Search

The water-dispersible electrochormic nanocomposites, polyaniline (PANI) connected multi-walled carbon nanotubes (MWCNTs) by covalent bond were synthesized through a grafting polymerization process of aniline monomer with functionalized MWCNTs. JV.N'-dicyclohexylcarbodiimide (DCC) was used as dehydrant for functionalization process of MWCNTs with p-phenylenediamine (PPD). The transmission electron microscope (TEM), scanning electron microscopy (SEM), thermal gravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FT-IR) were used to study the morphologies, thermal stabilities and chemical structures of the nanocomposites, respectively. The UV-vis absorbance spectra and cyclic voltammetry behaviors of nanocomposites were tested by UVvis spectrometer and electrochemical workstation, respectively. It is shown show that PANI-MWCNTs nanocomposites not only exhibit good water solubility, but also exhibit excellent film-forming properties. The results confirm that the [pi]-[pi] interactions and interfacial covalent bond between PANI and MWCNTs endow PANI-MWCNTs excellent electrochemical and electrochromic properties. The conductive network composed of PANI and MWCNTs can significantly improve the ion transports, electron conduct and electrochromic performance of PANI. The PANI-MWCNTs-0.6% nanocomposite film shows highest optical contrast value (0.78) and shortest response time (3.8 seconds for coloring and 3.2 seconds for bleaching) among neat PANI and PANI-MWCNTs nanocomposites. KEYWORDS electrochromism, interfacial covalent bond, multi-walled carbon nanotubes, polyaniline, 1 | INTRODUCTION Electrochromic materials can endow reversible color changes by applying difference potentials. Tungsten trioxide (W[O.sub.3]) was first applied as electrochromic materials by Deb in 1969, [1] and it [...]

Based on the discrete element method (DEM), four packed beds composed of soybean kernels with diameters of 6.4, 6.8, 7.4 mm, and the mixture of three kinds of particles were established. Then, a double-diffusion heat and mass transfer model between the grain pile and the interstitial air was established based on the local mass and thermal non-equilibrium (LMTNE) mechanism. Finally, employing particle-resolved computational fluid dynamics (PRCFD), the heat and mass transfer between the grain kernels and air during the drying process in the four packed beds were numerically resolved. It was found that the packed bed formed by stacking particles of different diameters had a minimum porosity of 0.4547. The radial porosity of the packed bed oscillates and decreases toward the central axis, while the tortuosity of the airflow path oscillates and decays toward the periphery. The mass transfer Biot number for soybean kernels with diameters of 6.4, 6.8, and 7.4 mm were 2.38 × 106, 2.44 × 106, and 2.53 × 106, respectively. This indicates that the mass transfer rate in the grain pile primarily depends on the magnitude of the moisture diffusion coefficient within the grain kernels. Compared with mass diffusion, thermal diffusion occurs much faster, which results in temperature gradients in packed beds only existing in the first 5 min of drying, and the drying rate in the early stage is higher than that in the later stage. Most importantly, the airflow characteristics, heat and moisture content are not in local equilibrium in the packed bed, and should be considered when designing drying systems. [ABSTRACT FROM AUTHOR]

Grain is an important material for human survival. However, the expanding world population is contributing to a growing grain shortage. In order to reduce the loss of grain due to pests and mold during storage, mechanical ventilation as the main method of ventilation has crucial research significance. This article proposed and analyzed the ventilation effect and the migration characteristics of ozone in the grain pile under the modified ventilation network (MVN) and compared it with the original ventilation network (OVN). The study found that the temperature, moisture, and ozone concentration in the grain pile of the two ventilation networks are not evenly distributed in the vertical direction, showing a layered pattern. That is, with an increase in grain stack height, the temperature and moisture content of the grain stack are higher, and the ozone concentration is lower. Moreover, in the pre-ventilation period, the average temperature decline rate of the MVN was 1.25 °C/d, which was better than that of the OVN (0.84 °C/d), and the maximum temperature difference between the MVN and the OVN was 0.89 °C. The vertical ducts added to the MVN improved the ventilation effect, maintaining high ozone concentrations within the grain pile. Notably, on the sixth day of fumigation, the average ozone concentration of the MVN exceeded that of the OVN. The MVN can solve the shortcomings of the OVN, where air intake and fumigation gas rise slowly in the vertical direction. These findings hold substantial significance for optimizing ventilation network structures, devising effective fumigation strategies, and enhancing the insecticidal effects of grain storage. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer‐related death worldwide. HCC exhibits strong inter‐tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine‐learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI‐enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI‐enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI‐enabled models, including aspects such as domain knowledge, novel algorithms, and multi‐dimension data sources. Overcoming these challenges would allow AI‐enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. Level of Evidence: 5 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]