Nanotechnology最新文献

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.

Background: Sepsis-induced acute respiratory distress syndrome (ARDS) is a life-threatening condition with uncontrolled inflammation and lung damage. Current therapies are limited, and reprogramming macrophages from pro-inflammatory M1 to anti-inflammatory M2 phenotypes via STAT6/IRF4 activation offers a promising strategy. Method: Dexamethasone-loaded glycyrrhiza protein nanoparticles (Dex@GNPs) were synthesized by extracting glycyrrhiza protein (GP), denaturing it with phosphoric acid, cross-linking with glutaraldehyde, and encapsulating dexamethasone. Physicochemical properties (size, ζ-potential, drug release) were characterized. In vitro studies used LPS-stimulated MH-S macrophages; in vivo efficacy was evaluated in murine ARDS models (LPS intratracheal injection or cecal ligation and puncture). Macrophage polarization (flow cytometry, immunofluorescence), STAT6/IRF4 pathway activation (Western blot), lung histopathology (H&E), and inflammation markers (BALF cytokines, ELISA) were assessed. Results: Dex@GNPs exhibited favorable physicochemical properties (hydrodynamic diameter: 374±12 nm; ζ-potential: -22±4 mV) with pH-responsive drug release (79% cumulative release at pH 5.5 within 24 h). In vitro, Dex@GNPs significantly reprogrammed M1 macrophages to M2 phenotypes, increasing CD206⁺ cells from 5% to 25% and upregulating STAT6/IRF4 expression compared to LPS-stimulated cells. In vivo, Dex@GNPs selectively targeted inflamed lungs, reduced alveolar damage, suppressed pro-inflammatory cytokines (TNF-α, IL-6, MCP-1 reduced by 81%, 83%, 86% respectively), and restored alveolar-capillary barrier integrity, outperforming free dexamethasone. Conclusion: Dex@GNPs synergize GP's targeting and dexamethasone's anti-inflammatory effects to alleviate sepsis-induced ARDS by STAT6/IRF4-mediated macrophage polarization, offering a biocompatible nanotherapeutic platform. Keywords: Sepsis-induced ARDS; Glycyrrhiza protein nanoparticles; Macrophage polarization; STAT6; IRF4; Targeted drug delivery .

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.

Deviatoric stress-induced coalescence of nanocrystal superlattices is a promising route for massively fabricating nanowires. We perform atomistic molecular dynamics simulations to characterize the deviatoric stress-induced fusion behavior of alkylthiol-capped gold superlattices and examine the influence of ligand length on the nanowire formation. The results show that a threshold deviatoric stress along the compression direction is essential for forming the ordered nanowire arrays, and it significantly increases with the ligand length. The ligand length dependence can be attributed to the fusion energy barrier between constituent gold nanocrystals as well as its alternation induced by the change in ligand length. We show that the ligands on neighboring gold nanocrystals abundantly interdigitate at the potential minimum but become highly splayed or bent at the repulsive maximum. Increasing the ligand length promotes ligand interdigitation at the potential minimum but causes a larger contact area between ligands on opposite nanocrystals at the repulsive maximum. This jointly results in a marked increase in fusion energy barrier with the increasing ligand length, thereby requiring a higher critical deviatoric stress to drive the gold nanocrystals into nanowires for longer ligands. This study reveals that reducing the ligand length can effectively decreases the operational stress required for the formation of nanowires, which can provide theoretical guidance for optimizing the stress-induced nanofabrication approaches.

This paper demonstrates a significant advance in creating ultra-flat Si(100) surfaces suitable for low thermal budget device fabrication. This is achieved by a two-step pre-flash and protect (PFP) process that locks in an atomically flat surface that survives subsequent device processing steps. The first PFP step is a high-temperature flash in ultra-high vacuum (UHV) that creates an atomically flat surface. The second PFP step is a Piranha solution treatment that preserves the surface with a thin oxide shortly after removal from UHV. This oxide can then be easily removed with buffered oxide etchant (BOE) as needed during subsequent device fabrication. Following BOE, a surface with angstrom-level flatness is recovered, obviating the need for more aggressive thermal or chemical surface flattening processes. With this new process no aggressive chemical cleaning, such as RCA cleaning, is needed and no high-temperature surface cleaning or flattening is required for nanoscale device fabrication. This method offers promising opportunities for device fabrication and other applications that require clean and atomically flat Si(100) surfaces and low thermal budget device processing.

Ionic polymer metal composites (IPMCs) are widely used in flexible actuation and sensing. Traditional IPMCs employ the commercial Nafion film as the electrolyte films, suffering from low water uptake (WU), high modulus, and high cost. This study reports a water-resistant polyvinyl alcohol (PVA) electrolyte film with high WU and ion exchange capacity (IEC), using PVA as the matrix, sulfonic SiO2 nanocolloids as the fillers, and glutaraldehyde as the crosslinking agent. Physicochemical tests reveal that the PVA film containing 9 wt% sulfonic SiO2 nanocolloids exhibits better IPMC-related properties when compared to Nafion: 3.92 folds in WU, 1.14 folds in IEC, and 0.238 folds in elastic modulus. After spraying an electrode slurry containing poly(3,4-ethylenedioxythiophene) /poly(styrenesulfonate) dispersed multi-walled carbon nanotubes, the SiO2/PVA film IPMC actuator demonstrated exceptional electromechanical response. Under DC signal, it maintains a steady one-way deflection with a large swelling angle of 48.1 ° and no significant back relaxation. Under AC signals, it generates periodic deflections with outputted displacements up to 8.19 mm for 440 s; after water replenishment, the actuator remains a repeatable and reliable deflection without an evident displacement decay. The SiO2/PVA film IPMC actuator demonstrates outstanding stability and actuation performance, making it suitable for applications in bionic robotics and wearable electronics.