Machine Learning Aided Design and Optimization of Thermal Metamaterials.

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.

Highly Thermally Conductive Triple-Level Ordered CNT/PVA Nanofibrous Films.

The escalating thermal power density in electronic devices necessitates advanced thermal management technologies. Polymer-based materials, prized for their electrical insulation, flexibility, light weight, and strength, are extensively used in this field. However, the inherent low thermal conductivity of polymers requires enhancement for effective heat dissipation. This work proposes a novel paradigm, emphasizing ordered structures with functional units, to create triple-level, ordered, low-filler loading of multi-walled carbon nanotube (MWCNT)/poly(vinyl alcohol)(PVA) nanofibrous films. By addressing interfacial thermal resistance through -OH groups, the coupling between polymer and MWCNT is strengthened. The triple-level ordered structure comprises aligned PVA chains, aligned MWCNTs, and aligned MWCNT/PVA composite fibers. Focusing on the filler's impact on thermal conductivity and chain orientation, the thermal transport mechanisms have been elucidated level by level. Our MWCNT/PVA composite, with lower filler loadings (10 wt.%), achieves a remarkable TC exceeding 35.4 W/(m·K), surpassing other PVA composites with filler loading below 50 wt.%.

Expression of SHP-1 and SOCS6 in patients with acute leukemia and their clinical implication.

BACKGROUND: To investigate the expression and clinical relevance of Src homology region 2 domain-containing phosphatase-1 (SHP-1) and suppressor of cytokine signaling 6 (SOCS6) in acute leukemia (AL). PATIENTS AND METHODS: The enrolled AL patients were divided into three groups (newly diagnosed, relapsed, and complete remission [CR]). Healthy donors were also included as a control group in this study. Semi-quantitative reverse transcription polymerase chain reaction (RT-PCR) was performed to measure messenger RNA (mRNA) expression of SHP-1 and SOCS6. Statistical analysis was conducted to analyze the correlation between mRNA levels of SHP-1 and SOCS6 with patient outcomes. RESULTS: mRNA expression of SHP-1 was significantly lower in AL patients than that in healthy donors. The newly diagnosed or relapsed AL patients had lower mRNA levels of SHP-1 than the patients in CR. In contrast, SOCS6 mRNA expression was significantly higher in newly diagnosed or relapsed patients than that in patients in CR as well as healthy donors. However, mRNA levels of both SHP-1 and SOCS6 were positively correlated with the patient remission. The chemotherapy-induced remission rate was higher in patients with detectable SHP-1 or SOCS6 expression than in patients with undetectable SHP-1 or SOCS6 expression. Furthermore, the AL patients with detectable SHP-1 mRNA expression had lower incidence rate of invasive fungal infection. CONCLUSION: The results suggest that expression patterns of SHP-1 and SOCS6 differ in AL patients. Despite the difference, expression of SHP-1 and SOCS6 is associated with favorable outcomes, suggesting an anticancer property of these two genes in AL.

Simultaneous determination of galanthamine and lycorine in Lycoris radiata by a capillary electrophoresis with an electrochemiluminescence method.

A novel capillary electrophoresis with electrochemiluminescence determination method was developed for the determination of two alkaloids based on the electrochemiluminescence signal enhancement effect of the tertiary amine group on tris(2,2'-bipyridyl)ruthenium(II). A linear relationship between the electrochemiluminescence peak area and concentrations of galanthamine and lycorine in the range of 0.07 ∼ 17 µg/mL and 0.07 ∼ 18 µg/mL was obtained and the detection limit was 0.008 and 0.002 µg/mL, respectively. The method is selective, simple, and convenient. It had been successfully applied to the analysis of galanthamine and lycorine in Lycoris radiata samples purchased from a local market.

Determination of gibberellins in soybean using tertiary amine labeling and capillary electrophoresis coupled with electrochemiluminescence detection.

A novel sensitive method based on tertiary amine labeling for the analysis of gibberellins (GAs) by capillary electrophoresis (CE) coupled with electrochemiluminescence (ECL) detection was proposed. GA3 was tagged with 2-(2-aminoethyl)-1-methylpyrrolidine (AEMP) using N, N'-dicyclohexylcarbodiimide (DCC) and 3,4-dihydro-3-hydroxy-4-oxo-1,2,3-benzotriazine (HOOBt) as coupling agents in acetonitrile to produce GA3-AEMP-derivative. The GA3-AEMP-derivative was injected into CE by electrokinetic injection and detected by Ru(bpy)3(2+)-based ECL. The parameters affecting derivatization, detection and separation such as concentration of reactants, detection potential, pH and concentration of separation buffer, were investigated in detail. Under optimum conditions, the linear concentration range for GA3 was from 2.0×10(-7) to 1.28×10(-4)M with a correlation coefficient of 0.9997. The detection limit was 8×10(-8)M (S/N=3). The relative standard deviations of migration time, peak intensity and peak area for nine continuous injections of 2.0×10(-5)M GA3-AEMP-derivative were 1.0%, 2.1% and 4.2%, respectively. The developed approach was successfully applied to the determination of total GAs in the stem, leaf and seed of soybean (Glycine max [L.] Merr.) with recoveries in the range from 89.6% to 99.3%.

Spectral properties of directed random networks with modular structure.

We study spectra of directed networks with inhibitory and excitatory couplings. We investigate in particular eigenvector localization properties of various model networks for different values of correlation among their entries. Spectra of random networks with completely uncorrelated entries show a circular distribution with delocalized eigenvectors, whereas networks with correlated entries have localized eigenvectors. In order to understand the origin of localization we track the spectra as a function of connection probability and directionality. As connections are made directed, eigenstates start occurring in complex-conjugate pairs and the eigenvalue distribution combined with the localization measure shows a rich pattern. Moreover, for a very well distinguished community structure, the whole spectrum is localized except few eigenstates at the boundary of the circular distribution. As the network deviates from the community structure there is a sudden change in the localization property for a very small value of deformation from the perfect community structure. We search for this effect for the whole range of correlation strengths and for different community configurations. Furthermore, we investigate spectral properties of a metabolic network of zebrafish and compare them with those of the model networks.

Localizations on complex networks.

We study the structural characteristics of complex networks using the representative eigenvectors of the adjacent matrix. The probability distribution function of the components of the representative eigenvectors are proposed to describe the localization on networks where the Euclidean distance is invalid. Several quantities are used to describe the localization properties of the representative states, such as the participation ratio, the structural entropy, and the probability distribution function of the nearest neighbor level spacings for spectra of complex networks. Whole-cell networks in the real world and the Watts-Strogatz small-world and Barabasi-Albert scale-free networks are considered. The networks have nontrivial localization properties due to the nontrivial topological structures. It is found that the ascending-order-ranked series of the occurrence probabilities at the nodes behave generally multifractally. This characteristic can be used as a structural measure of complex networks.

Self-affine fractals embedded in spectra of complex networks.

The scaling properties of spectra of real world complex networks are studied by using the wavelet transform. It is found that the spectra of networks are multifractal. According to the values of the long-range correlation exponent, the Hust exponent H, the networks can be classified into three types, namely, H>0.5, H=0.5, and H<0.5. All real world networks considered belong to the class of H>or=0.5, which may be explained by the hierarchical properties.