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Economic Analysis, Environmental Impact, Future Prospects and Mechanistic Understandings of Nanosensors and Nanocatalysis
It is crucial to understand the economic importance of sensors and catalysis. Economy always plays a major role in the field of nanotechnology. The ever-growing industrial revolution raises many concerns to understand the phenomena and to develop inexpensive devices for sensing applications. However, manufacturing such devices have caused a severe impact on environment. Thus, it is a requirement to understand the mechanistic aspects and also future prospects of nanosensors and catalysis to achieve sustainable technologies for the future. 2023 selection and editorial matter, Anitha Varghese and Gurumurthy Hegde; individual chapters, the contributors. -
Laccase mediated electrosynthesis of heliotropin on mango-kernel derived carbon nanosphere composite: A sustainable approach
Facile fabrication of enzyme immobilized carbon nanospheres (CNS) based catalysts with high electrical conductivity and catalytic efficiency are of decisive importance for their electrocatalysis. A novel, green and highly efficient synthesis route for the development of an electrode surface with enhanced electrical conductivity and better catalytic activity for the electrochemical synthesis of heliotropin. The obtained biowaste (mango seed kernels) was pyrolyzed and subjected to acid treatment to form functionalized CNS (f-CNS). The functionalized carbon fiber paper (CFP) electrode was employed as a template for laccase immobilization which was further treated with free laccase resulting in the formation of Lac-fCNS/CFP electrode. The developed electrode exhibited excellent electrooxidation of piperonyl alcohol in the presence of 2,2,6,6-Tetramethyl-1-piperidinyloxy (TEMPO), which served as the mediator. A high yield (78%) of heliotropin was achieved during the electrooxidation at 0.78 V via bulk electrolysis. The obtained product (heliotropin aka piperonal) was confirmed via 1H NMR and 13C NMR. Additionally, computational molecular docking analysis of f-CNS:laccase composite showed strong binding affinity (?6.2 kcal/mol) with TEMPO in comparison with free laccase (?5.1 kcal/mol). The excellent selectivity and efficiency of the developed electrocatalyst aim to surpass all other reported laccase-TEMPO mediated based electrocatalytic oxidation reactions. 2022 Vietnam National University, Hanoi -
A novel laccase-based biocatalyst for selective electro-oxidation of 2-thiophene methanol
An effective biocatalyst was fabricated for TEMPO-mediated electrooxidation of 2-thiophene methanol. Laccase obtained from Trametes versicolor was covalently immobilized onto electrochemically polymerized ortho-amino benzoic acid (PABA) layer on carbon fiber paper (CFP) electrode. The composite material was characterized by Fourier transformed infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), Optical profilometry (OP), and scanning electron microscopy (SEM). Electrochemical parameters were studied using cyclic voltammetry (CV). Moreover, the developed biocatalyst (Lac-PABA/CFP) was used for selective conversion of 2-thiophene methanol to 2-thiophene carboxaldehyde using 2,2,6,6-Tetramethyl-1-piperidinyloxy, free radical (TEMPO) as a mediator. The formation of the product was confirmed via FTIR, GCMS, 1HNMR and 13CNMR. The enzyme activity of free and immobilized laccase was studied using 2, 2?-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) substrate at optimal conditions. Computational In silico analysis also suggested the presence of active sites (T2/T3 trimeric sites-copper ions) in laccase (PDB id: 1KYA's) interacting amino acid residues with the TEMPO and 2-thiophene methanol. Additionally, molecular dynamics simulations revealed that 2-thiophene methanol as compared to TEMPO is more stable (better RMSD, RMSF) in interacting with laccase specifically having strong interaction residues at Asp206, Glu242, Gly262, Gln293, and Glu302. Furthermore, the proposed strategy was confirmed by assessing the various interactions using computational tools. This work would be highly beneficial to develop an electrocatalyst for effective synthesis of 2-thiophene carboxaldehyde, a common intermediate in pharmaceutical, agrochemical, dye, fertilizer and chemical industries. 2021 -
Waste elimination to porous carbonaceous materials for the application of electrochemical sensors: Recent developments
World is expected to face a big disaster from waste materials generated as a result of the ever-growing industrialization. Recent developments in the area of biomass-derived materials and valuable products has attracted many into its diverse applications. Biomass-derived porous carbonaceous materials are highly recommended for the development of electrochemical sensors due to their unique features like cost-effectiveness, distinctive structure, sustainability, and regenerative nature. The electrochemical and catalytic activity of the sensor differ based on their surface morphology specifically surface area, pore-volume, and pore size. Various techniques like activation, doping, and dispersion of metal nanoparticles are efficient in enhancing the performance of sensors. Some of the essential or seminal developments in the area of biomass-derived carbonaceous materials for detecting diverse target analytes like pharmaceutical drugs, metal ions, biomolecules, food additives, pollutants, and flavonoids are reviewed. 2020 Elsevier Ltd -
A sustainable non-enzymatic approach for determination of cholesterol using piper nigrum derived porous carbon/?-Fe2O3 composite electrode
Activated porous carbon (APC) obtained from Piper nigrum along with ?-Fe2O3 have been used to modify carbon paste electrode (CPE) for the highly sensitive and selective electrochemical determination of cholesterol. The enhanced synergistic properties observed between the biomass-derived APC and ?-Fe2O3 uplifts the electrocatalytic activity of the modified electrode (APC-Fe2O3/CPE). The prepared ?-Fe2O3 nanoparticles were characterised by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Dynamic light scattering (DLS), zeta potential measurements and Thermogravimetric analysis (TGA). High resolution transmission electron microscopy (HRTEM), Field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) and electrochemical techniques were used to study the physico-chemical properties of the modified electrodes. Experimental conditions such as effect of pH, scan rate and concentration of cholesterol were optimized. Wide linear dynamic range between 25 nM and 300 nM, low limit of detection (LOD) and limit of quantification (LOQ) of 8 nM and 26 nM respectively make the method very effective and sensitive. Cholesterol in human blood serum samples was non-enzymatically determined using the developed method. 2021 The Electrochemical Society. -
?-cyclodextrin functionalized graphitic carbon nitride as a promising electrocatalyst for the selective oxidation of Tetrahydrofurfuryl alcohol
Selective electrochemical conversion of Tetrahydrofurfuryl alcohol (THFA) was facilitated employing ??cyclodextrin (??CD) functionalized graphitic carbon nitride (GCN) based nanocomposite. The GCN bulk were obtained by subjecting melamine to pyrolysis and further the material was exfoliated to improve its optoelectronic properties. Non-covalent attachment of ??CD over GCN via ultrasonication creates reactive surface sites on the electrode (??CD/GCN/CFP) facilitating a better host-guest interaction. The cyclic voltammetry and chronoamperometry techniques were employed to investigate the reaction mechanism (qualitative) and kinetics (quantitative) respectively of 2,2,6,6-Tetramethyl-1-piperidinyloxy (TEMPO) mediated electrochemical oxidation of THFA. Further, on subjecting the reaction mixture to bulk electrolysis, the desired product was isolated with yield of 78%. The enhanced efficacy, stability and repeatability of the developed heterogenous catalyst aims to surpass all other conventional synthesis of Tetrahydrofurfural (THFF). 2022 Elsevier Ltd -
Conducting Polymers: A Versatile Material for Biomedical Applications
Conducting polymers (CPs) are organic polymers with metallic conductivity or semiconducting properties which have drawn considerable attention globally. They are versatile materials because of their excellent environmental stability, electrical conductivity, economic importance as well as optical and electronic properties. CPs are interesting because they can be functionalized in several ways and the chemical properties are fine-tuned by incorporating new functionalities, making them more suitable in biomedical and other applications. They act as appropriate mediums of biomolecules and can be employed to improve the speed, stability, and sensitivity of various biomedical devices. They can transit between conducting and semiconducting states and have the ability to change mechanical properties by regulated doping, chemical modifications, etc. In this paper, we review the potential biomedical uses of conducting polymers such as smart textiles, bioactuators, hydrogels, and the use of CPs in neural prosthetic devices. 2022 Wiley-VCH GmbH. -
Large Language Models in Economic Forecasting: A Comprehensive Analysis of Predictive Performance and Benchmarking Against Traditional Methods for India FY 2025-26
This study presents a comprehensive systematic evaluation of the performance of Large Language Models (LLMs) in economic forecasting, specifically examining their ability to predict key Indian macroeconomic indicators for the fiscal year 2025-26. Through a comparative analysis of ten prominent LLMs against traditional econometric models and expert forecasts from leading institutions, we assess the forecasting accuracy, reliability, and practical limitations of these models using a rigorous multistage validation framework. We validate predictions using actual quarterly data from Q1 and Q2 of FY 2025-26, providing a real-time assessment of forecasting capabilities with bootstrap confidence intervals and time series cross-validation techniques. Results reveal significant variations in LLM performance, with validation against Q1 2025-26 actual GDP growth of 6.7 per cent showing that several LLMs achieved superior accuracy (MAPE less than 3 per cent) compared to traditional ARIMA models (MAPE 13.58 per cent). Top-performing LLMs demonstrate forecasting capabilities that approach expert-level accuracy while maintaining computational efficiency and scalability. Statistical significance tests using the Diebold-Mariano framework confirm the superiority of ensemble LLM approaches over individual traditional methods. The findings demonstrate that leading LLMs can serve as valuable supplementary forecasting tools, positioning between conventional statistical methods and expert analysis in terms of accuracy, while offering advantages in processing qualitative information and adaptation to structural changes. 2025 IEEE. -
Artificial Intelligence: A Catalyst for Change in the Indian Automobile Industry
AI is becoming a major game-changer economically and technologically across various sectors in the world. The Indian automotive industry is one such area of development. This paper discusses AIs impact on Indian automotive sector right from supply chain management, boost in production, smart AI systems through predictive maintenance, customization capabilities and development of autonomous vehicles. The Indian automotive industry is one such industry that greatly adds to the countrys GDP and employment, but at the same time presents challenges in terms of infrastructure, logistics and changing consumer needs that AI can address. With the advent of campaigns like Make in India and Digital India, India seeks to position itself as one of the leading figures in international production, and for this, the adoption of AI measures seems of strategic importance as this will facilitate productivity growth, competitiveness and meeting the aims of sustainable development (Aggarwal et al. in J Technol Forecast Soc Change 170, 2021 [1]; Chui et al. in AI adoption and economic growth: The case of India. McKinsey Global Institute, 2022 [6]). Through case studies of Indian companies and new startups using AI technologies, this research focuses on how AI can tackle complex supply chains, cut production costs and satisfy consumer expectations for going green, safety and personalization. At the same time, AI usage in India has its own challenges such as expensive introduction, lack of skilled labour, protection of personal data and strict rules. This paper posits that given the necessary assistance from the state, together with the cooperation of the industry and investment in AI specialists, the Indian auto industry is able to use AI for scaling in a competitive environment and to become part of Indias economy in a larger context. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Fair and Inclusive Customer Segmentation in AI- Driven Marketing
This chapter explains how artificial intelligence has evolved customer segmentation from a marketing tool into a socio- technical decision mechanism with implications for fairness, inclusion, and cultural representation. In the chapter, algorithmic segmentation is analyzed using clustering methods, explainable frameworks such as LIME and SHAP, and fairness metrics to identify or alleviate structural bias in multicultural markets. It discusses accuracy fairness trade- offs, transparency, emotional trust, and organizational capability gaps, especially when segmentation outputs flow into generative AI driven personalization. Through case studies on multicultural targeting, AI sales agents, misinformation flows, and exclusion in finance, employment, and welfare, the authors show how segmentation systems affect society. The chapter concludes with strategic, ethical, and policy recommendations for responsible, inclusive AI marketing grounded in fairness aware segmentation. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Laccase mediated electrosynthesis of heliotropin on mango-kernel derived carbon nanosphere composite: A sustainable approach /
Journal of Science: Advanced Materials and Devices, Vol.7, Issue 4, pp.1-11, ISSN No: 2468-2179.
Facile fabrication of enzyme immobilized carbon nanospheres (CNS) based catalysts with high electrical conductivity and catalytic efficiency are of decisive importance for their electrocatalysis. A novel, green and highly efficient synthesis route is reported here for the development of an electrode surface with enhanced electrical conductivity and better catalytic activity for the electrochemical synthesis of heliotropin. The obtained biowaste (mango seed kernels) was pyrolyzed and subjected to acid treatment to form functionalized CNS (f-CNS). The functionalized carbon fiber paper (CFP) electrode was employed as a template for laccase immobilization which was further treated with free laccase resulting in the formation of Lac-fCNS/CFP electrode. -
β-cyclodextrin functionalized graphitic carbon nitride as a promising electrocatalyst for the selective oxidation of Tetrahydrofurfuryl alcohol /
Electrochimica Acta, Vol.430, ISSN No: 0013-4686.
Selective electrochemical conversion of Tetrahydrofurfuryl alcohol (THFA) was facilitated employing β−cyclodextrin (β−CD) functionalized graphitic carbon nitride (GCN) based nanocomposite. The GCN bulk were obtained by subjecting melamine to pyrolysis and further the material was exfoliated to improve its optoelectronic properties. Non-covalent attachment of β−CD over GCN via ultrasonication creates reactive surface sites on the electrode (β−CD/GCN/CFP) facilitating a better host-guest interaction. -
PM2.5 Prediction Models: A Systematic and Comparative Review
Airborne particulate matter (PM) is an amalgam of liquid droplets found in air and microscopic solid particles. The particles differ in size, shape, and chemical composition. PM has a significant impact on climate and precipitation and adversely affects human health as it can infiltrate the lungs and enter the cardiovascular system. This article explores the various PM2.5 prediction models proposed to date to predict a region's particulate matter (PM2.5) concentration. As prediction techniques evolve rapidly, this study aims to assess the various methodologies proposed for predicting PM2.5 concentration in different regions according to the factors that influence it. Various machine learning, deep learning, and statistical models have been proposed to predict hourly or daily PM2.5 concentrations in the air. The previously proposed models were compared using the RMSE, MAE, and R2 scores as the evaluation metrics. Since most of these models were region-specific and mostly used different parameters for the prediction, the comparison highlighted the need for a generalized model that could be fine-tuned based on the parameters of a particular region. Thus, this review points to the need for a high-accuracy generalized prediction model for PM2.5 that adapts to the diverse parameters region-wise. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Security and Privacy in Biometric Authentication: Advancements and Risks Across Platforms
Biometric identification systems like fingerprint, face, iris, and voice recognition have revolutionized user authentication with more convenience and increased security over conventional password based authentication. Nonetheless, the fact that biometric features are immutable introduces unprecedented issues around privacy, spoofing, and database compromise because once biometric data leaks out, it cannot be withdrawn or substituted. This work advances empirical testing with analytical analysis to test biometric authentication for face, fingerprint, and iris modalities. Models based on convolutional neural networks (CNNs) were used and evaluated on benchmarked datasets, and findings indicated that face and iris recognition performed nearly perfectly with zero false acceptance and rejection rates in controlled environments, while fingerprint recognition performed poorly because of dataset size and quality constraints. The conclusions identify the significance of data preparation and variation in ascertaining the reliability of biometric information. In an effort to cross-verify the experiments further, case studies of Aadhaar, Apple Face ID, and Biostar 2 also clarified the threats of central storage, spoofing, and regulatory loopholes. The research concludes by suggesting privacy preserving frameworks, encryption, and multimodal methods for securing the future of biometric authentication. 2025 IEEE. -
Characterization of nanocarbon based electrode material derived from anthracite coal
Nanocarbon derivatives (NCD's) have wide range of scope in the field of sensors, supercapacitors and charge storage application. In the present study, anthracite is used as a precursor to synthesis nano-carbon derivatives. One of the important aspects of this study is to intercalate the synthesized NCD's with Li-ion to enhance its electrochemical and optical properties. The prepared NCD with Li-ion interface is used as an electrode material to study charge-discharge capacity and cyclic stability. The NCD shows a specific capacitance 65.4 mF g-1 and retention of capacitance after 200 cycles. However, adding small amount of supportive electrode material with NCD's enhances the capacitance after 160 cycles. The drastic increase in electronic conductivity of NCD's by adding supportive Li-ion permits the electrochemical activity of electrode material to be effectively utilized for practical applications. 2020 IOP Publishing Ltd. -
Analysing grief on twitter: A study of digital expressions on Om Puri's death /
Funes Journal of Narratives And Social Sciences, Vol.2, pp. 136-152, ISSN No. 2532-6732. -
Football Player Substitution Analysis using NLP and Survival Analysis
Football player substitution is extremely significant in situations where the team is down by goals or attempting to retain a lead that can add value to the team's performance. However, substituting players based on their prior performance would not assist the squad in making good decisions. In one of the papers, they used an inverse gaussian hazard model to determine the survival rate of players. However, the main issue arises when players do not give their all due to their mental state, which plays a critical role during the game. Furthermore, most of the research papers relied solely on past performance of players and various analyses, which was insufficient. This study discovered that the player's mindset should be mentally stable and competitive which is also very crucial during the match by reading various research articles. Hence, this study proposes a framework which comprises of two models, namely Survival Analysis (Kaplan-Meier Fitter) and Natural Language Processing (Sentimental Analysis). Sentimental Analysis would hel p in determining a player's mindset before the match and Kaplan-Meier Fitter is used to find out the survival rate of player's performance based on several factors like goal scored, passing accuracy etc. which would allow the team to make better informed decisions. Comparison of these two models would yield the best results for substitute players on the bench on the basis of their past performance and their mental health which will allow them to make team management to make better judgments. 2023 IEEE. -
Hybrid Semantic Evaluation of Student Answers Using Rule Matching and BERT Embeddings
Accurate evaluation of student answers in online and traditional assessments is critical in education. In recent years, various text similarity-based methods have been proposed. However, there are certain challenges, such as the semantic and structural understanding of text. Thus, this paper uses the BERT model to present a hybrid evaluation framework that combines rule-based similarity techniques with deep semantic knowledge. The rule-based component utilizes predefined linguistic and domain-specific rules to ensure interpretability. At the same time, BERT-based similarity captures the semantic similarity and the paraphrased answers. Experimentation has been carried out on benchmark datasets, with the proposed hybrid model and human experts on manual evaluation. The performance comparison demonstrates that the hybrid model has performed significantly better than the traditional machine learning approaches in terms of accuracy and fairness of scoring. The proposed hybrid model is also compatible with deployment in educational platforms as it provides suitable feedback to learners. 2025 IEEE. -
Bioconversion of Feather Composts using Proteolytic Bacillus mycoides for their Possible Application as Biofertilizer in Agriculture
Proteolytic Bacillus strains were screened for highest protease production amongst which Bacillus mycoides (G2) was chosen as an assuring protease producer. Enzyme activity was maximum at 37C, pH-7, when the medium was supplemented with 0.5 and 0.75% of sucrose and beef extract respectively. Tapioca flour and soybean meal were capable of replacing commercial carbon and nitrogen sources respectively. Feather degradation studies revealed 62% of degradation with Quail feather (QF), followed by Chicken feather (CF) (58%), Guinea fowl feather (51%) and Pigeon feather (43%). Biodegradation of feather samples in soil evidenced degradation of Quail feather and Chicken feather at the following patternQF Treatment 1 (5%) ? CF Treatment 1 (5%) ? QF Treatment 2 (10%) ? CF Treatment 2 (10%). Maximum degradation of QF and sufficient release of free amino acids into the feather compost was obvious with Field Emission Scanning Electron Microscopic (FE-SEM) and High Performance Thin Layer Chromatographic (HPTLC) analyses respectively. In vitro plant growth studies of tomato and chilly plants were accomplished with feather composts. Maximum growth of 26.44cm (shoot length) was achieved when feather compost prepared with degraded QF (5%) was utilized as plant growth substrate, than other treatment pots (P < 0.05). Plant growth was exemplary in the case of tomato when compared to that of chilly. Sound degradation of QF, followed by CF using Bacillus mycoides could strengthen the efficacy of microbial fermentation processes. This significant attempt could support poultry farms as well as organic agricultural sectors ecologically. Graphic Abstract: [Figure not available: see fulltext.] 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
A new broad-band atmospheric dispersion corrector for HROS-TMT
Atmospheric dispersion causes light from celestial objects with different wavelengths to refract at varying angles as it passes through Earths atmosphere. This effect results in an elongated image at the focal plane of a telescope and diminishes fibre coupling efficiency into spectrographs. We propose an optical design that incorporates a Rotational Atmospheric Dispersion Corrector (RADC) to address the broad-band dispersion for the multi-object mode of the High-Resolution Optical Spectrograph (HROS) on the Thirty Meter Telescope (TMT). The RADC corrects the dispersion across the entire wavelength range (0.311 ?m), using Amici prisms optimized for over 90 per cent transmission efficiency and minimal angular deviation of the beam from the optical axis after dispersion correction. For enhanced accuracy, particularly in the blue region, we have, for the first time, implemented the Filippenko model in Zemax via a custom Dynamic-Link Library (DLL) file. The Author(s) 2025. Published by Oxford University Press on behalf of Royal Astronomical Society.