Search Results (265)

Free vibrations of axially moving beam-like nanostructures have been investigated in recent years; however, vibrations of moving nanochassis traveling over a surface with arbitrarily small irregularities have not been displayed yet due to some complexities in modeling. To address this challenge, a nonlinear, nonlocal surface energy-based composite beam-like model is established to fairly accurately capture the nanochassis’ vibrations. The nanocar consists of a composite-like nanochassis and the ends’ wheels, where the nanochassis is modeled by an appropriate beam model and the wheels are simulated as rigid solid elements that are attached to the beam’s ends. Both differential- and integral-based formulations are presented, and their nonlinear stiffness, as well as the procedure for capturing the nonlocal elastic field, is carefully explained using the assumed mode approach. For several particular cases, the predicted results by the suggested models are verified with those of several analytical solutions, and reasonably good agreements are achieved. Beyond the aforementioned comparison studies, the possible instabilities of the nanochassis that travels over a straight route were also identified and explained under a small deformation regime. Through conducting a fairly comprehensive parametric study, the roles of amplitude and frequencies of the harmonic route, axial velocity, length, diameter, nonlocality, surface energy, and geometrical nonlinearity on maximum deformations and internal forces are examined comprehensively. This study could be considered as basic scrutiny for the nonlinear analysis of more complex traveling nanostructures over arbitrarily shaped surfaces. Full article

Antimicrobial resistance and biofilm-associated contamination continue to pose critical challenges in food safety and biomedical applications, necessitating the development of advanced antimicrobial materials with enhanced efficacy, safety, and functional adaptability. Antimicrobial nanomaterials offer versatile solutions due to their tunable physicochemical properties, surface engineering capabilities, and controlled release behaviors, enabling improved antimicrobial and antibiofilm performance across diverse systems. This review highlights the main advancements in AI-assisted design of antimicrobial nanomaterials, demonstrating how data-driven approaches are increasingly used to predict antimicrobial activity, optimize synthesis parameters, model nanotoxicity, integrate multimodal datasets, and improve interpretability through explainable AI frameworks. Key findings indicate that machine learning-guided strategies and autonomous experimental platforms significantly accelerate material optimization while reducing reliance on traditional trial-and-error methods. The review further summarizes the performance and mechanisms of major antimicrobial nanomaterial systems, including metal and metal oxide nanoparticles, metal–organic frameworks, polymeric nanocarriers, nanoemulsions, and hybrid nanostructures, with emphasis on their translational applications in food preservation, antimicrobial coatings, wound healing, implant protection, and drug delivery. Despite these advances, challenges remain in data quality, model generalizability, toxicity prediction, reproducibility, and regulatory translation. AI-enabled and data-driven frameworks provide a powerful pathway for accelerating the rational design and practical implementation of next-generation antimicrobial nanomaterials. Full article

To address the erosion-induced failure of large-caliber gun barrels under extreme thermochemical coupling, this study systematically investigates the microstructural evolution of multi-layered gradient regions along the radial direction of 32CrNi3MoV steel under extreme thermochemical cycling. Leveraging SEM, EBSD, TKD, and double-beam aberration-corrected TEM, combined with JMatPro thermodynamic simulations, the phase transitions, crystallographic characteristics, and substructural evolution spanning from the bore surface to the matrix are elucidated. The results demonstrate that a three-layer gradient structure forms along the radial direction. The topmost layer is a chemically stabilized metastable austenite diffusion layer with a thickness of 1.5–4.0 μm. which is attributed to the suppression of martensitic transformation due to C/N interstitial diffusion lowering the MS temperature. The observed high-density dislocation tangles and stacking faults within this austenite diffusion layer result from thermal mismatch stresses during rapid thermal cycling. The subsurface region is a martensitic transformation layer with a thickness of 70–97 μm, exhibiting a substructural gradient from nanostructured high-density twinned martensite to refined lath martensite. Thermodynamic analysis indicates that rapid heating (≈10⁵ °C/s) facilitates significant austenite nucleation and growth during the reverse phase transformation, subsequently forming nanostructured martensitic grains via non-equilibrium transformation during rapid cooling. Adjacent to this is a matrix tempering layer extending approximately 160 μm. Nanoindentation hardness profiling reveals that the peak radial hardness (≈1000 HV) occurs within the fine-grained martensitic zone approximately 40 μm from the surface. In contrast, the tempered layer exhibits reduced hardness (≈400 HV) compared to the original matrix (≈500 HV). This is primarily attributed to transient high-temperature over-tempering effects, which induces carbide coarsening and the loss of solid solution strengthening, alongside the softening of prior austenite grain boundaries. This study clarifies the micro-to-nanoscale evolution of the barrel microstructure, providing critical theoretical insights for understanding erosion mechanisms and improving lifetime predictions. Full article

Poor bone quality in osteoporotic patients remains a major challenge for achieving predictable osseointegration. This study serves as a mechanistic complement to previously reported structural data, aiming to investigate the molecular pathways underlying the synergy between nanostructured surfaces and autologous blood concentrates in compromised bone. Ninety-six Wistar rats were divided into healthy (SHAM) and osteoporotic (OVX) groups. Implants with nanostructured hydroxyapatite (NanoHA) or dual acid-etched (DAE) surfaces were installed in the tibiae, associated or not with leukocyte- and platelet-rich fibrin (L-PRF). Gene expression (RT-qPCR) for Runx2, Alpl, Bglap, Spp1, Tnfrsf11, and Tnfrsf11b was assessed at 7 and 30 days. In compromised systemic conditions (OVX), the NanoHA + L-PRF association promoted a robust “molecular rescue” of bone metabolism. At 30 days, this synergistic group exhibited a significant upregulation of Alpl (mean: 11.69 ± 1.65) and Runx2 (mean: 4.49 ± 0.82) compared to DAE controls (p < 0.05). Crucially, the therapy orchestrated a protective remodeling environment by significantly inducing Tnfrsf11b expression (5.50 ± 0.88), effectively balancing the Tnfrsf11/Tnfrsf11b ratio. Late-stage maturation markers (Bglap and Spp1) were also significantly elevated, effectively mimicking healthy physiological levels observed in the SHAM group. NanoHA biofunctionalization, synergistically with L-PRF, triggers a transcriptional reprogramming of the peri-implant microenvironment, mitigating the catabolic effects of estrogen deficiency. These findings provide a biological foundation for enhanced clinical predictability in high-risk patients, suggesting that local interfacial modifications can overcome systemic bone compromise. Full article

Metallic nanolaminates (MNLs) exhibit excellent mechanical properties due to unique modulation and interface structures. However, the correlation between the deformation of nanostructures and the mechanical behavior of the materials remains inadequately elucidated. Molecular dynamics method is performed to investigate coupled effect of grain size (d = 7.5~25.0 nm) and layer thickness (λ = 1.31~15.15 nm) on the tensile behavior of Ti/Ni polycrystalline nanolaminates (PNLs). A plastic co-deformation mechanism involving crystalline phases, interface, and grain boundary under strong size and interface constraints is discovered. The dominant plastic deformation in Ti layer is size-independent HCP-BCC-HCP phase transformation. Dislocations propagation in Ni layer shifts with increasing layer thickness, which manifests as extended dislocations sliding, interaction between moving dislocations and interface dislocations, respectively. When grain sizes or layer thicknesses are small, interface migration, grain boundary diffusion, and grain boundary migration become prominent plastic deformation carriers. The coordinating effect of grain boundary and interface on deformations of different nanostructures endows materials with relatively favorable plastic properties. Moreover, a dimensionless parameter d/λ accounting for grain morphology and interface structure is found to predict the variations in flow stresses and characterize the dominating plastic deformation mechanisms of the stretched Ti/Ni PNLs. Full article

Nanostructured lipid carriers (NLCs) are colloidal delivery systems developed to address the low stability and limited bioavailability of sensitive active compounds. In this study, anthocyanin-rich barberry extract-loaded NLCs were prepared by a water-in-oil-in-water double emulsion method, using beeswax as the solid lipid and coriander essential oil as the liquid lipid. A combined D-optimal mixture design was employed to evaluate the effect of surfactant ratios (Tween 80/Tween 20 and polyglycerol ester (PGE)/polyglycerol polyricinoleate (PGPR)) on particle size, polydispersity index (PDI), zeta potential, and encapsulation efficiency. The optimized formulation suggested by Design-Expert^® software was obtained at 90/10 Tween 80/Tween 20 and 90/10 PGE/PGPR ratios and showed a particle size of 94.25 nm, PDI of 0.18, zeta potential of −23.4 mV, and encapsulation efficiency of 74%. The experimental values were in close agreement with the predicted responses. TEM observations indicated spherical morphology at the nanoscale, while FTIR, DSC, and XRD analyses confirmed successful incorporation of barberry extract into the lipid matrix and a less ordered crystalline structure. During one month of storage, the optimized NLC was more stable at 4 °C compared with 25 °C and showed higher antioxidant activity than the free extract. It also exhibited a higher inhibitory effect against S. aureus and E. coli than the free form in MIC and MBC assays. Overall, the developed NLCs could serve as an effective carrier system to improve the stability of anthocyanin-rich barberry extract and extend its application in food formulations. Full article

Mild steel remains one of the most widely used structural materials in mechanical and industrial engineering due to its favorable mechanical performance and low cost. However, its high susceptibility to corrosion continues to cause significant operational and economic losses across engineering systems. This study presents a unified analytical framework for analyzing corrosion-related molecular and nanostructured systems using reverse degree-based topological descriptors, namely, the Reverse M-polynomial and Reverse NM-polynomial. The framework is demonstrated in two complementary stages relevant to corrosion engineering. First, an exploratory structure–property correlation analysis based on Quantitative Structure–Property Relationship (QSPR) principles is conducted for furan-based organic inhibitors reported in the literature, examining the relationship between reverse degree-based descriptors and inhibition efficiency on mild steel surfaces. The analysis reveals a strong statistical correlation within the analyzed dataset (r = 0.958), indicating the sensitivity of selected reverse topological descriptors to molecular structural variations. The statistical significance of the correlations was evaluated using p-values and F-statistics, confirming the reliability of the observed associations within the analyzed dataset. However, owing to the limited dataset size, no claims of external predictivity are made. Second, the framework is extended to advanced protective materials through the analytical formulation of reverse descriptors for nanoporous graphene nanoribbons containing 14-annulene pores, focusing exclusively on structural and topological characterization. These graphene structures are considered as potential physical barrier materials; however, in this study, the analysis is limited to structural descriptor characterization without modeling corrosion performance. This work provides analytical results for reverse degree-based descriptors of such graphene architectures. Overall, the findings establish a versatile analytical framework that supports exploratory structure–property investigations of organic inhibitors and provides descriptor-based structural benchmarks for graphene nanostructures, offering theoretical insights relevant to corrosion mitigation research. Full article

Surface-enhanced Raman spectroscopy (SERS) enables ultrasensitive molecular detection but produces high-dimensional and substrate-dependent spectral data that are difficult to analyze using conventional methods. The integration of machine learning (ML) provides new opportunities for extracting chemical information from complex SERS datasets and for optimizing nanostructured substrates that determine signal enhancement. This review summarizes recent advances in ML-assisted SERS across the analytical workflow. Data characteristics and preprocessing strategies are first outlined, followed by an overview of supervised, unsupervised, and deep learning approaches for spectral classification and quantitative analysis. Applications in biomarker discovery and spectral fingerprint recognition are discussed, with emphasis on model interpretability. In addition, ML-driven strategies for substrate optimization, including surrogate modeling and inverse design, are highlighted as emerging directions for improving enhancement efficiency. Current challenges, such as data scarcity, limited generalization, and real-time deployment constraints, are also examined. The convergence of ML and SERS is gradually shifting Raman-based analysis toward more predictive and integrated sensing frameworks. Full article

The present research article deals with the thermal degradation study of epoxy resins filled with hybrid nanostructured forms of carbon under oxidative conditions. In particular, the formulated polymer composites (denoted as HYB_0.1%_CNTs:GNs and HYB_0.5%_CNTs:GNs, respectively) consist of two kinds of fillers, namely multi-walled carbon nanotubes (CNTs) and graphene nanosheets (GNs), mixed together with two different total mass amounts: 0.1 and 0.5%. In both kinds of nanocomposites, three different CNT:GN mixing ratios were considered (5:1, 1:1, and 1:5, respectively), thus providing a total of six hybrid samples. The thermal behavior of these samples was studied by simultaneous thermogravimetry and differential thermal analysis (TG/DTA) under flowing air, and two processes took place in distinct temperature ranges. In each step, about 50% of mass loss is detected with an exothermic effect in the corresponding DTA curve, with the second one accompanied by an intense heat release. The kinetic analysis of the two-stage oxidative thermal degradation was investigated using a model-free isoconversional approach. A non-Arrhenian behavior of the temperature function k(T) was assumed, and lifetime prediction was estimated at temperatures close to those of the possible applications. Isoconversional analysis shows nearly constant activation energies for all composites except HYB_0.1%_5:1 (from 142 to 96 kJ·mol⁻¹), while lifetime predictions indicate that thermal stability increases with graphene content at 0.1% loading (HYB_0.1%_1:5) and with CNT content at 0.5% loading (HYB_0.5%_5:1), with uncertainties below 7%. Finally, because of the π–π bond interactions between the CNTs and the GNs dispersed in the epoxy resin matrix, an effective and remarkable electrical performance was found and a correlation with both electrical and morphological properties was established. In this regard, Tunneling Atomic Force Microscopy (TUNA) proved to be particularly powerful in allowing the simultaneous mapping of topography and localized conductive networks with exceptional sensitivity to nanofiller dispersion, such as CNTs and GNs. DC conductivity increased by up to nine orders of magnitude at 0.1 wt% hybrid loading (up to 3.73 × 10⁻⁴ S/m vs. 1.06 × 10⁻¹³ S/m for CNT-only), with nanoscale TUNA currents (−1.9 to 4.5 pA) mirroring macroscopic trends, while at 0.5 wt% all hybrids reached 10⁻² S/m, indicating reduced synergy once a fully developed conductive network is established. Full article

Metal-functionalized boron nitride nanostructures represent promising platforms for lightweight solid-state hydrogen storage. In this work, we perform a comprehensive density functional theory (DFT) investigation of pristine and metal-decorated B₄N₄ quantum dots (M = Li, Ti) to evaluate their structural stability, adsorption energetics, and near-ambient storage performance. Pristine B₄N₄ is highly stable but interacts weakly with H₂ (E_ads ≈ −0.12 eV), leading to negligible uptake under operating conditions. Li decoration moderately enhances adsorption through charge-induced polarization (E_ads ≈ −0.15 eV) but offers limited stabilization beyond the first few molecules. In contrast, Ti decoration fundamentally reshapes the interaction landscape, strengthening electrostatic, polarization, and dispersion contributions and enabling significantly stronger yet reversible H₂ binding (E_ads ≈ −0.36 eV). Sequential adsorption calculations predict maximum theoretical capacities of 14, 18, and 20 H₂ molecules for pristine, Li-, and Ti-decorated systems, respectively. Grand canonical thermodynamics show that Ti–B₄N₄ retains nearly its full loading at 30 bar and 298 K, while pristine and Li-decorated clusters store only negligible amounts. Under desorption conditions (3 bar, 373 K), Ti–B₄N₄ releases most of its stored hydrogen, yielding an exceptional reversible capacity of 15.1 wt%. Energy decomposition analysis attributes this performance to cooperative electrostatic, polarization, and dispersion enhancements. Ti–B₄N₄ emerges as a highly promising theoretical candidate, warranting future experimental validation. Full article

Acute kidney injury (AKI) presents a critical clinical challenge due to its rapid progression and lack of effective targeted therapies. The herbal combination of rhubarb and Salvia miltiorrhiza, a cornerstone of Traditional Chinese Medicine (TCM) for renal protection, shows promise, yet its bioactive components and mode of action remain incompletely understood. This study identifies and characterizes inherent nanoscale entities from this herbal pair as a novel nanotherapeutic platform. Self-assembled nanoparticles (designated RSNPs) were isolated from the ethanol extract via differential centrifugation. Comprehensive characterization revealed that RSNPs form stable nanostructures through spontaneous self-assembly, primarily driven by supramolecular interactions (e.g., π-π stacking and hydrogen bonding). UPLC-MS/MS quantification confirmed the co-assembly of multiple bioactive constituents within RSNPs. Network pharmacology and molecular docking initially predicted their synergistic action on AKI-related pathways. In a cisplatin-induced murine AKI model, RSNP administration markedly attenuated renal dysfunction and histopathological damage, mechanistically linked to the mitigation of oxidative stress (e.g., decreased MDA and increased SOD) and inflammation (e.g., downregulated TNF-α and IL-6). In vitro, RSNPs demonstrated enhanced cellular internalization and superior cytoprotection against cisplatin toxicity in renal tubular epithelial cells, significantly reducing apoptosis. These findings unveil that the therapeutic efficacy of the Rheum palmatum L.–Salvia miltiorrhiza Bunge pair is intrinsically embedded within its nanoscale architecture. RSNPs represent a new class of TCM-derived nanotherapeutics with a well-defined material basis and multimodal mechanisms, offering a promising strategy for AKI treatment. Full article

This review surveys how nanoparticles and biomolecular nanosized structures interact with model membrane systems, and how these interfacial processes govern their performance in drug and gene delivery, antimicrobial strategies, biosensing, and nanotoxicology. The nanostructures covered include polymeric nanoparticles, lipid-based carriers, peptide nanostructures, dendrimers, and multifunctional hybrids. Model membranes span Langmuir monolayers, supported lipid bilayers, vesicles/liposomes across sizes, and emerging hybrid or asymmetric constructs that better approximate native complexity. Mechanistically, interactions follow recurrent routes—surface adsorption, bilayer insertion, pore formation, and lipid extraction/reorganization—regulated by particle size, morphology, charge, ligand architecture, and lipophilicity, in conjunction with membrane composition, phase state, curvature, and asymmetry. A multiscale toolkit links structure, mechanics, and dynamics: Langmuir troughs and Brewster Angle Microscopy map thermodynamics and mesoscale morphology; atomic force microscopy and quartz crystal microbalance with dissipation resolve nanoscale topography and viscoelasticity; fluorescence microscopy/spectroscopy reports on localization and packing; neutron and X-ray reflectometry quantify vertical structure; molecular dynamics provides atomistic pathways and design hypotheses. Historically, the field advanced from early monolayers and bilayers, through the fluid mosaic model, to raft microdomains and modern biomimetic systems, enabling increasingly realistic experiments. Key advances include cross-method integration linking experimental observations with image-based computational models; persistent debates concern the translation from simplified models to living membranes, the role of dynamic coronas, and scale/force-field limits in simulations. Future efforts should prioritize hybrid models incorporating proteins and asymmetric lipidomes, standardized reporting and reference systems, rigorous coupling of experiments with calibrated simulations and machine learning, and alignment with safety-by-design and regulatory expectations, thereby shifting interfacial measurements from descriptive observation to predictive design rules. Full article

Novel, accurate molecular diagnostics are driving new advances across medicine, public health, and environmental monitoring. Surface-enhanced Raman spectroscopy (SERS) nanotags are powerful platforms for ultrasensitive, multiplexed, and quantitative detection of molecular targets. This review focuses on indirect sensing strategies, where SERS nanotags act as signal transducers, resulting in enhanced and unique Raman spectra upon binding of target analytes (high specificity) and allowing for ultralow limits of detection. These indirect SERS sensors typically consist of a plasmonic core, a Raman reporter molecule, and a ligand that targets the analyte of interest. Each of these components contributes to the sensitivity, stability, and selectivity of the system. Rational design of SERS nanotags requires balancing enhancement efficiency with reproducibility, biocompatibility, and assay integration. The choice of reporter molecules, for instance, governs spectral uniqueness and enables multiplexed detection of multiple analytes within a single sample. Recent advances in artificial intelligence and machine learning are accelerating nanotag development by enabling predictive control over nanostructure geometry, composition, and optical response. SERS nanotags are increasingly being integrated into diagnostic formats, such as lateral flow assays and microfluidic devices, offering both qualitative and quantitative analysis at the point of care. This review provides an overview of key design principles, common strategies for nanostructure functionalization and stabilization, and emerging biosensing applications, serving as a practical guide for researchers seeking to design and implement SERS nanotags. Full article

Fluorescence remains a foundational optical phenomenon underpinning applications in sensing, imaging, diagnostics, and catalysis. Among the strategies developed to modulate fluorescence, coupling fluorophores with plasmonic metals has emerged as a powerful route for both enhancement and quenching. The collective excitation and decay of surface plasmons can profoundly alter fluorophore excitation rates, radiative pathways, and emission efficiencies. This review provides a mechanistic and historical synthesis of metal–fluorophore interactions, unifying enhancement and quenching phenomena under the term Metal Manipulated Fluorescence (MMF). We summarize the fundamental principles of fluorescence and plasmon resonance, discuss theoretical and computational approaches for predicting metal–fluorophore coupling, and critically examine recent advances in plasmonic nanostructure synthesis that enable precise control over fluorophore behaviour. By integrating experimental observations with theoretical models, we highlight the opportunities and limitations of current MMF strategies and outline future directions in materials design, synthesis methodologies, and predictive modelling for next-generation optical and optoelectronic technologies. Full article

The development of anisotropic gold nanostructures supporting localized surface plasmon (LSP) resonances in the near-infrared (NIR) biological window is of great interest for diagnostic and therapeutic nanotechnologies. Here, we report gold open-shell nanoparticles (AuOSNs), a symmetry-broken nanoshell architecture exhibiting strong NIR surface-enhanced Raman scattering (SERS) activity. AuOSNs were fabricated via a surfactant-free strategy combining bottom-up silica sphere assembly with a simple top-down gold deposition process, without using highly cytotoxic surfactants such as cetyltrimethylammonium bromide (CTAB). Boundary element method (BEM) simulations revealed that the asymmetric open-shell geometry induces NIR LSP resonances with pronounced electromagnetic field localization near the opening edges, depending on excitation configuration. Consistent with these predictions, extinction spectra of AuOSNs dispersed in water showed an LSP resonance peak at ~793 nm, close to the 785 nm excitation wavelength for SERS. In aqueous dispersion, AuOSNs modified with 4-mercaptobenzoic acid (4-MBA) exhibited strong SERS activity with enhancement factors of ~10⁶. Furthermore, polyethylene glycol (PEG)-modified MBA/AuOSNs showed negligible cytotoxicity in vitro. SERS imaging confirmed that PEG/MBA/AuOSNs enable visualization of HeLa cells via characteristic MBA SERS signals. These results demonstrate that surfactant-free AuOSNs provide a biocompatible platform for NIR-excited SERS sensing and cellular imaging, highlighting their potential in plasmonic bioimaging applications. Full article