Overall Spontaneous Water Splitting for Calcium Bismuthate Ca(BiO2)2: Flexible-Electronic-Controlled Band Edge Position and Adsorption-Site-Modulated Bond Strength.

Eco-friendly photocatalysts for water splitting, highly efficient in oxygen/hydrogen evolution reactions, hold great promise for the storage of inexhaustible solar energy and address environmental challenges. However, current common photocatalysts rarely exhibit both H2 and O2 production performances unless some regulatory measures, such as strain engineering, are implemented. Additionally, the extensive utilization of flexible electronics remains constrained by their high Young's modulus. Herein, on the basis of density functional theory calculations, we identify a novel spontaneous oxygen-producing two-dimensional Ca(BiO2)2 material, which can efficiently regulate the electronic structures of the surface active sites, optimize the reaction pathways, reduce the reaction energy barriers, and boost the overall water-splitting activity through biaxial strain modulation. In detail, an unstrained Ca(BiO2)2 monolayer not only possesses a suitable band gap value (2.02 eV) to fulfill the photocatalytic water-splitting band edge relationships but also holds favorable transport properties, excellent optical absorption across the visible light spectrum, and spontaneous oxygen production under neutral conditions. More excitingly, under application of a 7% biaxial tensile strain modulation with an ideal biaxial strength of 32.35 GPa nm, the Ca(BiO2)2 monolayer not only maintains its structural integrity but also exhibits a completely spontaneous reaction for photocatalytic hydrogen precipitation with superior optical absorption. This can primarily be attributed to the substantial reduction of the potential barrier through strain engineering as well as the weakening of bond energy resulting from changes of the adsorption site as calculated by crystal orbital Hamiltonian population analysis. This flexible stretchable electronic modulated the photocatalyst behavior and bond energy of O-Bi and O-Ca interactions, offering outstanding potential for photocatalytic water spontaneous oxygen and hydrogen evolution among all of the reported metal oxides, and is more likely to become a promising candidate for future flexible electronic devices.

An Early Warning System for Earthquake Prediction from Seismic Data Using Batch Normalized Graph Convolutional Neural Network with Attention Mechanism (BNGCNNATT).

Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events.

Effects of 25(OH)D3 supplementation during late gestation on the serum biochemistry and reproductive performance of aged sows and newborn piglets.

The purpose of this study was to investigate the effects of diet type (normal or low Ca and P diets) and 25(OH)D3 supplementation (with or with not 2000 IU/kg 25(OH)D3 ) during late gestation on the serum biochemistry and reproductive performance of aged sows and newborn piglets. A total of 40 sows, which are at their 7th parity, were divided into four groups: control group (standard diet), low Ca group, 25(OH)D3 group and low Ca plus 25(OH)D3 group respectively (10 in each group). The blood of sows on day 100 and 114 of gestation and newborn piglets was collected for serum biochemical analyses. Results showed that the reproductive performance of sows was not influenced by diet type or 25(OH)D3 supplementation (p > 0.05). And the addition of 25(OH)D3 to diet low Ca group caused that the content of serum TG in sows on day 100 of gestation was not different from that of the control group (p > 0.05). The addition of 25(OH)D3 significantly decreases the content of serum TG in sows on day 114 of gestation (p < 0.05). The addition of 25(OH)D3 significantly increased the content of serum UREA and CREA in newborn piglets (p < 0.05). Overall, feeding 2000 IU/kg 25(OH)D3 to aged sows at late gestation had no effects on reproductive performance, but partly contributed to keeping serum TG balance in sows and may indicate increased pressure on kidneys in newborn piglets.

Two-Dimensional Direct Semiconductor Boron Monochalcogenide γ-BTe: Room-Temperature Single-Bound Exciton and Novel Donor Material in Excitonic Solar Cells.

Recently, excitonic solar cells (XSCs) with high photovoltaic performance have raised research interests because of their high power conversion efficiencies (PCEs). Herein, by using first-principles calculations, we predict that γ-BX (X = S, Se, Te) monolayers are direct semiconductors with the band gaps of 2.94, 2.71, and 1.32 eV, respectively, and maintain semiconduction in the broad strain range of 0% ≤ δ ≤ 5%. The moderate direct band gap, high transport property, dramatically high absorption from visible to the ultraviolet region, and extraordinary excitonic behavior of monolayer γ-BTe, render it promising for next-generation optoelectronic and photovoltaic devices. By choosing monolayer GeP2 as a proper acceptor material, the practical upper limit of PCE for the heterobilayers of γ-BTe/GeP2 reaches up to 21.76% (22.95% under strain), comparable to typical heterobilayer solar cells, making it a competitive donor material for photovoltaic device applications.

Monolayer Bi2Se3-xTex: novel two-dimensional semiconductors with excellent stability and high electron mobility.

Two-dimensional materials play a vital role in next-generation microelectronics, optoelectronics and flexible electronics due to their novel physical properties caused by quantum-confinement effects. In this work, we investigate the stability and the possibility of exfoliation of monolayer Bi2Se3-xTex (x = 0, 1, 2) using first-principles calculations. Our calculations show that these materials are indirect bandgap semiconductors, and the elastic modulus is smaller than other conventional materials, which indicates better flexibility. We find that the electron mobility of monolayer Bi2SeTe2 along the armchair direction is higher than that of black phosphorene, reaching 2708 cm2 V-1 s-1, and the electron mobility of monolayer Bi2Se3 along the zigzag direction is about 24 times larger than the hole mobility. The remarkable electron mobilities and highly anisotropic properties of these new monolayers pave the way for future applications in high-speed (opto)electronic devices.

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

[Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale].

In the present study, an innovative method is proposed, employing both wavelet transform and neural network, to analyze the near-infrared spectrum data in oil shale survey. The method entails using db8 wavelet at 3 levels decomposition to process raw data, using the transformed data as the input matrix, and creating the model through neural network. To verify the validity of the method, this study analyzes 30 synthesized oil shale samples, in which 20 samples are randomly selected for network training, the other 10 for model prediction, and uses the full spectrum and the wavelet transformed spectrum to carry out 10 network models, respectively. Results show that the mean speed of the full spectrum neural network modeling is 570.33 seconds, and the predicted residual sum of squares (PRESS) and correlation coefficient of prediction are 0.006 012 and 0.843 75, respectively. In contrast, the mean speed of the wavelet network modeling method is 3.15 seconds, and the mean PRESS and correlation coefficient of prediction are 0.002 048 and 0.953 19, respectively. These results demonstrate that the wavelet neural network modeling method is significantly superior to the full spectrum neural network modeling method. This study not only provides a new method for more efficient and accurate detection of the oil content of oil shale, but also indicates the potential for applying wavelet transform and neutral network in broad near-infrared spectrum analysis.

The effect of salt concentration on DNA conformation transition: a molecular-dynamics study.

We performed three 3-ns molecular dynamics simulations of d(CGCGAATTCGCG)2 using the AMBER 8 package to determine the effect of salt concentration on DNA conformational transitions. All the simulations were started with A-DNA, with different salt concentrations, and converged with B-DNA with similar conformational parameters. However, the dynamic processes of the three MD simulations were very different. We found that the conformation transition was slow in the solution with higher salt concentration. To determine the cause of this retardation, we performed three additional 1.5-ns simulations starting with B-DNA and with the salt concentrations corresponding to the simulations mentioned above. However, astonishingly, there was no delayed conformation evolution found in any of the three simulations. Thus, our simulation conclusion is that higher salt concentrations slows the A --> B conformation transition, but have no effect on the final stable structure. [Figure: see text].

Distribution patterns and controllable transport of water inside and outside charged single-walled carbon nanotubes.

The density distribution patterns of water inside and outside neutral and charged single-walled carbon nanotubes (SWNTs) soaked in water have been studied using molecular dynamics simulations based on TIP3P potential and Lennard-Jones parameters of CHARMM force field, in conjunction with ab initio calculations to provide the electron density distributions of the systems. Water molecules show different electropism near positively and negatively charged SWNTs. Different density distribution patterns of water, depending on the diameter and chirality of the SWNTs, are observed inside and outside the tube wall. These special distribution patterns formed can be explained in terms of the van der Waals and electrostatic interactions between the water molecules and the carbon atoms on the hexagonal network of carbon nanotubes. The electric field produced by the highly charged SWNTs leads to high filling speed of water molecules, while it prevents them from flowing out of the nanotube. Water molecules enter the neutral SWNTs slowly and can flow out of the nanotube in a fluctuating manner. It indicates that by adjusting the electric charge on the SWNTs, one can control the adsorption and transport behavior of polar molecules in SWNTs to be used as stable storage medium with template effect or transport channels. The transport rate can be tailored by changing the charge on the SWNTs.

Cross sections of electron inelastic interactions in DNA.

The cross sections of electron inelastic interaction in DNA are calculated using the dielectric response theory and Penn statistical approximation, with the exchange correction included. An empirical approach to obtain optical energy loss function is given for the organic compounds without available optical data. Comparisons of the calculated data with available experimental and theoretical results have been done to show the reliability of the approach proposed in this work. Using this approach, the total inelastic cross sections for five bases: guanine, adenine, thymine, cytosine and uracil have been calculated in the energy range of E< or =10 keV and compared with those recently obtained with the Deutsch-Mark formalism and the Binary-Encounter-Bethe model, respectively. An equivalent unit of the DNA molecule is constructed according to the contents of A-T and G-C base pairs in DNA, and is divided into five constituents, i.e. sugar-phosphate and four bases. The total inelastic cross sections for the constructed unit of the DNA molecule and its constituents have also been calculated.