Carbon Trends最新文献

Supercapacitors have attracted significant attention in modern devices as a promising solution for electrical energy storage due to their remarkable capability to undergo rapid charge and discharge cycles. While various materials are employed in the construction of supercapacitors, carbon-based materials emerge as a predominant choice within the commercial realm. In present report, our intention is to develop an effective supercapacitor device derived from natural biomass. Therefore, we have synthesized water soluble, monodisperse and fluorescent carbon quantum dots (CQDs) from turmeric leaves (Curcuma caesia) via a single step hydrothermal carbonization. Further, the doctor blade technique was employed to coat a layer of CQDs on stainless steel substrate using PVA as a binder. We observed the functional groups associated with QDs triggers the fast diffusion of ions and transmission of electrons with conducting substrate and electrolyte and thereby effectively charge and discharge mechanism. The supercapacitor based on carbon quantum dots (CQDs) based electrode exhibits exceptional performance characteristics with a remarkable specific capacitance of 468 F/g and highest energy density of 78.6 Wh/kg, superior to the values reported for most carbon-based supercapacitors. Further, we demonstrated the light dependent capacitive enhancement by depositing a thin P3HT layer over CQDs. Moreover, CQDs-based supercapacitor achieves a maximum power density of 733.2 W/kg when operated in a 1 M KOH electrolyte solution and an excellent capacitive retention of about 80 % even after 5000 cycles.

Eutectic-Magnesium electrolytes are sparsely used electrolytes in Magnesium ion batteries. In this context, readily available less toxic precursors based eutectic electrolytes are attracting increasing interest owing to the focus of sustainable battery development. The unique benefits of magnesium such as high specific capacity, low reduction potential, and remarkable reversibility without dendrimer formation are highly advantages when compare to lithium based batteries. Developing an optimal electrolyte composition is a key area of study in the field of battery technology. With improved cell performance, stability across cycles, and general safety, we hope to reduce unwanted interfacial reactions. In this study, we examined eutectic combination of trimethylamine hydrochloride and aluminium chloride (TMA: AlCl₃ = TMA) along with magnesium perchlorate to understand ion-solvation, complexation, thermal stability, ion transport and conduction, and electrochemical stability, certain physico-chemical and electrochemical parameters were evaluated prior to assessing the cell's performance. The salient features being an ionic conductivity (σ) of 6.25×10⁻³ mS cm⁻¹ at 30 °C, remarkable performance retention with over 90 cycles of operation with the electrolyte and an impressive capacity of 90 mAh/g. The behaviour of ionic conductivity with temperature followed the Vogel-Tammann-Fulcher (VTF) equation. Moreover, the anodic stability around 2.5 V (Mg/Mg²⁺) when platinum is used as the working electrode endorses the suitability of the electrolyte for use in Rechargeable Magnesium Batteries (RMBs).The promising results of this first investigation open up new possibilities for investigating complementary pairings with the aim of improving the efficiency of magnesium-ion cells.

Use of waste polystyrene as an additive in the preparation of biowaste derived char can provide significant new properties that enhance the performance of the epoxy coating on steel surface. This work establishes a cost-effective Quasi-graphitic carbon (QGC) derived from the co-pyrolysis of Eucalyptus wood chips and polystyrene as a mix in epoxy (EP) matrix for enhanced the coating properties. The QGC material was characterized by FTIR, XRD, Raman, SEM-EDX, TGA, etc. Results show the incorporation of 0.1 wt.% QGC to the EP matrix enhances corrosion resistance by 98.6 % and boosts mechanical properties with a 245.45 % increase in hardness and a 57.31 % rise in elastic modulus compared to pure EP coatings. Microscopic analysis reveals a smoother, more compact surface with fewer structural defects comapred to pure EP coating. Adhesion tests score the category of 4B, 5B, and water contact angle improve to 102.8°, compared to 61.6° for pure EP coatings. These eco-friendly materials, created through environmentally conscious processes can be a safe alternative to the conventional toxic chemicals used to protect against corrosion,particularly in marine environments.

As an important intermediate for dual carbon targets, catalytic CO oxidation under mild conditions has received sufficient attention, as the reaction mechanism is directly related to the type of employed catalyst. High performance computing is performed with density functional theory to elucidate the mechanism of CO oxidation catalyzed by sulfur doped fullerene (C_60-xS_x (x = 1 ∼ 3)). The total activation energy for the first CO oxidation on C₅₉S, C₅₈S₂, and C₅₇S₃ increases gradually, as implies that the CO oxidation on C₅₉S should be easier than those on the other two dopants. Distinct electrons (0.852 e and 1.479 e) are transferred to oxygen atoms (O₂) from C₅₉S with the adsorption of O₂ and CO. There is no synergistic effect for the doping S atoms. All elementary reactions on C₅₉S are exothermic processes. This means that C₅₉S is a potential material for addressing environmental protection issues and H₂ purification for fuel cell applications.

High-strength and high-modulus carbon fibers are the basis of many composite materials used in power and automotive engineering as well as other mechanical engineering fields. Superstructural thermoplastic binders—like PPS, PSU, PES, and PEEK—are emerging quickly as a binder material. The mechanical properties of composite materials, especially tensile strength, are improved when high-strength and high-modulus fibers are combined with superstructural thermoplastic binders. However, the type of carbon fiber used, the concentration of thermoplastic binder, and the specifics of the production process all have a significant impact on the final mechanical properties of the composite material. As such, predicting these properties requires both a thorough analysis and a trustworthy mathematical model that predicts mechanical properties (tensile strength).

The study that is being presented takes a thorough approach to statistical analysis and model building that anticipates the tensile strength of composite material samples made of carbon filaments that have been impregnated with polysulfone (PSU), a thermoplastic polymer.

PSU thermoplastic polymer was used as a binder, and 817 samples of composite material with high-strength and high-modulus carbon fibers of four different grades were subjected to a thorough statistical analysis of the tensile test findings.

Nine distinct regression models and four CNN-based models with three distinct neuron activation functions were constructed based on the statistical analysis. The built-in models forecast the composite material's ultimate strength based on the specimen loading circumstances, filler qualities, and composition.

Significant differences were found in the mechanical properties of carbon fibers of different grades and types (high-strength and high-modulus) based on statistical analysis of the results of tensile tests. The results of Spearman's correlation study indicated a medium positive correlation between ultimate strength and polymer concentration and a weak negative association between ultimate strength and the density of the carbon fiber contained in the composite material. The strain corresponding to the ultimate strength and fiber density were found to have a medium negative correlation, whereas the polymer concentration showed a medium positive correlation. In the composite material, a very slight negative association was discovered between the concentration of polymers and the density of carbon fibers.

Test results were split into two categories while creating CNN and regression models: 75 % were used for model testing and 25 % were used for training. The CNN model with three layers of hidden parameters produced the best prediction results; the RMSE was 142.948 MPa and the Spearman correlation coefficient between the test strength and the anticipated values was 0.988.

Regression models' sensitivity analysis revealed that, up to a response variable (tens