The Global Research Network for Verified Scholars

Smart nanomaterials have emerged as a pivotal class of materials due to their unique and tunable properties making them ideal candidates for advanced technological applications. This review focuses on the magnetic, transport, and surface properties of these materials, which play a crucial role in their functionality across diverse fields. The discussion begins with an overview of magnetic properties, highlighting the role of size, morphology, and composition in tailoring magnetism for applications such as data storage, biomedical imaging, and drug delivery. Next, the transport properties, including electrical and thermal conductivity are analyzed with emphasis on the mechanisms driving charge transport and their implications for electronic devices, sensors, and energy storage systems. The surface properties, including surface reactivity, wettability, and functionalization potential, are also explored, demonstrating their importance in catalysis, environmental remediation, and biomedical interfaces. A comprehensive analysis of recent advances, experimental techniques, and theoretical frameworks is provided to connect these properties to practical applications. Finally, the review identifies current challenges and prospects in the development of smart nanomaterials, emphasizing their role in next-generation technologies.

This work investigates the fabrication of barium bismuth titanate (BaBi4Ti4O15) ceramic and its incorporation into polyvinylidene fluoride (PVDF) to produce composite films via solution casting. Scanning electron microscope images reveal a distinct microstructure, with grains uniformly dispersed and grain boundaries clearly visible, suggesting that BBTO ceramic particles are evenly incorporated into the PVDF polymer matrix. At lower frequencies, the nearly 50-fold increase in dielectric constant with temperature is attributed to the growing influence of interfacial polarisation. When BaBi4Ti4O15 (BBTO) is added to the PVDF matrix, the AC conductivity decreases dramatically, providing evidence for a prospective energy storage opportunity. Moreover, the work established the stability and compatibility of the composite by confirming that BBTO does not impact the β phase of PVDF. An impedance spectroscopic study revealed that upon dilution, both BBTO and PVDF samples exhibited a single partially formed semicircle, suggesting a combination of the electrode effect on conduction and grain boundary resistance. Therefore, the dielectric response, AC conductivity, and impedance behaviour of the PVDF-BBTO composites are mainly determined by interfacial polarisation in the double layer and by the thermally activated motion of the PVDF polymer chains. This work initiated a deeper understanding of the ceramic PVDF structure and its potential as a dielectric capacitor.

Lanthanum manganite (LaMnO₃) and its doped derivatives have become an important class of perovskite materials due to their adaptable structural, electronic, and magnetic properties. A-site substitution with rare-earth elements and alkaline-earth elements has been shown to manipulate the Mn³⁺/Mn⁴⁺ ratio, alter oxygen vacancy concentrations, and perturb lattice distortions; all of this can impact conductivity, catalytic activity, and magnetism. These properties render a wide variety of applications appealing for doped LaMnO₃ materials, including solid oxide fuel cells, catalysis, spintronics, and energy storage. Even so, several challenges have limited their broader use, such as secondary-phase formation, electrolyte reactivity, dopant segregation, and processing at high temperatures. This article discusses advancements toward addressing these challenges with synthesis, defect engineering, and computational methods. Additionally, it reflects on the potential of new tools (machine learning and nanoscale design) to speed up the discovery of optimized compositions and processing conditions. Despite present problems, doped LaMnO₃ perovskites remain a promising material for energy and electrical applications.

This article develops a new penalty-based aggregation operator known as the penalty-based induced ordered weighted averaging (P-IOWA) operator which is an extension of penalty-based ordered weighted averaging (P-OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P-OWA and P-IOWA operators with the different means such as generalized mean and quasi-arithmetic mean. This article also includes different families of P-OWA and P-IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty-based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.

Blockchain enables decentralized, tamper-evident, and auditable data sharing among untrusted parties, but deployments face a trade-off between scalability and privacy. Public blockchains expose transactional metadata, risking confidentiality, while privacy-preserving approaches like zero-knowledge proofs (ZKPs) often reduce throughput and increase latency due to proof computation. Scalability methods—Layer-2 rollups, sharding, state channels—typically offer limited privacy, leaving metadata open to inference attacks. We propose a privacy-preserving, scalable blockchain architecture for secure data sharing in distributed environments such as federated clouds, healthcare networks, IoT ecosystems, and inter-bank settlements. The design combines Layer-2 zero-knowledge rollups with a modular Layer-1 settlement layer (Ethereum or Hyperledger Fabric), decentralized storage (IPFS/Filecoin), and fine-grained access control. Transactions are batched off-chain, validated with succinct ZK proofs, and only aggregate proofs and state roots are committed on-chain, enabling confidentiality with high throughput. The architecture, deployed in a Kubernetes-orchestrated environment, supports horizontal scaling, automated failover, and observability via Prometheus, Grafana, and Jaeger. A prototype shows up to 5.8× throughput gains over baseline privacy-preserving blockchains while keeping latency within acceptable limits. Evaluation against three baselines—Layer-1 only, Layer-1 + privacy, and Layer-1 + scalability—using throughput, latency, cost, privacy efficacy, and fault tolerance confirms that integrating privacy-preserving cryptography with scalable rollup architectures is both feasible and effective for secure, high-performance blockchain systems.

Water scarcity is increasingly becoming a global concern, intensified by urbanization, climate change, and unsustainable water management practices. In India, major industrial cities like Jamshedpur remain under severe water stress situations arising from increasing demand, pollution, and infrastructure‐less efficiency. This study looks into the present water crisis in Jamshedpur by describing major drivers for it, reviewing past trends, and then suggesting sustainable options that might fit in well with the particular socio‐industrial conditions of the city. A GIS‐based spatial analysis using Kriging interpolation of groundwater records (2010–2022) was performed, followed by validation having an RMSE value of 0.00424 bcm. In addition, the study scrutinized government reports, local‐level case studies, and institutional records that attempted to gauge demand, pollution levels, and governance challenges. The findings highlight a 20%–30% gap between water demand and supply as well as a massive groundwater decline in some places where the water level is going down by over 10 m in 10 years. Such a scenario arises due to industrial overexploitation and a terribly fragmented governance scenario. Spatial maps are shown concerning at‐risk areas with respect to a water crisis in the future. To achieve long‐term water security, the study suggests the implementation of integrated water resources management (IWRM), rainwater harvesting, wastewater reuse, and making a strong push for public‐private partnerships while promoting community engagement, as well as data‐based decision‐making, to increase climate resilience. The findings are not only relevant to Jamshedpur but also offer a replicable framework for other industrial cities grappling with water scarcity under climate uncertainty.

Despite global progress, digital health adoption in low-income countries remains uneven. Pakistan presents a critical case due to its large population, rural-urban disparities, limited public health expenditure, and emerging yet fragmented digital initiatives. While global trends show a shift towards E-Health, Pakistan presents a unique case study where these global innovations meet specific local implementation barriers.