The Review of Carbon Capture-Storage Technologies and Developing Fuel Cells for Enhancing Utilization

OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications

The rapid advancement of climate change and global warming have widespread impacts on society, including ecosystems, water security, food production, health, and infrastructure. To achieve significant global emission reductions, approximately 74% is expected to come from cutting carbon dioxide (CO2) emissions in energy supply and demand. Carbon Capture and Storage (CCS) has attained global recognition as a preeminent approach for the mitigation of atmospheric carbon dioxide levels, primarily by means of capturing and storing CO2 emissions originating from fossil fuel systems. Currently, geological models for storage location determination in CCS rely on limited sampling data from borehole surveys, which poses accuracy challenges. To tackle this challenge, our research project focuses on analyzing exposed rock formations, known as outcrops, with the goal of identifying the most effective backbone networks for classifying various strata types in outcrop images. We leverage deep learning-based outcrop semantic segmentation techniques using hybrid backbone networks, named OutcropHyBNet, to achieve accurate and efficient lithological classification, while considering texture features and without compromising computational efficiency. We conducted accuracy comparisons using publicly available benchmark datasets, as well as an original dataset expanded through random sampling of 13 outcrop images obtained using a stationary camera, installed on the ground. Additionally, we evaluated the efficacy of data augmentation through image synthesis using Only Adversarial Supervision for Semantic Image Synthesis (OASIS). Evaluation experiments on two public benchmark datasets revealed insights into the classification characteristics of different classes. The results demonstrate the superiority of Convolutional Neural Networks (CNNs), specifically DeepLabv3, and Vision Transformers (ViTs), particularly SegFormer, under specific conditions. These findings contribute to advancing accurate lithological classification in geological studies using deep learning methodologies. In the evaluation experiments conducted on ground-level images obtained using a stationary camera and aerial images captured using a drone, we successfully demonstrated the superior performance of SegFormer across all categories.

Recent Advancements in Polyurethane-based Tissue Engineering

In tissue engineering, polyurethane-based implants have gained significant traction because of their high compatibility and inertness. The implants therefore show fewer side effects and lasts longer. Also, the mechanical properties can be tuned and morphed into a particular shape, owing to which polyurethanes show immense versatility. In the last 3 years, scientists have devised methods to enhance the strength of and induce dynamic properties in polyurethanes, and these developments offer an immense opportunity to use them in tissue engineering. The focus of this review is on applications of polyurethane implants for biomedical application with detailed analysis of hard tissue implants like bone tissues and soft tissues like cartilage, muscles, skeletal tissues, and blood vessels. The synthetic routes for the preparation of scaffolds have been discussed to gain a better understanding of the issues that arise regarding toxicity. The focus here is also on concerns regarding the biocompatibility of the implants, given that the precursors and byproducts are poisonous.

Use of 5,10-Disubstituted Dibenzoazaborines and Dibenzophosphaborines as Cyclic Supports of Frustrated Lewis Pairs for the Capture of CO2

The reactivity of 5,10-disubstituted dibenzoazaborines and dibenzophosphaborines towards carbon dioxide was studied at the DFT, M06-2X/def2-TZVP, computational level. The profile of this reaction comprises of three stationary points: the pre-reactive complex and adduct minima and the transition state(TS) linking both minima. Initial results show that dibenzoazaborines derivatives are less suitable to form adducts with CO₂ than dibenzophosphaborine systems. The influence of the basicity on the P atom and the acidity on the B center of the dibenzophosphaborine in the reaction with CO₂ was also explored. Thus, an equation was developed relating the properties (acidity, basicity and boron hybridization) of the isolated dibenzophosphaborine derivatives with the adduct energy. We found that modulation of the boron acidity allows to obtain more stable adducts than the pre-reactive complexes and isolated monomers.

Future of Hydrogen as an Alternative Fuel for Next-Generation Industrial Applications; Challenges and Expected Opportunities

A general rise in environmental and anthropogenically induced greenhouse gas emissions has resulted from worldwide population growth and a growing appetite for clean energy, industrial outputs, and consumer utilization. Furthermore, well-established, advanced, and emerging countries are seeking fossil fuel and petroleum resources to support their aviation, electric utilities, industrial sectors, and consumer processing essentials. There is an increasing tendency to overcome these challenging concerns and achieve the Paris Agreement’s priorities as emerging technological advances in clean energy technologies progress. Hydrogen is expected to be implemented in various production applications as a fundamental fuel in future energy carrier materials development and manufacturing processes. This paper summarizes recent developments and hydrogen technologies in fuel refining, hydrocarbon processing, materials manufacturing, pharmaceuticals, aircraft construction, electronics, and other hydrogen applications. It also highlights the existing industrialization scenario and describes prospective innovations, including theoretical scientific advancements, green raw materials production, potential exploration, and renewable resource integration. Moreover, this article further discusses some socioeconomic implications of hydrogen as a green resource.

Hybrid organic-inorganic membranes based on sulfonated poly (ether ether ketone) matrix and iron-encapsulated carbon nanotubes and their application in CO2 separation

The need to reduce greenhouse gas emissions dictates the search for new methods and materials. Here, a novel type of inorganic–organic hybrid materials Fe@MWCNT-OH/SPEEK (with a new type of CNT characterized by increased iron content, 5.80 wt%) for CO₂ separation is presented. The introduction of nanofillers into a polymer matrix has significantly improved hybrid membrane gas transport (D, P, S, and α_CO2/N2), and magnetic, thermal, and mechanical parameters. It was found that magnetic casting has improved the alignment and dispersion of Fe@MWCNT-OH carbon nanotubes. At the same time, CNT and polymer chemical modification enhanced interphase compatibility and membrane CO₂ separation efficiency. The thermooxidative stability, and mechanical and magnetic parameters of composites were improved by increasing new CNT loading. Cherazi's model turned out to be suitable for describing the CO₂ transport through analyzed hybrid membranes. The comparison of the transport and separation properties of the tested membranes with the literature data indicates their potential application in the future and the direction of further research.

Fe@MWCNT-OH/SPEEK hybrid membranes for CO₂ separation! Significant improvement of hybrid membrane's gas transport, magnetic, thermal, and mechanical parameters. Enhancement of interphase compatibility after CNT and polymer chemical modification.

Role of Microalgae in Global CO2 Sequestration: Physiological Mechanism, Recent Development, Challenges, and Future Prospective

The rising concentration of global atmospheric carbon dioxide (CO2) has severely affected our planet’s homeostasis. Efforts are being made worldwide to curb carbon dioxide emissions, but there is still no strategy or technology available to date that is widely accepted. Two basic strategies are employed for reducing CO2 emissions, viz. (i) a decrease in fossil fuel use, and increased use of renewable energy sources; and (ii) carbon sequestration by various biological, chemical, or physical methods. This review has explored microalgae’s role in carbon sequestration, the physiological apparatus, with special emphasis on the carbon concentration mechanism (CCM). A CCM is a specialized mechanism of microalgae. In this process, a sub-cellular organelle known as pyrenoid, containing a high concentration of Ribulose-1,5-bisphosphate carboxylase-oxygenase (Rubisco), helps in the fixation of CO2. One type of carbon concentration mechanism in Chlamydomonas reinhardtii and the association of pyrenoid tubules with thylakoids membrane is represented through a typical graphical model. Various environmental factors influencing carbon sequestration in microalgae and associated techno-economic challenges are analyzed critically.

Investigation of SiO2 Nanoparticle Retention in Flow Channels, Its Remediation Using Surfactants and Relevance of Artificial Intelligence in the Future

In this study, the effect of these variables on commercial silica NP retention was presented in a fabricated flow model considering only the physical adsorption aspects of silica NP retention. From our observations, it was established that while silica NP concentration, flow rate and salt are key variables in influencing silica NP agglomeration and retention, the effect of temperature was highly subdued. The effect of salt-induced agglomeration was particularly severe at moderate salinity (≈4 wt% NaCl). To mitigate the effect of salt-induced agglomeration, a commonly used anionic surfactant, sodium dodecyl sulfate (SDS) was added to the solution and the silica NP retention was tabulated. An amount of 0.3 wt% SDS was found to negate salt-induced agglomeration significantly, paving the way for use of silica NP solutions, even in the presence of saline conditions. A section on the prospective use of artificial intelligence for this purpose has been included. This study is useful for understanding NP retention behaviour, especially in the presence of salinity and its mitigation using surfactants, in flow applications.