelling and evaluation of jet fires from natural gas processing,storage, and transport

Jet fires and their repercussions play a significant role in catastrophic incidents that typically have a cascading impact in process industries. Several hydrocarbon experiments from 19 papers were incorporated into the current endeavour to develop simulations of jet flames using machine learning (ML) models. Dimensionless characteristics have been used as output and input variables, including mass flow rates, fuel density, jet flame length, and heat release fluxes. When training three layers of the multi-layer feedforward neural network (MLFFNN) method, a Bayesian regularization backpropagation approach was adopted and evaluated with the radial based functions (RBF) algorithm. Through an optimization procedure, the first and second hidden layers of the MLFFNN have been optimized to include 10 and five neurons, respectively. The RBF algorithm with 40 neurons in a single layer has been computed using the same method. The best mean square error (MSE) validation results for RBF and MLFFNN were 0.006 and 0.0002, respectively, for 40 and 100 epochs. The MLFFNN and RBF models' respective regression statistical analysis outputs were 0.9949 and 0.9645. The ML method has been identified as a potentially useful technique for precisely predicting the geometrical and radiative characteristics of jet flames.