Jaypee Institute of Information Technology

The focus of the present study is the synthesis and characterization of Plumeria obtusa leaf-derived carbon quantum dots (PCQDs) and their sensing capabilities for environmentally hazardous heavy metal ions. The hydrothermally synthesized PCQDs were morphologically and optically characterized using TEM and FESEM image analysis, XRD, UV–Vis absorption, photoluminescence, and FTIR spectroscopy. Dual emission was observed which confirms the existence of two emissive species with two lifetime decay profiles: one from the singled layer graphene core state and another from the passivated state of synthesized PCQDs. Furthermore, the applicability of PCQDs as fluorescent sensors for Fe3+ metal ions has been investigated up to 1.3 µM Fe3+ ion concentration. “Turnoff” fluorescence is observed in the presence of Fe3+ ions suggesting complex formation between PCQDs and Fe3+. Additionally, fluorescence lifetime decay curve showed that the average lifetime of PCQDs decreased with addition of Fe3+ metal ion. This reduction indicates that the Fe3+ ions introduced new energy levels or trap states within or around the quantum dot. These states provide alternate pathways for excited electrons and holes to recombine non-radiatively. The proposed optical techniques, along with principal component analysis, were successfully applied to detect dissolved metal ions in real samples such as paint, river water, and rainwater. This validates the effectiveness of the PCQD sensor as a reliable optical sensor for detecting water-dissolved metal ions, which contribute to water pollution.

Effect of multiwalled carbon nanotubes (MWCNTs) addition on thermoelectric properties of polycrystalline LSCCO (La1.98Sr0.02Cu0.94Co0.06O4) has been examined. The samples have been synthesized via the solid‐state reaction technique. Micro‐structural and surface morphology of the synthesized pellets have been investigated using X‐ray diffraction and Field Emission Scanning Electron Microscopy, respectively. The electrical resistivity and Seebeck coefficient of investigated pellets have been measured using a custom‐built apparatus between 300 and 450 K. Nevertheless, the transient heat transfer technique has been adopted for thermal conductivity measurement. The addition of MWCNTs significantly enhances the electrical conductivity and reduces the thermal conductivity of LSCCO. This results in a remarkable improvement in the figure of merit in spite of the reduction in Seebeck coefficient with MWCNTs addition. The maximum ZT value ~0.07 is achieved at 323 K for 0.05 wt% MWCNTs‐loaded LSCCO, which is ~28 times that of pristine LSCCO. The enhanced thermoelectric performance is attributed to the increased carrier concentration, reduced grain size, and improved interface phonon scattering due to MWCNTs addition. Our results demonstrate the potential of MWCNTs as an effective additive to enhance the thermoelectric properties of LSCCO‐based materials.

An unprecedented rise in the demand for the drug hydroxychloroquine (HCQ) worldwide and its utilisation has resulted in its upscale manufacturing. Being ecologically persistent and bioaccumulative, HCQ has come up as an emerging pollutant intensified by the COVID-19 pandemic occurring in low concentration in different environmental matrices due to improper treatment of waste and no proper legislation. Administration and excretion of HCQ, along with its toxic metabolites, contaminate surface and groundwater. This raises long-term toxic concerns from cellular biochemical changes to mortality in target and non-target organisms. To understand the potential environmental consequences of the indiscriminate use of HCQ, we reviewed some significantly unexplored sources, environmental behaviour, hazards, and the fate of pharmacological HCQ. We also discuss the potential risks of the degradation mechanism of the drug, its metabolite toxicity and the stress between ecological equilibrium with the emergence of antimicrobial resistance. Additionally, the review also emphasizes bioremediation and sustainable methods for treating industrial wastewater to reduce the ecotoxicological effects of pharmaceutical effluent. The present study is necessary to understand the severity and possible future scenarios by adopting the uncontrollable use of hazardous drugs and serve as a benchmark for prioritising environmentally safe concentrations in the absence of specific legislation.

Current vivid study reports synthesis, optical studies of functional carbonaceous material (FCM) as well as broad range photoluminescence including synergistic effect of metallized porphyrin (as visible range light absorbing material) on photocatalysis carried under visible light irradiation. This high-order self-assembly of functional carbonaceous material possess extraordinary photophysical-chemical properties with its surface rich in functional groups (thus making it suitable for π-π* transitions) and further its composite formation with a high absorption coefficient (more than 20,000 in broad visible range 400–700 nm) and non-radiative fluorescence quenching exhibiting (near IR) metallized porphyrin. The structural, compositional characteristics of FCM and its composite including phase purity, types of functional groups, bonding arrangement is analysed like XRD, FESEM, EDX, FTIR, UV–vis, FL and Raman spectroscopy. The optical as well as structural properties (exhibiting suitable ID/IG ratio ∼ 1.4) of the pure as well as composite of FCM and porphyrin (band gap shifting from UV (4.2 eV) to visible (3.0 eV)) indicated the robust and versatile behaviour to be explored in applications such as photodetection and catalysis. In this article, we report the broad range photoluminescence for pure FCM (300–550 nm) as well as its porphyrin-based composite (400–550 nm) and further the application of composite in the photocatalytic ability for the degradation of standard methylene blue dye for degradation percentage of 62% in 120 min in visible light while 30% in 180 min in dark. Its cost effectiveness, biocompatibility, solution processability, stability and the most crucial, its appreciable results in minute resource utilization indicates its appropriateness as competitive photocatalyst in similar as well as modified schemes.

This paper reports, the enhanced electrical parameters of sub-10 nm High-k SOI GaN FinFET by implementing fin optimization approach using TCAD simulation. The results show that as the fin aspect ratio (AR) increases, keeping the channel cross-sectional area constant, the static and analog performance of the suggested device enhances. On current of 0.15 mA, higher switching ratio (ION/IOFF) ratio (1.74 × 10⁹), reduced subthreshold swing (by 20%), and higher intrinsic gain has achieved for High-k SOI GaN FinFET having a higher fin AR (3.75) as compared to the lower fin aspect ratio (1.67) owing to the significant reduction in short channel effects. For more insight into the static/analog performances of the device; some other parameters such as transconductance (gm), energy band profile, surface potential, output conductance (gd), output resistance (Ro), and early voltage have also been investigated under fin optimization approach (fin aspect ratio modulation). Thus, the enhanced static/analog performances of the High-k SOI GaN FinFET clear the way for RFIC design.

The emerging field of epigenetics has been driving glioblastoma multiforme (GBM) development and progression. Various epigenetic alterations involving tumor suppressor genes, oncogenes, and signaling pathways have been identified in GBM. These alterations contribute to the aggressive behavior, therapeutic resistance, and tumor heterogeneity observed in GBM. Furthermore, the identification of specific genetic mutations associated with epigenetic dysregulation in GBM has provided new insights into the molecular subtypes and potential therapeutic targets within GBM. Understanding the complex interplay between genetic and epigenetic alterations in GBM is crucial for the development of effective and personalized therapies for this devastating disease. This review paper provides an overview of the epigenetic changes occurring in GBM and the potential of targeted epigenetic therapies as a promising avenue for GBM treatment, highlighting the challenges and future directions in this field has been deliberated.

In the past decade, the 2D materials-based electronic products market is rapidly growing especially in the areas of sensing, energy storage, biomedical, or ultrafast optoelectronics. Out of many 2D materials, transition-metal-dichalcogenides (TMDs) are one of the promising 2D materials which have weak van der-Waals force between each covalently bonded layer (X-M-X) of transition metals (M) and chalcogens (X). These individual-layer of TMDs undergo the transition from in-direct to direct band gap and have remarkable and fascinating electronic and optical properties. Moreover, TMDs have tremendous potential in several applications such as light emission, photodetection, gas sensing, biosensors, bio-imaging, and drug delivery because of their high density of surface sites. Furthermore, TMDs also act as associative biosensing elements for the real-time detection of biomarkers and, hence, exhibit great potential in biosensors and healthcare devices. The surface engineering and modification of TMDs can further enhance their electronic and sensing properties. Therefore, understanding the interfacial properties and atomic structures, and surface engineering of TMDs are thus of significant importance for the advancement of biosensors. The presented book chapter, comprehensively discusses the several deposition techniques for TMDs, their advantages, and disadvantages, the growth quality of deposited thin layers of TMDs, their morphological and structural analysis, several parameters for performance analysis of fabricated 2D-TMDs-based wearable biosensors and the effect of surface modification on device sensitivity, selectivity, and responsivity. Here, the various challenges and their solutions during bandgap tuneability and functionalization with various opportunities for application in advanced wearable biosensors have been discussed.

Ovarian aging is a major health concern for women. Ovarian aging is associated with reduced health span and longevity. Mitochondrial dysfunction is one of the hallmarks of ovarian aging. In addition to providing oocytes with optimal energy, the mitochondria provide a co-substrate that drives epigenetic processes. Studies show epigenetic alterations, both nuclear and mitochondrial contribute to ovarian aging. Both, nuclear and mitochondrial genomes cross-talk with each other, resulting in two ways orchestrated anterograde and retrograde response that involves epigenetic changes in nuclear and mitochondrial compartments. Epigenetic alterations causing changes in metabolism impact ovarian function. Key mitochondrial co-substrate includes acetyl CoA, NAD+, ATP, and α-KG. Thus, enhancing mitochondrial function in aging ovaries may preserve ovarian function and can lead to ovarian longevity and reproductive and better health outcomes in women. This article describes the role of mitochondria-led epigenetics involved in ovarian aging and discusses strategies to restore epigenetic reprogramming in oocytes by preserving, protecting, or promoting mitochondrial function.

Laser-based powder-bed fusion (LPBF) has become befit choice for fabricating intricate and precise metal parts using the additive manufacturing (AM) process. The micro-scale manufacturing industry continuously evolved and captured a huge market in various engineering applications for demand in miniaturized high-performance products. However, achieving precision and tolerance with a better surface finish has been challenging. In this article, the micro-scaled features (micro-holes) fabricated via LPBF process with SS316L material have been investigated for geometrical accuracy, followed by electrochemical micro-machining (ECMM) as post-processing for micro-hole finishing. The fabricated micro-holes with less than 100 µm diameter were found to be blind holes rather than complete through holes. The results show that the dimensional inaccuracy of micro-holes is in the range of 17–36%. However, the ECMM post-processing ensured the through-holes even for a hole diameter of 50 µm. Further, the post-processing operation improves dimensional accuracy and roundness by 31–79%, and 21–130%, respectively. In addition, the mechanical properties of the SS316L part printed with a hexagonal scanning strategy have been evaluated. The printed samples experienced residual stress from –91 MPa to 73 MPa, while a heat-treatment cycle relieved the induced residual stress without altering the microstructure significantly. The heat-treated samples exhibit noticeable improvement in yield and ultimate strengths. However, the elongation percentage of the heat-treated part is reduced by 2–10% at various strain rates. The heat-treatment cycle caused improvement in the specific wear rate.

In the realm of online social media, the proliferation of collusive behavior presents significant challenges for maintaining platform integrity and trust. This study introduces a primary labeled dataset focused on black-marketed collusive users on social media platforms, especially Twitter/X, aiming to classify collusive and genuine social media profiles. Collusive users, often operating in networks to manipulate metrics such as likes, retweets, and followers, were identified through specific patterns of interaction and engagement. Genuine users, on the other hand, were selected based on their organic and non-manipulative activity. The construction of our primary collusion dataset involved a meticulous process of data collection from 4 black marketing sites, followed by extracting features from Twitter/X. This collusive users data was merged with some genuine user data, which were heuristically collected from Twitter/X. Our primary dataset provides a valuable resource for research using machine learning, network science, and social media analysis, enabling the development and testing of algorithms designed to detect colluded users. By facilitating a deeper understanding of collusive dynamics, this work contributes to the broader efforts of safeguarding the authenticity and reliability of social media platforms. This comprehensive dataset will serve as a foundational tool for advancing research in addressing the collusive users Twitter/X social media. For elaborating the possibilities of model building, we have showcased the usage of our dataset with 15 machine learning classifiers, of which the LightGBM model outperformed with an AUC of 0.94. We have also demonstrated model enhancements using hyperparameter optimization with Bayesian Optimizer, Tree-structured Parzen Estimator, and Random Grid Search.

Stock price accelerates interest and preference of the young generation to explore the stock market with elicit interest. An autopilot system is needed where users choose beneficial stocks of their choice without paying attention to the manual inspection or implication. This study aims to form highly correlated stock communities based on fluctuations in stock price which will be helpful in the prediction and decision-making of users about their expenditure in the stock market. For the enormous volume of stock price time series data, the opted data representation method is the network which is generated with the help of a data statistical correlation test. Another research challenge was to detect the community in an optimized manner due to complex stock networks. For the same, BAT optimization algorithm and a proposed BAT-modified (BAT-M) algorithm are applied for complex stock network community formation. The better community formation results showcase that BAT-M gives better performance in comparison to the standard BAT algorithm on various performance evaluation factors.

Artificial intelligence (AI) generative tools offer students significant opportunities, albeit posing challenges to academic integrity. Given the constant advancements in generative AI, AI detection, and AI humanizer technologies, we provide three proactive strategies toward assuring such integrity.

Finance, as a multidimensional field, is constantly evolving, driven by economic, technological, and regulatory shifts. The dynamic nature of the field of finance research necessitates a continual identification of the latest areas of investigation for researchers to embark upon their future research projects. This study delves into the latest literature to uncover major trends in finance research, presenting a content analysis of 160 influential publications. Utilizing a systematic approach, six broad areas are identified, namely, behavioural finance, FinTech and digital finance, sustainable finance, financial risk management, financial econometrics, and asset pricing and portfolio management. Within these six areas, 14 prominent areas have been recognized, thoroughly discussed, and accompanied by relevant citations. The findings provide researchers with an overview of the current state of finance research and directions for future research. Future research will focus mainly on the application of machine learning, AI tools, big data, and quantum computing techniques for predictive analytics, fraud detection, cryptographic security, financial risk management, and improvement of financial services. Sustainable finance may be combined with ESG issues into FinTech, so promoting green investing and reporting. Embedded finance may be developed to incorporate financial services into non-financial platforms. The study contributes to new knowledge creation by identifying the emerging trends of finance research. The study’s focus on the most recent financial trends facilitates innovation-driven economic development and societal progress. The study leads to a better understanding of the impact of financial innovations on larger economic systems.