Nanotechnology最新文献

Recently, symmetry-broken ground states, such as correlated insulating states, magnetic order and superconductivity, have been discovered in twisted bilayer graphene (tBLG) and twisted trilayer graphene (tTLG) near the so-called magic angles. Understanding the magnetic order in these systems is challenging, however, as atomistic methods become extremely expensive near the magic angle and continuum approaches fail to capture important atomistic details. In this work, we develop an approach to incorporate short-ranged Hubbard interactions self-consistently in a continuum model. In addition, we include long-ranged Coulomb interactions which are known to be important when doping the flat bands of tBLG and tTLG. Therefore, for the first time, magnetic order in moiré graphene multilayers is self-consistently explored in a continuum model with atomistic detail. With this approach, we perform a systematic analysis of the magnetic phase diagram of tBLG as a function of doping level and twist angle, near the magic angle. Our results are consistent with previous perturbative atomistic Hartree+Ucalculations. Furthermore, we investigated magnetic order of tTLG, which were found to be similar to those in tBLG. In the future, the developed continuum model can be utilized to investigate magnetic ordering tendencies from short-range exchange interactions in other moiré graphene multilayers as a function of doping, twist angle, screening environment, among other variables.

This research outlines a simulation-based analysis of dual-gate semiconducting graphene field-effect transistors (GFETs) constructed using vertically aligned synthesized graphene via plasma-enhanced chemical vapor deposition (PECVD) technique. Using SILVACO TCAD software, the study investigates the impact of varying plasma parameters-specifically electron and ion temperatures, and densities-each associated with different graphene channel thicknesses. Distinct combinations of plasma electron/ion temperature and density were investigated; each linked to a specific graphene channel thickness. The study focused on the electrical properties of the dual gate semiconducting GFET, comparing them with the existing experimental observations and correlating these properties with the plasma processing parameters. It was seen that the values of these properties, like drain current,Ion/Ioff current ratio, transconductanceg_m, cutoff frequencyf_c, etc., increased on decreasing the plasma parameters of the PECVD process involved. The relations developed can be used to modulate the properties of plasma-grown GFETs, by scaling them down for industrial use in several concerned sectors of high-frequency circuits, solar cells, supercapacitors and biosensing technologies. These findings provide a theoretical framework to support future experimental validation and process optimization.

Non-van der Waals layered transition metal chalcogenide Cr₂Te₃ exhibits unique properties including perpendicular magnetic anisotropy, anomalous Hall effect, and non-trivial band topology. Our first principles study further reveals that, induced by the surface Cr atoms, a transition from metallicity to a half-metallicity could occur when Cr2Te3 is reduced to an ultrathin film. The synergistic effect induced by the surface termination and the thickness of the film was found to play a crucial role in this transition. Specifically, a correlation between the bonding symmetry broken on the surface Cr, the strong orbital hybridization between surface Cr and Te atoms, and the squeeze of spin-down valence bands associated with Te-5p orbitals below the Fermi level was found in such ultra-thin 2D Cr2Te3 film during this transition. Such a finding provides key insights into the tunability of Cr₂Te₃ on its spintronic properties by modulating its thickness and surface termination, making it a potential application for designing highperformance spintronic devices.

In this study, the growth of vertical graphene (VG) nanosheets on copper (Cu) substrates in a direct-current plasma-enhanced chemical vapor deposition (PECVD) system was studied. The plasma process during the VG growth was characterized using a high-speed camera and optical emission spectroscopy. Results showed that the plasma composition remained constant, but the overall plasma intensity increased with increasing substrate open area (OA). At low OAs of > 0.05, VG growth was limited to edges, and the VG height increased gradually to reach 700 nm as more reactants became readily available. Two distinctive regimes were identified: diffusion-limited growth at OAs < 0.6, and kinetic-limited growth at OAs > 0.6 for Cu meshes and screens. Under the diffusion-limited regime, VG growth occurred preferentially from the substrate edge toward the center. Therefore, the deposition time was extended to achieve uniform VG deposition. However, in the kinetic-limited regime, the increased availability of reactants did not alter the VG height, which remained at 700 nm. The kinetic-limited deposition was uniform across the substrate due to less plasma screening. This study sheds light on the growth mechanism of VG on perforated substrates, opening new avenues to control deposition on Cu substrates within plasma-screened interfaces.

The use of machine learning (ML) is reshaping the design and optimization of nanofiber-based drug delivery systems (N-DDS). Electrospun nanofibers offer high surface area, tunable porosity, and versatile drug encapsulation strategies, making them attractive for controlled release applications in multiple therapeutic areas. However, the optimization of materials, fabrication conditions, encapsulation strategies, and release mechanisms is challenging due to the multitude of interdependent parameters. This review outlines how ML has been applied to accelerate N-DDS development, replacing traditional trial-and-error approaches with predictive and adaptive models. We first present a bibliometric landscape of the literature on nanofibers and drug delivery systems (DDS), highlighting the role of electrospinning. We then discuss recent applications of ML in polymer selection, electrospinning optimization, encapsulation strategies, and drug release kinetics. Special attention is given to case studies where ML models achieved high predictive accuracy in tailoring nanofiber morphology, encapsulation efficiency, and release profiles. We also elaborate upon the key challenges for clinical translation, including data quality, scalability, sustainability, and ethical concerns. By integrating ML and other artificial intelligence (AI) methods with nanofiber engineering, N-DDS can progress toward patient-specific, sustainable, and industrially scalable therapeutic platforms, opening new frontiers in precision medicine.

Gold particles are commonly used as catalysts in the vapor-liquid-solid (VLS) growth of GaAs nanowires, but the incorporation of gold into the nanowires can negatively affect their electronic and optical properties. In this work, we investigate the equilibrium concentration of Au in GaAs nanowires using density functional theory calculations combined with thermodynamically assessed chemical potentials. Our results show that under typical VLS growth conditions, the Au concentration is strongly influenced by the growth temperature and the Ga concentration in the catalyst alloy particle. We find that minimizing Au incorporation requires low growth temperatures and high Ga content in the particles. The predicted equilibrium Au concentrations are consistent with experimental data, offering theoretical guidance for minimizing Au contamination during nanowire growth.

Black phosphorus (BP) is a novel two-dimensional (2D) material with tunable electronic and optical properties. Thickness is a pivotal parameter in defining the electronic, optical, and thermal properties of 2D crystals. Determining the thickness of a material is crucial to studying its properties. However, conventional characterization methods for the directly determination of thick layers of BP are complex and inefficient. In this paper, we propose a machine learning (ML)-based method that can efficiently and accurately determine the layer number of BP. The features of the three characteristic peaks (Ag1,B2g, andAg2) were extracted from the Raman spectra, including peak position, intensity, full width at half maximum, and integrated intensity. Subsequently, we found that the intensity ratio of the substrate (Si) peak to the Raman mode is crucial to predicting the number of layers by feature importance analysis. This study makes a key contribution by presenting, for the first time, a comparative analysis of multiple ML algorithms for identifying the layer number of BP. Furthermore, it identifies a specific set of discriminative features tailored for BP's Raman spectra. Finally, by synergistically augmenting the dataset and refining the model architecture, we effectively mitigated the performance limitations imposed by the small dataset. The performance of the model is evaluated based onR2, mean square error, and mean absolute error, where theR2of all algorithms is not less than 0.9. ML models can accurately predict the number of layers of BP material. ML algorithms can automatically learn from the data and optimize the algorithm to improve the efficiency and accuracy of the model. This not only reduces the analysis burden on researchers but also promotes the in-depth application of artificial intelligence in 2D material characterization.

Phosphorene exhibits promising tribological application due to its layered structure that imparts intrinsic lubricating properties. Understanding the mechanisms by which oxygen and other ambient species modify phosphorene remains a key challenge, with the impact of the layer thickness and point defects still unknown. Despite its promise as a solid-state lubricant, detailed nanoscale understanding of layer-dependent defect formation, surface reactivity, and potential degradation is still limited. In particular, the possible multilayer-dependent degradation behaviour of phosphorene in the presence of common environmental species such as hydrogen (H), oxygen (O), and hydroxyl (OH) has received little attention. In this work, we perform a systematic density functional theory investigation to explore how these chemical species interact with monolayer to four-layer phosphorene, including systems with and without phosphorus vacancies. Our findings show that H, OH adsorption is energetically not favourable in any layer configurations, while O shows strong exothermic interactions across all thicknesses, regardless of the presence of defects, with the bilayer showing the most favourable interaction with these species. Structural responses, including changes in bond lengths and interlayer spacing, were quantified and found to depend on both the type of adsorbate and the number of layers. The presence of vacancies induces localized distortions but does not compromise the overall structural integrity. Bader charge calculations show charge transfer between phosphorene layers and adsorbates. Overall, our results set a foundation for further work on phosphorene by providing a detailed, layer-by-layer understanding of phosphorene's chemical reactivity in ambient environments and highlight the need to consider layer number, intrinsic defects and environmental species in models of phosphorene.

In power electronics, silicon carbide (SiC) MOSFETs can experience ultra-high gate voltage pulses during electrostatic events, yet their reliability under such extreme conditions remains insufficiently explored. In this work, we fabricate SiC MOSFETs and present systematic reliability evaluation under ultra-high gate pulse stress. Our results reveal that hole-related charge trapping dominates the degradation for both positive and negative gate stress. Under high positive pulses, the threshold voltage (V_th) exhibits a non-monotonic shift driven by hole injection, whereas under high negative pulses,V_thdecreases rapidly due to hole capture and the formation of additional donor-like traps. Moreover, the time and field dependence ofV_thdegradation demonstrates that oxide breakdown is primarily caused by electric field stress. Overall, this study provides new insight into the degradation pathways of SiC MOSFETs under extreme electrical stress and offers practical guidance for improving device robustness in power applications.

Lithium-ion batteries face challenges in achieving high energy density and long cycle life, due to limitations of conventional anode materials. MXenes, a class of two-dimensional materials, show great potential as anodes but suffer from low intrinsic capacity and severe nanosheet self-stacking.To overcome these issues, this study developed a double transition metal MXene, Ti 2 NbC 2 T x , which offers enlarged interlayer spacing and high electrical conductivity. To further address the selfstacking issue, a Ti 2 NbC 2 T x @CNFs (carbon nanofibers) composite was fabricated via electrospinning and subsequent carbonization. This structure uniformly embedded the MXene within a continuous conductive carbon matrix, effectively inhibiting self-stacking and facilitating electron/ion transport. As a lithium-ion battery anode, the composite demonstrated excellent electrochemical performance. A reversible capacity of 246.5 mAh g -1 was retained after 7000 cycles at a high current density of 5 A g -1 , demonstrating outstanding specific capacity and cycling stability.This work provides a viable strategy for developing high-performance MXene-based anodes for next-generation energy storage.