Rajalakshmi Institute of Technology

The quest for high-efficiency and stable photoanodes remains a central challenge in dye-sensitized solar cells (DSSCs). Here, we report a hierarchical CNT-integrated lithium–zinc–aluminate (CNT–LiZn0.5Al2O4) spinel nanoarchitecture as a superior alternative to conventional CNT–ZnO and CNT–LiZn0.5O photoanodes. Using a combination of SILAR and doctor blade techniques, the nanocomposites were fabricated on FTO substrates and thoroughly characterized. X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analyses confirmed successful Li⁺ and Al3⁺ incorporation, driving a structural transition from wurtzite ZnO to a cubic spinel phase, effectively passivating defect states and tuning the electronic structure. UV–Vis Tauc analysis revealed significant bandgap narrowing from 3.04 eV (CNT–ZnO) to 2.34 eV (CNT–LiZn0.5Al2O4), thereby enhancing visible-light absorption. AFM and SEM studies further showed a highly textured, porous morphology, favorable for dye loading and light scattering. Combined with the CNT network’s superior charge transport, this structural and electronic synergy led to pronounced recombination suppression, as evidenced by quenched PL spectra. The resulting DSSCs exhibited stepwise performance improvement: CNT–ZnO (η = 5.55

Opinion mining has gradually become tough to handle as the amount of user-generated content on social media platforms increases exponentially. As a matter of fact, Twitter is the most common platform to collect voices in terms of products, advancements, and policies. Sentiment Analysis (SA) deals with people's thoughts, feelings, and opinions about various subjects. Through the examination of tweets, one can gauge public views on news, rules, the community, and even celebrities. Unfortunately, currently existing SA mechanisms often have limited prediction capabilities and are still a long way from being able to function in real, time commercial applications. The main causes of inaccuracies are lack of data and difficulties in model configuration in deep learning (DL). This research introduces a classification learning-based Optimal Tiered blocks of Convolutional Neural Long Short, Term Memory (OTCNLSTM) for emotion recognition. Four Local Features Training Blocks (LFTBs), which are capable of hierarchically extracting spatiotemporal local emotional cues, make up the OTCNLSTM model. On top of that, the Boosted Killer Whale Predation Optimization (BKWOP) technique is introduced to pinpoint the perfect hyperparameters and solution sets, thus forming a stable neural network model. The newly designed system efficiently categorizes the sentiments generated from the Twitter users' comments into four categories, namely, positive, negative, neutral, and irrelevant. The Kaggle Twitter dataset powered a thorough experimental study, which showed the model's performance like a rock. The OTCNLSTM model reached an overall accuracy.

Gold and silver nanoparticles are gaining attention as advanced tools for drug delivery, largely due to their ability to carry medications and facilitate imaging. By simulating blood flow in these constricted environments, the research aims to assess how these nanoparticles behave and interact within the arteries. This study delves into the potential application of gold (Au) and silver (Ag) nanoparticles in narrowed arteries (stenosis) analyzed through the use of computer simulations. The primary goal is to determine if Au and Ag nanoparticles can enhance blood flow and mitigate the severity of stenosis, thus offering a novel approach to treatment. Specifically, the study examines the impact of increasing the volume fraction of gold nanoparticles on blood flow dynamics and temperature distribution. The analysis also incorporates the effects of thermal radiation and heat exchange within the arterial system. Additionally, the study explores the magnetic properties of the nanoparticles, investigating how they can be designed to bind to specific sites or clots within the artery. By applying an external magnetic field, these particles can be concentrated in desired locations, potentially enhancing treatment efficacy. Numerical analysis indicates that increasing the volume fraction of gold nanoparticles results in lower blood temperature and reduced blood velocity. The study also observes significant fluctuations in the thermal distribution profile, attributed to the influence of thermal radiation. The findings suggest that gold and silver nanoparticles, due to their distinctive properties, can improve blood flow and reduce the severity of stenosis. The research further highlights that enhancing the magnetic properties of these nanoparticles can enable them to target specific sites within the artery more effectively.

Precise control of non-Newtonian biofluids is essential in biomedical and thermal engineering applications, where heat and mass transfer must be carefully regulated. This study explores the steady two-dimensional bioconvective flow of a Casson fluid over a slender needle under a localized magnetic dipole, incorporating the effects of thermal radiation, Joule heating, and homogeneous chemical reactions. The governing nonlinear equations are transformed via similarity techniques and solved numerically using a Runge-Kutta method with a shooting scheme, with accuracy ensured through convergence and mesh-independence tests. Results reveal that magnetic interactions profoundly alter the flow, enhancing surface shear stress and increasing skin friction by up to 16 %, while reducing heat transfer due to magnetic damping and thermal energy accumulation in the boundary layer. These findings demonstrate the novelty of employing localized magnetic fields to actively manipulate bioconvective transport in Casson fluids, offering valuable insights for the design of magnetically guided biomedical devices and advanced thermal management systems.

In the constantly evolving realm of blood-based fluid dynamics, meticulous management of thermal and fluidic properties within living organisms is crucial for advancing diagnostic and therapeutic applications. This study focuses on the complex interactions within a Casson hybrid nanofluid comprising ferrosoferric oxide (Fe3O₄) and molybdenum disulfide (MoS₂) nanostructures dispersed in blood plasma. The research explores the influence of thermal diffusion, diffusion-thermo, and non-linear (quadratic) thermal radiation on the fluid’s transportation dynamics, while taking into account sophisticated boundary conditions such as the Smoluchowski temperature jump and the Maxwell velocity slip. These boundary conditions closely replicate physiological environments at the micro/nanoscale. The governing PDEs that describe the flow of the nanomaterial are transmuted and parametrized through a similarity transformation. These resulting equations are simulated by utilizing the finite difference method to ensure numerical stability and accuracy. The results suggest that both variants of nanomaterials substantially improve thermal conductivity and energy transfer, with temperature and velocity distributions being influenced by temperature jump and velocity slip conditions. Moreover, thermal diffusion and diffusion-thermo processes result in a decrease in temperature and an increase in velocity and concentration. This study presents an advanced physics-driven framework for the development of biomedical devices and fluid-based therapeutic systems.