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In scenarios where smallpox has been eradicated, the most important human orthopoxvirus pandemic is monkeypox (MP), which is a zoonotic disease that spreads more often. MP is difficult to diagnose, as it has characteristics similar to smallpox, chickenpox (CP), and measles (MS). Polymerase chain reaction (PCR), computerized histopathological nucleic acid amplification test (NAAT), immunohistochemistry, enzyme-linked immunosorbent assay (ELISA), and electron microscope-based skin lesion testing are used for MP detection. But, these tests are time-consuming and may lead to false decision. Therefore, automation in MP detection using skin lesions images is needed for developing affordable and fast solutions. In this work, we have proposed a deep convolutional neural network architecture named the Softflatten network (Softflatten-Net) to identify and categorize MP using skin lesions. This architecture helps to avoid gradient vanishing and overfitting problems. Classification has been conducted in seven distinct scenarios: MP versus normal (NH), MP versus MS, MP versus CP, MP versus other, MP versus CP versus NH, MP versus CP versus MS, and MP versus CP versus MS versus NH. Among binary classification tasks, the Softflatten-Net achieved the maximum classification accuracy (Ac) of 97.25%, a sensitivity (Sen) of 92.94%, and a precision (Pr) of 96.48% in classifying MP from other categories of skin images. In the three-class classification task (MP versus CP versus NH), the proposed network achieved an Ac of 94.71%, a Pr of 92.11%, and a Sen of 87.87%. In the four-class classification scenario, namely, MP versus CP versus MS versus NH, the proposed model achieved the Ac, Pr, and Sen values of 90.73%, 84.21%, and 69.38%, respectively. Furthermore, we have considered 4241 skin images from IEEE data port for nine-class classification and 28771 images from Dermnet for 20-class classification to validate the performance of the proposed Softflatten-Net architecture. The proposed architecture performed satisfactorily on all the three datasets considered in this work and outperformed the existing state-of-the-art models in the literature. A postimage analysis is performed via local interpretable model-agnostic explanations (LIMEs), gradient class activation maps (G-CAMs), and occlusion sensitivity (OS) approaches. In addition, to analyze misclassification, the stochastic neighbor embedding distributed with t (t-SNE) is employed. Visualization techniques aided in identifying the image regions most pertinent for classification. The proposed network can be utilized for early and reliable detection of MP using images of skin lesions.

In this paper, we examine issues and policies for transformation of Indian economy to achieve higher growth with equity in the next 2.5 decades as India aspires to be a developed country by 2047. We also highlight the structural challenges and opportunities for the Indian economy. The objective is to provide a broad assessment of the factors that need to be critically addressed for India to not only achieve and sustain a high rate of growth but also equity.

In this paper, the effect of the addition of 2 wt. % of both copper oxide (CuO) and hexagonal boron nitride (h-BN) nanoparticles in rice bran and sesame grease on the tribological properties has been investigated. The nano additive-based grease synthesized by sonochemical homogenization technique was further analyzed to observe the physicochemical and rheological characteristics by carrying out FT-IR, dropping test, zeta potential, UV spectroscopy, and viscosity test. The nano additive-based grease samples showed a zeta-potential value of −43mV compared to virgin grease samples (<−30mV) which suggested a stable suspension of nano additive-based grease, which exhibited superior values even after 6 months. The nano additive-based grease also represented a consistent network microstructure that made the grease more resistant hence enhancing its viscosity; of which rice bran + CuO grease reported the highest viscosity. The tribological analysis was done by conducting experiments on the ball-on-disc configurations using a reciprocating test rig, under varying loading conditions from 10-50 N, 2 mm stroke length, and a frequency of 50 Hz. The addition of nanoparticles resulted in a substantial decrease in coefficient of friction (COF) and wear when compared to virgin grease. A reduction in COF by 27% and 43% upon the addition of h-BN and CuO respectively in virgin rice bran and by 23% and 36% upon the addition of h-BN and CuO respectively in virgin sesame grease was observed. The addition of CuO showed a better reduction in wear as compared to h-BN in both rice bran and sesame grease. Scanning electron microscopy analysis suggested that the film formation capability of the nanoparticles especially CuO nanoparticles is the main reason for the improved tribological behavior. It was further verified by the 3D profilometer analysis that reported the lower Ra values for the worn-out samples for grease containing h-BN and CuO nanoparticles.

Covalent organic frameworks (COFs) represent bulk crystalline organic polymers characterized by their significant surface area and well-organized pores. By carefully selecting building blocks, we can tailor COF structures with strategically placed heteroatoms. This deliberate inclusion enhances their stability, surface area, and guest-binding ability, which make them highly versatile catalyst supports. Importantly, heteroatoms within COF pores under an applied potential can amplify the catalyst’s desired properties. FeOOH, when grown as nanosized QDs, can exhibit semiconducting band gaps, and their amorphous form can be rich in defects, making them catalytically active. Here, we synthesized a heterogeneous electrocatalyst (FeOOH@COF) consisting of FeOOH supported on an imine-linked covalent organic framework (IISERP-COF33) under mild conditions. Electron microscopy, ICP, and XPS validate the homogeneous distribution (13.2 wt %) of nanosized FeOOH quantum dots (QDs) within the COF (average particle size distribution: 2.7 nm). Thus, the Lewis–Bronsted acidity-rich FeOOH accommodated at keto and nitrogen-rich anchoring sites within the COF pores defines the active and recyclable electrocatalyst. This composite system catalyzes ambient condition ammonia production from nitrogen with a yield of 77.4 μg h–1mgcat–1and faradaic efficiency of 46.4% at −0.4 V in 0.1 M LiClO4aqueous solution, surpassing other COF and iron-based electrocatalysts reported thus far. This work elucidates an accessible aqueous-stable COF-QD electrocatalyst for energy-efficient ammonia production.

In this study, we have investigated the ultrafast carrier dynamics in the binary transition metal oxide, α- CoV2O6, using nondegenerate pump (3.14 eV)–probe (1.57 eV) transmission spectroscopy at room temperature. A detailed look at the time-resolved differential transmission data taken at various pump fluence excitations reveals the presence of two types of traps in the journey of photoexcited electrons: the shallow trap states and deep trap states. This finding is corroborated by steady-state absorption and photoluminescence spectra. First-principles density functional theory calculations reveal that these trap states originate from oxygen vacancies in α- CoV2O6. The empirical kinetic model consisting of these two trap states further indicates that the photoexcited electrons become trapped in these states on ultrafast timeframes: the trapping time in shallower traps is about 2 ps, whereas the capture time in deeper midgap traps takes about 30 ps. It further indicates that approximately 44% and 40% of the excited electrons are trapped in the shallow and midgap trap states, respectively, highlighting their crucial role in the optical properties of α- CoV2O6.

In this article, for the first time, we demonstrate the fabrication of low-cost split gate bulk planar junctionless field-effect transistor (SG-BPJLFET) for biosensing applications. In the fabrication process, the junctionless concept has been incorporated to reduce the thermal budget, random dopant fluctuations, and the fabrication complexities of the device. The measured transfer characteristics of the fabricated SG-BPJLFET show improved $I_{{ON}}$ / $I_{{OFF}}$ ratio of $\sim 10^{{7}}$ with the calculated mobility of 9.72 cm2/V $\cdot $ s and the charge carrier density of $1 \times 10^{{18}}$ cm $^{-{3}}$ . For the fabricated device, Debye’s length is found to be 6.45 nm, which can be used for the detection of protein complexes (such as streptavidin–biotin) by using $0.01 \; \times $ phosphate buffer solution (PBS) in solution. The fabricated device is further simulated for the detection of biomolecules such as streptavidin and biotin. From the simulated results, it has been observed that the device has the capability to be used as a biosensor for detection of various protein complexes with higher sensitivity and selectivity.

Real-time rendering applications leverage heterogeneous computing to optimize performance. However, software development across multiple devices presents challenges, including data layout inconsistencies, synchronization issues, resource management complexities, and architectural disparities. Additionally, the creation of such systems requires verbose and unsafe programming models. Recent developments in domain-specific and unified shading languages aim to mitigate these issues. Yet, current programming models primarily address data layout consistency, neglecting other persistent challenges.In this paper, we introduce RenderKernel, a programming model designed to simplify the development of real-time rendering systems. Recognizing the need for a high-level approach, RenderKernel addresses the specific challenges of real-time rendering, enabling development on heterogeneous systems as if they were homogeneous. This model allows for early detection and prevention of errors due to system heterogeneity at compile-time. Furthermore, RenderKernel enables the use of common programming patterns from homogeneous environments, freeing developers from the complexities of underlying heterogeneous systems. Developers can focus on coding unique application features, thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.

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AbstractHoney bees provide key ecosystem services i.e., pollination but are functioning under various biotic and abiotic stresses. Among various stresses, thiamethoxam has been criticized worldwide. Since no scientific study on its sub-lethal effects on honey bees has been conducted in India, the present work was carried out at the Department of Entomology, Punjab Agricultural University, Ludhiana for two seasons during 2017-19. The median lethal dietary dose (LDD50) of thiamethoxam upon chronically feeding the sub-lethal doses for 10 days for a group of adult Apis melliferabees (age 10 days) was 0.421 ng bee−1 day−1. At this level, the reduction in the area of an acinus of the hypopharyngeal gland was significant i.e., 36.62 and 37.92 percent over control during spring and winter seasons, respectively. The adverse impact of thiamethoxam on hypopharyngeal glands will be limiting the development and productivity of the honey bee colonies.

IMPORTANCE: For patients treated with immune checkpoint inhibitors (ICIs), recent data suggest that obesity has a beneficial effect on survival outcomes in various cancer types. Reports on this association in head and neck cancer are limited. OBJECTIVES: To compare overall survival (OS) to 5 years and functional outcomes in patients with head and neck squamous cell carcinoma (HNSCC) treated with ICIs based on pretreatment body mass index (BMI). DESIGN, SETTING, AND PARTICIPANTS: This retrospective population-based cohort study used data obtained from the TriNetX Global Collaborative Network database to identify patients with HNSCC who received ICI treatment between January 1, 2012, and December 31, 2023, resulting in a total of 166 patients (83 with BMI of 20.0-24.9 [normal BMI] and 83 with BMI of ≥30.0 [obesity BMI]) after propensity score matching (PSM) for pretreatment medical comorbidities and oncologic staging. EXPOSURE: Normal BMI vs obesity BMI. MAIN OUTCOMES AND MEASURES: Overall survival and functional outcomes (dysphagia, tracheostomy dependence, and gastrostomy tube dependence) were measured to 5 years after ICI treatment and compared between patients with obesity BMI and normal BMI. Additional analyses compared OS and functional outcomes in the cohort with normal BMI and cohorts with overweight BMI (25.0-29.9) and underweight BMI (&lt;20.0). RESULTS: Among the 166 patients included in the PSM analysis (112 men [67.1%]; mean [SD] age, 62.9 [15.4] years), obesity BMI was associated with significantly improved OS at 6 months (hazard ratio [HR], 0.54 [95% CI, 0.31-0.96]), 3 years (HR, 0.56 [95% CI, 0.38-0.83]), and 5 years (HR, 0.62 [95% CI, 0.44-0.86]) after ICI treatment, compared with patients with normal BMI. Obesity BMI was also associated with decreased risk of gastrostomy tube dependence at 6 months (odds ratio [OR], 0.41 [95% CI, 0.21-0.80]), 1 year (OR, 0.41 [95% CI, 0.21-0.78]), 3 years (OR, 0.35 [95% CI, 0.18-0.65]), and 5 years (OR, 0.34 [95% CI, 0.18-0.65]). Obesity was also associated with decreased risk for tracheostomy dependence at 1 year (OR, 0.52 [95% CI, 0.28-0.90]), 3 years (OR, 0.45 [95% CI, 0.45-0.90]), and 5 years (OR, 0.45 [95% CI, 0.45-0.90]). There were no differences in rates of dysphagia or immune-related adverse events between cohorts at any points. CONCLUSIONS AND RELEVANCE: Using population-level data for patients with HNSCC treated with ICIs, these results suggest that having obesity was associated with improved 6-month, 3-year, and 5-year OS compared with having normal BMI. Additionally, obesity was associated with decreased gastrostomy and tracheostomy tube dependence compared with normal BMI. Further investigation is required to understand the mechanism of these findings.

The present work aims to develop an object tracking controller for the Stewart platform using a computer vision-assisted machine learning-based approach. This research is divided into two modules. The first module focuses on the design of a motion controller for the Physik Instrumente (PI)-based Stewart platform. In contrast, the second module deals with the development of a machine-learning-based spatial object tracking algorithm by collecting information from the Zed 2 stereo vision system. Presently, simple feed-forward neural networks (NN) are used to predict the orientation of the top table of the platform. While training, the x, y, and zcoordinates of the three-dimensional (3D) object, extracted from images, are used as the input to the NN. In contrast, the orientation information of the platform (that is, rotation about the x, y, and z-axes) is considered as the output from the network. The orientation information obtained from the network is fed to the inverse kinematics-based motion controller (module 1) to move the platform while tracking the object. After training, the optimised NN is used to track the continuously moving 3D object. The experimental results show that the developed NN-based controller has successfully tracked the moving spatial object with reasonably good accuracy.

AbstractMillettia pinnatais an important medicinal plant that has been used as a treatment of various diseases due to presence of wide range of pharmacological properties. The plant contains quercetin, kaempferol, karanjin, pongaglabrone, kanjone, kanugin, gammatin, pongaglabol, and other bioflavonoids. Kaempferol is a natural flavonol that shows many pharmacological properties including anti-inflammatory, antioxidant, anticancer, and antidiabetic activities etc. The enzyme flavonol synthase (FLS, EC 1.14.20.6) catalyses the conversion of dihydroflavonols to flavonols, i.e. biosynthesis of kaempferol from dihydrokaempferol. The current work examined the binding affinity-based approach to improve the enzyme catalytic activity using computational methods. Sequential site-directed mutagenesis was used to create four mutants with the goal to increase hydrogen bonds and further improving the ligand (dihydrokaempferol) binding efficiency. Simulations were done to monitor the stability of the mutants followed by molecular docking to confirm interactions with ligand. For structure validation, various dynamic analysis like RMSD, RMSF, ROG, SASA, H-bond, PCA, DCCM, and FEL were performed, which predicts the stability of wild-type (WT) proteins and mutants. The Mutant_2 and Mutant_3 showed maximum H-bonding and better stability than other mutants and WT that proved higher affinity suggesting improved catalysis. Mutant_2 and Mutant_3 exhibited binding affinities of −7.6 and −8.2 kcal/mol, respectively for the ligand. The outcome of present study will provide significant improvement in synthesis of kaempferol and other plant-based flavonoids.Communicated by Ramaswamy H. Sarma

Advancements in nanotechnology have brought conventional solar cell efficiency closer to the Shockley-Queisser limit (33%). However, challenges such as thermalization and rapid cooling dynamics impede the extraction of surplus energy. Carrier temperature (Tc) reflects post-photoexcitation hot carrier states, crucial for understanding cooling dynamics and material selection in hot carrier solar cell (HCSC) development. This study presents hot carrier temperature and its time evolution obtained from time-resolved emission spectra (TRES) of semiconductor nanostructures (CdS/ZnS core/shell and CsPb(Cl/Br)3perovskite quantum dots) using femtosecond optical gating (FOG) spectroscopy. We compare three TRES analysis methods: peak-frequency shift (PFS) using lognormal distribution modeling, Maxwell–Boltzmann distribution modeling of the high-energy tail (HET), and full lineshape (FLS) modeling using Gaussian linewidth convoluted with Maxwell–Boltzmann distribution. Our analysis demonstrates that PFS heavily overestimates cooling time and fails to report carrier temperatures. While HET provides valuable insights, it is susceptible to fitting-range bias. Conversely, FLS offers both carrier temperatures and cooling time metrics, making it the preferred method to obtain hot carrier dynamics from TRES, objectively.

AbstractAcute pancreatitis develops as a local inflammatory condition of the pancreas due to a wide range of factors including overdose of alcohol or drugs, gall stones, etc. At present, medications based on herbal formulations have gained importance due to their better safety profile. Current study assessed the pharmacological effects of Pancogrit, an herbal medicine, in caerulein and TNF-α-induced in vitropancreatitis models. A comprehensive evaluation of phytometabolites present in Pancogrit was conducted and 85 phytometabolites were identified through UPLC-QTOF-MS and UPLC-UV. The bioactivity and mode of action of Pancogrit were assessed in caerulein-induced AR42J (rat pancreatic acinar) cells, TNF-α-induced PANC-1 (human pancreatic duct) cells, and NF-κB reporter cells. Pancogrit was found to decrease the elevated amylase and lipase activity in caerulein-induced AR42J cells. It ameliorated the increased ROS levels, IL-6, IL-8 release and NF-κB activity in TNF-α-induced PANC-1 and NF-κB reporter cells. Pancogrit also downregulated IL-6, IL-1β, ICAM-1, and STAT3 gene expression in PANC-1 cells. Moreover, Pancogrit inhibited the protein expression levels of phosphorylated STAT3, phosphorylated and total JAK2 in TNF-α-induced PANC-1 cells. Taken together, Pancogrit is a clinically relevant therapeutic agent for mitigation of pancreatitis.

Purpose: Project-based learning is one of the most effective methods of transferring academic knowledge and skills to real-world situations in higher education. However, its effectiveness is not much investigated focusing on the students' narrative. This study aims at evaluating the students' experience and perspective on adopting project-based learning in master by research and doctoral programmes for proactive skills development. Design/methodology/approach: This study evaluates the self-reflection of 10 postgraduate students and their supervisor who have participated in developing a software tool for solar photovoltaics (PV) integrated building envelope design, management and the related education. Findings: Findings reveal that the students have effectively improved their knowledge on the subject via collaborating with the industry, self-learning/observation, peer learning, problem-solving and teamwork. Dividing the project into student-led tasks has improved the decision-making and leadership skills, risks identification, planning and time management skills. The overall experience has (1) built up confidence in students, (2) enhanced their creativity and critical thinking and (3) improved their proactive skills and context knowledge. Originality/value: A clear research gap can be seen in exploring the effectiveness of project-based learning for master by research and doctoral programmes, which mainly focus on extensive research. These programmes do not necessarily focus on developing students' proactive skills, which is the main requirement if they intend to work in the construction industry. This paper addresses the above research gap by demonstrating the effectiveness of project-based learning for developing the proactive skills in a research-intensive learning environment.

One-dimensional organic nanotubes feature unique properties, such as confined chemical environments and transport channels, which are highly desirable for many applications. Advances in synthetic methods have enabled the creation of different types of organic nanotubes, including supramolecular, hydrogen-bonded, and carbon nanotube analogues. However, challenges associated with chemical and mechanical stability along with difficulties in controlling aspect ratios remain a significant bottleneck. The fascination with structured porous materials has paved the way for the emergence of reticular solids such as metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and organic cages. Reticular materials with tubular morphology promise architectural stability with the additional benefit of permeant porosity. Despite this, the current synthetic approaches to these reticular nanotubes focus more on structural design resulting in less reliable morphological uniformity. This Account, highlights the design motivation behind various classes of organic nanotubes, emphasizing their porous interior space. We explore the strategic assembly of organic nanotubes based on their bonding characteristics, from weak supramolecular to robust covalent interactions. Special attention is given to reticular nanotubes, which have gained prominence over the past two decades due to their distinctive micro and mesoporous structures. We examine the synergy of covalent and noncovalent interactions in constructing assembly of these nanotube structures.

The government had circumscribed all the economic activities due to the COVID-19 pandemic. Consequently, this had a major impact on the self-employed. Using a structured questionnaire, this study intends to explore how self-employed people are rescued from detrimental situations. This is to assess the early impact of COVID-19 on self-employed people and their working hours. Most of these economic ventures operate on the borders between the formal and informal sectors. Those working in these sectors have generally been excluded from the discussion about the economic impact of this pandemic. This present study emphasised the after-effects of COVID-19 and how the self-employed community was rescued with or without government aid.

In the quest to fathom unknown horizons and to know what is beyond our home planet Earth, mankind has been trying since long, to reach into the vacuum of deeper space and beyond. Nations all over the world are working vigorously on manned space missions, in the hope of making humankind a multiplanetary species. Indian Space Research Organisation (ISRO) has also been moving forward in similar lines and has elaborate plans to send humans to space. Before sending humans, a Half Humanoid which is being designed, developed and manufactured by ISRO is planned to make the first space travel from the Indian soil. As part of it, an anthropomorphic humanoid skull was designed and manufactured to be used in the Half Humanoid model. The method selected for the manufacturing of the skull was Metal Additive Manufacturing (AM) via the DMLS process (Direct Metal Laser Sintering). As the AM route is capable of manufacturing complex features, further mass reduction of the skull was achieved with the help of artificially intelligent incorporation of lattice structures into the design. The strength and stiffness requirements along with the lattice parameters were given as constraints during the design optimisation. An elaborate coupon study was conducted to understand the right philosophy of incorporating lattices structures to replace solid material from the structure. Insights from this study were used to select appropriate material volume on the structure where lattices would be inducted. The skull design that is incorporated with lattice structure was metal additive manufactured.

It is a great honour and privilege to deliver the Presidential Address at the 58th Annual Conference of the Indian Econometric Society (TIES). I am grateful to the Society for conferring this honour on me. TIES is one of the oldest and most reputed professional societies in the country. Eminent econometricians, economists, Trustees, office bearers of the society and other stakeholders have strengthened TIES since 1960. The annual conferences of TIES provide opportunities for young scholars to present research papers and interact with experts from India and abroad. I remember the wide participation of experts, senior and young researchers and students when we organised the 50th annual conference (Golden Jubilee) of TIES at IGIDR, Mumbai in 2013. Personally, I have learned a lot from the activities and publications of the Society in the last four decades. I am happy to note that this conference has a session on the contributions of Prof. C.R. Rao, the doyen of statistics. I would like to acknowledge that my knowledge in econometrics is due to the excellent teaching of Prof. K.L. Krishna and late Prof. A.L. Nagar at the Delhi School of Economics. Keeping in view of the broader interests of TIES, I have chosen to speak on “Regional Dimensions: Economic Growth, Inclusive and Sustainable Development. My Ph.D. topic was on inter-regional disparities in India. I am happy to revisit on regional dimensions after 40 years in a comprehensive way covering growth, inclusion and sustainability.

A well-documented Achilles heel of current cancer immunotherapy approaches is T cell exhaustion within solid tumor tissues. The proinflammatory cytokine interleukin (IL)-23 has been utilized to augment chimeric antigen receptor (CAR) T cell survival and tumor immunity. However, in-depth interrogation of molecular events downstream of IL-23/IL-23 receptor signaling is hampered by a paucity of suitable cell models. The current study investigates the differential contribution of IL-2 and IL-23 to the maintenance and differentiation of the IL-23 responsive Kit225 T-cell line. We observed that IL-23 enhanced cellular fitness and survival but was insufficient to drive proliferation. IL-23 rapidly induced phosphorylation of STAT1, STAT3, and STAT4, and messenger RNA expression of IL17A, the archetypal effector cytokine of T helper 17 (Th17) cells, but not their lineage markers RORCand NCR1. These observations suggest that IL-23 endowed Th17/ILC3-like effector function but did not promote their differentiation. In contrast, spontaneous differentiation of Kit225 cells toward a Th17/ILC3-like phenotype was induced by prolonged IL-2 withdrawal. This was marked by strongly elevated basal IL17Aand IL17Fexpression and the secretion of IL-17. Together, our data present Kit225 cells as a valuable model for studying the interplay between cytokines and their contribution to T cell survival, proliferation, and differentiation.IL-2 maintained Kit225 cells in a proliferative and undifferentiated state. Its withdrawal initiated their spontaneous differentiation into a Th17/ILC3-like phenotype.

Smart sensors have revolutionized the world with their capability of capturing physical parameters. The sensors provide vital data to the monitoring station in the applications of environment, disaster management, transportation, etc. A framework is built up with an Internet of Things-based approach that detects vehicle accident with the sensors and the information is stored and monitored on the Thingspeak cloud. The alert is raised whenever an accident occurs to the vehicle with the help of an accelerometer, ultrasonic sensor, and GPS. The message of the real-time coordinates is transmitted with the communication module. The monitoring station takes the necessary action by recognizing the accident occurred and providing the emergency services to the hotspot immediately. The integrated approach will be beneficial as it will detect any mishap with the vehicle and facilitate the emergency help to the passengers in the vehicle. The proposed framework will be beneficial for saving the human life in any smart city.

Addressing the need for sustainable alternative fuels amid depleting petroleum resources and environmental concerns, this study investigates the conversion of abundant and eco-friendly lignocellulosic biomass, the invasive weedy plant Tithonia diversifolia(TD), through pyrolysis to bio-oil. The influence of ZSM-5 catalysts with Ni and Co impregnation is also explored on fixed bed-pyrolytic conversion as well as employing various kinetic and thermodynamic models. Characterization techniques including XRD, SEM, and EDX confirm successful metal loading while preserving microstructure. Catalysts significantly impact product distribution, and physicochemical analysis alongside NMR and GC-MS data reveal improved bio-oil properties, including reduced O content, increased C content, and higher calorific values when catalysts are employed. Kinetic analysis suggests lower activation energies for catalytic pyrolysis (Ea∼105–154 kJ/mol) compared to non-catalytic processes (Ea∼160 kJ/mol), emphasizing the catalytic role in enhancing biomass conversion. Thermodynamic analysis further supports catalytic enhancements of biomass devolatilization. This study offers valuable perspectives on the pivotal role of catalysts in biomass conversion.

Understanding the nanofluid flow boiling phenomena is essential for many engineering applications including atomic reactors, cooling of electronic devices, synthetic buildings and spacecraft. Hybrid nanofluids, which are commonly defined as mixtures of two or more nanoparticles, are yet unknown to have improved features. When the coupled nanoparticles are suitably coated with one another, one on top of the other, this would be a successful technique as the usage of such nanofluids is hardly reported. The current research aims to investigate the effects of various novel hybrid ZnOcoated Cu(Cu@ZnO) nanofluid concentrations on the critical heat flux (CHF) and heat transfer coefficient (HTC) for their potential real-life application. The Cu@ZnO nanoparticles with hybrid properties were selected because Cu increased the conductivity of heat whereas ZnO improved chemical durability. Efficient spark discharge in liquid nitrogen was employed in three stages to produce robust Cu@ZnO nanoparticles with hybrid properties. In order to create durable nanofluids, the nanoparticles with mixed properties were dispersed using an ultrasonication process at four different volumetric concentrations: 0.025%, 0.050%, 0.075% and 0.1%. At 45°C, the nanofluids’ thermal conduction was 31% higher than that of base fluid. The flow boiling (subcooled) tests are carried out in minichannel with novel nanofluids. When the flow boiling investigation was being conducted, a greater concentration improved the HTC and CHF. In comparison to water, 0.1% of nanofluid caused the maximum rises in CHF and HTC, which were found to be 168.09% and 186.9%, respectively. It is possible for the fluid to utilize latent heat, create bubbles, distinct and delay the commencement of CHF because the thick Cu@ZnO-coated surface possesses strong thermal conductivity.