Electrochemical C-N coupling offers a sustainable route for the synthesis of urea from CO2 and N2. However, it is challenged by the scarcity of active and highly selective catalysts. In this study, we present a rational strategy to design transition metal (TM)-doped single-atom alloy (SAA) electrocatalysts. We investigated a CuFe bimetallic alloy as a host to enable facile coactivation of CO2 and N2. Through a multistep process using quantum mechanical calculations, 3d, 4d, and 5d TM dopants on the CuFe surface were screened to assess catalyst synthesizability, activity, and selectivity against competing pathways, including hydrogen evolution reaction, ammonia formation, and C2 product formation. Among all TMs, Ti, Cr, Mo, and W were identified as promising candidates that can form thermodynamically stable SAAs in the CuFe bimetallic alloy. W-doped CuFe emerged as the most selective and favorable candidate for urea production, and the electronic structure analysis revealed the origin of the high selectivity. While Cr and Ti catalysts showed overbinding due to high d-band centers, strong hybridization, and excessive charge transfer, the W dopant achieves an optimal electronic balance, enabling controlled back-donation to both N2 and CO2 molecules, facilitating dual activation. Mechanistic investigation revealed the reaction pathway involving a C-N coupling step to form the *NCON intermediate, with an activation free-energy barrier of 1.08 eV on W SAA. Cr, Mo, and Ti were relatively less active and selective. This work presents a rational strategy for designing SAAs for targeted C-N bond formation.
Encapsulation increases the versatility of materials for synthetic purposes by changing their properties. In this study, the PANI-encapsulated CeO2 nanocomposite was prepared through hydrothermal treatment, followed by the oxidative polymerization method, which yields the CeO2 eggs in a PANI wasp nest structure-like morphology. The unique structure of the CeO2/PANI nanocomposite was used for supercapacitor and sensor applications. Under optimal conditions, the CeO2/PANI nanocomposite delivers a specific capacitance of 487.5 F g(-1) at a current density of 1 A g(-1) with a 79% rate capability at 20 A g(-1). The activated carbon//PANI/CeO2 device delivers a cell-specific capacitance of 242.7 F g(-1) at a current density of 1 A g(-1). It provides energy and power densities of 109.25 Wh kg(-1) and 9000 W kg(-1), respectively, with 95.7% cyclic stability after 9000 GCD cycles at 10 A g(-1). Furthermore, the fabricated GC-CeO2/PANI electrode surpasses previous electrodes for tryptophan oxidation by reducing the oxidation potential to 0.67 V, offering a wide linear range from 4.5 to 160 mu M and providing a low detection limit of 1.56 mu M. Apart from high sensitivity, the GC-CeO2/PANI electrode also exhibited selectivity and long-term stability for tryptophan detection. The remarkable performance of the CeO2/PANI nanocomposite highlights its potential for supercapacitor and sensor applications while also paving the way for advancements in multifunctional energy applications.
Buccal drug delivery is an attractive alternative to conventional oral dosage forms, which allows the administration of drugs through the vascularised mucosa of the oral cavity, potentially minimising exposure to gastrointestinal degradation and hepatic first-pass metabolism. In the present work, nitroglycerine-loaded buccal oral films were prepared by the solvent casting method and were evaluated for their physicochemical and mechanical properties. The main film-forming polymer used was hydroxypropyl methylcellulose (HPMC) 50 cps, and glycerol was used as a plasticiser. The prepared films were evaluated for appearance, weight variation, thickness, folding endurance, disintegration time and surface pH. The resulting films were homogeneous and transparent. Mean film weights for the formulations F1-F3 ranged from 71.6 to 73.0 mg, and the film thicknesses were from 0.45 to 0.63 mm.
The efficient path selection of data in wireless sensor networks is vital for enhancing total system performance. Traditional path selection protocols encounter challenges related to frequent node movements, optimization of energy efficiency, and the nature of network environments. To tackle these challenges, an innovative methodology termed Deep reinforcement arctic puffin federated stochastic gradient learning (DRAFSGL) integrates federated learning and puffin optimization for energy-efficient multipath routing. Multiview deep embedded clustering (MDEC), enhanced by coyote and badger optimization into efficient clusters to reduce routing complexity. To ensure privacy, quantum-resistant homomorphic encryption (QRHE) enables secure computation without safeguarding against quantum threats. The data monitoring system applies game theory to optimize the behavior of agents involved in monitoring activities in the system. The suggested approach has exhibited extraordinary evaluation, attaining a peak accuracy of 99.7% alongside a precision of 98.9%, a specificity of 98.4%, a delay of 18 ms, a throughput of 87.9 Kbps, and a remarkable recall rate of 98.9%, as well as an F1-score when compared to existing methods. Overall, DRAFSGL methodology improves adaptive, secure, and energy-efficient multipath routing through the combination of federated learning and arctic puffin optimization. Meanwhile, the MDEC technique with improved coyote and badger optimization (ICBO) simplifies routing complexity and QRHE guarantees quantum-resistant, secure data transmission, effectively addressing the shortcomings of current techniques in dynamic 6G wireless sensor network (WSN)-based Internet of Things (IoT) systems.
Breast cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, necessitating the development of effective and targeted therapeutic strategies. Flavonoids, a diverse group of naturally occurring polyphenolic compounds, have gained significant attention due to their potent anticancer properties and low toxicity. This study integrates network pharmacology and molecular docking approaches to elucidate the underlying mechanisms of flavonoids in the treatment of breast cancer. Initially, bioactive flavonoids such as apigenin, luteolin, kaempferol, chlorogenic acid, and caffeic acid were screened, and their potential targets were identified using public databases. A compound–target–pathway network was constructed to reveal the multitarget interactions, highlighting key proteins including CDK6, JUN, TNF, STAT3, ESR2, and MMPs. Pathway enrichment analysis indicated that these targets are mainly involved in critical signaling pathways such as pathways in cancer, JAK-STAT signaling, and hormone-related pathways.
