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In situ growth of octa-phenyl polyhedral oligomeric silsesquioxane nanocages over fluorinated graphene nanosheets: super-wetting coatings for oil and organic sorption
Superhydrophobic surfaces offer significant advantages through their hierarchical micro/nanostructures, which create optimal surface roughness and low surface energy, making the development of robust surfaces essential for enhancing their physical and chemical stability. Here, we introduce in situ growth of octa-phenyl polyhedral oligomeric silsesquioxane (O-Ph-POSS) nanocages over multi-layered fluorinated graphene (FG) nanosheets through hydrolysis/condensation of phenyl triethoxysilane in an alkaline medium to produce a robust POSS-FG superhydrophobic hybrid. The efficient in situ growth of O-Ph-POSS nanocages over FG nanosheets was confirmed by FT-IR spectroscopy, PXRD, SEM, TEM, TG analysis, 29Si NMR spectroscopy, N2 adsorption-desorption isotherms and XP spectroscopy. The as-synthesized O-Ph-POSS over FG becomes superhydrophobic with a water contact angle (WCA) of 152 2 and a surface free energy (SFE) of 5.6 mJ m?2. As a result of the superhydrophobic property and robust nature of the POSS nanocage, O-Ph-POSS over FG nanosheets revealed the absorption capability for oils/organic solvents ranging from 200 to 500 wt% and were applied to coat onto the polyurethane (PU) sponge to effectively separate various oils and organic solvents from water mixtures, achieving separation efficiencies between 90% and 99%. Importantly, O-Ph-POSS-FG@Sponge still retained a separation efficiency of over 95% even after 25 separation cycles for hexane spill in water. The sponge efficiently separates toluene and chloroform using a vacuum pump, achieving flux rates of up to 20 880 and 12 184 L m?2 h?1, respectively. Weather resistance tests of O-Ph-POSS-FG@Sponge, prepared at intervals of 1 week and 1 year, showed that aged samples retained similar WCA values to freshly prepared sponges, confirming their long-term durability and performance. Mechanical stability assessments indicated that O-Ph-POSS-FG@Sponge maintained superhydrophobic properties, with WCA values of 151 2 for tape peeling and emery paper treatments and 150 2 for knife cutting, highlighting its excellent stability under physical deformation. Additionally, leveraging the exceptional resistance of O-Ph-POSS, the superhydrophobic O-Ph-POSS-FG@Sponge exhibited excellent stability and durability, even under supercooled and hot conditions during oil/water separation. Optical microscopy analysis of O/W and W/O emulsions, both stabilized by a surfactant, revealed complete droplet separation, further confirming the O-Ph-POSS-FG@Sponge's effectiveness for emulsion separation applications. The present work provides a straightforward method for the large-scale production of robust, superhydrophobic materials suitable for cleaning up oil spills on water surfaces. 2025 The Royal Society of Chemistry. -
IndiaEuropean Union Trade Integration: An Analysis of Current and Future Trajectories
In a dynamic global environment of increased economic interdependence, nations are more than ever seeking to remove barriers to trade, despite growing trends of protectionism. In this context, India and the EU-27 have initiated talks for the establishment of a Bilateral Trade and Investment Agreement (BTIA) in an attempt to bring their economies together. However, after 16 rounds of negotiations, the failure to conclude this agreement has raised questions regarding the benefits of the agreement to India. This study attempts to examine the current trade scenario and the effects of the proposed regional trade agreement by estimating a structural gravity model. This study employs the Poisson Pseudo Maximum Likelihood (PPML) estimator for analysing the trade-creation and trade-diversion effects of the BTIA to overcome the shortcomings of ordinary least square (OLS) estimators. For the empirical analysis, the merchandise export data from the Gravity database has been taken for a period of 19 years from 2001 to 2019. The results indicate that the BTIA could lead to trade creation and trade diversion, highlighting the need for a re-evaluation of Indias trade policy. JEL Classification: F10, F13, F14, F15, O24 2021 National Council of Applied Economic Research. -
Analysis of indias trade patterns and trade possibilities with the european union
Trade has played a crucial role in the emergence of developing econo-mies. The global emergence of India is also linked to its role in global trade. In this context, the European Union and India initiated talks for a free trade agreement known as the Bilateral Trade and Investment Agreement (BTIA). However, this agreement has failed to materialise due to various challenges and disputes. Against this backdrop, the present study attempts to trace the existing pattern of trade relations between India and the EU and provide a preliminary analysis of the nature of trade in this proposed region. A modified gravity equation and indicators of regional trade interdependence have been estimat-ed. The results indicate that trade within this region is in line with cer-tain predictions of the gravity model. Additionally, it also indicates that such an agreement has little potential for expanding trade and might even result in unnatural trade. Thus, it provides evidence for the argu-ment that India can benefit from developing ties with similar emerging economies in the Asia-Pacific neighbourhood. 2020, WSB University. All rights reserved. -
Amine-functionalized MIL-101(Fe)-NH2@ZIF-8 composite for efficient adsorption of Pb2+ ions
Heavy metal contamination of water resources poses a serious environmental and public health threat, necessitating the development of efficient and selective adsorbent materials. In this study, a hierarchical MIL-101(Fe)-NH2@ZIF-8 composite was successfully fabricated via an interfacial growth strategy, integrating amine-functionalized MIL-101(Fe)-NH2 and ZIF-8 to achieve a synergistic micro-mesoporous architecture with accessible functional sites. The composite was thoroughly characterized by FTIR, PXRD, TGA, BET, and SEM-EDX analyses, with elemental mapping confirming the structural integration and resulting in enhanced porosity, thermal stability, and functional group availability. The material exhibited a remarkable Pb2+ adsorption efficiency of 94.9 % and a maximum adsorption capacity of 297 mg/g, significantly superior to the adsorption of other metal ions (Cd2+, Cu2+, Ni2+, and Cr2+). Atomic absorption spectroscopy (AAS) validated the exceptional selectivity of MIL-101(Fe)-NH2@ZIF-8 for Pb2+ ions. The enhanced performance is attributed to the synergistic effect of accessible amine (?NH2) functionalities, Fe?O coordination sites, and hierarchical porosity enabling strong metal binding and rapid diffusion. These findings highlight the exceptional potential of MIL-101(Fe)-NH2@ZIF-8 as an advanced adsorbent for Pb2+ removal from water, offering a practical pathway to address critical environmental challenges and promote sustainable human health and ecological protection. 2025 Elsevier B.V. -
Keggin-Type H5PMo10V2O40Intercalated MgAl-LDH: Structural Integrity and Bifunctional Electrocatalytic Activity
The development of earth-abundant electrocatalysts is central to sustainable water electrolysis, yet many systems are limited by poor electronic conductivity and inadequate durability. In particular, the high solubility of discrete polyoxometalates (POMs) clusters hinders their direct deployment as stable heterogeneous electrocatalysts. Here, a Keggin-type H5PMo10V2O40 POM is intercalated into MgAl layered double hydroxide (MgAl-LDH) by a formamide-assisted exfoliation-reassembly strategy to afford a POM@MgAl-LDH hybrid. Structural characterization confirms quantitative ion exchange of POM anions into the LDH galleries and an increase of the basal spacing to 9.210.5 Density functional theory calculations indicate thermodynamically favorable intercalation (?E ? ?2.3 eV per formula unit) and predict an equilibrium interlayer distance that matches the experiment. The hybrid exhibits a BET surface area of 50.6 m2 g1 and hierarchical porosity. In 1.0 M KOH, POM@MgAl-LDH functions as a bifunctional electrocatalyst, affording hydrogen and oxygen evolution overpotentials of 215 and 411 mV at 10 mA cm2, respectively, with ?97% current retention over 12 h of electrolysis. These results suggest that spatial confinement of redox-active POM clusters within an earth-abundant MgAl-LDH host reduces POM loss into solution and improves the electrocatalytic response of LDH framework, offering a practical route to nonprecious-metal bifunctional electrocatalysts for alkaline water splitting. 2026 American Chemical Society -
Intelligent YOLOv8-based Rover for Precision Agriculture: Tomato Ripeness and Disease Detection
Tomato cultivation has traditionally relied on manual inspection and generalized irrigation practices, often resulting in inefficient resource use and reduced yield quality. This work presents a compact rover designed to support precision agriculture in tomato farming. The rover is equipped with an OV2640 camera module, a humidity sensor, and a water-level sensor. The OV2640 captures images of tomato fruits and leaves, transmitting them via Wi-Fi to a laptop for analysis. Two custom-trained YOLOv8 deep learning models are employed for visual diagnostics: one determines tomato ripeness, enabling optimal harvest timing, and the other detects common leaf diseases, including Late Blight, Leaf Mold, Leaf Miner, Mosaic Virus, Septoria, Early Blight, Spider Mites, and Yellow Leaf Curl Virus. In addition to visual inspection, the rover measures environmental parameters such as soil moisture and ambient humidity, supporting data-driven irrigation decisions and early preventive measures. Communication between the rover and the processing unit is achieved through live video streaming from the ESP32-CAM, with processed results enabling either manual teleoperation or potential future autonomous navigation. By integrating AI-based plant health assessment with environmental monitoring, the proposed system offers a economically efficient and scalable solution to improve crop quality, optimize resource usage, and enhance decision-making in tomato farming operations. 2026 IEEE. -
The feasibility analysis of load based resource optimization algorithm for cooperative communication in 5G wireless ad-hoc networks
Efficient allocation of resources is crucial in wireless ad hoc networks (WANETs) as spectrum assets are costly. Cooperative communications were introduced as a solution to the problem of limited spectrum availability. In this approach, numerous nodes share their resources and increase the bandwidth available to end-users. This research investigates the practicality of a new algorithm that optimizes resources based on load for Cooperative Communications in 5 G WANETs. The algorithm consists of two components. Initially, a distributed algorithm for forming a topology is suggested. This algorithm employs a load-based approach to explore network conditions and efficiently choose the most suitable topology. An optimization algorithm that relies on a greedy strategy is suggested. In this approach, the chosen nodes send their bits to the receiver to maximize the attainable system throughput. A thorough simulation study is conducted to evaluate the overall performance of the proposed algorithm in assessing existing methods. The proposed model obtained 94.72 % energy efficiency, 91.69 % network throughput, 94.72 % spectrum utilization, 27.47 % network delay, 24.08 % packet loss rate, 94.38 % signal-to-noise ratio, 93.91 % data transfer rate, 95.87 % error detection rate, and 94.28 % link reliability rate. The results demonstrate that the suggested algorithm significantly enhances the system and the overall network performance compared to existing approaches. The proposed approach is feasible and environmentally friendly for optimizing bandwidth in 5 G wireless ad hoc Networks. 2024 The Authors -
Hybrid optimization for efficient 6G IoT traffic management and multi-routing strategy
Efficient traffic management solutions in 6G communication systems face challenges as the scale of the Internet of Things (IoT) grows. This paper aims to yield an all-inclusive framework ensuring reliable air pollution monitoring throughout smart cities, capitalizing on leading-edge techniques to encourage large coverage, high-accuracy data, and scalability. Dynamic sensors deployed to mobile ad-hoc pieces of fire networking sensors adapt to ambient changes. To address this issue, we proposed the Quantum-inspired Clustering Algorithm (QCA) and Quantum Entanglement and Mobility Metric (MoM) to enhance the efficiency and stability of clustering. Improved the sustainability and durability of the network by incorporating Dynamic CH selection employing Deep Reinforcement Learning (DRL). Data was successfully routed using a hybrid Quantum Genetic Algorithm and Ant Colony Optimization (QGA-ACO) approach. Simulation results were implemented using the ns-3 simulation tool, and the proposed model outperformed the traditional methods in deployment coverage (95%), cluster stability index (0.97), and CH selection efficiency (95%). This work is expected to study the 6G communication systems as a key enabler for IoT applications and as the title legible name explains, the solutions smartly done in a practical and scalable way gives a systematic approach towards solving the IoT traffic, and multi-routing challenges that are intended to be addressed in 6G era delivering a robust IoT ecosystem in securing the process. The Author(s) 2024. -
Improving crop production using an agro-deep learning framework in precision agriculture
Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments. The Author(s) 2024. -
Federated Learning for Privacy-Preserving Threat Detection in IoT-Enabled Networks
The advent of Internet of Things (IoT) devices at a continuous rapid pace has greatly increased the surface for cyberattacks to measure the effectiveness of threat detection mechanisms. Most conventional centralized threat detection frameworks require sending sensitive device data to a single central server for aggregation, with significant privacy risks and scalability challenges. Such challenges could be efficiently addressed with the use of Federated Learning (FL), an emerging decentralized paradigm of training machine learning models, through the collaboration of a large number of devices, such as IoT sensors, that store the data locally and do not share raw data. In this work, we integrate FL to propose a threat detection framework for preserving privacy in IoT-enabled networks. In this paper, we propose a system architecture in which edge devices perform local training of machine learning models on encrypted traffic and behavioral data and then periodically share only the model updates with a centralized aggregator. This approach ensures the privacy of the data, minimizes communication overhead, and improves detection capabilities for real-time threats. The efficacy of FL-based threat detection is examined through experimental evaluations on benchmark datasets of IoT attack traces, indicating that FL-based approaches achieve competitive accuracy versus prior centralized schemes while greatly mitigating risks of data leakage. We further address issues regarding heterogeneous device resources, communication efficiency, and adversarial attack resilience in this context. Our results indicate that federated learning is a very effective approach for providing IoT environment protection, as it securely balances privacy, scalability, and detection performance. 2025 IEEE. -
Machine Learning-Based Intrusion Detection Systems for 5G and beyond Networks
NextGen networks (5 G and beyond) have diversified their infrastructure. Traditional Intrusion Detection Systems (IDS) cannot effectively address the continuously evolving landscape of threats, which is why machine learning-based IDS has emerged as a crucial solution. This overview presents the trends in the application of machine learning techniques (deep learning and ensemble methods) for machine learning-based intrusion detection in 5 G and beyond networks. The important issues tackled encompass real-time anomaly detection, large-scale data processing, adaptive learning against unknown attacks, and detection outcomes. Specifically, we emphasize the promising combination of federated learning, reinforcement learning, and graph-based methods for deployment in distributed, resource-constrained network environments. We present a comprehensive overview of performance metrics such as accuracy, false positive rate, computational overhead, and scalability for each approach, highlighting the crucial trade-offs necessary for successful deployment in dynamic 5G scenarios. Furthermore, we prioritize privacy-preserving methods and secure model sharing. This abstract could further highlight that machine learning-based schemes for intrusion detection systems are important additions toward providing strong defences for cyberspace in 5 G and beyond. 2025 IEEE. -
Post-Quantum Cryptography for Securing Next-Generation Communication Networks
Advancements in Quantum Key Distribution (QKD) and lattice-based encryption are paving the way for PQC adoption, but challenges remain, such as performance overhead and compatibility with existing infrastructure. It evaluates whether PQC schemes are feasible for real-time applications in high-speed, low-latency networks and analyzes the security-performance trade-offs. We investigate standardized candidates from NIST's PQC Project (e.g., CRYSTALS-Kyber, Dilithium) and their resistance to hybrid attacks. In addition, we also investigate the hardware acceleration (e.g., FPGA, ASIC) approach to alleviate the latency bottleneck. Transition strategies, such as hybrid cryptography (the coupling of classical and PQC algorithms) and zero-trust frameworks to maintain backward compatibility, are a key focus here. We further discuss side-channel vulnerabilities specific to PQC implementations and suggest mitigation strategies. These findings emphasize the need for a continued focus in areas such as scalability, standardization and quantum secure key distribution and the importance of collaboration between academia, industry and policymakers."By tackling these issues, PQC can secure next-gen networks from quantum dangers while aging to be efficient and trustworthy. 2025 IEEE. -
An enhanced performance analysis of load based resource sharing framework for MIMO systems in 5G communication systems
Resource sharing serves as a cost-effective and dynamically adjustable method for alleviating traffic congestion in wireless networks. Advancements in multi-input multi-output (MIMO) technologies for 5G communication systems have led to the exploration of resource sharing across various cells or sectors. This approach aims to optimise network performance, focussing on coverage, capacity, and quality of service. This document presents a new load-based resource-sharing framework designed for multi-cell MIMO systems. The proposed framework utilises channel-loading data from local base stations and dynamically allocates available resources among adjacent base stations. The proposed framework facilitates dynamic resource sharing, effectively addressing traffic overload in 5G networks. The proposed LBRS achieved a delta-P value of 90.91%, a prevalence threshold value of 89.84%, a critical success index value of 91.01%, and a Mathews correlation coefficient value of 91.27% at the terminal access. At the resource transmission, the system recorded a delta-P value of 92.10%, a prevalence threshold value of 92.18%, a critical success index value of 91.65%, and a Mathews correlation coefficient value of 88.31%. The simulation results indicate that the proposed framework effectively enhances dynamic resource sharing, resulting in a notable improvement in network performance. The Author(s) 2025. -
An Improved Deep Learning Framework for Energy Management in Low-Energy Building Integrated Photovoltaics (LE-BIPV)
The possibility of incorporating photovoltaics (PV) as part of building design has opened a new approach to energy generation from sustainable resources. An effective method to facilitate the good operation of these systems would be efficient energy-level management. The existing Energy Management of LE-BIPV employs a conventional control strategy, which is inconvenient for operation and fails to properly deal with nonlinearity in the PV system. The proposed model aims to provide a new deep-learning framework for the energy management of LE-BIPV. The proposed neural network framework can learn the intricate relationships between PV generation and battery storage and enable accurate energy management predictions. This proposed deep learning framework can substantially upgrade the global energy control of building-integrated PV systems in low-energy buildings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Regulatory challenges and compliance in federated learning (FL) for financial applications
The financial sector is increasingly turning toward artificial intelligence (AI) for applications such as fraud detection, credit scoring, and risk management. But that makes it contrary to the regulatory environment. New data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the Digital Personal Data Protection Act (DPDPA) in India impose stringent conditions on data residency, minimization, and sovereignty. This chapter argues that traditional centralized AI systems which require sensitive data to be collected for processing at one site simply do not sit well with these legal requirements, thereby creating massive compliance risks for financial institutions. By way of an extensive architectural study and practical application, this chapter demonstrates that the very basic functions of a traditional AI system tend to contravene prohibitions on cross-border transfers of data. Instead, we propose Federated Learning (FL) as a compliance-by-design solution that solves this sticking point. In other words, by inverting the discredited approachand bringing the algorithm to the data rather than the other way aroundFL ensures that practitioners in different institutions and jurisdictions collaborate on model training without sharing raw data. Only aggregated and anonymized updates on the model are sentinherently complying with certain data residency and data minimization principles. Besides advocating for FL as a core compliant innovation pathway, this chapter also touches on a number of regulatory uncertainties and other potential issues arising from this technology, such as liability, model security, and a need for industry-wide standards. To this end, the chapter clearly states that the adoption of privacy-preserving technologies such as FL has become integral. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. -
Adaptive optimization with reinforcement learning for high utility itemset extraction
Extraction of High Utility Itemsets (HUI) plays a vital role in data mining that comprises several techniques developed to address it efficiently. However, when dealing with large itemsets and diverse items in a dataset, the problem's search space becomes notably complex and expansive. This makes the task of identifying HUIs more computationally expensive and time-consuming. In this paper, a novel Optimized Coverage list unit utilities-based High Utility Itemset (OCHUI) extraction approach is introduced for High Utility Itemset extraction. The extraction of high utility patterns and the extraction of qualified high utility itemsets are the two main processes in the suggested method. In the first step, high utility patterns are identified by mining metrics such as Redefined transaction-weighted utility, positive and negative Unit profit, Purchase quantity, and Coverage (RUPC) from the dataset. In the second step, qualified high utility itemsets are obtained optimally using an adaptive optimization algorithm called Cuckoo search Assisted Ant colony Optimization with Reinforcement Learning (CAAO-RL) is proposed. The Reinforcement Learning (RL) uses the On and Off policy method to intelligently leverage the tuning parameter of optimization. The RUPC model obtained the pattern score of 13600, runtime rate of 10.256 s and memory usage of 198 MB, respectively. 2025 Elsevier B.V. -
Synergistic fabrication, characterization, and prospective optoelectronic applications of DES grafted activated charcoal dispersed PVA films
This study investigates the synthesis, analysis, and utility of films comprising deep eutectic solvent (DES) grafted activated charcoal (AC) within a polyvinyl alcohol (PVA) matrix for optoelectronic device applications. The fabrication process involves the dispersion of DES functionalization AC into the PVA solution, followed by casting onto substrates with controlled drying. Comprehensive characterization encompassing X-ray diffraction (XRD), scanning electron microscopy (SEM), UVvis spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and impedance spectroscopy which discerns the films microstructure, morphology, conductance, band-gap, and optical traits. The DES grafted AC infusion with variable concentration has significantly influenced optical absorbance and reduced the band gap indicating efficient charge mobility. Furthermore, the impedance analysis has revealed the electrical conduction of the film to be 1.8 10?6 ??1 m?1. In summary, the dispersion of DES modified AC in the PVA matrix have converted the insulating PVA to a semiconducting polymeric film with reduced band-gap and increased absorption, which present a propitious avenue for wide array of optoelectronic devices, such as thin film transistors, photovoltaics, LEDs, photodetectors, and many such applications. 2024 The Authors. Polymers for Advanced Technologies published by John Wiley & Sons Ltd. -
Modified eco-friendly and biodegradable chitosan-based sustainable semiconducting thin films
Semiconducting materials are pivotal in various fields, such as solar cells, LEDs, photovoltaic cells, etc. A nature-friendly chitosan is a good film-forming, water-soluble polymer that is modified to a small band-gap polymer for various optoelectronic applications. Choline chloride:ethylene glycol:glycerin (1:1:1) deep eutectic solvent (DES)-modified activated carbon is incorporated into the chitosan matric and this composite is fabricated into thin films via spin coating methodology. The obtained films are subjected to multiple studies such as scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), impedance spectroscopy, and UVvis spectroscopy to perceive the thin-films microstructure, morphology, conductance, band gap, and optical nature. The integration of DES-modified activated carbon has significantly improved the charge transfer capacity of chitosan by reducing the band gap from 4.0 to 2.0 eV. These notable characteristics exhibited by the modified films can be key to sustainable semiconducting materials and have the potential to transform several optoelectronic applications. 2024 The Author(s). Polymers for Advanced Technologies published by John Wiley & Sons Ltd. -
Novel deep eutectic solvent catalysed Single-Pot open flask synthesis of Tetrasubstituted-1H-Pyrroles
Pyrrole and its analogs have garnered immense attention due to their multifaceted biological significance and versatile applications, ranging from medicinal agents to fundamental biological pigments. Despite their prominence, pyrrole synthesis with multiple substituents is complex and calls for innovative approaches to green chemistry. This study delves into synthesizing novel 3,5-dimethyl-1H-pyrroles via multicomponent reactions (MCRs) employing deep eutectic solvents (DES). Due to their eco-friendly nature, these DESs provide a safer substitute for traditional solvents. Specifically, a novel three-component DES (3CDES) was formulated, showcasing promising catalytic activity for multiple cycles with excellent product generation. The synergy between MCR and DES elucidates their combined potential in fostering a sustainable and efficient green synthesis route with the E-factor of 0.1699. 2024 Elsevier B.V. -
Tailored Optoelectronic Materials: DES-Modified MWCNTs in PVA Matrix for Advanced Polymeric Films
Integrating optoelectronic functionalities with advanced materials offers exciting potential for novel composite systems. This study investigates the synthesis, characterization, and optoelectronic applications of polyvinyl alcohol (PVA) films incorporating multi-walled carbon nanotubes (MWCNTs) surface-modified with choline chloride-urea (1:2) deep eutectic solvents (DES), known as Reline. Dispersion of Reline-enhanced MWCNT within the PVA matrix is meticulously described. DES-grafted MWCNTs demonstrate improved solubility, leading to superior dispersion within the polymer, confirmed by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The influence of Reline-grafted MWCNT loading on the films' optoelectronic properties, including optical absorbance, bandgap, and electrical conductivity, is systematically analyzed. Results show that DES-grafted MWCNTs significantly enhance these properties, indicating strong potential for these composites in optoelectronic devices such as solar cells, photovoltaics, photodetectors, and light-emitting devices. 2025 by the authors.