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A DSEESIPT-active organic luminogen for turn-on enantioselective recognition of chiral amino alcohols and selective hydrazine sensing
The development of dual-state emissive (DSE) organic luminogens has elevated the ease of recognition of various biological analytes, which demonstrates the multifaceted potential of dual-state emitters. Therefore, in this study, we synthesised a dual-state emissive excited-state intramolecular proton transfer (ESIPT)-based organic luminogen, (E)-4-(5-bromo-2-hydroxybenzylideneamino)-2,3-dimethyl-1-phenyl-1,2-dihydropyrazol-5-one (ANMB), exhibiting excitation-dependent phototunability with large Stokes shifts of 109 nm and 155 nm in both the solution and solid states, respectively, underscoring its potential as a biosensor. The metal-chelating ability of ANMB was investigated, revealing significant fluorescence quenching upon coordination with Cu2+ ions, leading to 96% reduction in emission intensity. The introduction of biological analytes, such as amino alcohols, enabled fluorescence recovery, where ANMB demonstrated enantioselective recognition: a single emission peak for the S-enantiomer and dual emission peaks for the R-enantiomer. Furthermore, ANMB demonstrated high selectivity for hydrazine detection in both the solution and solid states, with new emission bands observed at 411 nm and 432 nm, respectively, indicating a fluorescence shift from green to blue. Complementarily, ANMB was successfully applied for real-time imaging of hydrazine in food and plant samples, showcasing its practical adaptability. Additionally, in silico molecular docking studies were performed, revealing the potential therapeutic activity of ANMB against diarrheal targets. Overall, this work highlights the multifunctionality and tunability of DSEESIPT-based organic luminogens, positioning ANMB as a promising candidate for the selective recognition of biologically significant analytes in analytical and real-world contexts. This journal is The Royal Society of Chemistry, 2026 -
Hormonal-mediated Cicer arietinum L. leaf extract-assisted synthesis of a ternary g-C3N4/ZrTiO4/V2O5 nanocomposite for photocatalytic remediation of organic pollutants
A novel green synthesis approach was developed for the fabrication of a g-C3N4/ZrTiO4/V2O5 nanocomposite (NC) using a hormone-treated plant extract as a biogenic reducing and stabilizing agent. The hormone-assisted synthesis had a significant influence on the physical, chemical, and morphological properties of the nanocomposite compared to the control route. The obtained NCs, confirmed by XRD, FTIR, UV-vis, SEM, and EDX analyses, exhibited enhanced crystallinity, a reduced band gap, and a distinct morphological transformation from nanorods to nanocubic structures. Elemental composition analysis confirmed the successful integration of Zr, Ti, and V components, improving the photocatalytic performance of the material. The hormone-mediated g-C3N4/ZrTiO4/V2O5 NC achieved an 89.14% degradation efficiency of Rose Bengal dye, maintaining its activity over three successive cycles without notable loss of performance. Furthermore, the photocatalyst efficiently converted degradation intermediates, such as benzyl alcohols, into valuable substituted benzaldehyde derivatives with yields ranging from 75% to 92%, demonstrating sustained catalytic stability over four consecutive cycles. These findings highlight the potential of hormone-assisted green synthesis as a promising and sustainable approach for designing advanced photocatalytic nanomaterials. This journal is The Royal Society of Chemistry and the Centre National de la Recherche Scientifique, 2026 -
Nature-inspired ?-MnMoO4nanocubes from Arachis hypogaea for next-generation wastewater treatment and organic pollutant catalysis
In this study, bimetallic ?-MnMoO4 nanoparticles (NPs) were successfully synthesized via a one-step solution combustion method using Arachis hypogaea (peanut) seed powder as a green fuel. This eco-friendly route was adopted to explore the adsorption, photocatalytic, and catalytic properties of the resulting NPs. The structural, morphological, and optical characteristics of ?-MnMoO4 NPs were systematically characterized using XRD, FTIR, UV-vis, and PL spectroscopy, SEM, and EDX techniques. Notably, ?-MnMoO4 NPs demonstrated excellent adsorption capability toward methylene blue (MB) dye, achieving a removal efficiency of 86.70%, which was primarily attributed to their negatively charged surface. Moreover, the nanoparticles demonstrated a remarkable photocatalytic activity, achieving 81.55% degradation of MB through the photo-oxidation of water into hydroxyl (?H) radicals by photogenerated holes. Beyond dye remediation, the study further explored the catalytic capabilities of o-phenylenediamine via oxidative condensation with substituted aromatic aldehydes to synthesize benzimidazole derivatives, achieving yields ranging from 35% to 85%, depending on the substituents used. This integrated approach highlights the potential of ?-MnMoO4 NPs not only in pollutant removal but also in facilitating the green synthesis of high-value chemical products, demonstrating promising applications in environmental remediation and fine chemical industries. This journal is The Royal Society of Chemistry, 2026 -
Molecular energy transfer: utilizing biogenically-synthesized ZnMn2O4 nanoparticles from Arachis hypogaea seeds for photoluminescence, adsorption, and photocatalytic applications
The green synthesis of nanoparticles (NPs) has emerged as a sustainable alternative to conventional chemical approaches, primarily due to the use of phytochemicals as reducing and stabilizing agents. In the present study, bimetallic ZnMn2O4 nanoparticles were synthesized via a green combustion method employing Arachis hypogaea (peanut) seed powder as a natural fuel source. The synthesized ZnMn2O4 NPs were systematically characterized using XRD, FTIR, SEM, BET, UV-Vis, and PL spectroscopy to elucidate their structural, morphological, and optical properties. Distinct bluish-green fluorescence was observed under short-wave UV irradiation (254 nm), enabling their application in latent fingerprint visualization. The multifunctional performance of the ZnMn2O4 NPs was further demonstrated in environmental applications. The materials exhibited enhanced adsorption (63% 0.2%) and photocatalytic degradation (79% 0.3%) efficiencies against Methylene Blue (MB) dye under UV irradiation, with results statistically significant (p < 0.05). In addition, the NPs effectively reduced toxic Cr(vi) ions in aqueous media, highlighting their potential as efficient detoxification agents. Overall, this work demonstrates a novel, green synthesis route for ZnMn2O4 nanoparticles that uniquely integrates environmental remediation and forensic applications. The dual functionality addressing both pollutant degradation/detoxification and forensic fingerprint visualization positions this study as a rare and innovative contribution to the field of nanotechnology. 2025 The Royal Society of Chemistry. -
Green Synthesis of Reduced Graphene Nanostructure from Cinnamomum Camphora
A facile green synthesis for carbon nanoparticle production using Cinnamomum camphora (Camphor) is presented. Camphor upon carbonization and chemical oxidation leads to the formation of nano-carbon structures with lateral size 7.33nm to 4.14nm, respectively. The nanomaterial's stacking height is about 2.76nm and 3.10nm, leading to the formation of about 10 layers of carbon. The AFM analysis confirms that the graphene layer formed is wrinkled or folded. Developments of a layered structure with spheroids are observed on the sample's surface, confirming the graphitization of the amorphous carbon. The relative intensity of the defect to the graphite band is found to be 0.98 for the nanostructure indicating a lesser degree of defects. The C1s band of the nanostructure is deconvoluted to components at 284.7, 286.5, 287.3, and 289 eV, which are assigned to non-oxygenated ring carbon (sp2 carbon), C in C-O (bound to O either as epoxy or hydroxyl), C in C=O (of alcohols, phenols or ether), and C in C(O)O (carboxylic acid) respectively. The study reveals the formation of few-layer oxygenated carbon layers from the botanical hydrocarbon. 2020 by the authors. -
Novel carbon nano-onions from paraffinum liquidum for rapid and efficient removal of industrial dye from wastewater
Carbon nano-onions (CNOs) are fascinating zero-dimensional carbon materials owning distinct multi-shell architecture. Their physicochemical properties are highly related to the parent material selected and the synthesis protocol involved. In the present work, we report for the first time novel CNO structures encompassing discrete carbon allotropes, namely, H18 carbon, Rh6 carbon, and n-diamond. These structures were cost-effectively synthesized in gram scale by facile flame pyrolysis of paraffinum liquidum, a highly refined mineral oil. The as-synthesized and chemically refashioned CNOs are quasi-spherical self-assembled mesopores, manifesting remarkable stability and hydrophilicity. The CNO structures exhibit excellent dye adsorption characteristics with high removal capacity of 1397.35mg/g and rapid adsorption kinetics with a minimal adsorbent dosage of 10mg/L, for a low concentration of 20mg/L methylene blue dye. The novel CNOs assure potential implementation in the remediation of low concentration and high volume of dye-contaminated wastewater. Graphical abstract [Figure not available: see fulltext.] 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
A Novel Nature-Inspired Coconut Tree Optimization Technique forEngineering Applications
Coconut tree optimization technique is a novel optimization algorithm that is motivated by the physical structure of the coconut tree. The search for optima in a feasible region is chosen between a random root and any point in a leaf. Coconut tree optimization is a pseudo meta-heuristic algorithm wherein search of solution is carried out using random as well as gradient movement with a memory stack that contains local optima. Nonlinear optimization problems consisting of equality and inequality constraints were solved using the proposed algorithm. The algorithm is validated for linear and nonlinear optimization problems. The comparative study and analysis were detailed for existing algorithms used in domain-specific physical problems. The algorithm is compared with the genetic algorithm and particle swarm optimization by considering standard test functions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Internet of things based metaheuristic reliability centered maintenance of distribution transformers
The transformer is a vital component of the power system. Continuous stress on the transformer due to overload, transient and faults will lead to physical damages. The isolation of the transformer causes significant revenue loss and inconvenience to the consumers at the distribution level. This invites the need to achieve a reliable power supply to the consumers and to perform maintenance activity appropriately. Optimized and predictive maintenance strategies are evolved to improve power availability for consumers. The model considers dispersive generation at the customer end, namely solar photovoltaics standalone system, diesel generation, and vehicle to load capabilities. Incipient or critical status of transformers' functional parameters are observed through the transformer terminal unit and sent to the internet of things platform. The remote processing unit acquires the information from all the distribution transformer and generates the optimized and reliability-centered maintenance schedule. In the proposed work, new reliability indices concerning the consumer dispersive generation are defined. The maximization of the reliability problem is solved using the coconut tree optimization technique. The highest reliability of power supply to the consumer and maintenance schedule are obtained. Economic facet of the estimated maintenance schedule exhibit benefit for both utility and consumer as it encapsulate time of use tariff. The heuristic dataset is used to synthesize the trained model by the machine learning algorithm and future maintenance schedule is predicted. The comparative study is made for the outcome of time-based optimized and predicted maintenance schedules against reliability. 2020 Institute of Physics Publishing. All rights reserved. -
Deformation Diagnostic Methods for Transformer Winding through System Identification
Transformers play a critical role in the power system. Dynamics of the power system changes if the transformers are out of service for scheduled and unscheduled maintenance work under contingency situations. Faults, overloading, and mechanical abnormalities causes the incipient and critical damages to the transformer. The isolation of transformers leads to the voltage profile change, load curtailments, high compensation, economic loss, and many more problems. It is very important to know the problems occurred in the transformer parts to repair and restore it into the system to attain better stability, reliability, and economics. The transformer health monitoring system consisting of prediction, identification, and diagnostics in online as well as offline mode that will provide sufficient content to the managerial utility to take actions against the problem anticipated or occurred. The heuristic survey inks, the probability of damage in the transformer winding is more compared to the other parts. A novel method using system identification is proposed for the diagnosis of transformer winding. The location and extent of mechanical deformations can be ascertained along with specifically detecting radial and axial deformations in the transformer windings. A system identification approach in frequency and time domain were employed in the diagnostic algorithms for the sweep frequency response dataset. For both transfer function and state space model, a reference table called deformation information tableau has been synthesized for lumped parameter transformer model by varying series and shunt circuit elements systematically. The details of deformation are extracted from the tableau for actual frequency response data for a specified frequency range and winding type. The crosscorrelation of two-dimensional frequency response arrays, one being a signature array and other being deformation array, is used to represent relativity as a singleton. A toolbox is developed for the generation of heuristic deformation information tableau and to diagnose using the diagnostics algorithm developed. The proposed algorithms were verified and simulated for continuous disk type winding. 2019 IEEE. -
Development of Internet of Things Platform and Its Application in Remote Monitoring and Control of Transformer Operation
Internet of Things platforms deployed on the system will exhibit numerous benefits such as real time monitoring, faster operation and cost effectiveness. A system oriented IoT platform is developed which features database connotation, web services, setup portal, cloud hosting, drivers or listener for programming languages and hardware devices. The functional parameters of transformer in electrical power system vary around the limit and beyond, which is observed by the IoT platform for remote analysis and to report deformation in the winding. The frequency response measurement from the transformer terminal unit is send to cloud database which is then fetched to remote application through IoT client. At remote monitoring tool, the diagnostic algorithm is executed to estimate the location and extent of deformation. IoT based frequency response analyzer and transformer diagnostic tools developed reports the status of the transformer health condition. Depending upon the extent of deformation, the transformer is isolated from power system. Springer Nature Switzerland AG 2020. -
Transformer performance enhancement by optimized charging strategy for electric vehicles
Transformer efficiency and regulation, are to be maintained at maximum and minimum respectively by optimal loading, control, and compensation. Charging of electric vehicles at random charging stations will result in uncertain loading on the distribution transformer. The efficiency reduces and regulation increases as a consequence of this loading. In this work, a novel optimization strategy is proposed to map electric vehicles to a charging station, that is optimal with respect to the physical distance, traveling time, charging cost, the effect on transformer efficiency and regulation. Consumer and utility factors are considered for mapping electric vehicles to charging stations. An Internet of Things platform is used to fetch the dynamic location of electric vehicles. The dynamic locations are fed to a binary optimization problem to find an optimal routing table that maps electric vehicles to a charging station. A comparative study is carried out, with and without optimization, to validate the proposed methodology. 2022. The Author(s) -
Internet of Things Based Autonomous Borewell Management System
Water is a basic need for all living beings. At present, due to a large population, water level is getting depleted at an alarming rate particularly in urban region. During summer season, there is no continuous flow of water or availability of water. In electrical contingency situations, bore-wells are prone to damages. The utilization of power at dry run condition affects the economy of the consumers. Despite having no water in the bore-well, if the motor runs, the motor windings may burdened and gives rise to unnecessary power loss. In the present scenario, conservation of energy is a major concern. The conservation of energy as a whole will take place when an individual take an active part by using autonomous and effective methodologies or controllers. The issue is solved by managing the borewell using Internet of Things (IoT) as a platform to automate and manage. The IoT based borewell management system is designed to provision scheduling, manual operation, avoidance of borewell motor running at dry run condition and also nullifies energy loss. The automated borewell operations can be executed from a remote control and measurement unit by the measurement of electrical parameters and analytics. The proposed system minimizes man power, saves time and conserve energy loss. The paper presents operating the conventional borewell by deployment of smart controller which handles the information and communication technology at client and base units. Springer Nature Switzerland AG 2020. -
Optimal Charging Strategy for Spatially Distributed Electric Vehicles in Power System by Remote Analyser
The burden on the consumer for the price of fuel for classic vehicles is the root cause for the emergence of the fast growing trend in the power driven vehicles or electric vehicles. Less acceptance of electric vehicles by the customers and the hesitancy to replace traditional fuel powered vehicles by considering the economic factor is a major concern that existing in the current scenario. Therefore, for the proper balancing of the load with respect to the power available among different neighbouring charging stations in a given area, a load scheduling algorithm is used. The optimal route planner for the electric vehicles reaching the charging station is identified and then the power carried by each feeder is calculated by cumulative power of all the charging stations. The identification of the possible route is performed by the spatial network analysis which will be executing at remote analyzer. The location, state of charge, and other details of the electric vehicle through telemetry is used to find the best charging station for the particular vehicle in view of the cost, distance and the time. The performance of the technique is evaluated with and without optimization by considering the logical constraints; and the results are presented. Springer Nature Switzerland AG 2020. -
Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT
Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized. The Author(s) under exclusive licence to Biomedical Engineering Society 2023. -
Computer simulation of diesel fueled engine processes using matlab and experimental investigations on research engine
The depletion of conventional fuel source at a fast rate and increasing environmental pollution have motivated extensive research in combustion modeling and energy efficient engine design. In the present work, a computer simulation incorporating progressive combustion model using thermodynamic equations has been carried out using MATLAB to evaluate the performance of a diesel engine. Simulations at constant speed and variable load have been carried out for the experimental engine available in the laboratory. For simulation, speed and Air/Fuel ratios, which are measured during the experiment, have been used as input apart from other geometrical details. A state-of-the-art experimental facility has been developed in-house. The facility comprises of a hundred horsepower water cooled eddy current dynamometer with appropriate electronic controllers. A normal load test has been carried out and the required parameters were measured. A six gas analyzer was used for the measurement of NOx, HC, CO2, O2, CO and SOx. and a smoke meter was used for smoke opacity. The predicted Pressure-Volume (PV) diagram was compared with measurements and found to match closely. It is concluded that the developed simulation software could be used to get quick results for parametric studies. Copyright 2017 ASME. -
Impact of user-generated content on purchase intention for fashion products: A study on women consumers in Bangalore
The advent of online media has been instrumental in providing consumers with quick, relevant, and convenient information on products and services. The success of such media has been established for businesses such as tourism, automobile, and consumer electronics- wherein consumers tend to decide on final purchases based on user - generated content (UGC) such as customer reviews and feedback rather than on traditional advertising media. With short lead times, quick turnaround of products, and frequent changes in offerings, the fashion industry is also exploring the use of such user-generated content for marketing its products. This study sought to explore and understand the relevant factors that draw consumers towards the usage of user-generated content (UGC) in the online space for the fashion business, and its impact on the purchase intention for different categories of fashion products. The study focused on the cosmopolitan city of Bengaluru, known for its fashion centricity and brand awareness. It attempted to analyze the factors for reference to media content generated by co-consumers, especially amongst women, and inferred that content that provides them with gratifications relating to social acceptance are more liable to positively influence their intent to purchase. It also specifically identified product categories that are liable to benefit from such content. -
Effective proactive routing protocol using smart nodes system
Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires. 2022 The Authors -
Vitaware-culs 2020 vitamin awareness kit /
Patent Number: 202041002544, Applicant: Erumalla Venkatanagaraju. Rapid urbanization and increase in population have evoked tremendous attention for biofuels production to combat shortage of fuels, environmental concerns, foreign exchange savings and socioeconomic issues. In recent years bioethanol production from agro-industrial wastes acquired a prominent place to fulfil the gap between production and demand. -
Process for generation of bioethanol from mannuronic acid and guluronic acid /
Patent Number: 202041002544, Applicant: Erumalla Venkatanagaraju.
Rapid urbanization and increase in population have evoked tremendous attention for biofuels production to combat shortage of fuels, environmental concerns, foreign exchange savings and socioeconomic issues. In recent years bioethanol production from agro-industrial wastes acquired a prominent place to fulfil the gap between production and demand. -
Bioparametric Investigation of Mutant Bacillus subtilis MTCC 2414 Extracellular Laccase Production under Solid State Fermentation
This work has been undertaken to investigate the bio parameters such as various substrates, initial moisture level, inoculum size, pH, incubation temperature, incubation period, metal ions and nitrogen sources effect on the production of laccase in solid-state fermentation using mutant Bacillus subtilis MTCC 2414. The laccase production was observed with a sesame oil cake (183.32 0.29 U/g), initial moisture level 80% (189.28 0.52 U/ g), inoculum size 1.5% (196.12 0.26 U/g), initial pH 8 (215.20 0.48 U/g), incubation temperature 37C (225.80 0.52 U/g), incubation period 48h (258.80 0.29 U/g), CuSO4 (263.16 0.12 U/g) and yeast extract (268.14 0.16 U/g) in the production medium. 2018, Association of Biotechnology and Pharmacy. All rights reserved.