With the increasing demand for energy conservation and emissions reduction in the shipping industry, suctionbased turbine sails have emerged as a novel wind energy utilization technology and have become a research hotspot. This study focuses on the aerodynamic performance of suction-based turbine sails with the aim of investigating the effects of suction intensity and suction port position on their aerodynamic characteristics. By employing Computational Fluid Dynamics (CFD) numerical simulations using the Re-Normalization Group (RNG) k–ε turbulence model and the SIMPLE algorithm, this study provides a detailed analysis of lift and drag coefficients, pressure distribution, and vorticity distribution under various combinations of suction intensity (γ) and suction port position (α). The results show that variations in suction intensity significantly affect the lift and drag characteristics of the turbine sail, while changes in the suction port position directly influence the attachment and separation behavior of airflow on the sail surface. Furthermore, a synergistic effect is observed between γ and α—their interaction not only alters the flow distribution but also plays a critical role in determining the overall performance of the turbine sail.By comprehensively considering the influence of these two factors, the study draws key conclusions for optimizing the design of suction-based turbine sail, providing valuable theoretical insights and technical guidance for their practical application in wind-assisted marine propulsion.

Due to the sturdy photoluminescence and absorption, CQDs emerged as a suitable candidate for optical sensing probe. The present study deals with the synthesis of blue-fluorescent Carbon Quantum Dot (TAA-CQD) using tannic acid and glycine as novel precursors. The TAA-CQD were synthesised hydrothermally with the high production yield and QY to be 86.12 and 21%, respectively, and an average particle size of 1.9 nm. The TAA-CQD aqueous solution displays excitation-dependent fluorescence emission in the excited range from 420 to 650 nm. The CIE co-ordinates in a highly blue region at (0.14, 0.19) confirmed the synthesised TAA-CQD were blue in fluorescent. Fluorescence of TAA-CQD was stable under all pH range, resisted the high ionic strengths condition and stable over 8 months. Furthermore, the fluorescent TAA-CQD was capable in detecting a tetracycline-classed antibiotic Doxycycline (DXY) along with remarkable selectivity and sensitivity. The measures limit of detection (LOD) was very low 2.42 mM in comparison to other methods. Moreover, the applicability of the proposed work has been fruitfully employed on the pharmaceutical waste. Thus, our designed TAA-CQD based fluorescence sensing system hold great promise for the advanced sensing materials in the detection of DXY and we believe that our approach will be promising and viable in a clinical applications.

Aqueous zinc–iodine batteries (AZIBs) are gaining attention for their ability to store and convert electrical energy. Nevertheless, their performance is hindered by the continual migration of polyiodides towards the zinc anodes, leading to undesirable side reactions, diminished coulombic efficiency, and compromised cycling stability. Traditional carbon materials have proven inadequate in resolving these challenges, mainly due to their limited iodine capacity and weak binding forces. Herein, we explore the use of porous carbon nanosheets (PCNSs) synthesized via the “Pharaoh’s Serpent” reaction as cathode electrodes in AZIBs without pre-load iodine. The PCNSs, characterized by their nanosheet structure and expansive specific surface area, not only facilitate a shorter diffusion path for rapid electrolyte infiltration but also provide numerous sites for ion adsorption and capacitive storage, markedly improving the efficacy of electrochemical reactions and ion migration rates. Utilizing the synthesized PCNSs as the cathode electrode in AZIBs, a specific capacity of 296 mAh g− 1 was achieved at 0.3 A g− 1. Even when the current density increased to 30 A g− 1, a specific capacity of 144 mAh g− 1 was still attained, with a capacity retention ratio of up to 48.6%, which is competitive with that of supercapacitors. In addition, the AZIBs demonstrated impressive cycling stability, retaining 103% of their capacity after 10,000 cycles, and a notable energy density of 266.4 Wh kg− 1 based on the cathode material. These findings significantly broaden the application of carbon materials in AZIBs research, emphasizing their potential in advancing AZIB technology.

Marine biomass (MB) is gaining attention as a sustainable and eco-friendly carbon source within the carbon cycle, particularly in regions with extensive coastlines. However, the high content of alkali and alkaline earth metals (AAEMs) in MB poses challenges in producing functional carbon materials, like activated carbon (AC), with a high specific surface area (SSA). In this study, we employed a two-step CO2 activation process, coupled with acid treatment, to successfully convert MB into highly porous AC. Preheating followed by nitric acid washing reduced AAEM content from 22.4 to 2.5 wt%, and subsequent atmospheric CO2 activation produced AC with an SSA of 1700 m2/ g and mesopores of 3–5 nm. A further treatment with a mixed acid solution of nitric and acetic acids reduced impurities to below 1.0 wt%. A second pressurized CO2 activation at 1 MPa yielded AC with an SSA exceeding 2100 m2/ g, with mesopores accounting for more than 50% of the total pore volume. This method demonstrates an effective approach to producing high-performance AC from MB for advanced applications.

Optimizing business strategies for energy through machine learning involves using predictive analytics for accurate energy demand and price forecasting, enhancing operational efficiency through resource optimization and predictive maintenance, and optimizing renewable energy integration into the energy grid. This approach maximizes production, reduces costs, and ensures stability in energy supply. The novelty of integrating deep reinforcement learning (DRL) in energy management lies in its ability to adapt and optimize operational strategies in real-time, autonomously leveraging advanced machine learning techniques to handle dynamic and complex energy environments. The study’s outcomes demonstrate the effectiveness of DRL in optimizing energy management strategies. Statistical validity tests revealed shallow error values [MAE: 1.056 × 10(− 13) and RMSE: 1.253 × 10(− 13)], indicating strong predictive accuracy and model robustness. Sensitivity analysis showed that heating and cooling energy consumption variations significantly impact total energy consumption, with predicted changes ranging from 734.66 to 835.46 units. Monte Carlo simulations revealed a mean total energy consumption of 850 units with a standard deviation of 50 units, underscoring the model’s robustness under various stochastic scenarios. Another significant result of the economic impact analysis was the comparison of different operational strategies. The analysis indicated that scenario 1 (high operational costs) and scenario 2 (lower operational costs) both resulted in profits of $70,000, despite differences in operational costs and revenues. However, scenario 3 (optimized strategy) demonstrated superior financial performance with a profit of $78,500. This highlights the importance of strategic operational improvements and suggests that efficiency optimization can significantly enhance profitability. In addition, the DRL-enhanced strategies showed a marked improvement in forecasting and managing demand fluctuations, leading to better resource allocation and reduced energy wastage. Integrating DRL improves operational efficiency and supports long-term financial viability, positioning energy systems for a more sustainable future.

Industrialization and increasing consumerism have driven up energy demand and fossil fuel consumption, significantly contributing to global climate change and environmental pollution. While renewable energy sources are sustainable, their intermittent nature necessitates the development of efficient energy storage devices to ensure uninterrupted power supply and optimal energy utilization. Electrochemical energy storage devices are promising for sustainable energy. Traditionally, carbon electrode materials for these devices come from non-renewable sources. However, using biomass and biomass–coal blends can help substitute fossil fuels, reducing environmental impact. Recent advancements in carbon materials have achieved specific surface areas of over 2500 m2/ g, resulting in supercapacitor capacitances of 250–350 F/g and cycling stability exceeding 10,000 cycles with < 5% capacity loss. In lithium-ion batteries, biomass-based anodes deliver 400–600 mA h/g, outperforming graphite. Doped carbon materials enhance charge-transfer efficiency by 20–30%, while CO₂ emissions from production are reduced by 40–60%. With 50–70% lower costs than fossil-based alternatives, biomass-derived carbons present a viable pathway for scalable, eco-friendly energy storage solutions, accelerating the transition toward sustainable energy systems. Overall, this work highlights the influence of carbon materials on the electrochemical properties and hydrogen storage capacity of biomass-based carbon materials. This also underscores their potential application in energy storage.

In recent years, high-entropy alloys (HEAs) have attracted considerable attention in materials engineering due to their unique phase stability and mechanical properties compared to conventional alloys. Since the inception of HEAs, CoCrFeMnNi alloys have been widely investigated due to their outstanding strength and fracture toughness at cryogenic temperatures. However, their lower yield strength at room temperature limits their structural applications. The mechanical properties of HEAs are greatly influenced by their processing methods and microstructural features. Unlike traditional melting techniques, powder metallurgy (PM) provides a unique opportunity to produce HEAs with nanocrystalline structures and uniform compositions. The current review explores recent advances in optimizing the microstructural characteristics in CoCrFeMnNi HEAs by using PM techniques to improve mechanical performance. The most promising strategies include grain refinement, dispersion strengthening, and the development of heterogeneous microstructures (e.g., harmonic, bimodal, and multi-metal lamellar structures). Thermomechanical treatments along with additive manufacturing techniques are also summarized. Additionally, the review addresses current challenges and suggests future research directions for designing advanced HEAs through PM techniques.

Though Farnesiferol C (FC) derived from Ferula asafoetida is known to have antiangiogenic and apoptotic effect in gastric, breast, nonsmall lung cancers, the underlying antitumor mechanism of FC is not fully understood so far. Hence, in the current study, apoptotic mechanism of FC was explored in colon cancers in association with carbon catabolite repression 4-negative on TATA-less 2 (CNOT2)/c-Myc signaling. Herein FC significantly increased cytotoxicity and reduced the number of colonies in HCT116 cells more effectively than in SW480 cells, though FC enhanced sub-G1 cell population in HCT116 and SW480 cells compared to untreated control. Consistently, FC activated the cleavages of Poly ADP-ribose polymerase (PARP) and Bax and attenuated the expression of pro-PARP and Cyclin D1 in HCT116 cells better than SW480 cells. Also, FC significantly reduced the expression of CNOT2 and c-Myc. Also, FC reduced of c-Myc stability in HCT116 cells by cycloheximide assay. Notably, CNOT2 depletion reduced the expression of c-Myc, while c-Myc depletion also attenuated the expression of CNOT2 in HCT116 cells, implying the crosstalk between CNOT2 and c-Myc. Furthermore, overexpression of c-Myc or CNOT2 promoted the expression of pro-PARP in HCT116 cells. Overall, these findings suggest that FC induces apoptosis via inhibition of CNOT2 and c-Myc in colon cancers for a potent anticancer candidate for further agriculture cultivation in Korea.