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Analysing the impact of oil prices, economic activity, and trade policy uncertainty on CO2 emissions in the US context: A wavelet approach
This study examines the simultaneous co-movements between oil prices, economic activity, trade policy uncertainty, and CO2 emissions in the United States using a series of wavelet methodologies. Unlike traditional approaches, the wavelet approach is appropriate for understanding the time-varying associations at different frequencies and is designed to efficiently handle the non-stationary nature of economic and environmental time series data. The empirical results highlight the potential of a leading relationship where economic activities and trade policy uncertainties drive CO2 emissions in the US during the period from January 1990 to January 2022. Contrarily, the link between oil prices and CO2 emissions is characterized by intricate dynamics, exhibiting both lagging and leading co-movements at different frequencies. Moreover, economic activities have a positive impact on CO2 emissions, while in the high quantile tails, trade policy uncertainty decreases CO2 emissions. This means economic activity is slowing down during the period of high trade policy uncertainty. Our findings highlight the necessity of specific policies that reconcile economic growth with environmental sustainability, manage the effect of oil price changes on CO2 emissions, and match trade policies with emission-minimizing goals. Based on the results, this research offers important implications for policymakers to ensure the equilibrium between economic activity and environmental management within the scope of sustainable development goals. 2025 International Association for Gondwana Research -
A heavy metal tolerant Thiopseudomonas alkaliphila strain as a potential plant growth promoter isolated from Bengaluru region
Thiopseudomonas alkaliphila, a Pseudomonadaceae has diverse environmental role that has not been much explored. Current study highlights, the isolated strain from industrial sites of Bengaluru with heavy metal tolerance against lead, chromium and cadmium. The antibiotic susceptibility test (AST) and minimum inhibitory concentration (MIC) showed sensitive against all the antibiotics used in the study. Subsequently, 16s rRNA analysis established and closely related to T. alkaliphila D2441 strain, whole genome was submitted, GenBank SRA database accession number is as follows PRJNA1258058. The unravelling of genetic determinants analyzed for heavy metals, antibiotic resistance and plant growth promoting traits were compared with related strains. A single chromosome with 2,400,551 bp length, average GC ratio 49.44 % and with 1941 protein-encoding genes (PEGs), the strain can bioremediate different heavy metals (354 genes/proteins), along with an aptitude as plant growth promoting rhizobacteria (PGPR) evidenced by genes showcasing tolerance against adverse environmental conditions under stress for phytohormones, plant nutrient acquisition, heat and shock chaperones, siderophore etc. The study highlights, T. alkaliphila as a non-pathogenic, potential heavy metal remediator with potential activity for PGPR traits at genetic levels. 2025 -
Aqueous symmetric supercapacitor based on hydrothermally grown reduced graphene oxide wrapped cobalt oxide nanocomposites: An efficient paradigm for enhanced performance in supercapacitors
Extensive research has been carried out on the development of electrode materials for energy storage applications, especially in the field of supercapacitors. The present work is the first report on the effect of rGO concentration on the electrochemical properties of reduced graphene oxide-cobalt oxide (rGO-Co3O4) nanocomposites. A symmetric supercapacitor device is assembled by compounding two hydrothermally grown rGO-Co3O4 nanocomposite electrodes separated by a membrane dipped in a 3 M KOH aqueous electrolyte solution. The device delivered a specific capacitance of 1006 Fg?1, energy density of 357.44 W h kg?1, and a power density of 1600 W kg?1 at a current density of 2 Ag?1. It showed a cyclic stability of 80 % during 10,000 cycles at a very high current density of 5 Ag?1 and a coulombic efficiency of 100 %, which maintained a better electrochemical performance, implying that the as-synthesized electrodes are useful for portable energy storage devices. 2025 Elsevier Ltd. -
Biomass-derived N-doped carbon to anchor bimetallic-phospho boride for hydrogen evolution from alkaline seawater
Seawater electrolysis offers a sustainable pathway for hydrogen production, but is hindered by the limited activity and stability of electrocatalysts, with Pt-based materials being highly active yet costly and scarce. To address these issues, we synthesize nitrogen-doped carbon (NC) via a solvent-free method from golden shower biomass. NC is integrated with CoMoPB catalysts using a facile chemical reduction process. The resulting CoMoPB/NC catalyst exhibited superior HER activity, achieving a low overpotential of 34 mV at 10 mA/cm2 in alkaline natural seawater, outperforming the commercial Pt/C catalyst under similar conditions. The CoMoPB/NC catalyst demonstrated considerable stability at ?500 mA/cm2 for 100 h and showed strong HER performance in seawater electrolyzers, reaching ?1.98 V at 500 mA/cm2. This study explores the potential of biomass-derived catalysts to rival and surpass commercial noble metal-based systems, offering a cost-effective and sustainable solution for industrial-scale seawater electrolysis and renewable energy applications. 2025 Elsevier Ltd -
Instigating the mixed phases of cobalt oxide in nanowires for electrolysis of urea-based water
The urea oxidation reaction (UOR) offers a more energy-efficient alternative to water splitting, with a lower theoretical potential of 0.37 V and the possibility of using urea-based wastewater as an electrolyte. In this study, phosphorus/boron-incorporated cobalt oxide nanowires supported on nickel foam (P,B-CoxOy NW@NF) are synthesized by hydrothermal and reduction methods as an electrocatalyst for UOR. The P,B-CoxOy NW@NF demonstrates exceptional electrocatalytic performance with a low UOR potential of 1.33 V at 50 mA/cm2 in alkaline media. Comprehensive structural and morphological analyses reveal the formation of mixed Co3O4-CoO phases with abundant oxygen vacancies (Ov) and Co2+ species, which synergistically enhance conductivity and provide ideal surroundings for active M?OOH species formation. In-situ electrochemical kinetic studies highlight the superior catalytic activity of P,B-CoxOy NW@NF, attributed to a high density of active sites, improved reactant adsorption, and efficient desorption of byproducts, including CO2. The catalyst exhibits excellent long-term stability with minimal degradation of 7 % over 100 h of continuous chronoamperometry testing and 2 % loss after 10,000 cycles. Furthermore, the activity of P,B-CoxOy NW@NF is evaluated in alkaline natural cow urine, requiring just 1.35 V at 50 mA/cm2 for UOR, demonstrating its practical relevance for real-world applications. These findings showcase the significant potential of P,B-CoxOy NW@NF as a scalable and stable electrocatalyst for sustainable hydrogen production from wastewater. 2025 Elsevier Ltd -
Surface modified Cobalt Oxide Nanostructures for hydrogen generation from catalytic dissociation of NaBH4
Liquid chemical hydrides, such as aqueous sodium borohydride (NaBH4), offer a safer, energy-dense alternative for fuel cell vehicles, enabling on-demand hydrogen release under ambient conditions. However, achieving large-scale viability for this system requires the development of a cost-effective and durable catalyst to improve hydrogen release efficiency. In this study, three distinct nanostructured Co3O4 catalysts (nanorods (NR), nanosheets (NS), and nanocubes (NC)) were synthesised via a hydrothermal method and further modified by incorporating B and P heteroatoms on the surface. Among these, the B/P-Co3O4-NS catalyst with its 2D nanosheet structure exhibited the highest catalytic activity, achieving an activation energy of 17.7 kJ/mol and a maximum hydrogen generation rate (HGR) of 5.6 L/min/g for hydrolysis of NaBH4. All three B/P-modified Co3O4 catalysts outperformed both CoPB nanoparticles and unmodified Co3O4, attributed to enhanced electronic interactions and induced lattice strain from B and P incorporation, with the nanosheet morphology providing a large surface area for improved efficiency. The B/P- Co3O4-NS catalyst also demonstrated notable stability, successfully enduring recycling and high-temperature treatment (773 K). These results highlight B/P-Co3O4-NS as a promising candidate for practical hydrogen generation, combining high catalytic performance with robust stability. 2025 Elsevier Ltd -
Quantum-enhanced neuro-fusion framework for intelligent decision-making in smart home IoT surveillance
Smart-home surveillance systems increasingly rely on heterogeneous IoT data streams, requiring efficient fusion, scalability, and robustness under noisy sensing conditions. This paper proposes a Quantum-Inspired Deep Neuro-Fusion Architecture (QDNFA) for anomaly detection in edgecloud IoT environments. The framework integrates modular encoders, temporal alignment, and a quantum-inspired optimisation mechanism to support multi-modal data processing while maintaining real-time performance. Experimental evaluation is conducted on the CASAS Smart Home dataset to validate sensor-centric anomaly detection, scalability across multiple devices, and edgecloud inference efficiency. While the architecture is designed to support audio and video modalities, the present study focuses on low-dimensional sensor data, and large-scale benchmarking on audiovisual surveillance datasets is identified as future work. Results demonstrate improved detection accuracy and reduced latency compared to baseline methods in sensor-driven smart-home scenarios. 2026 The Author(s). -
Analysis of passive bloodstain morphology across surface textures and drop heights using deep learning
Bloodstain pattern analysis (BPA) is a critical forensic science tool for reconstructing crime scene events. In this study, the effect of substrate type and drop height on the morphology of passive bloodstains was examined under controlled laboratory conditions. Blood samples were dropped vertically at 90 angle from three different heights, and the drops were permitted to strike five different surfaces, including curved cups, crushed chart paper, jute cloth, jelly stone, and concrete. These substrates were chosen to represent a realistic range of porous, semi-porous, non-porous, textured, and curved materials that are commonly encountered in crime scenes. The features of the substrate affect stain morphology, including shape irregularity and satellite formation, but not the measured angle of impact. These findings validate the consistency of impact angle determination using BPA, wherein the nature of the substrate primarily affects stain morphology but not necessarily the accuracy of angles. The large image data sets were tested using deep learning approaches, which effectively differentiate bloodstain patterns generated from varying fall heights. MobileNet model, leveraging pretrained ImageNet features, achieved superior accuracy and generalisation, underscoring the value of transfer learning for small forensic datasets. Future extensions of this work will include multiple impact angles, motion-related effects and temperature-controlled conditions to represent the actual crime scene scenarios. Deep learningbased analysis of these data may improve the understanding of bloodstain morphology and strengthen the forensic applicability. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
SS-CNN BruiseFinder: Hyperspectral imaging and CNN-driven spatial-spectral fusion for non-destructive plum bruise analysis
Plum fruit is susceptible to damage at various stages, from growth to packaging, and such bruising is often difficult to detect visually due to its subtle surface appearance. This research seeks to develop a convolutional neural network (CNN) model that leverages 3D convolutional layers to integrate spatial and spectral features from hyperspectral data, enabling accurate bruise analysis in plum fruit. In this study, plums sourced from a Norwegian fruit store were intentionally bruised and then imaged using hyperspectral technology at various time intervals (30 min to 48 h post-bruising). A novel CNN model, dubbed SS-CNN BruiseFinder, is developed to harness the spatial and spectral characteristics of these hyperspectral images for accurate bruise detection and classification. The SS-CNN BruiseFinder model demonstrates detection accuracy ranging from 68.5% to 91.5% and categorization accuracy between 67.39% and 98.16%. To further establish the effectiveness of this approach, three additional deep learning models a custom spectral CNN, ResNet 101, and a bidirectional LSTM model are developed and evaluated on the same dataset, providing a comprehensive validation of the proposed method's superiority. Timely detection of bruising helps prevent contaminated plums from entering the supply chain during transportation or storage. By categorizing plums based on bruise age, retailers can offer consumers more accurate freshness and quality information, enabling them to make better-informed purchasing choices and ultimately enhancing the overall shopping experience. To encourage community engagement and re-implementation, our code is available at https://github.com/SS-CNN BruiseFinder. 2025 Elsevier Ltd -
Early bruise detection, classification and prediction in strawberry using Vis-NIR hyperspectral imaging
The most frequent kind of damage to strawberries is bruising. However, most of the bruises are so barely perceptible at an early stage on the surface, that detection of them with the human eye is quite challenging. This study proposes a method for accurately detecting and classifying the damage using reflectance imaging spectroscopy. In order to carry out the study, an experiment was devised to artificially induce bruises and a dataset was generated at different bruise intervals. A model for detecting and classifying bruises at their latent stage was developed using machine learning classifiers, including support vector machines (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT), to investigate the changes over time after bruise occurrence on the detection performance. Regression models for the prediction of bruising time were developed using partial least square regression (PLSR), RF, gradient boosting (GB), support vector regression (SVR), and DT. Among the compared models, both SVM and LDA could achieve 99.99 % classification accuracy. RF was regarded as being the most advisable for detection and prediction jobs due to its high performance. It achieved MSE of 0.052 and R2 of 0.989 for prediction. 2024 Elsevier Ltd -
Innovative fish peptide-loaded chitosomes: Advancing bioactive delivery through comprehensive in vitro and in vivo assessments
Considerable advancements have been achieved in controlled delivery systems, yet ensuring optimal stability, bioavailability, and precise targeting of bioactive compounds continues to present challenges. Addressing this gap, the present study explores chitosomeslipid vesicles stabilized by chitosanas a promising approach to enhance the delivery efficiency of bioactive molecules. This study investigate fish peptide-loaded chitosomes, leveraging chitosan's biocompatibility, biodegradability, and encapsulation capabilities. A comprehensive evaluation was conducted under both in vitro and in vivo conditions to assess their potential applications. In vitro studies using L929 cell lines demonstrated high biocompatibility, efficient cellular uptake, and sustained cell viability, with a dose-dependent cytotoxicity profile, leading to early and late apoptosis. The tolerance, favourable metabolic stability, and biomarker responses were validated by in vivo evaluations in Albino Wistar rats, demonstrating systemic efficacy and safety. Moreover, the peptide-chitosome formulation demonstrated a lipid-lowering impact, as evidenced by increases in high-density lipoprotein (HDL) and unsaturated fatty acids and decreases in triglycerides, saturated fatty acid content, and low-density lipoprotein (LDL). These findings highlight the potential of fish peptide-loaded chitosomes as an advanced bioactive delivery system, addressing existing limitations and expanding their applicability in nutraceutical and therapeutic formulations. 2025 The Author(s) -
Shear Waves Induced Vibration in a Size-dependent Loosely-bonded ViscoelasticFlexoelectric Material Structure Subjected to Fractional Derivative
The present study investigates the dispersive and damping limitations of shear horizontal waves (SH-waves) in an imperfectly bonded size-dependent layer over layer (LoL) structure. The LoL model consists of a nonlocal flexoelectric layer (NFL) coated by a thin nonlocal viscoelastic layer (NVL) with fractional elastic and viscoelastic properties. Utilizing Eringen's nonlocal elasticity theory, the governing equations for both NVL and NFL have been established and a complex frequency relation through analytical methods is obtained by applying appropriate boundary conditions at the imperfect interface and free surfaces. The complex frequency relation was then separated into dispersion and attenuation equations to represent the dispersive and damping characteristics of SH-waves in the LoL model. The study presents the classical case as a particular instance along with various other cases obtained by relaxing certain assumptions from the present model. To visualize the impact of key parameters such as viscosity, NVL thickness, permittivity, piezoelectricity, nonlocality parameters of NVL and NFL, imperfectness, fractional-order derivative, and flexoelectricity on dispersive and damping natures, several graphs have been plotted and discussed the distinguished region of existence for dispersion and attenuation curves. This was achieved by deriving the lower and upper bounds for SH-wave velocity. Additionally, the influence of key parameters on the surface response of nonlocal shear stresses and particle displacement within the LoL structure is graphically depicted as a function of depth. The findings reveal that SH-wave characteristics are significantly more diverse in the size-dependent LoL model compared to the classical LoL model. The findings of this study hold significant promise for advancing the design and functionality of various technological applications. By enhancing our understanding of surface wave dynamics in size-dependent structures combining flexoelectric and viscoelastic materials, this research paves the way for innovations in sensor technology, energy harvesting systems, and devices capable of manipulating waves. 2025 Elsevier Masson SAS -
Fractional and memory effects on wave reflection in pre-stressed microstructured solids with dual porosity
The present work investigates the influence of fractional-order derivative and memory-dependent derivative on the behavior of various waves reflected at the free surface of a size-dependent, pre-stressed, microstructured thermoelastic solid with a dual porosity framework. A generalized MooreGibsonThomson (MGT) model, incorporating higher-order terms and memory effects, is adopted to describe the complex heat transfer behavior within the material. A nonlocal framework based on Eringen's theory is utilized to derive the basic relations of the considered medium. An examination of the non-dimensionalized governing equations is conducted employing the normal mode technique to provide accurate solutions. The research demonstrates the presence of six separate wave modes that travel at varying speeds within the medium. The energy and amplitude ratios of reflected waves are determined by applying suitable boundary conditions. The influence of varying incidence angles on the reflected wave energy distribution is investigated numerically and visualized using MATLAB software. The study reveals that the energy ratios of the reflected waves are sensitive to the fractional-order parameter, kernel functions, initial stress, and nonlocality parameter. The analysis suggests a conservative reflection process, indicating minimal energy loss during reflection. Key findings and their implications for relevant scenarios are presented in the conclusion. Comparisons with existing models for certain cases demonstrate good agreement, supporting the validity of the present model. 2025 Elsevier Masson SAS -
Study of heat transfer in a rotating weakly electrically conducting Newtonian fluid: Primary and Kpers-Lortz regimes
In this paper, we study the primary and secondary (Kpers-Lortz) instabilities of rotating RayleighBard convection for a weakly electrically conducting Newtonian fluid with idealistic boundaries. The critical Rayleigh number is obtained for each instability. Fourth-order and ninth-order Lorenz model are derived using the truncated Fourier-Galerkin expansion and the onset of primary and secondary instabilities is studied. Using a non-linear analysis, we derive the expression for the Nusselt number for both primary and secondary instabilities. The analysis reveals that the heat transfer in the case of primary instability is an over-prediction when compared with that of the secondary instability. An increase in the strength of the magnetic field is to delay the onset of primary and secondary instabilities and decrease the heat transfer. These insights advance the understanding of magnetohydrodynamic stability in rotating convective systems and have implications for geophysical and astrophysical fluid dynamics. 2025 Elsevier Masson SAS -
Autonomous green vegetable growth monitoring via YOLOv9 and a vine robot with tracked mobility
Urban agriculture is facing shrinking land while demand for food is increasing. The study introduces a vine-like, soft robot for non-destructive tracking of green vegetable development using a tracked mobile platform. Although an inbuilt camera and YOLOv9 object detector classify in real time and generate results in four size categories, very small, small, medium, and large, a flexible tube is everted into dense greenery through a pneumatic eversion process. Sensor fusion and hierarchical control are integrated to enable navigation through the complex canopies of crops with accurate control of pressure and direction, and steering. A field trial found 91% mAP detection accuracy at 38 FPS, accurate vine extension (1.2 m @ 4 cm/s), and stable locomotion over uneven terrain, resulting in constant coverage without harming the plants. The system provides a scalable solution for precision agriculture, enhancing crop inspection, disease diagnosis, and harvest planning through continuous data insights. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Optimizing supercapacitor electrodes via lithium-induced JahnTeller modulation in CuO
AbstractThe development of advanced electrode materials with superior electrochemical properties is essential to meet the growing demand for efficient energy storage technologies. While surface engineering is common to address this fundamental challenge, the present work shifts the focus from external morphology to internal structural stabilization. Through an integrated experimental and density functional theory (DFT) approach, we demonstrate that a moderate lithium incorporation of 4at. % achieves an optimal balance in CuO properties by suppressing subtle JahnTeller distortions, enhancing crystallite size, narrowing the band gap, and improving both optical and electrical conductivity. X-ray Absorption Spectroscopy (XAS) confirms that Li-ion incorporation increases local symmetry around Cu sites, while EXAFS analysis identifies localized structural disorder associated with dopant substitution. This dual effect stabilizes the CuO lattice while simultaneously creating additional redox-active sites. Electrochemical testing validates this approach, as the optimized 4at. % Li-doped CuO electrode delivers a high specific capacitance of 656F/g at 1 A g?1. The fabricated symmetric supercapacitor device delivers an energy density of ~7Whkg?1 at a power density of ~700Wkg?1, demonstrating the feasibility of Li-doped CuO thin films for supercapacitor applications, although further optimization is required to improve long-term cycling stability. This synergistic experimentaltheoretical framework provides both fundamental insight and practical guidelines for the rational design of doped transition-metal oxides, offering a cost-effective and scalable strategy for next-generation energy storage applications. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Cobalt oxide intercalated graphitic carbon nitride- polyaniline hybrid architecture for supercapacitors
In this study, a graphitic carbon nitride/Cobalt oxide/Polyaniline (g-C3N4/Co3O4/PANI) ternary nanocomposite was synthesized through an integrated approach combining a simple hydrothermal method with in-situ oxidative polymerization. The binary g-C3N4/Co3O4 and g-C3N4/PANI hybrid composites were also synthesized to elucidate the synergistic effects of the individual components. The structural and morphological analysis confirms the successful formation of binary and ternary composites. The porous architecture of g-C3N4/Co3O4/PANI nanocomposite synergistically combines the pseudocapacitive contributions of Co3O4, the conductive pathways of PANI, and the stabilizing role of g-C3N4, resulting in enhanced surface accessibility and improved electrolyte wettability. Strong interfacial interactions, including ?-? conjugation between g-C3N4 and PANI with Co3O4 induced electrostatic stabilization, ensuring considerable mechanical robustness. Electrochemical assessments reveal that the g-C3N4/Co3O4/PANI composite showcased a remarkable specific capacitance of 1152 F g?1 and 93 % capacitance retention over 5000 galvanostatic charge-discharge cycles. The configured asymmetric supercapacitor (g-C3N4/Co3O4/PANI//activated carbon) delivers superior energy and power densities of 59.1 Wh kg?1 and 2693 W kg?1, respectively. The developed nanocomposite represents a significant advancement in hybrid electrode materials, offering substantial potential for next-generation high-performance energy storage systems. 2025 Elsevier Ltd -
Phytoremediated nickel-enriched biochar composite for high-performance supercapacitors
Renewable and sustainable high-performance energy storage devices are essential to meet the needs of next-generation power sources. This study explores the use of the hyperaccumulator Dracaena trifasciata (snake plant) grown in manipulated soil (with Nickel) to explore a cost-effective, sustainable phytoremediation technique for synthesizing high-performance biocarbon electrode material. The synthesized Nickel-Biochar (Ni-Biochar) is treated with acid to enhance its processability and is then combined with an optimal amount of Polyaniline (PANI) to improve charge conductivity. The Ni-Biochar/PANI electrode demonstrates excellent electrochemical performance, with a specific capacitance of 638 F g?1 at 0.5 A g?1 in a three-electrode cell and notable stability, retaining 92 % of its capacity after 10,000 cycles. Additionally, the asymmetric supercapacitor made with Ni-Biochar/PANI achieves a specific capacitance of 163 F g?1 in a 3 M KOH solution. The Ragone plot for this device reveals an energy density of 57 Whkg?1 and a power density of 1259 W kg?1. The device also shows outstanding long-term cyclic stability, retaining 90 % of its capacity after 5000 charge-discharge cycles. This high level of performance underscores the potential of utilizing plants as green carbon sources, which can be combined with various metal oxides and conducting polymers to produce hybrid nanomaterials, making them highly promising for sustainable supercapacitor electrode applications. 2025 Elsevier Ltd -
Amide-enriched pod-based carbon nanospheres for enhancing supercapacitor performance: A value-added approach for solid state supercapacitors
The present work involves the fabrication of symmetric solid-state supercapacitors (SSSCs) using amide-functionalized carbon nanospheres (CNS) derived from Magnolia champaca pods, a bio-waste material. The pods were carbonized at temperatures ranging from 400 C to 1000 C, with CNS at 800 C (MC800) showing best electrochemical performance. The synthesized materials, i.e., MC400, MC600, MC800, MC1000, were characterized by techniques such as FESEM, HR-TEM, FTIR, XRD, Raman spectroscopy, and BET. Amide functionalization, achieved through the use of 2,3,4-trifluoroaniline (TFA), enhanced charge storage capacity by improving ion transport and surface interaction, resulting in the functionalized CNS labeled as MC800/COOH-TFA. The electrochemical investigation of the CNS was studied via techniques such as cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS). The functionalization led to two-fold increase in specific capacitance from 243 Fg?1 to 410 Fg?1 at a current density of 0.25Ag?1 in 3 M KOH. The SSSCs was fabricated using MC800/COOH-TFA with a PVA-KOH gel electrolyte demonstrating a good areal capacitance of 40 mFcm?2 at 1.0 mAcm?2. Moreover, the device exhibited excellent energy density of 5.54 ?Whcm?2 and cycle stability, retaining 71.75 % of its capacitance after 10,000 charge-discharge cycles. The response time of the functionalized sample has been reduced to 2.31 s (MC800/COOH-TFA) from 4.73 s (MC800). These results highlight the potential of amide functionalized CNS in producing efficient, sustainable energy storage devices with improved performance. 2025 Elsevier Ltd -
Biowaste-derived hierarchical activated porous carbon with heteroatom-doping (N/S) for efficient symmetrical supercapacitors: A cow urine approach
The continuous accumulation of biowaste in the environment over extended periods can pose considerable ecological challenges. Hence, the conversion of natural biowaste into value-added products is essential. In this study, for the first time, carbon materials derived from cow urine, an animal waste, were explored as potential electrode materials for supercapacitors (SCs). Hierarchical, highly porous carbonaceous materials containing heteroatoms such as N and S were synthesized using a simple, template-free pyrolysis method, involving the direct carbonization of cow urine as a single precursor at 700 C (CCUR-700) and pre-KOH activation of the resulting cow urine deposit pyrolyzed at 700 C (A-CCUR-700) with a removal of inherent mineral salts. The resulting porous carbon materials were then employed as electrode materials for SC applications. The A-CCUR-700 electrode, with its abundant surface functionalities, high specific surface area (2651.7 m2/g), high porosity, good conductivity, and self-doped heteroatoms (N and S), demonstrated better charge storage performance compared to the CCUR-700 electrode. Notably, a two-electrode symmetric SC assembled using the A-CCUR-700 electrode demonstrated an excellent specific capacitance of 165 F/g at a current density of 0.5 A g?1. Furthermore, the A-CCUR-700 symmetric SC device achieved a high energy and power density of 22.9 Wh/kg and 5100 W/kg, respectively, with a capacitance retention of 95.3 % over 5000 cycles. Overall, the results of this study suggest that the synthesis of functionalized carbonaceous materials from cow urine may open up new possibilities for producing inexpensive electrode materials for electrochemical value-added applications. 2025