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Tetracycline antibiotics are among the most prevalent emerging pollutants in aquatic environments, posing significant concerns owing to the emergence of resistance genes. Although biochar-based adsorbent materials have shown success in removing TCs from water sources, the implications of surface alteration on these processes and their ecological consequences remain little understood. This research presents the systematic comparative assessment of three biochar-based adsorbents: pristine biochar (BC), activated biochar (AC/BC), and Fe/N codoped biochar (Fe/N BC), comprising comprehensive physicochemical analyses, adsorption studies, and life cycle analysis, for tetracycline removal. Thorough characterization through BET, SEM, TEM, XRD, FTIR, Raman spectroscopy, elemental analysis, and zeta potential measurements demonstrated that Fe/N co-doping significantly increased specific surface area, introduced nitrogen functionalities, and created iron-oxide active sites. Fe/ N BC exhibited a TC adsorption capacity of 283 mg g(-1), surpassing that of BC and AC/BC. Such a high adsorption capacity is attributable to a synergistic interplay of mechanisms, including electrostatics, it-it interactions, Femediated surface complexation, and pore-filling effects, as demonstrated by pseudo-second-order kinetics and by the Sips isotherm model. Significantly, Fe/N BC demonstrated efficacy in adsorption, maintaining performance after several regeneration cycles, and its adsorption capabilities were evaluated using actual wastewater samples. Ultimately, life cycle evaluation revealed that the superior removal effectiveness of TC per kilogram of adsorbent by Fe/N BC and AC/BC renders the environmental consequences of their manufacture insignificant compared to BC. The findings of this study indicate that Fe/N-doped biochar is an ecologically acceptable adsorbent and can be used in scaled-up adsorption-based treatment systems.
A novel ionic liquid-functionalized graphitic carbon nitride/graphene oxide (g-C3N4-IL@GO) nanocomposite was successfully synthesized through a facile multi step strategy. Graphitic carbon nitride (g-C3N4) was prepared by thermal polymerization and then functionalized with a DABCO-based ionic liquid via the substitution of surface active sites with 1,4-dibromobutane and functionalized DABCO to afford g-C3N4-IL. The functionalized material was then combined with graphene oxide sheets by interfacial assembly. This approach facilitated the development of a hybrid layered nanostructure with enhanced physicochemical properties for energy storage applications. The successful functionalization and nanocomposite formation were verified by complementary structural and surface analysis methods such as (XRD), (FTIR), (SEM), (EDS), (XPS), (BET), and (TGA). The electrochemical behavior of the g-C3N4-IL@GO electrode material was comprehensively analyzed using CV, GCD, and EIS in a three electrode system. The CV curves exhibited redox features and a total charge-storage mechanism comprising of Faradaic and electric double-layer capacitance. The GCD curves showed a high specific capacitance value of 425F/g at 2 A/g with a high current processing ability and good utilization of electroactive sites with a favorable ion-accessibility in the electrode. The excellent cycling stability of the electrode with 93% of initial capacitance retained after 10,000 successive cycles demonstrated that the electrode possessed robust structural integrity and long-term electrochemical durability. The energy and power densities were calculated from the GCD curves of the three-electrode system and found to be as high as 35 Wh/kg and 549 W/kg, respectively. To sum up, these results demonstrate the synergistic effect of the g-C3N4 framework, ionic liquid group and graphene oxide layer structure, which leads to fast charge transport, high reversibility, and better electrochemical performance. Thus the composite could be considered a potential candidate for supercapacitor electrodes.
Emotional Intelligence (EI) is progressively regarded as a key aspect in determining how well employees perform, especially in service-focused fields like banking. The paper explores how EI affects the Job Performance (JP) of personnel working in public sector banks in India. To investigate this, a descriptive research method is used and gathered primary data from 400 employees at branches of SBI and PNB in Himachal Pradesh, utilizing a structured questionnaire. Using SmartPLS 4 for PLS- SEM, measurement model and structural linkages are assessed. Results demonstrate that various dimensions of EI have a statistically significant and positive impact on JP. This highlights value of emotional skills in fostering employee adaptability, resilience, and productivity. The study expands present literature by presenting a multi-faceted view of linkage between EI and JP in realm of public banking. Additionally, it presents practical recommendations for HR management, highlighting need to incorporate EI into training, performance management, and organizational policies to maximize efficiency.
This study introduces an AI-driven integrated framework for predicting and optimizing the performance of turbo air classifiers, addressing the limited application of advanced intelligence techniques in fine-particle processing. A turbo air classifier was examined using three operational inputs, rotor speed (561-1739 rpm), primary air flow (98.87-351.13 m(3)/h), and secondary air flow (6-74 m(3)/h), to predict two key performance indicators: cut size (CS) and classification accuracy index (CAI). Multilayer perceptron neural networks (MLPNNs) were optimized using modified particle swarm optimization (MPSO), marine predators algorithm (MPA), and gray wolf optimizer (GWO). MPSO-MLPNN yielded the best CS predictions (R > 0.999), while GWO-MLPNN achieved the most accurate CAI predictions (R > 0.99). Pareto-based multi-objective bat algorithm (MOBA) was then applied to minimize CAI while constraining CS within 15-18 mu m and 18-21 mu m. The Pareto results revealed a clear trade-off: CAI decreased from similar to 2.30 to similar to 1.65 as CS increased slightly in the fine separation regime and stabilized at similar to 1.58-1.60 for coarser separation. Optimal conditions showed that fine separation requires high rotor speed with moderate-high airflow, whereas coarser, energy-efficient operation is achievable with lower rotor speeds and high airflow.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive failure, memory impairment, and behavioral disturbances. The disease is associated with complex pathological mechanisms including amyloid-β (Aβ) plaque deposition, tau hyperphosphorylation, oxidative stress, mitochondrial dysfunction, and chronic neuroinflammation. Despite extensive research, currently available therapeutic options provide only symptomatic relief and fail to halt disease progression. Consequently, increasing attention has been directed toward natural bioactive compounds with multi-target therapeutic potential. Marine ecosystems represent a vast reservoir of structurally unique biomolecules, among which marine-derived polysaccharides have emerged as promising candidates for neuroprotection. Polysaccharides such as fucoidan, alginate, carrageenan, chitosan, ulvan, chondroitin sulfate, and hyaluronic acid exhibit diverse biological activities, including antioxidant, anti-inflammatory, anti-amyloidogenic, and neuroprotective effects. These biomolecules can modulate several critical intracellular signaling pathways implicated in AD pathology, including the NF-κB, MAPK, PI3K/Akt/GSK-3β, Nrf2/ARE, STAT3, and NLRP3 inflammasome pathways. By regulating these pathways, marine polysaccharides can reduce oxidative stress, suppress neuroinflammatory responses, inhibit amyloid aggregation, attenuate tau pathology, and promote neuronal survival. Additionally, certain polysaccharides such as chitosan and alginate have demonstrated significant potential as nanocarriers for targeted drug delivery across the blood–brain barrier. This review summarizes recent advances in understanding the signaling pathways associated with AD and highlights the emerging therapeutic potential of marine-derived polysaccharides as multi-target neuroprotective agents. Overall, these marine biomolecules represent promising candidates for developing novel therapeutic strategies to mitigate neurodegeneration and improve cognitive function in Alzheimer’s disease.
