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Fabrication of LaCoO3/g-C3N5 Z-scheme photo catalyst for Allura Red dye degradation, ascorbic acid sensing and hydrogen evolution studies
Production of hydrogen by water splitting through photocatalytic process under visible light from waste water is one of the potential green energy technologies. In this study, LaCoO3, g-C3N5, LaCoO3/g-C3N5 (1:1), (1:2) and (2:1) weight ratio nanocomposites (NCs) have been successfully synthesized using a solution combustion, hydrothermal and probe sonication method. The X-Ray Diffraction (XRD) confirmed the compound formed with the crystal size range 20 ?30?nm. The studies of Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) verify the morphology of the particles; band gap of the range 1.52?eV identified from Ultraviolet-Visible (UVvis) studies. The findings demonstrate that Z-scheme heterostructures have developed on the interfaces between the layered flake-like g-C3N5 and the perovskite-type oxides LaCoO3, which improve the absorption of visible light, the separation of photogenerated electron-hole pairs, and the transformation of photogenerated electrons. From the different ratio of synthesized nanoparticles (NPs), the LaCoO3/g-C3N5 (2:1) shows enhanced photocatalytic activity of 99.87?% for degradation of Allura red dye in visible light irradiation. For the first time, the produced nanomaterials were tested for ascorbic acid sensing at extremely low concentrations with a 0.12??M detection limit. The prepared nanomaterials were assessed for their electrocatalytic water splitting operation. Especially, the nanomaterial, LaCoO3/g-C3N5 (2:1) reveal exceptional hydrogen evolution reaction (HER) and also oxygen evolution reaction (OER) capabilities with overpotential of 79?mV and 450?mV, respectively. Hence, the prepared nanomaterials are used for multifunctional applications. 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Fabrication of liquid-crystal retarders for solar polarimetry: A facile method
A high-precision polarimeter for simultaneous multi-line spectropolarimetric Sun observations is under development at Indian Institute of Astrophysics. Towards this end, we plan to use liquid-crystal retarders as the polarization modulators. A prototype liquid-crystal variable retarder (LCVR) is fabricated and characterized. A solution-processed method is adapted to fabricate the LCVR using commercially available E7 nematic liquid-crystal material. Thickness of the alignment layer of the LC retarder was optimized to achieve uniformity. The fabricated LCVR demonstrates spatial uniformity of retardance comparable to a commercial waveplate. The device is found to have a low-range of operational voltage of <20 V and a very short response time of <1 ms. Also, the device shows consistent operational stability. Indian Academy of Sciences 2025. -
Fabrication of Molecularly Imprinted Electrochemical Sensors for Food Additives
Molecularly imprinted polymers (MIPs) have emerged as a promising technique for the newlinepreparation of synthetic polymers with specific binding sites for target molecules. These polymers have found applications in various fields, including sensing, where they serve as a recognition element for the detection and quantification of analytes in chemical and biological environments. In recent years, MIPs have been utilized as sensing materials for biomolecules, food additives, pesticides, metal ions, and other target species. This work presents the development of MIP-based electrochemical sensors for the selective and rapid detection of food additives, namely tartrazine, 4-hexylresorcinol, butylated hydroxy anisole, and brilliant blue FCF. Conducting polymers, metal nanoparticles and 2D material-based electrode modifications have been employed in newlinethe preparation of MIPs for electrochemical sensing applications. Investigations reveal newlinea significant enhancement in the electrochemical oxidation/reduction current of the analytes upon the surface modifications applied to the Carbon Fibre Paper (CFP) substrate. The surface morphology of the modified electrodes was characterized using techniques such as Field Emission Scanning Electron Microscopy (FESEM), Electron Diffraction X-ray (EDX), X-Ray Photoelectron Spectroscopy (XPS), Optical newlineProfilometry, and Fourier Transform Infrared Spectroscopy (FTIR). Nyquist plots newlinedemonstrated the lowest charge transfer resistance at the finally modified working newlineelectrodes compared to other control electrodes. Optimization of experimental newlineconditions, including pH effects, investigation of reaction mechanisms through scan rate variations, determination of the number of cycles required for film newlineelectrodeposition to achieve maximum current response, and determination of the potential window, was carried out using cyclic voltammetry (CV). The quantification newlineof analytes was performed using Differential Pulse Voltammetry (DPV). -
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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 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 Robust Wettability Gradients on Soft Surfaces Through Physicochemical Modulations
The creation of robust surface gradients on soft materials is an emerging area of research in materials chemistry. Polydimethylsiloxane (PDMS), an elastomeric soft material, is widely employed in diverse research fields due to its exceptional properties including ease of processability, newlinebiocompatibility, and transparency. These properties make it an ideal choice for applications in microfluidics, soft robotics, and biomedical devices. Creating surface gradients on soft surfaces can be challenging, requiring expensive chemicals, sophisticated instrumentation, time, and complex experimental setups. This study presents simple and cost-effective methods newlinefor creating chemical (wettability) and physical (morphological) gradients on newlinePDMS surfaces. The methods we developed to create wettability gradients involves (i) newlinecreation of a gradient of crosslinking density on the PDMS surface by using newlinea differential curing method and (ii) selective inhibition of normal curing newlineusing an inhibitor. Contact angle measurements confirm the successful newlinecreation of both radial and linear gradient of surface wettability using both these methods with regions of higher crosslinking density exhibiting higher hydrophobicity. We have also devised an innovative technique for fabricating morphological gradients on soft surfaces. The method makes use of newlinedifferential curing and buckling instability to create hierarchical wrinkled patterns on the PDMS surface. Optical microscopy and profilometry confirm the uniformity, reproducibility, and controlled optical properties of the wrinkled surface patterns. newlineGradients we prepared demonstrated excellent performance in various applications, including water collection, cell adhesion, and triboelectric charge generation. They can be utilized in microfluidics, sensors, and newlinebiomedical devices due to their structural consistency, controllable physical newlineresponses, and reproducibility of the performances. -
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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. -
Fabrics of Power: Cutting Through the Noise in the Classroom
The hijab, purdah and veil though differently named constitute a continuum of meanings shaped by social, cultural and personal contexts. A womans decision to adopt or reject these garments situates her within a shifting spectrum of religious expression and secular alternatives. The volatility of these meanings renders the garments vulnerable to political appropriation, transforming them into contested symbols that are difficult to address pedagogically, therefore becoming a fabric of power. The hijab controversy that unfolded in Karnatakas educational institutions in early 2022 sharpened these complexities, prompting extensive public commentary on the purpose of education, the responsibilities of institutions, and the rhetorics of liberty, secularism, nation and religion. This article examines these commentarial responses ranging from editorials to columns in Kannada and the English media while reflecting on the parallel experience of teaching concepts such as liberty, dissent, secularism and religion during the period of unrest. In doing so, it foregrounds the paradox inherent in the politics of teaching literature, the framing of literature as political, and the pedagogical negotiations required when instruction unfolds within a charged and highly politicised atmosphere. 2025, Unisa Press. All rights reserved. -
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. -
Face-Based Kinship Verification using Deep Embeddings for Low-Cost Health Record Linkage
Precise linkage of health records is essential for continuity of care, reducing duplicate health records, and accurately documenting family medical histories. Genomic testing offers the evidence-based biological 'gold standard' for verifying kinship; however, access to testing is either impossible or unavailable in most low-resourced environments due to prohibitive costs, long timelines, and/or lack of infrastructure. This study provides a low cost and interpretable pipeline for kinship verification in the form of Siamese deep embeddings. The processed facial image embeddings produced by a ResNet-18 backbone using 256-dimensional and L2-normalized embeddings, are then compared using cosine similarity. A validation-based calibration process selects the logit polarity and decision threshold that support stable deployment decisions. Grad-CAM visualizations can be interpreted frame-by-frame and allow for pair-specific attributions of faces that were more relevant or important in decisions of similarity. In experiments on the Families in the Wild (FIW) dataset (family-disjoint splits), we report ROC-AUC of 0.834, target balanced accuracy of ?0.88, with similar precision, recall, and specificity. The confusion matrices also illustrate a near symmetric distribution of errors by family and both Grad-CAM explanations highlight how the model came to a decision for true cases and hard cases. The above results illustrate how we can deploy a lightweight, explainable, and face-based kinship verification pipeline on a CPU-only system. Our study therefore provides a feasible assistive tool for health record linkage where genomic validation is not possible. 2025 IEEE. -
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 Augmented with CNNs and Face Detection: Toward Emotive Emoji Synthesis
Emotion recognition is a crucial component with broad applications in technology and healthcare industries specifically in humancomputer interaction. To improve emotion recognition accuracy, this research introduces an innovative technique that integrates face detection with Convolutional Neural Networks (CNNs). Using the Fer2013 dataset, the approach consists of carefully identifying faces in images as a preprocessing step, followed by training a CNN network to identify emotions and create corresponding emojis. After conducting extensive testing and assessment, it is determined that after employing a face detection algorithm the suggested framework is effective in both correctly identifying emotions and producing visually appealing emojis. This helps to create an interface for emotional communication that is more user-friendly and captivating. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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.