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Diamond has an ultrawide bandgap with excellent physical properties, such as high critical electric field, excellent thermal conductivity, high carrier mobility, etc. Diamond with a hydrogen-terminated (H-terminated) surface has a negative electron affinity (NEA) and can easily produce surface electrons from valence or trapped electrons via optical absorption, thermal heating energy or carrier transport in a PN junction. The NEA of the H-terminated surface enables surface electrons to emit with high efficiency into the vacuum without encountering additional barriers and promotes further development and application of diamond-based emitting devices. This article reviews the electron emission properties of H-terminated diamond surfaces exhibiting NEA characteristics. The electron emission is induced by different physical mechanisms. Recent advancements in electron-emitting devices based on diamond are also summarized. Finally, the current challenges and future development opportunities are discussed to further develop the relevant applications of diamond-based electron-emitting devices. [ABSTRACT FROM AUTHOR]

This paper proposes a novel dynamic model considering manufacturing errors and eccentricity errors to analyze the dynamics of planetary gear transmission (PGT). The dynamic model is established based on the fractional-order calculus and solved by an enhanced fourth-order Lagrange interpolation polynomials method. Three numerical examples and the vibration experiments of PGT are employed for verification. The comparison results indicate that the proposed solution method has higher solution accuracy and efficient than the existing algorithms in solving fractional equations, and the relative errors of the proposed solution method in three examples are 0.32%, 0.78% and 0.16%, respectively. The proposed dynamic model of PGT has better agreement with the experimentally measured signal compared with the integer-order dynamic model, and the maximum error and average error of the characteristic frequency amplitude between the proposed dynamic model and the measured signal are 4.76% and 3.57%, respectively. The proposed method contributes to the theoretical foundation for the signal monitoring of PGT. [ABSTRACT FROM AUTHOR]

We present a catalog of stellar parameters (effective temperature T eff, surface gravity log g , age, and metallicity [Fe/H]) and elemental-abundance ratios ([C/Fe], [Mg/Fe], and [ α /Fe]) for some five million stars (4.5 million dwarfs and 0.5 million giant stars) in the Milky Way, based on stellar colors from the Javalambre Photometric Local Universe Survey (J-PLUS) DR3 and Gaia EDR3. These estimates are obtained through the construction of a large spectroscopic training set with parameters and abundances adjusted to uniform scales, and trained with a kernel principal component analysis. Owing to the seven narrow/medium-band filters employed by J-PLUS, we obtain precisions in the abundance estimates that are as good as or better than those derived from medium-resolution spectroscopy for stars covering a wide range of the parameter space: 0.10–0.20 dex for [Fe/H] and [C/Fe], and 0.05 dex for [Mg/Fe] and [ α /Fe]. Moreover, systematic errors due to the influence of molecular carbon bands on previous photometric-metallicity estimates (which only included two narrow/medium-band blue filters) have now been removed, resulting in photometric-metallicity estimates down to [Fe/H] ∼ −4.0, with typical uncertainties of 0.40 dex and 0.25 dex for dwarfs and giants, respectively. This large photometric sample should prove useful for the exploration of the assembly and chemical-evolution history of our Galaxy. [ABSTRACT FROM AUTHOR]

The manual identification of brain cancer types is often fraught with inaccuracies, leading to potential delays in diagnosis and treatment planning. This study presents a novel approach to predict brain cancer types using advanced machine learning (ML) algorithms integrated with sophisticated feature selection techniques. A multi-class classification framework was developed and evaluated, incorporating six ML models: Bernoulli Naive Bayes, K-nearest neighbors classifier, decision tree classifier, Gaussian process classifier (GPC), passive aggressive classifier, and perceptron. To enhance model performance, feature selection methods including the Gini index, mutual information, and principal component analysis (PCA) were employed. A comprehensive case study was conducted to assess the predictive accuracy of these models. The GPC, when trained and validated on features derived via PCA, outperformed other models in terms of predictive accuracy and generalization. Specifically, the dimensions identified by PCA (d 1, d 2, d 3, and d 4) were most effective in distinguishing between different brain cancer types. This methodology resulted in a significant improvement across various performance metrics. Compared to the baseline GPC model using all original features, the PCA-enhanced GPC achieved remarkable increases in Accuracy, Precision, Recall, and F1 Score by 294.31%, 22.14%, 294.31%, and 878.18%, respectively. These findings underscore the potential of combining ML algorithms with targeted feature selection techniques to advance the accuracy of brain cancer type prediction, offering substantial benefits for clinical decision-making and patient outcomes. [ABSTRACT FROM AUTHOR]

Unintentionally doped carbon impurities from organometallic precursors are primary sources of carrier compensation and mobility degradation in wide bandgap semiconductors, leading to lowered performance of power electronic devices. To address this challenge, carbon-free α- Ga2O3 single-crystalline thin films were heteroepitaxially grown on sapphire substrates by using gallium inorganic precursors through a mist chemical vapor deposition technique. Determined through a temperature dependence of growth rates, three distinct growth regimes are identified: the surface reaction limited regime below 480 °C, the mid-temperature mass-transport limited regime (480 °C–530 °C) and the high temperature limited regime related to desorption or phase transition. With an optimized around 530 °C, the densities of screw and edge dislocations are reduced to 7.17 × 106 and 7.60 × 109 cm−2, respectively. Notably, carbon incorporation was eliminated in the α -Ga2O3 grown by inorganic GaCl3, as evidenced by the absence of carbon-related vibrational bands in Raman scattering analysis, while crystalline quality was comparable to that grown with organometallic precursors. The high solubility of GaCl3 in water is expected to enable the rapid growth of high purity α -Ga2O3 with improved electronic transport performances. [ABSTRACT FROM AUTHOR]