RSC Advances最新文献

Two new macrolides, penicurvularins A and B (1 and 2), and the three known compounds (3-5) were isolated from cultures of the ascomycete fungus Alternaria sp. Their structures were elucidated primarily by NMR experiments. The absolute configuration of 1 was assigned from electronic circular dichroism calculations. Compounds 1 and 5 are active against aquatic pathogenic bacteria Vibrio alginolyticus and V. harveyi with MIC values ranging from 4 to 8 µg mL^-1, while compound 5 is cytotoxic against tumor cell lines Huh7, 786-O, and 5673 with IC₅₀ values of 11.6, 10.7, and 3.5 µM, respectively.

The interaction of cationic species with aza-homocubanes provides a powerful platform for probing the effects of cage topology on electronic structure and noncovalent binding phenomena. Here, density functional theory (DFT) calculations were employed to investigate the complexation of 1-azahomocubane and 9-azahomocubane with H⁺, Li⁺, Na⁺, K⁺, Mg²⁺, and Ca²⁺. Optimized geometries reveal systematic trends in cation-nitrogen bond distances, with protonation yielding short covalent-type N-H⁺ interactions (∼1.0 Å), alkali metals displaying increasing separation with ionic radius (N-Li ≈ 1.9 Å, N-Na ≈ 2.3 Å, N-K ≈ 2.7 Å), and alkaline earth dications forming significantly shorter and stronger bonds than size-comparable alkali cations (N-Mg²⁺ ≈ 2.0 Å vs. N-Na ≈ 2.3 Å). Analysis of Hirshfeld charges confirmed substantial electron transfer from nitrogen to the bound cation, with the charge on N shifting from -0.134e in free 1-aza to +0.067e upon protonation, and from -0.163e in free 9-aza to +0.058e in its protonated form. For metal complexes, the nitrogen charges became even more positive, e.g., -0.141e → +0.666e in (1-aza + Li)⁺ and -0.158e → +0.663e in (9-aza + Li)⁺, highlighting significant charge redistribution. The cation affinity (CA) and CB (cation basicity) indices quantified the stabilization: for instance, CA/CB values of 48.84/41.50 kcal mol^-1 were obtained for (1-aza + Li)⁺ compared to 46.13/38.91 kcal mol^-1 for (9-aza + Li)⁺, confirming stronger binding in 1-aza. The non-covalent interaction (NCI) isosurfaces and reduced density gradient (RDG) profiles revealed localized covalent-like interactions for protonated species, while alkali metals exhibited more diffuse electrostatic contacts, with dications displaying highly concentrated interaction regions. Collectively, the results reveal subtle but systematic differences between the two isomers, with 1-azahomocubane often exhibiting slightly shorter interaction distances and marginally enhanced stabilization relative to 9-azahomocubane, highlighting the influence of nitrogen topology on cation binding.

Conventional thin-film supercapacitors are limited by low energy density and poor charge balance between electrodes, restricting their integration into miniaturized electronic devices. In this study, reduced TiO₂ nanotubes (R-TiO₂ NTs) were fabricated via a straightforward anodization process followed by electrochemical reduction (self-doping) and further decorated with Ni(OH)₂ nanospheres. These R-TiO₂ NTs/Ni(OH)₂ NSs electrodes were employed as both positive and negative electrodes for symmetric supercapacitors, and as positive electrodes in asymmetric configurations. To develop a suitable negative electrode, few-layer graphene (FLG) and graphene nanoplatelets (GNP) were combined, and the optimal FLG/GNP weight ratio was identified to balance charge storage. This electrode design enabled the fabrication of an asymmetric supercapacitor (ASC) with significantly enhanced energy storage performance. The superior performance of the ASC is attributed to a synergistic charge storage mechanism, where surface-controlled pseudocapacitive reactions of Ni(OH)₂ nanosheets complement the double-layer capacitance of the FLG-GNP electrode, ensuring rapid charge-discharge kinetics, high rate capability, and excellent cycling stability. The ASC achieved an areal capacitance of 118.26 mF cm^-2 and an energy density of 42.05 µWh cm^-2 at 0.25 mA cm^-2, compared to 19.38 mF cm^-2 and 6.89 µWh cm^-2 for the symmetric device. This work demonstrates a promising strategy for high-performance, scalable micro-supercapacitors with potential applications in flexible and miniaturized electronics.

The study investigates a consistent technique for tracing the cashmere breeds and their geographical origins using mineral element fingerprinting from Inner Mongolia, China. 237 cashmere samples were collected from three different regions (Ordos, Chifeng and Alxa League) and four breeds (Albas, Mingan, Hanshan, Alxa white cashmere goats). Concentrations of 21 mineral elements were quantified by inductively coupled plasma mass spectrometry (ICP-MS) and assessed using multivariate statistical methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA). Among them, six elements (Mg, Mn, As, Sr, Ce and U; with Ce in regions and Sr in breeds, while the other five are common) discriminated significant regional and breed-specific differences (P < 0.05). Their concentrations ranged from 189.25-639.49 µg g^-1, 7.48-16.24 µg g^-1, 19.03-66.73 µg/100 g, 311.26-888.30 µg/100 g, 56.03-113.92 µg/100 g and 1.64-9.87 µg/100 g respectively across the regions with highly enriched Alxa samples. These six mineral elements served as key identifiers for traceability with high variable importance in projection (VIP > 1.0). The OPLS-DA model achieved excellent classification accuracy for both origin (R ² = 0.97) and breeds (R ² = 0.787). This novel study presents an integrated ICP-MS and OPLS-DA approach for authenticating geographical origins and breeds, providing a robust analytical basis for preventing fraud, certifying products and ensuring supply chain transparency in the luxury textile sector.

In this work, tin selenide (SnSe) thin films were successfully synthesized using a cost-effective proximity co-evaporation method within a chemical vapor deposition (CVD) system. The process involved a thermally evaporated Sn thin film and selenium powder as precursors, arranged in a source-substrate-face-to-face configuration to enable uniform and self-limiting lateral growth under an inert atmosphere. X-ray diffraction (XRD) confirmed the formation of orthorhombic SnSe with high crystallinity and phase purity. Field emission scanning electron microscopy (FESEM) and energy-dispersive X-ray spectroscopy (EDS) revealed a uniform grain morphology and near-stoichiometric composition, while X-ray photoelectron spectroscopy (XPS) confirmed the presence of Sn²⁺ and Se^2- oxidation states. Optical studies revealed a band gap of ∼1.15 eV, aligning well with the ideal range for optoelectronic applications. Electrical measurements demonstrated ohmic contact behavior, and photoresponse analysis under white LED illumination exhibited a significant enhancement in photocurrent, with a responsivity of 29.9 A W^-1, detectivity of 3.8 × 10¹¹ Jones, and quantum efficiency of 67.45%. These results show that the fabricated SnSe thin films could be suitable candidates for high-performance photodetectors and optoelectronic devices.

The incorporation of microstructural designs into flexible sensors has emerged as a potent strategy to enhance the capability of capturing fine information. Herein, an ionic liquid microemulsion-template strategy is presented to fabricate conductive heterogels with a bicontinuous structure of interpenetrating hydrophilic and hydrophobic phases, in which the hydrophilic ionic liquid domain functions as ion-rich channels and provides conductivity. The hydrophobic polymer domain serves as a supporting skeleton. Amphiphilic zwitterions anchored at the phase interface serve as effective ion transport sites to improve ion conduction. The bicontinuous structure enhances the mechanical performance, anti-swelling, thermal stability and ion conductivity of the heterogels. The assembled flexible sensor exhibits the sensitive detection of subtle muscle movements and specific recognition of speech, weight and temperature. This study is expected to provide innovative insights into the design and regulation of ion channels in flexible sensing devices.

Acne vulgaris (acne) is a common skin disorder associated with significant psychosocial impact. Current clinical therapies include topical and systemic antibiotics, benzoyl peroxide, and retinoids. While moderately effective, these clinical therapies fail to target all four major pathogenic causes of acne and are associated with painful side effects. Nitric oxide (NO), an endogenous signaling molecule, represents a promising alternative to conventional acne treatments due to its innate antibacterial and immunomodulatory functions. As NO is highly reactive, macromolecular NO donors are required for its controlled, solution-phase delivery. Prior work has utilized silica nanoparticle scaffolds to store and deliver NO, with the silica scaffold being considered inert. Herein, NO-releasing hyaluronic acid (HA), an endogenously produced biopolymer, was modified with NO donors to enable a dual-action therapeutic capable of addressing the pathogenic factors responsible for acne development. The molecular weight of these HA derivatives proved important with respect to bactericidal activity against Cutibacterium acnes and ability to modulate keratinocyte proliferation, sebum production, and inflammation.

Due to its capacity to form complexes with polyphenols and to self-assemble as nanoparticles, zein could be utilized as an excellent carrier for polyphenols. The objective of this study was to examine the interaction between zein and phloretin (PHL) through multispectral analysis and molecular docking, and to prepare and characterize PHL-loaded zein nanoparticles. Spectral analysis and docking data confirmed that the binding process of the zein-PHL complex is mainly influenced by hydrogen bonding and van der Waals forces, and hydrophobic interaction was auxiliary, with static quenching as the primary fluorescence quenching mechanism. Meanwhile, zein nanoparticles loaded with PHL were successfully prepared using the anti-solvent precipitation method, which was evidenced by the morphology and size characterization. The hydrogen bond and hydrophobic interaction in the nanoparticles were further confirmed by Fourier transform infrared spectroscopy. This study elucidates the noncovalent interaction mechanism between zein and PHL, providing a theoretical foundation for the design of zein-polyphenol nanocarriers. These carriers show promising applications as emulsion stabilizers or delivery systems for lipophilic bioactives, thereby facilitating the development of functional foods with improved stability and enhanced bioavailability.

Lead-free halide perovskites have emerged as promising alternatives to toxic Pb-based photovoltaic absorbers, yet many candidates suffer from poor stability or unfavorable electronic properties. In this work, we present the first comprehensive first-principles and device-level investigation of the novel vacancy-ordered perovskites Q₃GaBr₆ (Q = Na, K) to evaluate their potential for next-generation optoelectronic and solar-cell applications. Density functional theory (DFT) calculations confirm that both compounds crystallize in a stable cubic Fm3̄m phase with negative formation energies, favorable tolerance factors, and strong Ga-Br bonding within rigid octahedral frameworks. Electronic-structure analysis reveals direct band gaps of 1.445 eV (K₃GaBr₆) and 1.991 eV (Na₃GaBr₆), with Br-4p states dominating the valence band and Ga-/Q-site orbitals contributing to the conduction band. Optical studies show high absorption (>10⁴ cm^-1 in the visible region), low reflectivity, strong dielectric response, and pronounced UV absorption, indicating suitability for broadband optoelectronics. Mechanical and phonon analyses further confirm mechanical stability, moderate stiffness, and absence of imaginary phonon modes, while AIMD simulations validate excellent thermal robustness at elevated temperatures. Incorporating DFT-extracted parameters into SCAPS-1D device modeling demonstrates promising photovoltaic performance, with efficiency, current density, and fill factor strongly influenced by absorber thickness, defect density, and doping concentration. Under ideal simulated conditions, the device shows a theoretical upper-limit efficiency of 22.21%. The proposed DFT-SCAPS integrated approach provides an efficient and computationally economical route to screen and optimize lead-free perovskite absorbers, significantly reducing experimental trial-and-error while enabling accurate prediction of photovoltaic performance.

Fuel cells have lately garnered interest as a potentially advantageous technology for clean and efficient energy conversion. One type that has caught people's attention is the anion exchange membrane fuel cell (AEMFC), which can run on a variety of fuels and operates at low and high temperatures. Exploring its basic working principles, important materials, obstacles, and recent breakthroughs, this perspective presents a comprehensive introduction to AEMFC technology. The anion exchange membrane (AEM) and the electrodes of the AEMFC work together to improve the cell performance and the efficiency of the system as a whole. Furthermore, this review emphasizes the ways in which AEMFC technology is being improved by ML and AI technologies. Through the identification of crucial parameters and the improvement of the membrane electrode assembly (MEA), these technologies have the potential to optimize the performance of AEMFCs while drastically cutting down on the time and effort needed for experimental testing. Finally, we take a look at the possibilities and threats for further study of fuel cell technology-based sustainable energy generation using AEMs in conjunction with new electrode materials. This article introduces a structured framework and categorizes the following key concepts: need for anion exchange membranes (AEM) > mechanisms of anion conductivities > ORR (oxygen reduction reaction) > interfacial phenomena at the electrode-AEM interface > water management > integration of artificial intelligence (AI)/machine learning > neural networks (NN) > schemes for learning > predictive modeling > optimization algorithms and optimizing algorithms > AI in fault detection > AI in maintenance of fuel cells and in materials discovery.