Nanomaterials最新文献

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Indium phosphide (InP) is an excellent material used in space electronic devices due to its direct band gap, high electron mobility, and high radiation resistance. Displacement damage in InP, such as vacancies, interstitials, and clusters, induced by cosmic particles can lead to the serious degradation of InP devices. In this work, the analytical bond order potential of InP is modified with the short-range repulsive potential, and the hybrid potential is verified for its reliability to simulate the atomic cascade collisions. By using molecular dynamics simulations with the modified potential, the primary damage defects evolution of InP caused by 1-10 keV primary knock-on atoms (PKAs) are studied. The effects of electronic energy loss are also considered in our research. The results show that the addition of electronic stopping loss reduces the number of point defects and weakens the damage regions. The reduction rates of point defects caused by electronic energy loss at the stable state are 32.2% and 27.4% for 10 keV In-PKA and P-PKA, respectively. In addition, the effects of electronic energy loss can lead to an extreme decline in the number of medium clusters, cause large clusters to vanish, and make the small clusters dominant damage products in InP. These findings are helpful to explain the radiation-induced damage mechanism of InP and expand the application of InP devices.

The family of BiS₂-based superconductors has attracted considerable attention since their discovery in 2012 due to the unique structural and electronic properties of these materials. Several experimental and theoretical studies have been performed to explore the basic properties and the underlying mechanism for superconductivity. In this review, we discuss the current understanding of pairing symmetry in BiS₂-based superconductors and particularly the role of point-contact spectroscopy in unravelling the mechanism underlying the superconducting state. We also review experimental results obtained with different techniques including angle-resolved photoemission spectroscopy, scanning tunnelling spectroscopy, specific heat measurements, and nuclear magnetic resonance spectroscopy. The integration of experimental results and theoretical predictions sheds light on the complex interplay between electronic correlations, spin fluctuations, and Fermi surface topology in determining the coupling mechanism. Finally, we highlight recent advances and future directions in the field of BiS₂-based superconductors, underlining the potential technological applications.

Innovative methods for substrate patterning provide intriguing possibilities for the development of devices based on ordered arrays of semiconductor nanowires. Control over the nanostructures' morphology in situ can be obtained via extensive theoretical studies of their formation. In this paper, we carry out an investigation of the ordered nanowires' formation kinetics depending on the growth mask geometry. Diffusion equations for the growth species on both substrate and nanowire sidewalls depending on the spacing arrangement of the nanostructures and deposition rate are considered. The value of the pitch corresponding to the maximum diffusion flux from the substrate is obtained. The latter is assumed to be the optimum in terms of the nanowire elongation rate. Further study of the adatom kinetics demonstrates that the temporal dependence of a nanowire's length is strongly affected by the ratio of the adatom's diffusion length on the substrate and sidewalls, providing insights into the proper choice of a growth wafer. The developed model allows for customization of the growth protocols and estimation of the important diffusion parameters of the growth species.

Single-vesicle molecular profiling of cancer-associated extracellular vesicles (EVs) is increasingly being recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging is a facile method to quantify the fraction of the molecularly targeted population of EVs in biofluids at the single-vesicle level. However, accurate and efficient dual imaging vesicle analysis has been challenging due to the interference of false signals on the mask images and the need to analyze a large number of images in clinical samples. In this work, we report a fully automatic dual imaging analysis method based on machine learning and use it with dual imaging single-vesicle technology (DISVT) to detect breast cancer at different stages. The convolutional neural network Resnet34 was used along with transfer learning to produce a suitable machine learning model that could accurately identify areas of interest in experimental data. A combination of experimental and synthetic data were used to train the model. Using DISVT and our machine learning-assisted image analysis platform, we determined the fractions of EpCAM-positive EVs and CD24-positive EVs over captured plasma EVs with CD81 marker in the blood plasma of pilot HER2-positive breast cancer patients and compared to those from healthy donors. The amount of both EpCAM-positive and CD24-positive EVs was found negligible for both healthy donors and Stage I patients. The amount of EpCAM-positive EVs (also CD81-positive) increased from 18% to 29% as the cancer progressed from Stage II to III. No significant increase was found with further progression to Stage IV. A similar trend was found for the CD24-positive EVs. Statistical analysis showed that both EpCAM and CD24 markers can detect HER2-positive breast cancer at Stages II, III, or IV. They can also differentiate individual cancer stages except those between Stage III and Stage IV. Due to the simplicity, high sensitivity, and high efficiency, the DISVT with the AI-assisted dual imaging analysis can be widely used for both basic research and clinical applications to quantitatively characterize molecularly targeted EV subtypes in biofluids.

It is expected that two-dimensional (2D) metal nitrides (MNs) consisting of the 13th group elements of the periodic table and nitrogen, namely aluminium nitride (AlN), gallium nitride (GaN), indium nitride (InN) and thallium nitride (TlN), have enhanced physical and mechanical properties due to the honeycomb, graphene-like atomic arrangement characteristic of these compounds. The basis for the correct design and improved performance of nanodevices and complex structures based on 2D MNs from the 13th group is an understanding of the mechanical response of their components. In this context, a comparative study to determine the elastic properties of metal nitride nanosheets was carried out making use of the nanoscale continuum modelling (or molecular structural mechanics) method. The differences in the elastic properties (surface shear and Young's moduli and Poisson's ratio) found for the 2D 13th group MNs are attributed to the bond length of the respective hexagonal lattice of their diatomic nanostructure. The outcomes obtained contribute to a benchmark in the evaluation of the mechanical properties of AlN, GaN, InN and TlN monolayers using analytical and numerical approaches.

We present a MgB₂-based Microwave Kinetic Inductance Detector (MKID) featuring a quality factor Q_i ~ 10⁵ and noise equivalent power NEP ~ 10^-14 W/Hz at 2 K. In comparison to YBCO-based MKIDs, the MgB₂ detector shows greater sensitivity to both temperature and magnetic field, a result of its two-gap nature and relatively low critical Hc2 field. Our data indicate that MgB₂ is more advantageous for MKID applications at temperatures lower than 3 K.

The magnetic toroidal dipole moment, which is induced by a vortex-type spin texture, manifests itself in parity-breaking physical phenomena, such as a linear magnetoelectric effect and nonreciprocal transport. We elucidate that a staggered alignment of the magnetic toroidal dipole can give rise to spontaneous magnetization even under antiferromagnetic structures. We demonstrate the emergence of uniform magnetization by considering the collinear antiferromagnetic structure with the staggered magnetic toroidal dipole moment on a bilayer zigzag chain. Based on the model calculations, we show that the interplay between the collinear antiferromagnetic mean field and relativistic spin-orbit coupling plays an important role in inducing the magnetization.

Two-dimensional (2D) semiconductor components have excellent physical attributes, such as excellent mechanical ductility, high mobility, low dielectric constant, and tunable bandgap, which have attracted much attention to the fields of flexible devices, optoelectronic conversion, and microelectronic devices. Additionally, one-dimensional (1D) semiconductor materials with unique physical attributes, such as high surface area and mechanical potency, show great potential in many applications. However, isolated 1D and 2D materials often do not meet the demand for multifunctionality. Therefore, more functionality is achieved by reconstructing new composite structures from 1D and 2D materials, and according to the current study, it has been demonstrated that hybrid dimensional integration yields a significant enhancement in performance and functionality, which is widely promising in the field of constructing novel electronic and optoelectronic nanodevices. In this review, we first briefly introduce the preparation methods of 1D materials, 2D materials, and 1D/2D heterostructures, as well as their advantages and limitations. The applications of 1D/2D heterostructures in photodetectors, gas sensors, pressure and strain sensors, as well as photoelectrical synapses and biosensors are then discussed, along with the opportunities and challenges of their current applications. Finally, the outlook of the emerging field of 1D/2D heterojunction structures is given.

In this study, we demonstrate the reflectance spectrum of one-dimensional photonic crystals comprising two different types of metamaterials. In this regard, the designed structure can act as a simple and efficient detector for fat concentrations in milk samples. Here, the hyperbolic and gyroidal metamaterials represent the two types of metamaterials that are stacked together to construct the candidate structure; meanwhile, the designed 1D PCs can be simply configured as [G(ED)^m]^S. Here, G refers to the gyroidal metamaterial layers in which Ag is designed in a gyroidal configuration form inside a hosting medium of TiO₂. In contrast, (ED) defines a single unit cell of the hyperbolic metamaterials in which two layers of porous SiC (E) and Ag (D) are combined together. It is worth noting that our theoretical and simulation methodology is essentially based on the effective medium theory, characteristic matrix method, Drude model, Bruggeman's approximation, and Sellmeier formula. Accordingly, the numerical findings demonstrate the emergence of three resonant peaks at a specified wavelength between 0.8 μm and 3.5 μm. In this context, the first peak located at 1.025 μm represents the optimal one regarding the detection of fat concentrations in milk samples due to its low reflectivity and narrow full bandwidth. Accordingly, the candidate detector could provide a relatively high sensitivity of 3864 nm/RIU based on the optimal values of the different parameters. Finally, we believe that the proposed sensor may be more efficient compared to other counterparts in monitoring different concentrations of liquid, similar to fats in milk.