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Emerging Nanomaterials for Catalysis and Sensor Applications
This book reviews emerging nanomaterials in catalysis and sensors. The catalysis section covers the role of nano-photocatalysts in organic synthesis and health care application, oxidation and sulphoxidation reactions, liquid phase oxidation, hydrogen evolution and environmental remediation. It highlights the correlation of surface properties and catalytic activity of the mesoporous materials. The sensor section discusses the fabrication and development of various electrochemical, chemical, and biosensors. Features: Combines catalysis and sensor applications of nanomaterials, including detailed synthesis techniques of these materials. Explores methods of designing, engineering, and fabricating nanomaterials. Covers material efficiency, their detection limit for sensing different analytes and other properties of the materials. Discusses sustainability of nano materials in the industrial sector. Includes case studies to address the challenges faced by research and development sectors. This book is aimed at researchers and graduate students in Chemical Engineering, Nanochemistry, Water Treatment Engineering and Labs, Industries, Research Labs in Catalysis and Sensors, Environmental Engineering, and Process Engineering. 2023 selection and editorial matter, Anitha Varghese and Gurumurthy Hegde; individual chapters, the contributors. -
Simultaneous first order derivative spectrophotometric determination of vanadium and zirconium in alloy steels and minerals
A simple, selective and sensitive spectrophotometric method has been developed for the individual and simultaneous determination of trace amounts of vanadium(V) and zirconium(IV) in acetic acid medium using a newly synthesised reagent diacetylmonoxime salicyloylhydrazone (DMSH), without any prior separation. The molar absorptivity and Sandell's sensitivity of the coloured species are 1.30 x 10 4 and 1.82 x 10 4 Lmol -1cm -1 and 3.9 and 2.5 ng cm -2 for V(V) and Zr(IV), respectively. Beer's law is obeyed between 0.26-2.80 and 0.30-3.20 ?g mL -1 concentration of vanadium (V) and zirconium (IV) at 405 and 380 nm respectively. The stoichiometry of the complex was found to be 1:1 (metal:ligand) for V(V) and 1:2 for Zr(IV) complexes. These metal ions interfere with the determination of each other in zero order spectrophotometry. The first derivative spectra of these complexes permitted a simultaneous determination of V(V) and Zr(IV) at zero crossing wavelengths of 445 nm and 405 nm, respectively. The optimum conditions for maximum colour development and other analytical parameters were evaluated. The proposed method has been successfully applied for the determination of zirconium and vanadium in standard alloy steel samples, mineral and soil samples. 2012 Elsevier B.V. All rights reserved. -
Molecular architecture of PANI/V2O5/MnO2 composite designed for hydrogen evolution reaction
An ever increasing demand for energy has mandated scientists towards exploring innovative and environmentally friendly energy production techniques that can meet the needs of human beings and the world at large. Among the various techniques, hydrogen evolution reaction (HER) is a cost-effective and efficient method that produces hydrogen, a better fuel, for meeting our energy requirements. The large surface area, good redox capacity, high electroactivity, and tunable bandgap of polyaniline (PANI) makes it a preferred candidate for various energy-related applications. Incorporating mixed metal oxides into a polymer enhances its catalytic activities and can be used as an electrocatalyst for HER. In situ chemical oxidative polymerization method has been carried out to synthesize PANI/V2O5/MnO2 composite. The characterization studies of PANI/V2O5/MnO2 composite are done using XRD, FT-IR, BET, XPS, and FE-SEM analysis. The PANI/V2O5/MnO2 composite is used for linear sweep voltammetry studies and shows that it acts as an efficient electrocatalyst which gives an overpotential of 130 mV at 10 mA/cm2. The high electrocatalytic activity of the composite is due to the better surface phenomenon that is enhanced by the high porosity and surface area. The electrochemical impedance spectroscopy also shows lower charge transfer resistance for the PANI/V2O5/MnO2, confirming its excellent electroactivity. 90% of the current density is retained even after 7200 seconds, validating its stability. 2023 Elsevier B.V. -
Rational design of PANI incorporated PEG capped CuO/TiO2 for electrocatalytic hydrogen evolution and supercapattery applications
Synthesis of efficient electrocatalysts for energy applications is a major area scientists are currently focusing on to address the twin challenges of energy shortfall and the production of clean energy. Herein, an efficient electrocatalyst, polyaniline incorporated with polyethylene glycol capped CuO/TiO2 is prepared, which is effective in hydrogen evolution reactions and energy storage applications. The characterizations like XPS, XRD, FT-IR, FE-SEM, HR-TEM, and BET have been carried out to confirm the successful formation of the synthesized PANI/CuO/TiO2 composite. At 10 mA/cm2 current density, the prepared composite exhibits a lesser overpotential of 536 mV and 1587.2 C/g at 1 A/g as the specific capacity. The electrode prepared using the PANI/CuO/TiO2 composite also shows cyclic stability up to 2000 cycles. The synthesized composite is an efficient electrocatalyst for energy related applications. 2023 Hydrogen Energy Publications LLC -
Evaluative study on supercapacitance behavior of polyaniline/polypyrrole metal oxide based composites electrodes: a review
Electricity is a versatile form of energy but suffers from a drawback in that it cannot be stored easily. Supercapacitors are devices that can address this problem. Fabrication of efficient supercapacitors is the need of the hour, which requires an intelligent selection of the electrode materials and electrochemical conditions. In this review, electrochemical studies and synthesis methods of polymer based metal oxide composites, especially polyaniline and polypyrrole, are discussed in detail. Various fabrication methods that are in use for the preparation of the supercapacitor electrodes are evaluated, which gives an idea of the selection of suitable materials for electrochemical applications. The supercapacitance studies of the reported works are also discussed, which help to understand the efficiency and working of different polymer based metal oxide composites for energy applications. Conducting polymers have good capacitance behavior but low cyclic stability. Incorporating metal oxides, graphene, noble metals, MXenes, and carbon nanotubes enhances the capacitance of conducting polymers. Polyaniline based electrodes show comparatively higher capacitance values compared to polypyrrole based electrodes. The types of supercapacitors, the importance of polymers in supercapacitance applications, and the improvement of the polymer substrates by using various materials like metal oxides to enhance the supercapacitance ability are discussed in depth in this review. 2023 Elsevier Ltd -
Tailoring a Multifunctional PEDOT/Co3O4-CeO2 Composite for Sustainable Energy Applications
Energy sources play a crucial role in the development of the society. The gargantuan depletion of fossil fuels creates new glitches in the routine activities of human beings. Electrocatalysts can efficiently produce and store energy and, therefore, have the potential to alleviate this situation. A multifunctional electrocatalyst, Poly (3,4-ethylenedioxythiophene)/cobalt oxide-cerium oxide(PEDOT/Co3O4-CeO2), is synthesized by incorporating mixed metal oxide to 3,4-ethylenedioxythiophene (EDOT) using the in situ chemical oxidative polymerization method and is employed for both energy production and storage applications. The successful synthesis of the catalyst is confirmed through various characterization techniques. The composite shows a specific capacity of 617.8Cg?1 and a specific capacitance value of 1298.1Fg?1. The hydrogen evolution reaction (HER) analysis shows that the composite requires a low overpotential of 163.1mV at a current density of 10mAcm?2. Synthesized electrocatalysts can effectively handle the energy related issues prevailing in the society. 2024 Wiley-VCH GmbH. -
Electrocatalytic oxidation and determination of morin at a poly(2,5-dimercapto-1,3,4-thiadiazole) modified carbon fiber paper electrode
Voltammetric determination of morin on carbon fiber paper (CFP) electrode modified by electropolymerization of 2,5-dimercapto- 1,3,4-thiadiazole (DMTD) in phosphate buffer solution (PB, pH 9.0) have been studied. This modified electrode showed strong electrocatalytic activity toward the oxidation of morin, a flavonoid at physiological pH (PB, pH 7.0). Morin gave a sensitive anodic peak at 0.245 V (vs. SCE). The parameters influencing the anodic peak of morin such as effect of pH, effect of scan rate and concentration have been optimized. The electrochemical process was found to be irreversible and adsorption-controlled. Under the optimum conditions, the anodic peak current was linear to concentration of morin in the range of 2.5 10-10-2.75 109 M and detection limit was found to be 8.3 10-11 M. The practical application of the modified electrode was successfully demonstrated for the determination of morin in mulberry leaves. 2016 The Electrochemical Society. All rights reserved. -
Executive Function Decline and Its Association With TNF-? in the Later Stages of Post-Acute Sequelae of COVID
Beyond the immediate impact of the COVID-19 pandemic, survivors often grapple with incapacitating post-infection symptoms, referred to as Post-Acute Sequelae of COVID (PASC) when persistent beyond 90 days. Cognitive manifestations, encompassing attention, memory, and executive functions (EF), collectively termed brain fog, contribute to functional challenges in PASC. This infection also elicits a long-lasting pro-inflammatory response that persists even after viral clearance, potentially correlated with brain fog. However, it is unclear whether pro-inflammatory responses and cognitive sequelae persist beyond 1 year after the onset of infection. Thus, this study sought to investigate the long-term consequences of PASC on EFs as well as a potential association with markers of inflammation. Forty individuals with PASC who passed performance validity testing (PVT) and 40 matched healthy controls (HC) underwent neuropsychological assessments, including the Montreal Cognitive Assessment to assess global cognition, Victoria Stroop Test to assess inhibitory control, Wisconsin Card Sorting Test to assess cognitive flexibility, Digit Span Task to assess working memory, and Mackworth Clock Test to assess sustained attention on the Psychology Experiment Building Language (PEBL) toolkit. Serum was assayed for tumor necrosis factor-? (TNF-?) and interleukin-10 (IL-10). Results indicate significant EF decline in PASC, inversely correlated with serum TNF-? concentrations, approximately 562 225 days after the onset of infection. Thus, there exists protracted EF decline in PASC, persistent even beyond 1 year after the onset of infection. Increased levels of TNF-? are observed to be associated with poorer executive functioning in PASC. 2025 Wiley Periodicals LLC. -
Implementation of Morphological Gradient Algorithm for Edge Detection
This paper shows the implementation of a morphological gradient in MATLAB and colab platforms to analyze the time consumed on different sizes of grayscale images and structuring elements. A morphological gradient is an edge detecting technique that can be derived from the difference of two morphological operations called dilation and erosion. In order to apply the morphological operations to an image, padding is carried out which involves inserting 0 for dilation operation and 225 for erosion. Padding for the number of rows or columns is based on the size of the structuring element. Further, dilation and erosion are implemented on the image to obtain morphological gradient. Since central processing unit (CPU) implementation follows sequential computing, with the increase in the image size, the time consumption also increases significantly. To analyze the time consumption and to verify the performance across various platforms, the morphological gradient algorithm is implemented in MATLAB and colab. The results demonstrate that colab implementation is ten times faster when constant structuring element with varying image size is used and five times faster when constant image size with varying structuring element size is used than the MATLAB implementation. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deploying a Multi-Model Forecasting System for Bitcoin Prices: Bridging Statistical Forecasting and Deep Learning Innovations
In this study, we investigate and compare several forecasting models for predicting Bitcoin market prices using historical data sourced from Nasdaq Data Link (formerly Quandl) spanning from 2016 to 2025. Our analysis evaluates traditional time series methods - such as ARIMA and Holt Winters exponential smoothing - alongside modern machine learning and deep learning techniques including LSTM, Prophet, XGBoost, SVR, Random Forest, and GRU. Performance was assessed via metrics such as RMSE, MAE, MAPE, sMAPE, directional accuracy, and R-squared. Our experiments reveal that while classical methods (e.g., ARIMA and Holt Winters) exhibit large estimation errors and limited explanatory capacity, advanced neural network architectures - particularly the GRU - demonstrate superior accuracy with an RMSE of 2,505.84, MAE of 1,760.93, MAPE of 2.79%, and an R-squared of 0.99. The best-performing model (GRU) was deployed as a web application on PythonAnywhere, providing real-time forecasts through an interactive dashboard. This deployment not only validates the predictive efficacy of the GRU model but also offers a practical tool for investors and financial analysts to monitor and predict Bitcoin price movements using reliable Nasdaq data. 2025 IEEE. -
Harnessing Machine Learning for Mental Health: A Study on Classifying Depression-Related Social Media Posts
This study is of particular relevance in the way it identifies depression-related content on social media using a machine learning model to classify posts and comments. This dataset, encompassing around 6500 entries from various platforms including Facebook, was rigorously annotated by four proficient English-speaking undergraduate students together with the final label which is established via majority voting. Data Preprocessing, initial cleaning, normalization and TF-IDF feature creation through vectorization for the output of POS tags. The different machine learning models that were trained and tested are Logistic Regression, Random Forest, SVM (Support Vector Machine), Naive Bayes Gradient Boosting Algorithm K-NN (K nearest Neighbors) AdaBoost Decision Tree. Authors evaluated the models and measured their accuracy, precision score, recall rate (also known as sensitivity) in addition to F1-score. Gradient Boost, Random Forest, and SVM were top performers among which Gradient boosting was found to be an overall best one with almost 98.5%. They show that machine learning model can successfully predict the label of social media posts, as a way for accurately identifying depression from text data. This detailed model performance evaluation is useful in understanding what each approach does well and poorly, shedding light into whether they are / would be actually suitable for real-world applications. This study not only developed discriminative classifiers, but also included detailed analysis of their performance which should hopefully guide future work and help in practical implementations for real-time mental health monitoring. Through this work, this study aim to facilitate timely identification of depression-related posts, ultimately supporting mental health awareness and intervention efforts on social media platforms. 2024 IEEE. -
The usage of gold and the investment analysis based on gold rate in India
Gold is one of the main commodities where the customers invest their money comparatively with bank for better interest. In the Indian context people purchase gold for their children's marriages for later period. The investment in gold is better suits for easy conversion into money with quickest possible time from the bank and gold merchants. The appreciation or depreciation of gold based on other investment options like fixed deposit, provident fund, international crude oil price, stock market, mutual fund etc. The comparative analysis of gold with other investment options give an edge to the customer to clearly understand the investment pattern for their hard-earned money expected to give good returns in the future. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Physical ageing in Se-Te-Sb glasses
Bulk Se60-xTe40Sbx glasses in the composition range 0?x?14 were prepared by the melt quenching method. Differential Scanning Calorimetric (DSC) and thermal crystallization studies were performed to understand the thermodynamic property like glass transition and structural transformations. These glasses exhibit sharp endothermic peak at the glass transition (Tg). Disappearance of the endothermic peak at Tg in the rejuvenated samples clearly indicates the ageing effect in these glasses. Addition of Sb to Se-Te increases the connectivity of the structural network which is evidenced from the increase in Tg. A distinct change in the slope of the Tg at x=6, indicates a major change in the way the network is connected. The glass forming ability and the thermal stability also exhibit a maximum at x=6. Tg increases with the ageing time and the corresponding fictive temperature (Tf) calculated from the specific heat curves shows a decreasing trend. The molecular movements along the polymeric Se chains might cause the structural relaxation and the physical ageing. The physical ageing effect has been understood on the basis of the Bond Free Solid Angle (BFSA) model proposed by Kastner. Thermally crystallized samples show the formation of rhombohedral Sb2Te 3, rhombohedral Sb2Se3 and hexagonal Te phases. 2013 Elsevier Ltd. -
An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders
Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indi cating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model. The Author(s) 2024. -
Getting Back to Work: Cognitive-Communicative Predictors for Work Re-entry Following Traumatic Brain Injury
Return to work following a Traumatic Brain Injury (TBI) is affected by deficits across the cognitive, psycho-social and physical domains. The specific role of cognitive -communicative abilities influencing work re-entry is understudied. This study aimed at identifying the cognitive-communicative predictors for work re-entry following TBI. Thirty patients with TBI employed pre morbidly were categorized into two groups- 14 employed and 16 unemployed post TBI. Those having sustained mild, moderate or severe head injury and in the post injury period of 648months were recruited and majority belonged to skilled/ professional type of premorbid occupational status. They underwent a detailed assessment of cognition, language and communication using NIMHANS Neuropsychology Battery, Indian adapted versions of Western Aphasia Battery and La Trobe Communication Questionnaire (LCQ) respectively. Patients employed post TBI had better Aphasia Quotient (AQ) and better performance on all the cognitive domains and few domains of LCQ than those who remained unemployed. On step-wise Discriminant Function Analysis (DFA), injury severity and AQ could significantly differentiate between the two groups with an overall accuracy of 80%. Severity of head injury is a significant predictor for employability post TBI and evaluation of language along with cognitive abilities is crucial for patients with TBI for work re-entry. The study highlights the importance of a multi-disciplinary team in the assessment and management of cognitive-communication impairments following a TBI. 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Polymer Nanocomposite Graphene Quantum Dots for High-Efficiency Ultraviolet Photodetector
Influence on photocurrent sensitivity of hydrothermally synthesized electrochemically active graphene quantum dots on conjugated polymer utilized for a novel single-layer device has been performed. Fabrications of high-performance ultraviolet photodetector by depositing the polypyrrole-graphene quantum dots (PPy-GQDs) active layer of the ITO electrode were exposed to an Ultraviolet (UV) source with 265 and 355 nm wavelengths for about 200 s, and we examined the time-dependent photoresponse. The excellent performance of GQDs was exploited as a light absorber, acting as an electron donor to improve the carrier concentration. PGC4 exhibits high photoresponsivity up to the 2.33 A/W at 6 V bias and the photocurrent changes from 2.9 to 18 A. The electrochemical measurement was studied using an electrochemical workstation. The cyclic voltammetry (CV) results show that the hysteresis loop is optically tunable with a UV light source with 265 and 355 nm at 0.1 to 0.5 V/s. The photocurrent response in PPy-GQDs devices may be applicable to optoelectronics devices. 2022 by the authors. -
Recent advances in the development, design and mechanism of negative electrodes for asymmetric supercapacitor applications
Continuous technical advancements in a variety of industries, such as portable electronics, transportation, green energy, are frequently hampered by the inadequacy of energy-storage technologies. Asymmetric supercapacitors can expand their operating voltage window past the thermodynamic breakdown voltage of electrolytes by utilizing two distinct electrode materials, providing a workaround for the symmetric supercapacitors energy storage constraints. This evaluation offers a thorough understanding of this area. To comprehend the extensive research done in this field, we first examine the fundamental energy-storage mechanisms and performance evaluation standards for asymmetric supercapacitors. The most recent developments in the design and manufacture of electrode materials as well as the general structure of asymmetric supercapacitors. We have also discussed a number of significant scientific issues and offer our opinions on how to improve the electrochemical properties of future asymmetric energy storage devices. First, methods for designing high-performance electrode materials for supercapacitors must be developed; next, controllably built supercapacitor types must be attained (such as symmetric capacitors including double-layer and pseudocapacitors, asymmetric capacitors, and Li-ion capacitors). This review is timely because of the rapid expansion of research in this area. It summarizes recent developments in the study and creation of high-performance electrode materials with high supercapacitors. A number of crucial topics for enhancing the energy density of supercapacitors are examined, along with some reciprocal correlations between the main impacting parameters. Difficulties and prospects in this fascinating field are also covered. This offers a fundamental understanding of supercapacitors and serves as a crucial design rule for enhanced next-generation supercapacitors that will be used in both industrial and consumer applications. In this context, we extensively reviewed the classification of supercapacitor, EDLC (activated carbon, carbon aerogel, carbon nanotube), Pseudocapacitors, conducting polymers, metal oxides, hybrid materials, composite hybrids, rechargeable batteries, asymmetric devices and its design, aqueous solid state, fiber based asymmetric device, graphene based asymmetric device, terminologies used during the electrode selection, positive and negative electrodes in asymmetric device, material used for fabrication of negative electrodes, electrochemical performance of various devices which are fabricated by different electrode materials. Performance of material for various asymmetric device applications, conclusions outlook, recent developments in asymmetric devices. The current review may offer a thorough understanding and future prospects for developing negative electrodes to enhance asymmetric supercapacitor performance. 2023 Taylor & Francis Group, LLC. -
An Intelligent System to Forecast COVID-19 Pandemic using Hybrid Neural Network
A current outbreak known as COVID-19 has been discovered from the coronavirus was informed by WHO. COVID-19 is a universal pandemic that has brought out the best and the worst of humanity. Due to an increase in the cases daily, COVID-19 is creating a menace to public health and establishes a disruption of the social and economic development of the countries. The problem is the hospitals are not able to provide proper facilities and treatments on time due to the lack of facilities in India. The purpose of this project to build an efficient hybrid deep learning model for forecasting the COVID-19 pandemic with multiple features that are responsible for the spread of COVID-19 in the top five states in India. In particular, a hybrid model that incorporates Auto-Regressive Integrated Moving Average and Long-term Short Memory is been used to forecast confirmed cases. The linear and non-linear dependencies in the dataset is been dealt with by an ARIMA-LSTM hybrid model. As a result, when compared to the outcomes of ARIMA, LSTM models independently, the hybrid model was giving better results and was performing well in forecasting COVID-19 cases. Through this, the policymakers will get prior information on COVID-19 cases in states which will help the government and healthcare departments to take prominent measures to prevent it. 2021 IEEE. -
District Level Analytical Study of Infant Malnutrition in Madhya Pradesh
One of the main causes for Indias high infant mortality rate is malnutrition. It can be addressed using three broad groups of conditions: stunting, wasting, and underweight. Other factors such as sanitation, poverty, breastfeeding also contribute to the prevalence of malnutrition. Understanding the contribution of these factors and thus, eliminating them, to reduce malnutrition, is the purpose of this study. In this chapter, the district-level data obtained through NFHS-4 is used for analytical study for infant malnutrition, in Madhya Pradesh. Hierarchical Agglomerative clustering is used to group the districts based on the factors such as exclusively breastfeeding, inoculation, breastfeeding within one hour, no inoculation. The proposed model presents the effect of each factor, on infant malnutrition. It will help decision-makers and the government to shortlist the most appropriate districts contributing to malnutrition and to take curative action to reduce the rate of infant malnutrition. It is a generic model which can be utilized by other states to study infant malnutrition. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.