Developing cost-effective and durable trimetallic electrocatalysts is crucial for efficient electrochemical water splitting. Herein, we report an efficient electrocatalyst of the carbon nanotube-supported trimetallic Ni–Al–Ti nanocomposites (CNT–NAT NCs) fabricated using a simple ultrasonication-assisted assembly of co-precipitated NAT nanoparticles and CNTs. The CNT–NAT NCs exhibit an interconnected architecture, where NAT nanoparticles are uniformly anchored on a conductive CNT network, providing a high specific surface area of 60 m2/g. The CNT–NAT NCs show excellent hydrogen-evolution reaction activity in 1 M KOH, delivering an overpotential of 86 mV at 10 mA/cm2 with a Tafel slope of 64 mV/dec and long-term stability. For the oxygen evolution reaction, the catalyst achieves a low overpotential of 225 mV at 10 mA/cm2 and a Tafel slope of 54 mV/dec and excellent long-term stability in 1 M KOH. When employed as both electrodes in a two-electrode configuration, the CNT–NAT NC electrolyzer operates at a low cell voltage of 1.55 V and sustains stable water splitting for 100 h. The enhanced bifunctional performance arises from the synergistic combination of the highly conductive CNT framework and the abundant active sites of the Ni–Al–Ti nanocomposites, demonstrating the promise of CNT–NAT NCs for efficient water electrolysis.

The healing of large segmental bone defects remains a major challenge in regenerative medicine, prompting the development of biomimetic scaffolds that exhibit osteoconductivity, biocompatibility, bioactivity, and biodegradability similar to native bone tissue. In this study, a flexible electrospun nanofiber scaffold composed of bovine bone-derived strontium-doped hydroxyapatite (Sr-HAp), polymethylmethacrylate (PMMA), and cellulose acetate (CA) was fabricated to replicate the natural extracellular matrix of human bone. Sr-HAp was synthesized through thermal degradation of bovine bone followed by incorporation of strontium nitrate, and the resulting nanoparticles were uniformly embedded in the PMMA/CA nanofiber network using electrospinning. Morphological and structural analyses confirmed that Sr-HAp was well dispersed within the fibers, maintaining a crystalline structure without aggregation. The addition of CA enhanced the hydrophilicity and water absorption capacity of the scaffold, leading to improved swelling and degradation behaviour. Moreover, the Sr-HAp/PMMA/CA nanofiber scaffold exhibited significant cytotoxic effects against human prostate cancer (PC-3) and epidermoid carcinoma (A431) cell lines, with inhibition rates of 34.43 % and 61.65 % at 200 μg/mL, respectively. Further, the cytocompatibility of the Sr-HAp/PMMA/CA nanofiber scaffold against the human embryonic kidney (HEK293) cell line shows a non-toxic nature with the cytocompatibility of 94.9 % at 200 μg/mL. Overall, the biogenic Sr-HAp/PMMA/CA electrospun nanofiber scaffold demonstrates excellent physicochemical and biological properties, highlighting its potential as a promising material for tissue regeneration applications.

We report a highly sensitive and interference-free electrochemical sensor for dopamine (DA) detection in the presence of uric acid (UA) and ascorbic acid (AA), based on an in situ deposited graphitic carbon nitride (g-C3N4) and polymethyl thymol blue (PMTB) nanohybrid modified screen-printed carbon electrode (SPCE). The as-fabricated g-C3N4/PMTB/SPCE was thoroughly characterized using various physicochemical techniques. The electrochemical behavior of the modified electrode was systematically investigated by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The g-C3N4/PMTB/SPCE exhibited excellent electrocatalytic activity toward the selective oxidation of DA under optimized experimental conditions, including pH and scan rate. Interference-free detection of DA in the presence of AA and UA was achieved using DPV and chronoamperometric methods, revealing a wide linear concentration range, an ultralow limit of detection, and high sensitivity. Furthermore, the practical applicability of the proposed sensor was validated by determining DA in artificial biofluid samples, including blood serum, and urine. The recovery results obtained good agreement with those obtained using high-performance liquid chromatography (HPLC), confirming the reliability and accuracy of the developed sensing platform.

During recent decades, bone cancer-related diseases have remained hard to treat because of poor diagnosis, systemic toxicity, and restricted conventional treatments. Hence, the fabrication of functionalised nanoparticles offers a promising alternative by limiting side effects and improving therapeutic outcomes. In this study, zinc-substituted hydroxyapatite (Zn-HA) nanoparticles were fabricated from biogenic tuna fish bone waste via a thermal decomposition method and subsequently functionalised with Catharanthus roseus (CR) flower extract to synthesise a Zn-HA/CR nanocomposite. Structural and compositional characterisations verified Zn ions incorporation into the HA lattice and efficient CR-derived phytochemical functionalisation without altering the hexagonal HA phase. Compared to pure hydroxyapatite, the Zn-HA/CR nanocomposite exhibited improved surface morphology, enhanced swelling behaviour and degradation, and increased microhardness. The nanocomposite demonstrated significantly enhanced antibacterial activity against Staphylococcus aureus and Escherichia coli. The Zn-HA/CR nanocomposite also showed strong, dose-dependent antioxidant activity in DPPH assays. Furthermore, in vitro cytotoxicity studies using MG-63 (HOS) osteosarcoma cancer cells revealed that the proposed nanocomposite leads to pronounced morphological alterations and reduced cell viability. The prepared Zn-HA/CR nanocomposite would be a potential nanocomposite for enhanced antioxidant and anticancer activity, which highlights this composite as a multifunctional biomaterial platform for therapeutic applications.

Developing inexpensive, durable, and high-performance trimetallic catalysts is critical for producing hydrogen and oxygen through electrocatalytic water splitting. In this study, activated carbon-decorated trimetallic Ni-Al-La (AC-TM) nanocomposites were synthesized via a facile ultrasonication process using coprecipitation-derived TM nanoparticles and pyrolytically fabricated biomass AC nanosheets. The resulting AC-TM nanocomposites exhibited a microstructure consisting of AC-anchored TM nanoparticles with a high specific surface area of 64 m2/g. For the oxygen evolution reaction (OER), the AC-TM catalyst demonstrated excellent electrocatalytic activity, achieving a low overpotential of 180 mV and a Tafel slope of 52 mV/dec, alongside outstanding stability at 10 mA/cm2 in 1 M KOH. Similarly, for the hydrogen evolution reaction (HER), the AC-TM catalyst exhibited a low overpotential of 195 mV and a Tafel slope of 93 mV/dec with good durability. Consequently, the AC-TM nanocomposites delivered exceptional overall water-splitting performance, requiring a low full-cell voltage of 1.57 V at 10 mA/cm2 and maintaining long-term durability for up to 100 h. The remarkable performance of the AC-TM nanocomposites can be attributed to the synergistic effect of the high conductivity of the AC nanosheets and the enhanced catalytic active surface area provided by the TM nanoparticles. These findings indicate that AC-TM nanocomposites are highly suitable as bifunctional electrocatalyst materials for efficient water splitting.

Spinel-type transition metal oxide-based catalysts are crucial for efficient electrocatalytic oxygen and hydrogen generation. Therefore, in this study, zinc manganite (ZnMn2O4) nanostructures were synthesized using two different reducing agents, including sodium carbonate (Na2CO3) and sodium hydroxide (NaOH), through the coprecipitation method. When ZnMn2O4 was synthesized using NaOH, the sample exhibited a morphology with the aggregated and stacked nanobundles. In contrast, the Na2CO3-derived ZnMn2O4 sample demonstrated an interconnected and agglomerated spherical nanoparticle structure. For the oxygen evolution reaction, the spherical ZnMn2O4 nanoparticles exhibited the exceptional electrocatalytic performances, with an overpotential of 110 mV and a low Tafel slope of 47 mV/dec, showing excellent durability at 10 mA/cm2 in an alkaline electrolyte. For the hydrogen evolution reaction, the spherical ZnMn2O4 nanoparticles indicated a low overpotential of 158 mV and a Tafel slope of 120 mV/dec, with excellent stability at -10 mA/cm2. These findings suggest that the spherical ZnMn2O4 nanoparticles are effective electrocatalysts for highly efficient watersplitting.

Carbon-based metal-free catalysts, particularly those such as biomass-derived mesoporous activated carbon (AC) nanostructures, hold great promises for cost-effective and sustainable electrocatalysis for enhancing hydrogen evolution reaction (HER) performance in green energy technology. Neem and ginkgo leaves are rich in bioactive compounds and self-doping heteroatoms with naturally porous structures and act as a low-cost, sustainable biomass precursors for high-performance HER catalysts. In this study, mesoporous AC nanoflakes and nanosponges were synthesized using biomass precursors of neem and ginkgo leaves through a KOH activation process. Notably, AC nanosponges derived from ginkgo leaves exhibited outstanding physicochemical characteristics, including a sponge-like porous morphology with a large specific surface area of 1025 m2/g. For electrochemical evaluation in 0.5 M H2SO4, the G-AC sample revealed superior electrocatalytic HER performance, with a remarkably low overpotential of 26 mV at -10 mA/cm2, a small Tafel slope of 24 mV/dec, and long-term durability over 30 h. These results depict biomass-derived mesoporous AC nanosponges to hold substantial potential for highly efficient hydrogen production, contributing significantly to the advancement of eco-friendly energy solutions.

Transition-metal dichalcogenides have emerged as promising non-noble-metal electrocatalysts for efficient hydrogen production through the hydrogen evolution reaction (HER). In this work, we fabricated the graphitic carbon nitride-decorated cobalt diselenide (gC3N4-CoSe2) nanocomposites via the facile hydrothermal method. The prepared gC3N4-CoSe2 nanocomposites displayed an interconnected and aggregated morphology of gC3N4-decorated CoSe2 nanoparticles with offering large surface area of 82 m2/g. The gC3N4-CoSe2 nanocomposites exhibited excellent HER activity with a low overpotential (141 mV) and tiny Tafel slope (62 mV/dec) with excellent durability for 100 h at 10 mA/cm2 in an alkaline electrolyte. These outstanding HER performances of gC3N4-CoSe2 can be ascribed to the synergistic interaction between the electrochemically active porous CoSe2 nanoparticles and the highly conductive gC3N4 nanosheets. These results indicate that the gC3N4-CoSe2 nanocomposites hold promising and efficient HER electrocatalysts for sustainable green hydrogen production.

Foodborne illnesses remain a global challenge, requiring rapid and sensitive detection platforms. We developed a magnetosome-based electrochemical immunosensor for lipopolysaccharide (LPS) antigens from Escherichia coli and Salmonella typhimurium. Magnetosomes isolated from Magnetospirillum sp. RJS1 were characterized by HR-TEM and functionalized with antibodies (2 CFU mL−1), with FTIR confirming successful conjugation. The antibody–magnetosome complexes were immobilized on a chitosan/glutaraldehyde-modified glassy carbon electrode. AFM revealed globular (200–700 nm) and island-like (1–3 µm) features after antigen binding. Electrochemical impedance spectroscopy showed stepwise increases in charge-transfer resistance upon electrode modification and antigen interaction. The sensor exhibited high sensitivity toward E. coli (3–7 CFU mL−1) and Salmonella (3–8 CFU mL−1), achieving an immune sensitivity of 36.24 Ω/CFU mL−1 and a detection limit of 1 CFU mL−1. These results demonstrate the potential of magnetosome-based immunosensors as portable, efficient platforms for the rapid detection of foodborne pathogens in real samples.

Current investigations into the fabrication of innovative biomaterials that stimulate cartilage development result from increasing interest due to emerging bone defects. In particular, the investigation of biomaterials for musculoskeletal therapies extensively depends on the development of various hydroxyapatite (HA)/sodium alginate (SA) composites. Cuttlefish bone (CFB)-derived composite scaffolds for hard tissue regeneration have been effectively illustrated in this investigation using a hydrothermal technique. In this, the HA was prepared from the CFB source without altering its biological properties. The as-developed HA nanocomposites were investigated through XRD, FTIR, SEM, and EDX analyses to confirm their structural, functional, and morphological orientation. The higher the interfacial density of the HA/SA nanocomposites, the more the hardness of the scaffold increased with the higher applied load. Furthermore, the HA/SA nanocomposite revealed a remarkable antibacterial activity against the bacterial strains such as E. coli and S. aureus through the inhibition zones measured as 18 mm and 20 mm, respectively. The results demonstrated a minor decrease in cell viability compared with the untreated culture, with an observed percentage of cell viability at 97.2% for the HA/SA nanocomposites. Hence, the proposed HA/SA scaffold would be an excellent alternative for tissue engineering applications.

Open Cycle -Ocean Thermal Energy Conversion (OC-OTEC) is one of the most important renewable energy sources that generate electricity and fresh water from seawater utilizing the temperature gradient between the warm surface seawater and the cold deep seawater. This paper aims to develop non-linear data-driven model-based adaptive Feedback Control (FBC) schemes for the OC-OTEC process to track the output power and a dy-namic Feed-Forward Control (FFC) scheme to reject the effects of temperature disturbance on power caused by climate variations in OC-OTEC. The experiments are conducted on a laboratory-scale OC-OTEC experimental setup at the National Institute of Ocean Technology, Chennai. Firstly, linear data-driven models are developed using system identification techniques. Based on the developed models, gain scheduling-based adaptive control schemes are developed: Proportional Integral (PI) control and Model Predictive Control (MPC). Secondly, the closed-loop performances of the developed FBC schemes are analysed under servo and regulatory operations. Furthermore, it is observed from the sensitivity analysis results that the temperature disturbance highly in-fluences output over the manipulated variable. Hence, a dynamic FFC scheme is implemented to reject known disturbance of 1 degrees C variation in temperature gradient from 18 degrees C to 19 degrees C due to Sea Surface Temperature (SST) changes in temperature gradient. The results show that the proposed MPC-based FBC-FF scheme effectively enhances the tracking and disturbance rejection performance compared to the PI-based FBC-FF control scheme with a minimum Integral Square Error (ISE) of 3.055 and Control Effort (CE) of 7.505. This experimental data-based, data-driven modelling and adaptive control research provides a new direction for automating OC-OTEC. Further, these studies will help to develop a rugged and automated upscaling OC-OTEC plant control system with proposed feasible control schemes.

Electric Vehicles (EV) have gained popularity in recent years to reduce the amount of greenhouse gas emissions and utilize renewable energy sources more effectively. Fast charging of Lithium-Ion batteries (Li-Ion) in EV is a serious issue affecting battery life. The main objective of the proposed work is to develop a Hybrid Electro-Thermal Model (H-ETM) using multiple model approach to generate an optimal charging profile to enhance State-of-Health (SoH) of Li-Ion based on Multi-Objective Genetic Algorithm (MOGA). The hybrid model is developed by integrating four local models based on a multi-model approach to improve accuracy. For the dataset collected from real battery, a single model over the entire SoC range can provide terminal voltage accuracy of ±10 mV and the proposed multi-model approach yields an improved accuracy of ±5 mV. Further, the optimal current profiles under varying weight coefficients for charging time and temperature rise are generated using the proposed H-ETM. The suggested strategy's Pareto fronts are used as references to alter charging current rate to further satisfy diversified user demands, particularly for charging speed and temperature fluctuations in different charging applications. The proposed method provides more feasibility to select optimal charging patterns based on the requirements of the user by taking the trade-off between charging time and internal battery temperature rise while maintaining the constraints in state-of-charge, charging current, internal temperature rise, and charging time

This paper aims at the development of advanced process control technique that aids for the accurate on-line measurement of biomass concentration. Control of bioprocess is a challenging task mainly due to the nonlinearity of the process, complex nature of microorganisms, variations in critical parameters such as temperature, pH and agitator speed. In this study, the Event Triggered Feed Forward Control (ET-FFC) scheme is proposed and developed in order to diminish the effects of temperature, pH and DO variations during Escherichia coli ( E.coli ) K-12 fed-batch. Initially, the data are collected from the laboratory scaled 3L bioreactor setup under Fed-batch operating condition and data driven models are developed using system identification techniques. Then Proportional Integral (PI) and Model Predictive Controller (MPC) feedback controllers are designed to control biomass concentration by varying feed rate of substrate and their performances are compared. To suppress the effect of known disturbances due to critical parameters, an Event Triggered Feed Forward Control (ET-FFC) is designed to change the control action when the event is detected. The closed loop performance of PI and MPC based ET-FFC are obtained in simulation and compared. The results reveal that the proposed MPC based ET-FFC scheme enhances the biomass yield. Also, the proposed control scheme helps to reduce the frequency of communication between controller and actuator which leads to reduction in power consumption.

Precision medicine and personalized treatment approaches are imperative for effectively managing neurological disorders such as Epilepsy, Parkinson's disease, and Mental Depression. Deep brain stimulation is one of the important treatment techniques to suppress epilepsy for drug-resistant epilepsy. Advances in computational modeling and control schemes have received more attention for the development of patient-specific treatment of these diseases. For the effective treatment of these diseases, the design of smart deep brain stimulators (SDBS) with patient-specific models based on individual pathological conditions becomes essential. This paper focuses on developing learning-based control schemes for SDBS to suppress epilepsy using the Hodgkin–Huxley (HH) conductance-based single-neuron model of the brain and real-time validation of the control algorithms using Controller Hardware-in-the-Loop (CHIL). Firstly, patient-specific linear models are developed using a single-neuron cellular model. Secondly Reinforcement Learning (RL) based control schemes namely RL, RL-based Proportional Integral (RL-PI) controller, and RL-based Model Predictive Controller (RL-MPC) are developed for SDBS using Deep Deterministic Policy Gradient (DDPG) algorithm for epilepsy suppression. Finally, the developed control schemes are validated using CHIL with OP4200 real-time simulator and Arduino Mega 2560. The results show that RL-MPC outperforms other controllers and suppresses epilepsy effectively by providing optimal stimulation based on individualized pathological conditions.

Bismuth titanate (Bi 4 Ti 3 O 12 ) thin films were deposited on a platinized silicon (Pt/Ti/SiO 2 /Si) substrate using a spin-coating technique; they exhibited an excellent dielectric constant of 4228 and a tangent loss of 0.074.

This present study deals with the facile synthesis of Zinc (Zn), Strontium (Sr) Co‐substituted hydroxyapatite (M‐HAP)/lignin composite for biomedical applications. For composite formation, HAP was isolated from egg shell through wet precipitation method, biopolymer of lignin was extracted from neem wood through organosolve technique then HAP composites (HAP, HAP/lignin, Zn‐HAP/lignin, Sr‐HAP/lignin, Zn, Sr‐HAP/lignin) were obtained using freeze drying performance. The synthesized HAP composites were characterized using X‐ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and Energy‐dispersive X‐ray analysis (EDAX). The antibacterial behavior of the HAP composites was investigated against E. Coli, B. Subtilies, P. Aeruginosa and S. Aureus pathogens. Then the mechanical property of the HAP composites was carried out using Vicker's microhardness test which reveals a better improvement in mechanical hardness. The antibacterial activity confirms that the HAP composite exhibits enhanced antibacterial activity. Further, the cell viability analysis shows that the co‐substitution of Zn and Sr in HAP lattice improved cell viability analysis, which indicates that the obtained Zn, Sr co‐substituted HAP/lignin biocomposite acts as a potential biomaterial for better biomedical application.

Open cycle-ocean thermal energy conversion (OC-OTEC) is identified as an eco-friendly, self-powered desalination technology in tropical islands for harnessing freshwater from seawater. Automation of OC-OTEC is a major challenge due to the influence of sea surface temperature (SST) variations on its performances. This paper focuses on the development of multi-input multi-output model-based decentralized feedback control (DFBC) scheme with predictive disturbance-based feed-forward control (FFC) for laboratory-scale OC-OTEC plant for tracking and disturbance rejection. Experimental data were collected from a Realtime-setup and data-driven models were developed for process and disturbance. Similarly, daily temperature dataset of Kavaratti Island from European centre for medium-range weather forecasts was used for SST prediction using long short-term memory model and analysis of its robustness was done against sampling uncertainty. DFBCs such as proportional-integral (PI) controller and model predictive controller (MPC) were developed for tracking power and freshwater flow rate through regulation of warm water Flowrate and cold water flow rate. Prediction based FFC scheme with non-causal dynamics was developed with strong disturbance rejection capability. The closed-loop performances of the proposed control schemes results reveal that the decentralized MPC-FFC outperforms PI-FFC, with minimum integral square error of 0.355 (WW loop) and 0.31 (CW loop) while compromising the control effort of 575.3 (WW Loop) and 552.1 (CW Loop).

The oil and gas industries are actively seeking eco-friendly, oil-based drilling muds to enhance drilling performance. This paper investigates the suitability of invert emulsion fish oil-based drilling mud (IEFOBDM) through rheological parameters study in high-pressure, high-temperature wells and automates the prediction of rheological parameters using artificial neural networks (ANN). The IEFOBDM sample, with oil-water ratio of 70:30 was used to collect experimental data at different temperatures (40°C to 80°C) using Model 800 8-speed rotational viscometer after aging of 16hrs at 100°C.Further, the developed conventional and ANN models for predicting rheological parameters were analysed for their performance.

Battery is one of the major components of electric vehicles, which highly influences the performance of Electric Vehicles (EVs). However, enhancing the life of a Lithium-ion (Li-ion) battery is a challenging task because they have a high risk of fire hazards due to the electrochemical properties of Li-ion. Various key factors that have a significant influence on the health of the battery include a number of charge-discharge cycles, temperature, voltage, and current profiles. Hence, a highly efficient Battery Management System (BMS) with an optimal charging facility is needed. The main objective of the proposed work is to generate Multiple Hybrid Artificial Intelligence (MHAI)-based optimal charging current profiles for Li-ion batteries with minimum charging time and temperature rise in order to enhance the State Of Health (SOH). In this work, an open-source dataset of Li-ion-18650 from the National Aeronautics and Space Administration (NASA) was used. Firstly, the battery charging profile range from 0 to 100% is divided into 4 groups (0-25%, 26-50%, 51-75%,76-100%), and four hybrid AI models are developed and validated. to find the optimal charging current in each of the 4 regions instead of using a single AI model for the entire charging profile. For model development, temperature, maximum chargeable capacity, and charging time are considered as outputs, and charging voltage and current are taken as inputs. Long Short Term Memory (LSTM), Random Forest (RF), and Coulomb Counting (White Box Model) are used to develop models to predict temperature, maximum chargeable capacity, and charging time, respectively. Finally, Particle Swarm Optimization (PSO) is used to find the optimal current value for the developed models to minimize both the charging time and temperature rise using the weighted-sum method of the MultiObjective Particle Swarm Optimization (MOPSO) technique. The results show the feasibility of the proposed approach.

The development of renewable and efficient electrocatalysts for hydrogen evolution reaction (HER) is energetic for clean and green hydrogen production. Herein, we prepared the nanocomposites of narthan-gai leaves-derived activated carbon decorated-NiO (NiO/AC) by using the deep eutectic solvents (DES) process. The prepared nanocomposites showed an aggregated structure of AC-decorated NiO nanoparti-cles with a high specific surface area (118.6 m2/g) and large porosity. When utilizing the NiO/AC nanocomposite as an HER catalyst, the catalyst showed an excellent HER electrocatalytic behavior i.e., low overpotential (385 mV), very lower Tafel slope (127 mV/dec), and long-term stability. These owing HER features are considered accrediting to the coactive properties of the material active surface of NiO nanoparticles and the high conductivity of the AC nanoflakes. The results suggested that the DES-supported synthesized NiO/AC nanocomposites could play a significant role in electrocatalytic perfor-mance for wastewater treatment. (c) 2023 Elsevier B.V. All rights reserved.