Importance of Micropore–Mesopore Interfaces in Carbon Dioxide Capture by Carbon‐Based Materials

Investigating the effect of textural properties on CO2 adsorption in porous carbons via deep neural networks using various training algorithms

The adsorption of carbon dioxide (CO2) on porous carbon materials offers a promising avenue for cost-effective CO2 emissions mitigation. This study investigates the impact of textural properties, particularly micropores, on CO2 adsorption capacity. Multilayer perceptron (MLP) neural networks were employed and trained with various algorithms to simulate CO2 adsorption. Study findings reveal that the Levenberg-Marquardt (LM) algorithm excels with a remarkable mean squared error (MSE) of 2.6293E-5, indicating its superior accuracy. Efficiency analysis demonstrates that the scaled conjugate gradient (SCG) algorithm boasts the shortest runtime, while the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm requires the longest. The LM algorithm also converges with the fewest epochs, highlighting its efficiency. Furthermore, optimization identifies an optimal radial basis function (RBF) network configuration with nine neurons in the hidden layer and an MSE of 9.840E-5. Evaluation with new data points shows that the MLP network using the LM and bayesian regularization (BR) algorithms achieves the highest accuracy. This research underscores the potential of MLP deep neural networks with the LM and BR training algorithms for process simulation and provides insights into the pressure-dependent behavior of CO2 adsorption. These findings contribute to our understanding of CO2 adsorption processes and offer valuable insights for predicting gas adsorption behavior, especially in scenarios where micropores dominate at lower pressures and mesopores at higher pressures.

Mechanochemical activation of palm kernel shell using the L9 Taguchi orthogonal array for carbon dioxide adsorption

The problem faced during bio-based activated carbon synthesis is related to its low yield production, which is plausibly due to intricate conventional activation processes, along with utilization of corrosive chemical activator. Therefore, in this study, the activated carbon synthesis from palm kernel shell as starting material has been carried out via a facile solid-solid mixing (mechanochemical) activation. The feasibility and optimization of the high-yielded palm kernel shell activated carbon production has been done via the L9 Taguchi orthogonal array, whereby the larger-the-better signal to noise (S/N) ratio has been applied to determine the optimum operating conditions. Four parameters have been studied including the activation temperature (600-800 °C), impregnation ratio (1-3:1), activation time (60-120 min), and nitrogen flow rate (300-900 mL/min). Depending on the operating conditions, the activated carbon yield is ranging from 10 to 50 wt.%. Upon optimization, both the pristine precursor and activated carbon at the optimal conditions are characterized in terms of their surface morphology, porosity, and the surface functionalities. In context of carbon dioxide adsorption, the adsorption capacity at an ambient condition is found to be approximately 1.65 mmol/g, which is comparable to the values reported in the literatures.

Quantitative Structure-Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels

This work is devoted to the development of quantitative structure-property relationship (QSPR) models using various regression analyses to predict propylene (C₃H₆) adsorption capacity at various pressures in zeolites from a topologically diverse International Zeolite Association database. Based on univariate and multilinear regression analysis, the accessible volume and largest cavity diameter are the most crucial factors determining C₃H₆ uptake at high and low pressures, respectively. An artificial neural network (ANN) model with five structural descriptors is sufficient to predict C₃H₆ uptake at high pressures. For combined pressures, the prediction of an ANN model with pore size distribution is pleasing. The isosteric heat of adsorption (Q _st) has a significant impact on the improvement of the prediction of low-pressure gas adsorption, which finely classifies zeolites into high or low C₃H₆ adsorbers. The conjunction of high-throughput screening and QSPR models contributes to being able to prescreen the database rapidly and accurately for top performers and perform further detailed and time-consuming computational-intensive molecular simulations on these candidates for other gas adsorption applications.

Unravelling free volume in branched-cation ionic liquids based on silicon

The branching of ionic liquid cation sidechains utilizing silicon as the backbone was explored and it was found that this structural feature leads to fluids with remarkably low density and viscosity. The relatively low liquid densities suggest a large free volume in these liquids. Argon solubility was measured using a precise saturation method to probe the relative free volumes. Argon molar solubilities were slightly higher in ionic liquids with alkylsilane and siloxane groups within the cation, compared to carbon-based branched groups. The anion size, however, showed by far the dominant effect on argon solubility. Thermodynamic solvation parameters were derived from the solubility data and the argon solvation environment was modelled utilizing the polarizable CL&Pol force field. Semiquantitative analysis was in agreement with trends established from the experimental data. The results of this investigation demonstrate design principles for targeted ionic liquids when optimisation for the free volume is required, and demonstrate the utility of argon as a simple, noninteracting probe. As more ionic liquids find their way into industrial processes of scale, these findings are important for their utilisation in the capture of any gaseous solute, gas separation, or in processes involving the transformation of gases or small molecules.

The branching of ionic liquid cation sidechains utilizing silicon as the backbone was explored and it was found that this structural feature leads to fluids with remarkably low density and viscosity.

Prediction of Carbon Dioxide Adsorption via Deep Learning

Porous carbons with different textural properties exhibit great differences in CO₂ adsorption capacity. It is generally known that narrow micropores contribute to higher CO₂ adsorption capacity. However, it is still unclear what role each variable in the textural properties plays in CO₂ adsorption. Herein, a deep neural network is trained as a generative model to direct the relationship between CO₂ adsorption of porous carbons and corresponding textural properties. The trained neural network is further employed as an implicit model to estimate its ability to predict the CO₂ adsorption capacity of unknown porous carbons. Interestingly, the practical CO₂ adsorption amounts are in good agreement with predicted values using surface area, micropore and mesopore volumes as the input values simultaneously. This unprecedented deep learning neural network (DNN) approach, a type of machine learning algorithm, exhibits great potential to predict gas adsorption and guide the development of next-generation carbons.

Aqueous and Template-Free Synthesis of Meso-Macroporous Polymers for Highly Selective Capture and Conversion of Carbon Dioxide

Meso-macroporous polymers possessing nitrogen functionality were innovatively synthesized through an aqueous and template-free route herein. Specifically, the polymerization of 1-(4-vinylbenzyl)-1,3,5,7-tetraazaadamantan-1-ium chloride in aqueous solution under high temperatures induces the decomposition of the hexamethylenetetramine unit into ammonia and formaldehyde molecules, followed by the cross-linking of benzene rings through "resol chemistry". During this process, extended meso-macroporous frameworks were constructed, and active nitrogen species were incorporated. Taking the advantage of the meso-macroporosity and nitrogen functionality, the synthesized polymers offer competitive CO₂ capacities (0.37-1.58 mmol g^-1 at 0 °C and 0.15 bar) and outstanding CO₂ /N₂ selectivities (155-324 at 0 °C). Furthermore, after complexed with metal ions, the synthesized polymers show excellent activity for catalyzing the cycloaddition of propylene oxide with CO₂ (yield>98.5 %, turnover frequency: 612.9-761.1 h^-1 ).

Salt Templating with Pore Padding: Hierarchical Pore Tailoring towards Functionalised Porous Carbons

We propose a new synthetic route towards nanoporous functional carbon materials based on salt templating with pore-padding approach (STPP). STPP relies on the use of a pore-padding agent that undergoes an initial polymerisation/ condensation process prior to the formation of a solid carbon framework. The pore-padding agent allows tailoring hierarchically the pore-size distribution and controlling the amount of heteroatom (nitrogen in this case) functionalities as well as the type of nitrogen (graphitic, pyridinic, oxides of nitrogen) incorporated within the carbon framework in a single-step-process. Our newly developed STPP method offers a unique pathway and new design principle to create simultaneously high surface area, microporosity, functionality and pore hierarchy. The functional carbon materials produced by STPP showed a remarkable CO₂ /N₂ selectivity. At 273 K, a carbon with only micropores offered an exceptionally high CO₂ adsorption capacity whereas a carbon with only mesopores showed promising CO₂ -philicity with high CO₂ /N₂ selectivity in the range of 46-60 %, making them excellent candidates for CO₂ capture from flue gas or for CO₂ storage.