Greenhouse Gases Reduction: Conversion of Methane and Carbon Dioxide into Clean Energy
In the upcoming years, both population and energy consumption are expected to increase dramatically [1]. Industrialization has led to a dramatic shift in the energy environment [2], with predictions of a 57% increase in demand for energy between 2002 and 2025 [3]. In addition to organic materials like trees and solid waste, fossil fuels like coal, natural gas, and oil provide more than 90% of the world's energy needs. Their overuse has resulted in the release of climate-altering greenhouse gases like carbon dioxide (CO2) and methane (CH4) into the atmosphere [4]. Scientists and other stakeholders are putting more emphasis on finding solutions to global warming, increasing energy production in order to meet increasing demands, and decreasing emissions of greenhouse gases. Using greenhouse gasses to make useful chemicals or fuels is one solution to both problems [5]. This motivated researchers to investigate the potential of CO2 and CH4 as clean energy sources. The process of dry reforming of methane (DRM) has been identified as a potentially successful strategy for transforming CO2 into marketable syngas with a balanced H2/CO composition [6], [7], [8], [9]. The economic viability of DRM, the reactor type, the availability of raw materials, and the intended use of the produced syngas are all-important considerations. Though DRM is gaining popularity, maintaining its long-term stability is difficult due to carbon accumulation from CO disproportionation and methane degradation [10], [11]. The catalyst used, as well as other parameters like as pressure, temperature, feed concentration, and reactor size, are critical to the process's effectiveness. In this scenario, a nickel catalyst on a La2O3/SiO2 substrate with microspheres and a core-shell structure will be developed to improve the conversion of greenhouse gases into profitable syngas. This catalyst is projected to improve the efficiency and performance of the DRM process significantly.
Real-Time Ensemble Model for Stroke, Drowsy, and Distracted Driver Detection Using Transfer Learning Models
Road safety remains a global concern, with driver-related factors like distraction, drowsiness, and medical conditions such as stroke being leading causes of accidents. In this paper, we propose a real-time ensemble learning framework that leverages transfer learning for the detection of stroke, drowsiness, and distracted driving. Our model integrates multiple Convolutional Neural Networks (CNNs) fine-tuned for each specific task, and employs a stacking method to combine the predictions of these models using a meta-classifier. Notably, the model is optimized to enhance stroke detection, minimizing false negatives— an essential aspect for timely medical intervention. Experimental evaluations on diverse datasets demonstrate the efficacy of our approach, achieving an overall accuracy of 92.5%. The results emphasize the model’s potential for real-time driver monitoring, offering critical safety features that could reduce accidents and save lives.
Application of Carbon Aerogels in Lithium-Air Batteries
One of the main challenges with today’s batteries is their relatively low volumetric and specific capacities. The highest specific capacity can be achieved with lithium-air batteries, which use metallic lithium as the anode and typically some form of porous carbon as the cathode. To enhance performance, aerogels—among the world’s lightest solid materials—are ideal candidates for cathodes. Resorcinol-formaldehyde (RF)-based carbon aerogels, for example, serve this purpose well. In my work, I utilized two types of carbon aerogels as cathode materials: one derived from pyrolyzed resorcinol-formaldehyde polymer and the other a graphene-oxide-modified version of this carbon gel. I integrated the carbon aerogels I had pyrolyzed into lithium-air batteries to improve the cell’s performance, energy density, and capacity compared to cells using activated carbon. In my research, I examined the pore structure and surface properties of these materials in aqueous media using NMR (nuclear magnetic resonance) relaxometry and cryoporometry, exploring their impact on battery efficiency. I found that the graphene-oxide-containing sample's pores filled with water in a layered manner, indicating a more hydrophilic surface, which suggests a denser arrangement of oxygen-containing functional groups compared to the unmodified carbon aerogel. The pore sizes were reduced after adding graphene oxide, resulting in an increased specific surface area for the sample. Incorporating the reduced graphene-oxide-containing carbon aerogel enabled the creation of a more efficient, higher-capacity battery than with the RF carbon aerogel. This improved performance is likely due to the aerogel’s higher oxygen content and altered morphology. The increased oxygen content provides more active sites for oxygen reduction, meaning that a greater specific power output can be obtained from the battery.