Bhairab Ganguly College

A fluorescent probe, H3L (1), has been synthesized via the 1:2 condensation of 4-ethyl-2,6-diformylphenol with 2-amino-4-methylphenol and characterized by elemental analysis, FT-IR, 1H and 13C NMR spectroscopy and ESIMS. UV-Vis absorption and fluorescence studies reveal pronounced solvent-dependent photophysical behaviour. The fluorescence spectra exhibit dual emission arising from the locally excited enol form and the excited-state intramolecular proton transfer (ESIPT)-generated keto tautomer. Fluorescence studies in the presence of triethylamine and acetic acid, together with DFT and TD-DFT calculations and frontier molecular orbital analyses, confirm the ESIPT process and suggest the involvement of competing excited-state non-radiative pathways, including ICT and PET. Probe 1 exhibits pronounced aggregation-induced emission enhancement (AIEE) in methanol-water mixtures, with maximum fluorescence intensity at a water fraction of 20%. The AIEE phenomenon is attributed to the formation of crystalline nanoaggregates that restricts intramolecular motions and is supported by DLS and SEM analyses as well as fluorescence studies at different temperatures and in methanolethyleneglycol mixtures of varying viscocity. Fluorescence investigations in the presence of various metal ions, including anti-interference experiments, demonstrate that 1 functions as a selective fluorescence turn-on sensor for Al3+. The sensing response originates from chelation enhanced fluorescence (CHEF) accompanied by suppression of non-radiative decay processes. An association constant of 1.6 & times; 104 M-1 and a detection limit of 0.427 mu M have been obtained for Al3+ sensing. Job's plot, Benesi-Hildebrand analysis, ESI-MS and 1H NMR titration collectively indicate 1:1 complexation between H3L and Al3+, for which a composition [Al(HL)(NO3)2]- has been proposed. Quantum-yield and time-resolved fluorescence measurements corroborate both AIEE and Al3+ sensing. Cell viability assays using MC3T3 murine osteoblast precursor cells demonstrate low cytotoxicity of both the probe and its Al3+ complex. Furthermore, fluorescence microscopy studies establish good cellmembrane permeability and effective intracellular response toward Al3+, highlighting the potential bioimaging applicability of the system. Comparison with previously reported systems highlights the salient features of the present study.

This study assesses landslide susceptibility in the Rudraprayag to Badrinath region of the North Western Himalayas, Uttarakhand, India, using spatial statistics and machine learning techniques. A dataset of 268 landslide and 268 non-landslide points was analyzed to explore the spatial distribution of landslides. To achieve this, advanced spatial analysis methods including Average Nearest Neighbor (ANN), Moran’s I, K Function Analysis, and Getis-Ord Gi* Hotspot Analysis were integrated with Random Forest modeling. This integration aims to improve landslide susceptibility mapping. As a result of these analyses, the study found notable clustering of landslides impacted by environmental and geomorphological elements such as slope, elevation, seismic activity, and river proximity. Furthermore, cold spots were identified in more stable locations, while hotspot analysis highlighted high-risk areas, mostly in regions with steep slopes, weak lithology, and proximity to river systems. The Random Forest model further revealed elevation, slope, seismic activity, urbanization, and rainfall as key contributing factors in landslide susceptibility. Based on these findings, the study highlights the necessity of focused mitigation methods in high landslide prone locations and clearly identifies zones of varied susceptibility. In summary, the findings provide valuable insights into landslide dynamics and contribute to more effective landslide risk management in the region.

Polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP) blends was used to reduce AgNO3 to silver nanoparticles (AgNP<50 nm) and fabricate electrospun fibrous films (EFFs) of PVA-PVP-AgNP, graphene oxide (GO)-embedded PVA-PVP-AgNP-GO, and PVA-PVP-GO composites. Compared to PVA-PVP, PVA-PVP-AgNP-GO EFFs exhibited significant enhancements in crystallinity (45-65 %), protein adsorption (PA: 59-238 mu g, indicating improved biocompatibility), AC electrical conductivity (sigma: 0.051-2.78 S/m), dielectric permittivity (epsilon: 8.0-28, while maintaining a low loss tangent tan delta approximate to 0.14 at 1 kHz), and optical band gap (E-g: 1.1-0.78 eV). These findings highlight that GO/AgNP incorporated PVA-PVP composites for the next-generation biomedical, optoelectronic, and electronic applications.

Low-Density Polyethylene (LDPE) accumulation in marine and terrestrial ecosystems poses a planetary crisis, requiring sustainable bioremediation. Existing microbial degradation studies are often limited by a reliance on abiotic pretreatments (e.g., UV or thermal oxidation) and terrestrial isolates that lack environmental resilience. To address this gap, this study investigates the biodegradation potential of novel lipolytic bacterial strains Bacillus tropicus SBAA01 and Pseudomonas aeruginosa SBAA02 isolated from the Sundarbans mangrove ecosystem, India; a UNESCO World Heritage Site & Ramsar wetland. The untreated LDPE films were incubated for 42 days under co-metabolic conditions. Upon benchmarking against a previously isolated reference strain, Bacillus pacificus SBAA07, B. tropicus SBAA01 emerged as the most potent candidate. It achieved a net dry weight loss of 13.63% ± 0.01 with a calculated half-life (t1/2) of 198 days. Topographical analysis via Atomic Force Microscopy (AFM) revealed a significant 736% increase in surface root mean square roughness (Rq), correlating with deep pitting observed in Scanning Electron Microscopy (SEM). Conversely, P. aeruginosa SBAA02 showed significant surface disruption Rq value increase of 142% but lower weight loss efficiency of 8.89% ± 0.01. Critically, while B. tropicus SBAA01 drove the highest physical erosion; it maintained a moderate Carbonyl Index (CI) of 0.184. This suggests oxidative carbonyl intermediates are assimilated into biomass faster than they accumulate on the surface. These findings suggest the Sundarbans as a genetic reservoir for stress-tolerant biocatalysts capable of degrading pristine polymers without prior oxidation. This offers a scalable, nature-based solution for plastic waste management.

Electroencephalography (EEG) is a noninvasive technique used to record brain electrical activity. It has been widely explored for brain–computer interface applications, including imagined speech analysis. However, handling and utilizing large EEG datasets have challenges due to their size. We utilize Siamese neural networks (SNNs) to improve data utilization through pairwise learning by creating multiple training combinations of EEG data. SNNs are well-suited for tasks that involve comparing two or more inputs, making them ideal for analyzing EEG signals. This research focuses on utilizing SNNs to identify different brain states based on EEG signal analysis. A novel publicly available collection of electroencephalogram (EEG) recordings extracted from the imaginary pronunciation of two sets of Spanish words by 15 healthy people is presented. The first set includes 5 Spanish vowels, while the second set represents the instructions up, down, front, back, right and left. EEG signals were captured at 1024 Hz using a six-channel acquisition device. Each word was repeated fifty times by every randomly chosen participant. For contrast, certain blocks were recorded under the "pronounced speech" setting, which involves simultaneously acquiring audio and EEG signals. Subsequently, a DWT-based preprocessing and autoencoder-assisted Siamese Neural Network with triplet loss is proposed for EEG signal analysis. The results indicate that different brain states can be accurately identified with a high degree of accuracy using the proposed methodology.