Vellore Institute of Technology University
Recent publications
Amidines are a vital class of bioactive compounds and often necessitate multiple components for their synthesis. Therefore, exploring efficient and sustainable methodologies for their synthesis is indispensable. Herein, we disclose an alternative and greener method for synthesizing an unexplored new class of amidines through the photochemical synergistic effect of copper/nitroxyl radical catalysis. This approach facilitates site‐selective radical amination of unactivated imine C(sp²)−H bond in C,N,N‐cyclic imines over favored selectivity via halogen‐atom transfer (XAT). This greener method ticks 11 out of 12 green chemistry metrics (GCM), effectively sidestepping the need for oxidants, bases, ligands, multistep processes, and harsh conditions, distinguishing it from conventional methods described in previous studies. Kinetic, spectroscopic, and computational tools have been employed to elucidate the synergistic effect of Cu/nitroxyl radical, the role of light, XAT, the influence of substituents, and the order of the reaction in the catalytic cycle.
In this study, for the first time we devised a one‐pot telescopic method for synthesizing thiazol‐2(3H)‐imine using mortar‐pestle grinding under solvent‐free conditions. The reaction involves isothiocyanate, primary amine, and α‐bromoketones as precursors. The protocol demonstrated a broad substrate scope of 19 derivatives confirmed by various spectroscopic techniques. The reported approach is simple to operate, has mild reaction conditions, and affords the desired products in good to outstanding yields up to 98% in 10 to 20 min. UV‐visible and fluorescence spectroscopic techniques were used to investigate the photophysical characteristics of 9 selected thiazol‐2(3H)‐imine derivatives. The absorption and emission spectra show a maximum peak wavelength (λmax) at 295 ̶ 305 nm and 369 ̶ 370 nm, respectively.
A new phenanthridine appended quinoline-based chemoreceptor 5-(5-(quinolin-8-yl)thiophen-2-yl)-tetrahydrodibenzo[a,i]phenanthridine (PHQBA) was successfully synthesized and characterized by ¹H, ¹³C, and HRMS spectral analyses. The promising chelation-induced process in PHQBA was accelerated by Th⁴⁺ ions, which impart robust ratiometric green fluorescence at 515 nm. The host–guest complex formed in a 1 : 1 binding stoichiometry between Th⁴⁺ ions and PHQBA was demonstrated by Jobs plot experiments. The Benesi–Hildebrand (BH) plot was employed to compute the binding constant for the complexation of PHQBA + Th⁴⁺, which was determined to be 3.77 × 10⁵ M⁻¹. To comprehend the detection mechanism of PHQBA, DFT, and TDDFT, eased computational studies were conducted and well supported by the experimental results. Moreover, the limit of detection (LOD) of PHQBA was determined as 223 nM, which defines the remarkable optical sensitivity of the sensor PHQBA. Further, portable paper strip detection and real-time determination of Th⁴⁺ ions in real water samples ensure the practical applications of PHQBA.
The surge in global fruit and vegetable waste (FVW), influenced by increasing population and shifting consumption patterns, presents both a challenge and an opportunity. FVW represents a considerable global challenge; however, it harbors potential bioactive compounds with various health-promoting properties, including polyphenols, antimicrobial peptides, functional lipids, and dietary fibers. This review examines efficient methods for extracting and isolating these bioactive, including advanced techniques such as ultrasonication, supercritical fluid extraction, and enzymatic treatments, which maximize yields while maintaining bioactivity. The antioxidant, antimicrobial, and anti-inflammatory properties of these compounds make them valuable for functional food applications aimed at improving public health. Additionally, integrating food waste bioactive into wellness product aligns with consumer demand for sustainable and health-promoting food options. However, translating this potential into practice requires overcoming the barriers related to large-scale production, regulatory approval, and public acceptance. The novelty of this review lies in its comprehensive analysis of advanced technologies for enhancing bioactive stability and retention, providing practical strategies for overcoming challenges in large-scale production, regulatory compliance, and consumer acceptance. This review also aims to shift food systems toward sustainability, transforming waste into valuable resources for human health. Graphical Abstract
Parkinson’s disease (PD) can be symptomatically detected in its early stage by vocal impairments. A vocal feature-based PD classifier using the long short-term memory-recurrent neural network (LSTM-RNN) model is accurate, reliable, and suitable for early diagnosis. The classification accuracy primarily depends on the feature extraction method. Speech signals are fed framewise to a 256-point radix-2 discrete-in-time fast Fourier transform (R2-DIT-FFT), then to a 26-coefficient mel-frequency cepstral coefficient (MFCC) unit and finally to a discrete cosine transform (DCT) to get 12 features/frame. The majority of the computational complexity is due to the FFT unit. Therefore, an approximate arithmetic-based FFT design can provide higher hardware efficiency without losing the required classification accuracy. The range and accuracy analyses are conducted with different data formats. For this, a MATLAB LSTM-RNN model is trained and validated with the Italian Parkinson’s voice and speech dataset (in.wav format). Thus, an approximate 12-bit customized floating-point (CFP) representation is chosen, and it provides a classification accuracy of 85.34% and an F1 score of 86.61%. Later, the Radix-2 butterfly unit (R2BU) is implemented using 2 multipliers and 3 adders in the proposed data format. This 12-bit CFP multiplier requires 343.93μ\mu m2\hbox {m}^2 with a mean relative error distance (MRED) of 2.26×1042.26 \times 10^{-4}.The resulting MRED is 93.11% closer with an area overhead of 4.4% than the existing works. The optimal scheduling and pipelining strategies on the proposed R2BU provide 1378.944μ\mu m2\hbox {m}^2 area, 387.10μ\mu W power, and 500MFLOPS performance at 500 MHz when the design is synthesized using Nangate open-cell library 45-nm technology.
This study aims to exploit the resistance states of Cr/ZnO/TiN memristors to generate random numbers. A random number generator (RNG) circuit employing a single memristor is proposed. We suggest that current compliance (CC) is a significant parameter in determining the quality of randomness; it is found that low CCs (20‐50 µA) have a wide resistance state distribution that facilitates random bits. This work provides insight into the implementation of memristors for data security applications.
The Internet of Things (IoT) has transformed vehicular ad hoc networks (VANETs), leading to the Internet of Vehicles (IOV). VANETs are wireless networks without fixed infrastructure, designed to improve traffic safety in real time, supporting intelligent transportation systems (ITS). Due to their unpredictable nature, VANETs face major challenges like frequent link failures, scalability, reliability, network layout issues, quality of service (QoS), and security, all of which are complex and difficult to solve (NP‐hard problems). Traditional protocols are unsuitable for VANETs due to their unique properties. To accomplish the optimal number of clusters and achieve stability in VANETs within a dynamic environment, we propose a swarm‐based metaheuristic algorithm called the rat swarm optimization (RSO) algorithm. The RSO algorithm employs a clustering technique to optimize the network performance and ensure efficient communication in VANETs. The RSO algorithm optimizes load based on node transmission range (Tx range) through effective resource utilization and coordination. RSO organizes the unstructured network into cluster structures and generates near‐optimal clusters and CHs to reduce network randomness and maintain stability with lower communication costs. By keeping the number of clusters at an optimal level, the RSO algorithm enhances cluster lifetime and overall network performance. To assess the effectiveness and efficiency of the RSO algorithm, numerous experiments are performed by using various grid sizes, Tx ranges, and nodes in the network. The generated results demonstrate that the RSO algorithm stimulates 50.96%, 33.15%, 88.73%, and 96.70% optimal number of clusters when contrasted with the clustering algorithm–based on ant colony optimization (CACONET), moth flame clustering algorithm for IoV (MFCA‐IoV), the whale optimization algorithm for clustering in vehicular ad hoc networks (WOACNET), and grasshoppers' optimization‐based node clustering technique for VANETs (GOA) when the Tx range and nodes are taken into consideration. But, when the grid size is considered, the RSO generates 32.31%, 15.23%, 47.04%, and 58.33% optimal number of clusters when compared to cutting‐edge algorithms. Hence, the quantitative results and the statistical representation show the proposed RSO algorithm's effectiveness over cutting‐edge algorithms under the unpredictable nature of VANETs.
Aberrant activation of the Wnt/β-catenin signaling pathway, primarily driven by APC mutation and AXIN degradation via Tankyrase, contributes significantly to colorectal cancer (CRC) progression and metastasis. The accumulation of β-catenin, resulting from the dysregulated ubiquitination, underscores the need for alternative therapeutic strategies targeting Tankyrase and β-catenin. This present study explores microbial metabolites as a source of novel anti-cancer agents, leveraging their unique bioactivity and structural diversity, often exhibiting superior target specificity and lower toxicity than synthetic drugs. Through a computational drug discovery pipeline, a large library of 27641 microbial metabolites was initially screened based on multiple drug-likeliness criteria, resulting in the selection of 2527 compounds. Among the screened compounds, an integrated computational workflow comprising molecular docking, molecular dynamic simulations (MDS), MM/PBSA analysis, and Principal component analysis (PCA) identified Terreustoxin I (T1) as a potential Tankyrase inhibitor. In contrast, compound 10- phenyl-[12]-cytochalasin Z16 (B1) demonstrated a strong binding affinity within the β-catenin active site. Under physiological conditions, these lead compounds were evaluated for conformational stability, binding efficacy, and dynamic behavior. Additionally, ADMET profiling, physiochemical properties, and bioactivity score predictions confirmed the identified compounds’ pharmacokinetic suitability and reduced toxicity profile. In silico, cytotoxicity predictions showed significant activity against SW480 and HCT90 colorectal cell lines, with additional anti-neoplastic and anti-leukemic properties, strengthening their candidacy as effective anti-cancer agents. These findings provide a foundation for further experimental validation and development of novel CRC therapies with improved safety and efficacy potential.
Pharmaceutical products (PPs) and nanoplastics (NPs) are prominent emerging contaminants that pose serious threats to marine ecosystems. The present study aimed to investigate both pristine and combined toxicity of PPs...
In this paper, a hybrid controller with a sampled data control is investigated to achieve finite-time master–slave synchronization of delayed fractional-order neural networks (DFONNs). A Lyapunov-Krasovskii functional is constructed to obtain the sufficient conditions that incorporate delay information. For the first time, the asymptotic stability of the error system is guaranteed in a finite-time using the inequality technique and a sampled-data hybrid controller. The obtained conditions are expressed via linear matrix inequality. Notably, the proposed approach outperforms existing methods, demonstrating improved results in a comparative analysis. An explicit formula is utilized to calculate the settling time, which is significantly influenced by the fractional order 0<β10<\beta \le 1. The superior performance of the proposed control method is evident, showcasing its effectiveness through numerical simulations and addressing the synchronization problem in DFONNs.
One of the important tools used by Natural Disaster Mitigation and Management (NDMM) in recent times is drone surveillance, particularly effective in disaster-affected areas. Drones equipped with modern imaging capabilities offer enhanced coverage and accessibility in disaster-affected areas, making them particularly effective in locating individuals trapped during floods and earthquakes. Traditional human-operated video analysis has limitations such as low processing speed, insufficient dataset resulting in poor performance, higher implementation cost, longer computation time, and so on. To address the traditional approach's complications posed in complex search and rescue scenarios, this paper suggest a Double Transfer Bidirectional Feature Pyramid-based YOLOv8. This innovative model leverages the Bidirectional Feature Pyramid to improve the extraction of image features through the pyramid with features at multiple scales, and uses a Double Transfer Strategy to transfer the pre-trained models to different operational environments. YOLOv8 architecture is employed due to its proven effectiveness in achieving high-speed and precise object detection under real-time constraints. These models bring a considerable enhancement of the accuracy and stability of the person detection in aerial images. Performance analysis proved that the proposed model achieves 99.68% accuracy on the 4 K Drone, which is more efficient than the current methods. Moreover, the mean inference time of the model is lower to only 0.30 ms of the total time per image, which may be considered a great improvement compared to the time-consuming traditional approaches. The evaluation process included cross-validation and testing across diverse disaster scenarios to ensure the robustness and reliability of the results. The proposed model is more accurate and with less execution time in real-time SAR applications, making it an essential tool for quick detection and leads to individuals during natural disasters. The model also enhances emergency response efforts, demonstrating its potential to save lives in critical situations.
Through the DFT computations, the structural, vibrational, electronic, elastic, optical and thermal (thermoelectric, thermodynamic) properties of the two-dimensional Rb2Te monolayer are briefly contemplated. The Perdew-Bruke-Ernzerhof (PBE) form of generalized gradient approximation (GGA) functional in WIEN2k was deployed for the analysis of all these material properties. The trigonally crystallizing monolayer with an indirect band gap of 1.72 eV may be an upright single-layer that suffices distinct applications. ‘No negative’ phonon bands confirm the dynamical stability of the monolayer. The Rb2Te monolayer has large indirect band gap than Rb2S and Rb2Se. It exhibits mechanical stability with positive elastic constants satisfying the Born-Huang criterion for two-dimensional materials. The absorption coefficient spanning largely in the ultra-violet range makes the monolayer a congruous material for UV applications. Also, the thermoelectric figure of merit for p-type Rb2Te single-layer at room temperature is high (0.67) compared to the analogous series of compounds, that makes the monolayer a viable one for thermoelectric flexibility and experimental synthesis. The monolayer has high hole effective mass and D ratio. The obtained results aids in revealing the outstanding properties and excellent stability of the monolayer. Based on these findings the Rb2Te monolayer paves the way for promising applications in the fields of photovoltaics, thermoelectrics and UV-based applications.
5-tetrakis(carbazole-9-yl)-4,6-dicyanobenzene) has emerged as a key metal-free photocatalyst for sustainable organic synthesis. Due to its unique design enabling high photoluminescence quantum yield, thermally activated delayed fluorescence (TADF) and long excited state lifetime, 4CzIPN facilitates diverse reactions, such as CC and C-X bond formation reactions, under mild reaction conditions. This review highlights its application in decarboxylation, acylation and cyclisation reactions involving a-keto acids, carboxylic acids and aldehydes in a single catalytic system, as well as the combination of a dual catalytic system with transition metals to enhance selectivity and scope. 4CzIPN contributes to the advancement of sustainable chemistry by enabling green, efficient and scalable reactions and this review covers studies published between 2020 and 2024.
Although uncivil behaviors are rising across organizations and previous studies have associated workplace and personal characteristics with uncivil behavior, a study on the interactive effect of workplace incivility and ineffectual silence on turnover intention through the boundaries has yet to be ascertained. Therefore, this research explores the association of workplace incivility and ineffectual silence with turnover intention via rationalized knowledge hiding and regulation of emotion by applying conservation of resources and social exchange theories. The study adopted a time lag (time I and II), gathering responses (n = 504) from IT (information technology) professionals working for Indian IT firms in various capacities. The partial least squares - structural equational modeling algorithm investigates reliability, validity, and hypotheses. As per results, workplace incivility, rationalized knowledge hiding, and ineffectual silence significantly exacerbate turnover intention, while regulation of emotion decreases the negative impact of workplace incivility and helps minimize ineffectual silence. Consequently, the study helps organizations take measures to alleviate the prevalence of uncivil behaviors and prevent concealing information/knowledge behavior as well as the desire to quit the firm. Firms and individuals ought to promote and adopt the regulation of emotion that promotes positive conduct and protects individuals from abuse or mistreatment. The study adds substantial value to the existing literature through the relationship between workplace incivility, silence, and intention to leave.
Antibiotics are widely used in health care to treat various infections. Excessive use and poor waste management make antibiotics a serious threat to aquatic ecosystems. Most of the ecotoxicological assessments on freshwater microalgae are evaluated in the defined culture medium, which lacks environmental relevance. Hence, this study aimed to evaluate the toxicity of amoxicillin, ciprofloxacin, and levofloxacin on the freshwater microalga Scenedesmus obliquus in both lake water medium (LWM) and Blue green-11 medium (BG-11 M). Results indicated that levofloxacin exhibited the highest toxicity, followed by amoxicillin and ciprofloxacin in both LWM and BG-11 M. Pearson correlation and cluster heat map analyses revealed that oxidative stress generation was directly linked to reduced photosynthetic activity and cell viability. These findings suggest toxicity assessments conducted in defined culture media may modulate the toxicity levels compared to environmentally relevant media, like lake water.
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39,797 members
Priti Talwar
  • Division of Biomedical Sciences
Velusamy Vijayakumar
  • Department of Mathematics, School of Advanced Sciences (SAS)
K.V.N. Kavitha
  • Department of Communication Engineering
Senthur Pandi R
  • Division of Crystal Growth & Crystallography
S Kalaivani Narayanan
  • School of Electronics Engineering (SENSE)
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Vellore, India
Head of institution
Dr. G. Viswanathan, the Founder and Chancellor of VIT University