Journal of Chemistry and Environment

Properties of Electrode Materials and Electrolytes in Supercapacitor Technology

Syed Shaheen Shah — 2024-02-22

This thorough review article offers a cutting-edge analysis of the essential characteristics and developments in electrode materials and electrolytes for supercapacitor technology. We start by going over the basics of supercapacitors and how important characterization methods like electrochemical impedance spectroscopy, galvanostatic charge-discharge, and cyclic voltammetry work. Specific capacitance, energy, and power densities, three essential characteristics that are crucial for assessing supercapacitor performance, are carefully covered in this work. We also analyze the many kinds of capacitors, including hybrid supercapacitors, electric double-layer capacitors, pseudocapacitors, and supercapacitors, and explain their working principles and material-specific characteristics. The study highlights the importance of metal oxides and hydroxides, carbon-based materials, conductive polymers, and novel and hybrid materials such as MXenes and metal-organic frameworks. The special qualities of each material class, such as large surface area, electrical conductivity, and particular redox properties, are highlighted in this section. These qualities are crucial for maximizing the performance of supercapacitors. The topic of electrode materials is discussed in detail, including their benefits and the difficulties and chances to improve energy storage, stability, and affordability. Parallel to this, the study thoroughly examines various electrolyte kinds, a sometimes overlooked yet essential part of supercapacitor technology. Discussed include ionic conductivity, operating voltage windows, safety profiles, and electrochemical stability of aqueous, organic, ionic liquid, gel, and solid-state electrolytes. This paper highlights the relationship between supercapacitor performance and electrolyte type, explaining how electrolyte selection affects total energy density, power density, and operational longevity. This review article covers supercapacitor technology in detail and with a wide scope and is an invaluable resource. It is a fundamental work for scholars and practitioners new to the area. It offers sophisticated insights that may encourage creativity and application-specific advancement in this quickly changing field. The study presents a comprehensive analysis of the present and future developments in supercapacitor materials and technology, establishing it as a vital resource in the continuous search for cutting-edge energy storage solutions.

Simulation for migration and treatment of groundwater contamination in coal mining subsidence area: A case study of Datong dump, Huainan, China

Ayesha Selhaba — 2024-03-12

The liquid that seeps into the earth from landfills and removes garbage is called landfill leachate. Pollutants like organic and inorganic chemicals, xenobiotic substances, etc. are found in landfill leachate which contaminates groundwater. To study the contamination and treatment of landfill leachate to groundwater, Datong landfill was selected as a target zone. Samples of monitoring wells in the landfill site, the eastern collapse pond, and wells in groundwater from the goaf were tested and analyzed for TDS, conductivity, and dissolved oxygen (DO). To simulate all processes, various modules were built under different conditions. It was found that the leachate not only had polluted surface water but also groundwater. The simulation results showed that the pollution plume expanded spatially and temporally, mainly flowing from west to east and spreading to the north-south direction and reverse flow direction due to the low water level between the east and west sides. Anti-seepage walls and pumping-injection methods were used to control the migration and diffusion of pollution from landfill leachate. The simulation results also showed that both methods were effective in reducing the concentration and range of pollution plumes which would provide a theoretical basis for the treatment of pollutants in the Datong landfill site.

0D-3D Superstructure of Biocarbon with FeCl3-Assisted for Electrochemical Symmetrical Supercapacitor

Sri Ayunda — 2024-03-17

Biomass materials exhibit a diversity of macrostructures that can be derived or developed into various 0D-3D nanoscale structural designs with various different dimensions such as spherical, nanofiber, tubular, nanosheet, hierarchical, nanosphere, and honeycomb structures, when converted into activated carbon. This research converts oil palm empty fruit bunch fiber biomass into supercapacitor carbon material by optimizing the chemical activation process by varying the activating reagent. The active carbon conversion process includes precarbonization stages, chemical activation (KOH, FeCl₃, and ZnCl₂), carbonization, and physical activation. The resulting electrode was confirmed to have a morphology with a 0D to 3D structure (nanosphere, nanofiber and nanopore) due to the FeCl₃ activator, so the resulting surface area was high (517.998 m/g). Biomass was successfully converted into carbon which was confirmed through through the resulting physical and electrochemical properties. The electrochemical performance of the sample supercapacitor cell was analyzed in a symmetric two-electrode system in 1 M H₂SO₄ electrolyte solution. The FeCl₃-activated samples shows the highest specific capacitance of 256 F/g at a scan rate of 1 mV/s. In addition, oil palm empty fruit bunch fiber biomass is a potential raw material, as a carbon material for supercapacitor cells with high performance.

Seasonal Variation of Fe, Mn, and Pb in Groundwater of Northwestern Bangladesh

Md. Zahidul Islam — 2024-03-17

Groundwater is one of the most significant natural resources on earth and also stands as the largest source of all-purpose water in Bangladesh. The higher concentrations of iron (Fe), manganese (Mn), and lead (Pb) in water constitute a threat to human health and the environment. The research aimed to assess the seasonal variations of heavy metal concentrations, including Fe, Mn, and Pb, and the water type controlling the geochemistry of groundwater. A total of sixty groundwater samples were collected over a year in three seasons and analyzed for several physicochemical parameters using the standard analysis methods The highest concentrations of Fe, Mn, and Pb in investigated groundwater samples were found to be 3.69, 2.50, and 0.17 mg/L, respectively. The study observed that the higher concentrations of Fe and Mn were in the pre-monsoon but Pb was in the post-monsoon. The contaminations of Fe, Mn, and Pb in the groundwater follows the order Fe > Mn > Pb in three seasons, and their concentrations of abundance follow the seasons: PRM > POM > MON. The groundwater samples showed the dominance of Ca⁺⁺, Mg⁺⁺, and HCO₃^-, indicating temporary hardness and the Ca-Mg-HCO₃, hydrochemical facies, controlling the groundwater geochemistry in the study area. These findings would provide an in-depth understanding of water quality, potential risks to human health, and coping mechanisms for sustainable drinking water management.

Sequencing Initial Conditions in Physics-Informed Neural Networks

Saman Hooshyar — 2024-03-26

The scientific machine learning (SciML) field has introduced a new class of models called physics-informed neural networks (PINNs). These models incorporate domain-specific knowledge as soft constraints on a loss function and use machine learning techniques to train the model. Although PINN models have shown promising results for simple problems, they are prone to failure when moderate level of complexities are added to the problems. We demonstrate that the existing baseline models, in particular PINN and evolutionary sampling (Evo), are unable to capture the solution to differential equations with convection, reaction, and diffusion operators when the imposed initial condition is non-trivial. We then propose a promising solution to address these types of failure modes. This approach involves coupling Curriculum learning with the baseline models, where the network first trains on PDEs with simple initial conditions and is progressively exposed to more complex initial conditions. Our results show that we can reduce the error by 1 – 2 orders of magnitude with our proposed method compared to regular PINN and Evo.