An Enhanced Carbon Capture and Storage Process (e-CCS) Applied to Shallow Reservoirs Using Nanofluids Based on Nitrogen-Rich Carbon Nanospheres

Preparation, Characterization, Evaluation of Neuroprotective Effect, and Related Mechanisms of Phosphatidylserine Emulsion in 5- and 12-Week Old Mice

Phosphatidylserine (PS) improves learning and memory capacity. In this study, PS formulation was optimized by a response surface methodology. Moreover, we found that PS not only functions as a biologically active component in food preparations but also improves the emulsion's physical stability. Our results showed that the PS emulsions are characterized by a smaller particle size, higher ζ-potential (negative), higher viscosity, and lower surface tension and centrifugal stability constants than the emulsion without PS. Furthermore, we explored the neuroprotective effects of PS emulsion and its underlying mechanisms. Treatment with 2% (w/w) PS emulsion for three months enhanced spatial learning and memory in 5- and 12-week old mice in the Morris water maze test. Western-blotting analysis displayed that the 2% (w/w) PS emulsion treated group upregulated BDNF, TrkB, PSD95, mTOR, MBP, and ErbB4 expression in the hippocampus of 5- and 12-week old mice. Reverse transcription polymerase chain reaction (RT-PCR) analysis revealed elevated Nrg-1 and ErbB4 mRNA expression in the 2% (w/w) PS emulsion treated groups, and high Nrg-1 and ErbB4 expression levels were associated with better myelination. In conclusion, we reported PS emulsions with high stability and high bioavailability. Meanwhile, 2% (w/w) PS emulsion enhances learning, memory, and myelination in mice by activating the BDNF/TrkB and Nrg-1/ErbB4 signaling.

Electrospun Composites Made of Reduced Graphene Oxide and Polyacrylonitrile-Based Activated Carbon Nanofibers (rGO/ACNF) for Enhanced CO2 Adsorption

In this work, we report the preparation of polyacrylonitrile (PAN)-based activated carbon nanofibers composited with different concentrations of reduced graphene oxide (rGO/ACNF) (1%, 5%, and 10% relative to PAN weight) by a simple electrospinning method. The electrospun nanofibers (NFs) were carbonized and physically activated to obtain activated carbon nanofibers (ACNFs). Texture, surface and elemental properties of the pristine ACNFs and composites were characterized using various techniques. In comparison to pristine ACNF, the incorporation of rGO led to changes in surface and textural characteristics such as specific surface area (S_BET), total pore volume (V_total), and micropore volume (V_micro) of 373 m²/g, 0.22 cm³/g, and 0.15 cm³/g, respectively, which is much higher than the pristine ACNFs (e.g., S_BET = 139 m²/g). The structural and morphological properties of the pristine ACNFs and their composites were studied by Raman spectroscopy and X-ray diffraction (XRD), and field emission scanning electron microscopy (FE-SEM) respectively. Carbon dioxide (CO₂) adsorption on the pristine ACNFs and rGO/ACNF composites was evaluated at different pressures (5, 10, and 15 bars) based on static volumetric adsorption. At 15 bar, the composite with 10% of rGO (rGO/ACNF0.1) that had the highest S_BET, V_total, and V_micro, as confirmed with BET model, exhibited the highest CO₂ uptake of 58 mmol/g. These results point out that both surface and texture have a strong influence on the performance of CO₂ adsorption. Interestingly, at p < 10 bar, the adsorption process of CO₂ was found to be quite well fitted by pseudo-second order model (i.e., the chemisorption), whilst at 15 bar, physisorption prevailed, which was explained by the pseudo-first order model.

Biomass-Derived Carbon Molecular Sieves Applied to an Enhanced Carbon Capture and Storage Process (e-CCS) for Flue Gas Streams in Shallow Reservoirs

It is possible to take advantage of shallow reservoirs (<300 m) for CO₂ capture and storage in the post-combustion process. This process is called enhanced carbon capture and storage (e-CCS). In this process, it is necessary to use a nano-modifying agent to improve the chemical-physical properties of geological media, which allows the performance of CO₂ selective adsorption to be enhanced. Therefore, this study presents the development and evaluation of carbon sphere molecular nano-sieves (CSMNS) from cane molasses for e-CSS. This is the first report in the scientific literature on CSMNS, due to their size and structure. In this study, sandstone was used as geological media, and was functionalized using a nanofluid, which was composed of CNMNS dispersed in deionized water. Finally, CO₂ or N₂ streams were used for evaluating the adsorption process at different conditions of pressure and temperature. As the main result, the nanomaterial allowed a natural selectivity towards CO₂, and the sandstone enhanced the adsorption capacity by an incremental factor of 730 at reservoir conditions (50 °C and 2.5 MPa) using a nanoparticle mass fraction of 20%. These nanofluids applied to a new concept of carbon capture and storage for shallow reservoirs present a novel landscape for the control of industrial CO₂ emissions.

Element-Doped Functional Carbon-Based Materials

Carbon materials are one of the most fascinating materials because of their unique properties and potential use in several applications. They can be obtained from agricultural waste, organic polymers, or by using advanced synthesizing technologies. The carbon family is very wide, it includes classical activated carbons to more advanced types like carbon gels, graphene, and so on. The surface chemistry of these materials is one of the most interesting aspects to be studied. The incorporation of different types of chemical functionalities and/or heteroatoms such as O, N, B, S, or P on the carbon surface enables the modification of the acidic–basic character, hydrophilicity–hydrophobicity, and the electron properties of these materials, which in turn determines the final application. This book collects original research articles focused on the synthesis, properties, and applications of heteroatom-doped functional carbon materials.

Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al₂O₃-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.