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Fabrication of anchored complexes as electrodes for sensing heavy metal ions by electrochemical method
Anchored coordination complexes as electrochemical sensors play a significant role in the modern era. It is evident that this becomes a fact on account of their practical convenience. Furthermore, they have unlimited scope in ecological, therapeutic, experimental and biomedical applications. It has been observed that 165 such papers have reported on anchored complexes for electrochemical sensing during the past two years. While human vitality is rigorously threatened by heavy metal ions today, numerous trials are restrained for screening these in nature. This retrace highlights the electro analytical methods and the masterpiece contribution of anchored coordination complexes as electrochemical sensors for the identification of heavy metals such as indium, uranium, lead, beryllium, and mercury in 2015 and 2016. 2017, Oriental Scientific Publishing Company. All rights reserved. -
Fabrication of biopolymeric sheets using cellulose extracted from water hyacinth and its application studies for reactive red dye removal
Driven by the imperative need for sustainable and biodegradable materials, this study focuses on two pivotal aspects: cellulose extraction and dye removal. The alarming repercussions of non-biodegradable food packaging materials on health and the environment necessitate the exploration of viable alternatives. Herein, we embark on creating easily degradable biopolymer substitutes, achieved through innovative crafting of a biodegradable cellulose sheet sourced from extracted cellulose. Concurrently, the significant environmental and health hazards posed by textile industry discharge of wastewater laden with persistent dyes demand innovative treatment strategies. This study extensively investigated four distinct methods of cellulose extraction from water hyacinth, a complex aquatic weed. The functional groups, crystallinity index, thermal stability, thermal effects, and morphology of the extracted cellulose were characterized by FTIR, XRD, TGA, DSC, and SEM. This exploration yielded a notable outcome, as the most promising yield (39.4 0.02% w/w) emerged using 2% sodium chlorite and 2% glacial acetic acid as bleaching agents, surpassing other methods. Building on this foundational cellulose extraction process, the extracted fibers were transformed into highly biodegradable cellulose sheets, outlining conventional packaging materials. Moreover, these cellulose sheets exhibit exceptional efficacy in adsorbing reactive red dye, with the adsorption capacity of 71.43 mg/g by following pseudo-second kinetics. This study establishes an economically viable avenue for repurposing challenging aquatic weeds into commercially valuable biopolymers. The potential of these sheets for dye removal, coupled with their innate biodegradability, opens auspicious avenues for broader applications encompassing commercial wastewater treatment procedures. 2023 Elsevier Inc. -
Fabrication of bismuth ferrite/graphitic carbon nitride/N-doped graphene quantum dots composite for high performance supercapacitors
Supercapacitors are potential energy storage devices with a broad range of applications. In this study, we are investigating a bismuth ferrite/graphitic carbon nitride/N-doped graphene quantum dots composite as an electrode material for supercapacitor applications. XRD patterns of the composite exhibit the different crystalline phases of the individual component and confirm the rhombohedral structure of the composite. The wafer-like structure of bismuth ferrite is produced via hydrothermal technique supported on 2D structures viz. graphitic carbon nitride and N-doped graphene quantum dots. Compared to bismuth ferrite and bismuth ferrite/graphitic carbon nitride (g-CN) binary composite, the bismuth ferrite/g-CN/N-doped graphene quantum dots demonstrates a superior specific capacitance of 1472 F g?1 at 1 A g?1 current density. After 3000 charging-discharging cycles, the device maintains its cycling stability with 87% capacitance retention. A supercapacitor device is assembled utilizing bismuth ferrite/graphitic carbon nitride/N-doped graphene quantum dots and activated carbon as electrodes. This device shows a significantly improved performance with an energy density of 53.1 Wh kg?1 and a power density of 705.4 W kg?1. As a result, the composite electrode developed in this study is proved to be a potential electrode material for high-performance energy storage devices. 2022 -
Fabrication of cobalt oxide@cellulose/nitrogen doped carbon nanotubes decorated metal organic frameworks composite for symmetric supercapacitor applications
The two main issues facing the world's population now are energy storage needs and environmental protection. A lot of work has gone into creating electrochemical energy storage using chemical processes and a variety of possible electrode active materials. Supercapacitors, which are energy storage devices with a unique structure and morphology of cellulose materials for green energy resource. In this regard, solid state hydrothermal process is used to fabricate Co3O4@Cellulose (CE), Co3O4@CE/N-MWCNT, and Co3O4@CE/N-MWCNT/ZIF-67 composite materials. XRD, XPS, BET, and HR-TEM analyses verified the structural, surface, and morphological analysis. The electrochemical studies by a three- and two-electrode fabrication in presence of 1M KOH electrolyte for supercapacitor applications. When 1M KOH electrolyte is present, the fabricated Co3O4@CE/N-MWCNT/ZIF-67composite electrode displayed exceptional cyclic stability and a specific capacitance of ?835 F g?1 at 1 A/g. The constructed composite electrodes of Co3O4, Co3O4@CE, and Co3O4@CE/N-MWCNT have specific capacitances of 263, 406, and 576 F g?1 at 1 A/g, respectively, which improves electrochemical properties using a three-electrode design. The Co3O4@CE-N-MWCNT/ZIF-67//1MKOH/SSC composite is produced using two electrode configurations. The final material showed a capacitance of 258 F g?1 at 1 A/g, a capacitance retention of 84.95 % across 8000 cycles, and an energy density of 30.99 W h kg?1 at a power density of 5409 W kg?1. Hence, the composite electrodes that have been produced have the potential to be used in electrochemical systems. 2025 Elsevier B.V. -
Fabrication of Didactic Model to Demonstrate Bottle Filling System Using Programmable Logic Controller
Automation is the evolution of manufacturing process which will ideally lead to e-governance. It helps humans and machines to be connected at the hip, blending the cerebral aptitudes of human and specialized abilities of automated bots to immune the workspace. Automated system has enhanced the modern day market by increasing the quality of the product as well as making the fabricating process time efficient. Lights out technology in industry promotes the robots to do work even after working hours when the lights are shut down in industry. In this research paper, a unique approach is used to fabricate a didactic model which demonstrates the working of a bottling plant which may be preferred for medium and small scale industries. To implement the process, CODESYS is used to program CPX-CEC-C1 PLC, a digital computerized system which performs logical decisions and provides outputs based on sensor inputs. The main focus is towards interfacing pneumatics and hydraulic components with PLC. 2021, Springer Nature Singapore Pte Ltd. -
Fabrication of disposable sensor strips for point-of-care testing of environmental pollutants
Biosensors are potentially used in detection of trace amount of environmental pollutants. Nanostructured materials are being widely explored for its application in the field of biosensors for monitoring environmental pollutants. Advances in biosensor technology with the use of micro-/nanomaterials can detect and analyze living and chemical matter with high specificity, which is relatively fast, sensitive, accurate, and inexpensive for the determination of chemical and biological contaminants. Recent finding shows that carbon nanotubes (CNTs) and nanomaterial-based biosensors utilizing the electrochemical and optical properties are being used for the analysis of contaminants at an incredible sensitivity and accuracy. In this chapter, the application of CNT-based biosensors and the fabrication of paper-based sensors in monitoring hazardous environmental pollutants are discussed. 2022 Elsevier Inc. All rights reserved. -
Fabrication of NiO nanoparticles modified with carboxymethyl cellulose and D-carvone for enhanced antimicrobial, antioxidant and anti-cancer activities
Colon cancer is a deadly disease while pathogens such as Klebsiella pneumoniae (K. pneumoniae), Shigella dysenteriae (S. dysenteriae), Bacillus subtilis (B. subtilis), Staphylococcus aureus (S. aureus), and Candida albicans (C. albicans) are serious threat to the human health due to their persistent nature and resistant to conventional drugs. This study aims to develop NiO nanoparticles via single one pot chemical approach and to modifying them with natural molecules carboxymethyl cellulose and D-carvone to enhance antioxidant, anticancer and antibacterial activity. The NiO and NiO-CMC-Dcar exhibit fcc structure confirmed by XRD. The band gap values were found be 4.15 eV for NiO and 4.23 eV for NiO-CMC-Dcar nanocomposite. DLS study confirmed that the mean particles diameter of NiO and NiO-CMC-Dcar were 154.1 nm and 130.3 nm respectively. The TEM and SEM analysis confirmed that both NiO and NiO-CMC-Dcar samples were roughly spherical. PL emission spectra of NiO-CMC- Dcar nanoparticles at 426 nm and 506 nm indicate the electronic structural modification due to incorporation of CMC and Dcar molecules in to NiO materials. The green emission observed at 506 nm is due to oxygen vacancy that can be correlated to production of more reactive oxygen species (ROS) to kill microorganism. The experimental results show that the NiO-CMC- Dcar nanoparticles exhibit enhanced antimicrobial, anticancer and antioxidant activity when compared to NiO alone. 2024 Elsevier B.V. -
Fabrication of silver nanoparticle decorated graphene oxide membranes for water purification, antifouling and antibacterial applications
The quality of portable water is adversely affected by inadequate wastewater treatment, increase in domestic & industrial waste, and microbial contamination of surface water sources. Purification techniques such as sedimentation, precipitation, filtration, and ion exchange can be employed to recover clean water from various impurities. Among these, membrane-based purification methods have become more appealing in recent years due to its cost-effective and energy-saving features. However, fouling is a ubiquitous problem in membrane-based purification technologies, which leads to reduced water permeation and quality. Present study embodies the development of silver decorated graphene oxide coated nylon membrane with remarkable antibacterial and antifouling properties. Antibacterial analysis of bacteria Staphylococcus aureus and Escherichia coli, validates that higher concentration of silver in GO (GO A500) composites hinder the growth of bacteria. The antifouling properties of GO A500 membrane showed flux recovery ratio of 96 % with irreversible fouling ratio of 3 % during the filtration of BSA (Bovine serum albumin) protein. Further fabricated composite membrane exhibited pure water flux of 46.7 L m?2 h?1 with dye removal rate of 95 %, 88 % and 85 % for Congo red, Rhodamine-B and Methylene blue respectively. Catalytic studies conducted on GO A500 membrane demonstrated the efficacy of their antifouling properties. The investigations revealed that the composite (GO A500) membrane has excellent antibacterial and antifouling properties, making it a suitable option for wastewater treatment applications. 2023 Elsevier B.V. -
Face and Emotion Recognition from Real-Time Facial Expressions Using Deep Learning Algorithms
Emotions are faster than words in the field of humancomputer interaction. Identifying human facial expressions can be performed by a multimodal approach that includes body language, gestures, speech, and facial expressions. This paper throws light on emotion recognition via facial expressions, as the face is the basic index of expressing our emotions. Though emotions are universal, they have a slight variation from one person to another. Hence, the proposed model first detects the face using histogram of gradients (HOG) recognized by deep learning algorithms such as linear support vector machine (LSVM), and then, the emotion of that person is detected through deep learning techniques to increase the accuracy percentage. The paper also highlights the data collection and preprocessing techniques. Images were collected using a simple HAAR classifier program, resized, and preprocessed by removing noise using a mean filter. The model resulted in an accuracy percentage for face and emotion being 97% and 92%, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Face Detection-Based Border Security System Using Haar-Cascade and LBPH Algorithm
Border security is a process which measures the border management ideas by a country or set of countries to wage against unspecific and unauthorized travel or trade across the country borders, to bound non-legal deport, various crimes combat, and foreclose dangerous criminals from entering in the country. A system will help in keeping a check on those personnels who forge with the legal document with an intension to cross the border. This article discusses about border security of whole Indian context, and there are various such systems which have been built since 2010 as wireless sensor network system named Panchendriya. Remote and instruction manual switch mode arm system using ultrasonic sensor for security of border. This article we have made use of Haar-Cascadian along with LBPH algorithm with their functioning. The result and discussion section we compare most recent face recognition techniques that have been used in the last ten years. The proposed prototype is discussed and shown through simulation model, it provide better result compare to existing model. The proposed Haar-Cascade and LBPH algorithm provide 10% better performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Facebook as a Socialising Agent and its Impact on Academic Achievements on an Individual
Golden Research Thoughts Vol.2,Issue 9,pp.1-3 ISSN No. 2231-5063 -
Facebook post analysis for a celebrity page
Social Media is a subject that is large and extremely talked about in the present day. The expanding accessibility of the web and the development of social media brought in a necessity for the analysis of data based on a Facebook page. The number of information generated by social media platforms are expanding exponentially. The best approach to understand data of this magnitude lies in mining and analysis of such big data. The present focus is on the analysis of a Facebook Fan Page. This research work concentrates on the examination of the number of likes, post shares and correlation of posts and comments on various topics, fields or areas of a Facebook page. 'Like' is not the only way of rating a post nowadays. There are also recently added features like emoticons which display emotions such as 'love', 'wow', 'angry', 'sad', etc., to rate a post. The objective of this paper is to extract the data available and to perform analysis on a cricket fan page. Python is used to extract and analyse the information. IAEME Publication. -
Facial Emotion Detection Using Deep Learning: A Survey
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE. -
Facial emotion recognition using convolutional neural networks
Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). 2021 -
Facial Expression Recognition Using Pre-trained Architectures
In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health assessment, and humancomputer interaction. In this study, we explore the effectiveness of ensemble methods that combine pre-trained deep learning architectures, specifically AlexNet, ResNet50, and Inception V3, to enhance FER performance on the FER2013 dataset. The results from this study offer insights into the potential advantages of ensemble-based approaches for FER, demonstrating that combining pre-trained architectures can yield superior recognition outcomes. 2024 by the authors. -
Facial Expression Recognition with Transfer Learning: A Deep Dive
In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. 2024 IEEE. -
Facial pain expression recognition in real-time videos
Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well. 2018 Pranti Dutta and Nachamai M. -
Facial Recognition Model Using Custom Designed Deep Learning Architecture
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Nae Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Facile combustion synthesis of highly active Mo doped BiVO4 for photocatalytic dye degradation, photo-oxidation of alcohols, antifungal and antioxidant activities
This work represents the facile and green synthesis of Molybdenum (Mo)-doped bismuth vanadate (BiVO4). Green synthesis of Mo-doped BiVO4 was done using combustion technique using Mangifera indica (Mango) leaf extract as the fuel for combustion. The material synthesised was pure and characterised using X-ray diffraction, scanning electron microscopy, high resolution transmission electron microscopy, ultravioletvisible diffuse reflectance spectroscopy, Fourier transform infrared spectroscopy and photoluminescence (PL). It was found that Mo-doped BiVO4 had monoclinic scheelite phase, with a bandgap of 3.71eV. Various application was possible from the synthesised material like photodegradation of Malachite Green, a typical organic which showed excellent degradation efficiency of 99% under 120minutes. The catalyst also gave up to 95% yields in the light-assisted oxidation of aromatic alcohols to corresponding aldehydes. The material also showed excellent antioxidant properties showing 6.7g of ascorbic acid equivalence (AAE). It gave an excellent minimum lethal dosage (MLD) of 500g against Penicillium and Trichoderma fungal strains and showed maximum of 32 mm zone of inhibition. These applications show the versatility of the material to be used in various fields. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Facile construction of gefitinib-loaded zeolitic imidazolate framework nanocomposites for the treatment of different lung cancer cells
Gefitinib (GET) is a revolutionary targeted treatment inhibiting the epidermal growth factor receptor's tyrosine kinase action by competitively inhibiting the ATP binding site. In preclinical trials, several lung cancer cell lines and xenografts have demonstrated potential activity with GET. Response rates neared 25% in preclinical trials for non-small cell lung cancer. Here, we describe the one-pot synthesis of GET@ZIF-8 nanocomposites (NCs) in pure water, encapsulating zeolitic imidazolate framework 8 (ZIF-8). This method developed NCs with consistent morphology and a loading efficiency of 9%, resulting in a loading capacity of 20wt%. Cell proliferation assay assessed the anticancer effect of GET@ZIF-8 NCs on A549 and H1299 cells. The different biochemical staining (Calcein-AM and PI and 4?,6-Diamidino-2-phenylindole nuclear staining) assays assessed the cell death and morphological examination. Additionally, the mode of apoptosis was evaluated by mitochondrial membrane potential (??m) and reactive oxygen species. Therefore, the study concludes that GET@ZIF-8 NCs are pledged to treat lung cancer cells. 2024 International Union of Biochemistry and Molecular Biology, Inc.