Search Results (556)

This mini-review is intended to show how multimodal imaging could be developed from prototypes and proofs of concept by controlling the nanoscale for improved resolution of life science imaging for broad applications such as bioassays, early diagnoses and further applications. It intends to afford the presentation of multimodal approaches for imaging and bioimaging uses with potential applications to biological media. The application of multimodal nanoemitters provides enhanced bioimaging through the generation of various targeted and well-defined signals. Radionuclides and luminescent emitters were considered in the discussion for improved and enhanced signaling. In this manner, we intended to show the increase in the power of information by collecting varied optical signal–matter interactions. This could be important for Positron Emission Tomography and Computed Tomography (PET-CT), Fluorescence Tomography (FT), and other new modes of imaging contemplating the incorporation of nanotechnology. In the context of the design of new multimodal energy modes, key examples were shown from the literature, where the interactions of different energy modes could lead to enhanced and improved signaling. Electromagnetic fields and nanoplasmonics are involved in these different energy modes involving varied quantum particle interactions with modified properties. In this regard, multimodal imaging has experienced developments in nanoemitters and nanobiolabeling to track biomolecular events and targeted cells. A large quantity of research output actually focuses on nano- and quantum emissions. However, there are not as many studies dealing with enhanced emissions or laser emissions coupled with radionuclide emitters. A non-classical form of light emission, considering varied luminescent phenomena as well as further quantum signaling, showed interesting and high-impact perspectives when combined with nuclear emissions. This is the case for current trends focusing on innovative single-cell analysis. For example, the characterization and diagnosis of cells where the targeting of antibody–antigen interactions is required, providing light and energy from different sources to produce different details and imaging resolutions, has been noted. These are potential approaches that could be developed through various strategies targeting life science applications. In this regard, this article puts forward a discussion focused on nanotechnology contemplating radio-pharmacy and enhanced nanoemitters. Full article

Magnetopolymeric nanocomposites represent promising platforms for advanced nanomedicine due to their ability to combine magnetic responsiveness with polymer-mediated biological functionality. In this study, a hybrid nanocomposite system based on maghemite (Mh), poly(ethyl cyanoacrylate) (PECA), and chitosan (Cs) was developed and systematically characterized for biomedical applications. Mh nanoparticles (NPs) were incorporated as magnetic cores, while PECA acted as a biodegradable polymeric matrix, and chitosan provided surface functionalization and enhanced biocompatibility. The nanocomposites were prepared following anionic polymerization and coacervation methods and characterized in terms of structure particle size, electrokinetics and magnetic responsiveness. The results demonstrated the formation of a nanoscale (core/shell)/shell system with superparamagnetic properties. Importantly, the nanocomposites were evaluated as magnetic resonance imaging (MRI) contrast agents, exhibiting significant transverse relaxivity. In vitro cytotoxicity assays demonstrated the absence of significant toxic effects, while ex vivo hemocompatibility studies confirmed their compatibility with blood components. Furthermore, their potential as hyperthermia agents was demonstrated in vitro under the influence of an alternating magnetic field (AMF). Overall, the (core@shell)@shell nanocomposites combined superparamagnetic functionalities with favorable biological properties, supporting their potential use as multifunctional platforms for theranostic applications, including MRI contrast enhancement and magnetically induced hyperthermia. Full article

Purpose: Marking the one hundredth anniversary of quantum mechanics in 2025, quantum science has become foundational for the development of contemporary technologies, enabling advances in sensing, imaging, computing, and materials engineering. Cancer radiotherapy, although traditionally developed within the scope of classical dosimetric models and phenomenological biological frameworks, is fundamentally initiated by quantum-mechanical radiation-matter interactions. Radiation-induced DNA damage, which ultimately determines therapeutic effectiveness, originates from primary quantum-mechanical processes involving particle transport, electronic excitation and ionisation, followed by successive physicochemical and chemical stages including water radiolysis and radical formation. As scientific disciplines undergo a rapid “quantum transition,” radiation cancer treatment is increasingly positioned to benefit from deeper integration of quantum principles and emerging quantum technologies. Methods: This review examines how quantum mechanics governs the primary radiation-matter interactions that initiate the physical, physicochemical, chemical, and ultimately biological stages of radiation action at the (sub)cellular level, with particular emphasis on track structure, water radiolysis, DNA damage induction, and multiscale biological response. Contemporary approaches to DNA damage modeling are discussed, including track-structure Monte Carlo methods, nanodosimetric frameworks, and multi-scale simulation approaches that connect microscopic interaction events with biological outcomes. Key quantum concepts relevant to radiation therapy are outlined, together with emerging quantum technologies such as nanoscale quantum sensing, quantum lasers, quantum dots, and quantum computing, which are evaluated for their potential roles in dosimetry, imaging, treatment planning, and radiation transport simulations. In this context, artificial intelligence (AI) is considered a complementary tool to accelerate computation and integrate quantum-informed data across multiple scales. Results: The review highlights that quantum-informed modeling enables a more consistent description of radiation-induced processes across spatial and temporal scales, linking microscopic interaction mechanisms to DNA damage formation and macroscopic biological outcomes. Recent advances in track-structure and radiobiological modeling provide new opportunities for improving predictions of radiation effects and treatment response. Emerging quantum technologies show potential to enhance measurement sensitivity, improve simulation efficiency, and enable more precise control of radiation delivery. Furthermore, AI-assisted approaches facilitate the extraction of predictive patterns from complex datasets, supporting faster and more accurate estimation of biological endpoints such as DNA damage and cell survival. Conclusions: The quantum aspects of advanced treatment modalities, including proton and heavy-ion therapy, ultrafast radiation delivery, and the FLASH effect, as well as future concepts such as laser-plasma-driven and coherence-informed radiotherapy systems, indicate a promising direction for next-generation cancer treatment. By critically assessing both opportunities and limitations, this work provides a coherent framework for integrating DNA damage modeling, quantum principles, quantum-inspired techniques, emerging quantum technologies, and advanced computational tools to guide future developments in radiation oncology. Full article

Scanning magnetometry based on diamond nitrogen-vacancy (NV) centers enables quantitative magnetic imaging with nanoscale spatial resolution. However, large-area scanning is often limited by geometric distortion arising from the nonlinear hysteresis of piezoelectric positioners, leading to non-uniform pixel spacing and reduced image fidelity. Here, we present a software-based polynomial compensation method that corrects piezoelectric hysteresis without requiring additional hardware or closed-loop position control. By calibrating and analytically inverting the forward and backward hysteresis responses, compensated driving voltages are generated and directly applied during scanning. The method is experimentally validated using a current-carrying patterned gold device and magnetic domains in a hard-disk sample, demonstrating significantly improved geometric accuracy and the removal of scanning artifacts in both topographic and magnetic images. This simple and cost-effective approach provides an effective solution for improving large-area NV scanning magnetometry and other scanning probe imaging techniques based on open-loop piezoelectric positioners. Full article

Atomic force microscopy (AFM) has emerged as a powerful platform for quantifying nanoscale mechanical properties of cells, tissues, and extracellular matrix (ECM) components, providing candidate biomarkers for disease diagnosis, classification, prognosis, and treatment monitoring. However, the clinical translation of AFM-based nanomechanical biomarkers remains limited by low throughput, operator dependence, complex force-curve interpretation, heterogeneous biological samples, and the lack of standardized analytical pipelines. Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used to address these limitations by enabling automated AFM image and force-curve analysis, multiparametric feature extraction, cell and tissue classification, quality control, and high-throughput mechanophenotyping. This review summarizes how AI and ML have already been applied to AFM-derived nanomechanical and morphological data, with emphasis on cancer, fibrotic disease, and treatment response monitoring. We discuss classical ML models, deep learning approaches, clustering, fuzzy logic methods, and emerging automated Bio-AFM workflows. We further highlight current limitations, including small datasets, limited external validation, lack of reproducible reporting standards, and insufficient integration with clinical metadata. Finally, we propose a roadmap for AI-enabled AFM mechanobiomarkers, focusing on standardized datasets, explainable models, multimodal mechano-optical imaging, and clinically relevant validation strategies. Full article

Raman spectroscopy is a commonly used label-free technique for the chemical analysis of microplastics (MPs), which are defined as plastic particles larger than 1 μm but smaller than 5 mm size; given its small signal strength and diffraction-limited spatial resolution, Raman spectroscopy has limited applicability in the study of nanoplastics (NPs), which are defined as particles smaller than 1 μm. Tip-enhanced Raman scattering (TERS) is a promising technique that can overcome these limitations by using a single plasmonic tip of an atomic force microscope (AFM) to enhance the Raman signal from a small sample volume. Here we used TERS for the analysis of NPs in human bronchoalveolar lavage (BAL) fluid. We developed a sub-sampling procedure for nanoscale TERS imaging on an SiO₂/Si substrate and performed control experiments. We investigated the experimental coffee-ring effect on the spatial distribution of NPs on the substrate. We compared the results of TERS imaging with the conventional confocal Raman microscopy and observed the presence of similar types of plastic, pigments, and mineral particles, revealing the origin of NPs from the corresponding MPs. The total nanoparticle density observed in BAL using TERS was ~50 billion particles per liter, most of which were NPs. Our TERS approach may be extended to other types of human fluids and tissue samples to provide insights into the health effects of NPs. Full article

The spatial organisation of plasma membrane proteins such as G protein-coupled receptors (GPCRs) plays a critical role in regulating cell signalling, function, and ultimately cell fate. Resolving this organisation requires techniques capable of probing dynamics at the single-molecule level with high spatial and temporal resolution. In this study, we employ the complementary fluorescence fluctuation spectroscopy approaches, Fluorescence Correlation Spectroscopy (FCS), Photon Counting Histogram Analysis (PCH), Raster Image Correlation Spectroscopy (RICS) and Number and Brightness Analysis (N&B), in conjunction with Fluorescence Recovery After Photobleaching (FRAP), to investigate the membrane organisation of the C-X-C chemokine receptor 4 (CXCR4), a GPCR known to undergo ligand-induced reorganisation. At the nanoscale, FCS highlighted opposing effects on diffusion after agonist (CXCL12) and inverse agonist (IT1t) treatment, whilst RICS also showed ligand-mediated changes in particle number. Both single-point and image-based brightness analyses (PCH and N&B) showed increased brightness after CXCL12 treatment, consistent with the pre-internalisation clustering of CXCR4. At the microscale, FRAP showed an increase in immobile CXCR4, not visible to FFS approaches, following CXCL12 stimulation. This integrated approach, performed on a single commercial confocal microscope, provides valuable insight into the reorganisation of CXCR4 in the plasma membrane over a range of temporal and spatial scales, which are not detectable using standard imaging. Full article

Dynamic atomic force microscopy (AFM) is a key technique for nanoscale characterization and mechanical property measurement, where the geometric shape of the probe tip critically determines imaging quality and measurement accuracy. This study proposes a tip shape classification framework based on the dynamic response of the AFM microcantilever. First, a dimensionless dynamic model of the microcantilever is established, and its vibrational response is solved using a finite-difference scheme. For conical, spherical, and flat tip geometries, interaction force models are provided under both non-contact and tapping-mode AFM. Based on these formulations, multidimensional dynamic feature parameters, including amplitude, phase, virial, and root-mean-square force, are extracted. On this basis, an XGBoost-based classifier is constructed for tip shape identification, and the model’s decision-making mechanism is further interpreted through a SHAP-based explainability framework combined with dimensionality reduction and visualization techniques. Results show that, under non-contact conditions, the overall classification accuracy on the test set reaches 96.7%, with a 100% recognition rate for conical tips. Under tapping-mode conditions, the classification accuracies for conical, spherical, and flat tips are 100%, 85.5%, and 98.3%, respectively. The results demonstrate the feasibility of identifying tip shapes from dynamic responses using simulated data, thereby establishing a theoretical and methodological basis for future experimental validation and the development of tip diagnostic techniques. Full article

Background and Objectives: Acral peeling skin syndrome (APSS) is a rare autosomal recessive genodermatosis primarily affecting the skin. Although subtle hair abnormalities have been reported, data on hair morphology and age-related differences in APSS remain limited. Materials and Methods: Hair samples were collected from two pediatric patients with APSS aged 2 and 6 years and age-matched healthy controls. Five hair shafts from each participant were examined at two standardized distances from the root (5 mm and 15 mm). A total of ten atomic force microscopy (AFM) images were obtained for each hair shaft. Cuticle scale length, width, and deviation were analyzed using line profile measurements. Most analyses were performed on scan areas of 40 × 20 µm. Results: AFM revealed distinct nanoscale differences in hair cuticle morphology between children with APSS and healthy controls, including differences in cuticle scale step height, apparent cuticle scale length, and cuticle scale width. Conclusions: This exploratory study provides the first AFM characterization of hair shaft morphology in pediatric patients with APSS and provides a foundation for future research. Full article

Understanding the mechanical behavior of bimetallic nanoparticles under compressive stress is relevant for the use of these nanostructures in catalysis and nanomechanics. In this work, we present molecular dynamics (MD) simulations of compressive deformation in Pt–Ni nanoparticles—and bulk systems for comparison—with varying compositions (Pt_xNi_1−x) and local distributions. The simulations show that the mechanical response is governed by local strain fields, which influence both elastic and plastic regimes. The final trajectories were analyzed by dislocation analysis (DXA), simulated STEM imaging, and geometric phase analysis (GPA), which allowed the obtention of high-resolution strain maps. Analysis of von Mises stress distribution allowed us to correlate composition and atomic ordering with the formation and evolution of dislocations in the nanoparticles. The Pt_0.5Ni_0.5 intermetallic compound exhibits superior mechanical performance under uniaxial compression; in bulk, this composition also shows enhanced elastic energy storage. In polycrystalline nanoparticles, energy dissipation increased with decreasing average grain size, which is attributed to elevated plastic activity induced by the presence of multiple crystallographic orientations. GPA results show that it is possible to discriminate between compositions differing by as little as $\mathrm{\Delta }x$ = 0.1 based on local strain distributions, and the comparison with GPA performed on real STEM micrographs gives a fair agreement. GPA and atomistic stress maps reveal how strain fields evolve during compression and how they correlate with the development of plasticity. These findings highlight the critical role of local structural heterogeneities in dictating the mechanical behavior of nanoscale Pt–Ni systems, and provide strong evidence that GPA can correlate local strain and composition in real high-resolution micrographs. Full article

Ni–Co γ/γ′-strengthened superalloys are key structural materials for modern energy and flow turbomachinery systems due to their exceptional high-temperature strength, creep resistance, as well as hydrogen and corrosion stability. However, operation in gaseous hydrogen environments typical of hydrogen-cooled generators, cooled gas-turbine blades, and emerging hydrogen-energy technologies can significantly affect their microstructural stability and fracture behavior. This study presents a comprehensive multiscale review of hydrogen-induced nanoscale degradation and crack initiation mechanisms in Ni–Co superalloys produced by wrought, powder metallurgy, and additive manufacturing routes. Transmission electron microscopy combined with quantitative morphometric analysis was employed to characterize the size, morphology, and spatial distribution of γ′ precipitates, revealing a dense population of coherent particles predominantly in the 40–120 nm range, governed by a log-normal distribution. Correlations between precipitate size, aspect ratio, and circularity indicate the onset of partial loss of coherency and coarsening for particles exceeding ~80 nm, creating favorable sites for hydrogen localization. The presence of TCP phases (η, σ, μ, Laves) and carbides at grain boundaries and within grains was shown to enhance microstructural heterogeneity and act as effective hydrogen traps, promoting interfacial decohesion and microcrack initiation. To support microstructural interpretation, convolutional neural network analysis with Grad-CAM visualization was applied to SEM images, enabling the identification of the structural regions most sensitive to hydrogen-assisted damage, particularly γ/γ′ interfaces and defect clusters. The results demonstrate that hydrogen-induced degradation in Ni–Co superalloys is governed by the coupled interactions among microstructure, hydrogen distribution, and local stress state. The findings provide a physically grounded basis for optimizing alloy chemistry, heat treatment, and additive manufacturing parameters, as well as for developing AI-assisted predictive models for the durability of critical components in hydrogen-energy and high-temperature power-generation systems to increase hydrogen safety. Full article

In recent years, hybrid materials have been widely applied in the cultural heritage conservation field, especially to preserve color pigments. Among these, one of the most problematic (in terms of conservation science) is the red pigment cinnabar/vermilion. The challenge of this work was to prepare a hybrid coating consisting of a fluorinated polymer (known to protect cinnabar/vermilion), further modified with an inorganic filler based on anatase TiO₂. The latter is suitable because it is functionalized with quenchers, the particles are well above the nanoscale (≥200 nm in diameter), and it was added to the polymer matrix in small quantities. These characteristics made it suitable as a hybrid coating for protecting natural cinnabar, as demonstrated by the results obtained through a multi-analytical approach, based on multispectral imaging, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray analysis (EDX), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), X-ray fluorescence (XRF), contact angle, spectrophotometry and mechanical tests, which were applied to evaluate the performances of the hybrid coating on laboratory specimens (after aging) and original samples. The experimental results provide insight into both the physicochemical decomposition mechanism of natural cinnabar under laboratory-simulated aging conditions and the benefits of the coating. In particular, the treatment did not induce electrochemical changes in the mercury, which remained in its oxidized state (+2) rather than being further reduced to elemental mercury (Hg⁰), the species responsible for the blackening of cinnabar/vermilion (also combined with meta-cinnabar). In the oxidized form (Hg²⁺), the protein binder was altered, yet the application of the hybrid coating did not cause further physicochemical changes (i.e., red shift) to the Hg²⁺/egg-based binder system. This was also reflected in the color properties, which underwent no significant alteration. Finally, the mechanical tests yielded satisfactory results, particularly regarding water vapor permeability and treatment efficiency (even eight months after the initial application, although studies on the same samples are still ongoing). The hybrid coating was ultimately applied to original samples collected at Poggio Spaccasasso (Tuscany, Italy), which could be representative of prehistoric artworks based on natural cinnabar and traces of prehistoric adhesives made from beeswax, natural oils, and plant resins. Full article

The spatial organization of the eukaryotic nucleus plays a pivotal role in regulating pre-mRNA splicing; however, the underlying principles governing this organization remain incompletely understood. Recent advances in imaging and sequencing technologies have revealed that splicing regulation is orchestrated across multiple hierarchical levels, from nanoscale protein–RNA interactions to large-scale nuclear architecture. Intrinsically disordered regions (IDRs) in RNA-binding proteins (RBPs) mediate multivalent interactions that drive liquid–liquid phase separation, leading to the formation of dynamic biomolecular condensates, such as nuclear speckles, paraspeckles, and nuclear stress bodies (nSBs). These structures act as functional hubs that modulate RNA processing efficiency and respond to cellular stress. In addition, emerging evidence highlights nucleus-wide RBP meshworks that spatially organize co-transcriptional splicing through dynamic RNA-dependent interactions. The interplay between these condensates and meshworks forms a spatially organized network that fine-tunes the efficiency and fidelity of pre-mRNA splicing. Collectively, this review presents a unified model in which phase separation and higher-order nuclear architecture coordinately regulate transcriptomic output in space and time. Full article

Monocrystalline silicon, with its high refractive index, high infrared transmittance, and excellent dimensional stability, serves as a key optical component in high-energy laser systems, infrared imaging, and guidance fields. Its processing quality directly affects the performance indicators of related systems. To address the challenges of nanoscale precision shape and property control during processing, methods such as ultra-precision cutting, magnetorheological polishing, laser micromachining, ion beam processing, plasma etching, and chemical–mechanical polishing have been adopted to improve the surface shape accuracy and repair defects of monocrystalline silicon components. This paper reviews the research progress of key technologies, including nanoscale precision surface shape control manufacturing technology, nanoscale precision property control generation methods, and combined processes for its nanoscale shape and property control, providing technical support for achieving nanoscale precision shape and property control manufacturing of monocrystalline silicon components. Full article

Early and reliable detection of forest fires is essential for reducing environmental damage and ensuring public safety. Deep learning-based object detection enables automated fire monitoring across heterogeneous sensing platforms, including satellite, Unmanned Aerial Vehicle (UAV), and ground-based imaging systems. However, differences in spatial resolution, viewing geometry, and computational constraints present challenges for developing unified detection models. This study presents a comparative benchmarking analysis of the lightweight YOLOv26-nano model for forest fire detection using the FASDD dataset, comprising satellite, UAV, and ground-based imagery. A unified experimental protocol with five-fold cross-validation is adopted to ensure robustness and cross-platform generalization. Performance is enhanced through data augmentation, contrast-limited adaptive histogram equalization, and stochastic gradient descent optimization. Experimental results demonstrate that YOLOv26-nano achieves reliable detection accuracy and demonstrates promising computational characteristics under simulated resource-constrained edge-computing conditions. The proposed benchmarking framework provides a standardized reference for multi-platform fire detection and highlights the suitability of nano-scale object detection models for scalable wildfire monitoring and early-warning systems. Full article