Endophytic fungi are important components of healthy plants and are increasingly recognized as potential sources of biologically active compounds. In the present study, the diversity of culturable endophytic fungi associated with the medicinal plant Clitoria ternatea L. was investigated. A total of 100 healthy plant segments, consisting of 25 segments each from leaves, stems, roots and flowers, were examined using a culture-dependent isolation method. Overall, 71 fungal isolates representing 10 taxa were recovered and identified on the basis of their morphological characteristics. The recovered fungi included Aspergillus nidulans, Fusarium oxysporum, Fusarium sp., Aspergillus flavus, Chaetomium sp., Penicillium sp., Alternaria alternata, Curvularia lunata, Cladosporium sp. and Aspergillus niger. Fungal recovery varied among the plant tissues, with the highest number of isolates obtained from leaves (27), followed by stems (19), roots (17) and flowers (8). The recovery of diverse fungal taxa from all four tissues indicates that C. ternatea harbours a varied culturable endophytic fungal community. These isolates provide a useful foundation for further studies on their biological activities and production of potentially valuable secondary metabolites.
Thin films of α-Fe₂O₃ (hematite) are desirable materials for optoelectronic, sensing, photocatalytic and photoelectrochemical applications. Fe₂O₃ thin film was deposited on glass using hydrothermal method using concentration of FeCl₃.6H₂O 0.25 M at PH 3.0, treated at 160 °C for 4 h and annealed at 500 °C for 5 h. XRD, SEM, EDS and current-voltage measurements were used to characterize the films. XRD peak matching resulted in the presence of characteristic peaks of α-Fe₂O₃. The SEM images showed a densely compacted granular structure with grains having a faceted shape and dimensions of around 1.5–2.5 µm. The major elements detected by EDS analysis were iron (54.08 wt%) and oxygen (45.92 wt%). The absorbance value for the UV-Visible reading ranged in the 370–1100 nm range between 0.82850 to 1.16931, with the maximum reading obtained at 838 nm. The Tauc analysis resulted in an apparent optical band gap of ~1.29 eV. This value however, should be interpreted with caution as it is based on results obtained from a film thickness measurement and no validated bare-glass reference. Current response was measured over the range of 0-14 V, and it was observed that the current response increased in this range, which shows a highly resistive film. Linear regression gave the result of I = 0.1179V − 0.0250 (I in pA), with a coefficient of determination (R²) of 0.9871, which is basically linear. However, it is not possible to calculate an intrinsic conductivity or resistivity reliably because of the unknown geometry of the electrodes and the thickness of the film.
Bacopa Monnieri (L.) Wettst., commonly known as Brahmi, is a well-known medicinal plant extensively used in traditional Ayurvedic medicine for its cognitive-enhancing and neuroprotective properties. This study aims to perform comprehensive phytochemical profiling and develop a High-Performance Thin Layer Chromatography (HPTLC) fingerprinting method to identify and characterize the bioactive markers responsible for its therapeutic potential. Preliminary phytochemical screening was carried out to detect major classes of secondary metabolites such as alkaloids, saponins, flavonoids, glycosides, terpenoids, and phenolic compounds. Silica gel 60 F254 plates were used for HPTLC analysis, and solvent systems were tuned to differentiate various phytochemicals, including terpenoids, alkaloids, flavonoids, tannins, and saponins. UV 254 nm, UV 366 nm, and visible light were used for detection after derivatization. Methanolic extracts of various plant parts of Bacopa Monnieri were subjected to HPTLC analysis using various solvent systems to achieve optimal separation. The developed chromatographic profiles were analyzed for the presence of key bioactive markers, including bacosides, alkaloids, saponins, flavonoids, sterols etc which are considered the principal active constituents present in the herbal plant Bacopa Monnieri. The HPTLC method provided clear and reproducible fingerprint patterns that can serve as a reliable tool for quality control, standardization, and authentication of Bacopa Monnieri extracts. The study not only confirms the presence of pharmacologically important phytochemicals but also demonstrates the effectiveness of HPTLC as a rapid and cost-efficient technique for bioactive compound identification. HPTLC fingerprinting and bioactivity assessment. HPTLC profiling revealed diverse band patterns, with notable peaks corresponding to various phytochemicals that may be narrowed down and validated by mass spectrometry. This work supports the integration of traditional knowledge with modern analytical tools to ensure the efficacy and safety of herbal formulations and paves the way for future pharmacological investigations of Bacopa Monnieri.
Nonlinear ordinary differential equations provide an important mathematical framework for studying changing systems. Many real world processes show nonlinear behaviour. Population growth. disease transmission. chemical reactions. biological interactions and physical processes often depend on several interacting factors. This paper discusses the basic concepts of nonlinear ordinary differential equations with a focus on analytical and numerical analysis. The study explains the difference between linear and nonlinear equations. It also discusses the role ofinitial conditions and boundary conditions in obtaining meaningful solutions. Analytical methods provide useful information about the behaviour of a mathematical model. Numerical methods help researchers study problems that do not provide simple analytical solutions. The paper also discusses the use of nonlinear ordinary differential equations in population growth. Disease modelling. Chemical processes and biological systems. The study shows that analytical and numerical approaches support mathematical modelling in different ways. Analytical analysis helps explain the theoretical behaviour of a system. Numerical analysis helps study complex systems through approximate solutions.
An estimated 22% of global greenhouse gas emissions result from the transportation industry, due principally to long-haul freight's reliance on fossil fuel. India's economy is growing faster than ever before, with an increasing demand for logistics; hence, the emission from the road transport sector is also on the rise. Thus, decarbonization has now an indispensable component of the nation’s action against climate change. This study examines the decision-making process of individuals and/or corporates in India’s long-haul truck segment and their willingness to pay for low-carbon fuels. By employing a mixed-methods approach, the study investigates the influence of climate awareness, behavioral attitudes, cost sensitivity, risk perception, and social norms on fuel choice. Through a structured questionnaire, primary data was collected from fleet owners, transport managers long haul truck drivers and a small sample of consumers. Secondary data was collected from policy report and academic studies. The awareness of climate change may be much higher than the actual responses. Many people know a lot about climate issues, but they are unwilling to pay for low-carbon fuels because they are more sensitive to price and more mindful of risks in infrastructure and operations. Nonetheless, social norms and client pressure can lead some people towards cleaner fuels. The study calls for a combination of policy coherence and market-driven approaches alongside behavioral efforts to enhance transport decarbonization in India.
