Gate-tunable room-temperature ferromagnetism in two-dimensional Fe3GeTe2.

Materials research has driven the development of modern nano-electronic devices. In particular, research in magnetic thin films has revolutionized the development of spintronic devices1,2 because identifying new magnetic materials is key to better device performance and design. Van der Waals crystals retain their chemical stability and structural integrity down to the monolayer and, being atomically thin, are readily tuned by various kinds of gate modulation3,4. Recent experiments have demonstrated that it is possible to obtain two-dimensional ferromagnetic order in insulating Cr2Ge2Te6 (ref. 5) and CrI3 (ref. 6) at low temperatures. Here we develop a device fabrication technique and isolate monolayers from the layered metallic magnet Fe3GeTe2 to study magnetotransport. We find that the itinerant ferromagnetism persists in Fe3GeTe2 down to the monolayer with an out-of-plane magnetocrystalline anisotropy. The ferromagnetic transition temperature, Tc, is suppressed relative to the bulk Tc of 205 kelvin in pristine Fe3GeTe2 thin flakes. An ionic gate, however, raises Tc to room temperature, much higher than the bulk Tc. The gate-tunable room-temperature ferromagnetism in two-dimensional Fe3GeTe2 opens up opportunities for potential voltage-controlled magnetoelectronics7-11 based on atomically thin van der Waals crystals.

Anthropogenic effects on soils in the eastern Tibetan Plateau revealed by geochemical elemental characteristics.

The Tibetan Plateau (TP) constitutes a fragile and sensitive ecological environment, which is vulnerable to global climate change and human activities. To investigate the anthropogenic effects on the TP's environmental system is valuable for guiding human responses and adaptations to future environmental changes. In this study, we detailedly analyzed the geochemical elements of four representative soil sections developed on loess from Ganzi, Jinchuan, Aba, and Chuanzhusi in the eastern TP. The chemical elemental profiles distinctly indicated the presence of typical anthropogenic elements (Cu, Zn, Ni, Cr, Pb, Mn, and Fe), underscoring the substantial influence of human activities on TP soil, and showing spatial variance. Our results indicate that anthropogenic impacts were relatively low at Aba and Ganzi, resulting in a deficit of anthropogenic elements at the surface layer. Whereas at Jinchuan and Chuanzhusi, relatively intense anthropogenic impacts have led to the enrichment of anthropogenic elements in the topsoil. We infer that agricultural activities, increased traffic, and expansion of tourism activities were the major factors affecting the anthropogenic elements of TP soils. Our study highlights the impact of human activities on soil geochemical processes in the Tibetan Plateau.

Time Series Prediction of Gas Emission in Coal Mining Face Based on Optimized Variational Mode Decomposition and SSA-LSTM.

The accurate prediction of gas emissions has important guiding significance for the prevention and control of gas disasters in order to further improve the prediction accuracy of gas emissions in the mining face. According to the absolute gas emission monitoring data of the 1417 working face in a coal mine in Shaanxi Province, a GA-VMD-SSA-LSTM gas emission prediction model (GVSL) based on genetic algorithm (GA)-optimized variational mode decomposition (VMD) and sparrow search algorithm (SSA)-optimized long short-term memory (LSTM) is proposed. Firstly, a VMD evaluation standard for evaluating the amount of decomposition loss is proposed. Under this standard, the GA is used to find the optimal parameters of the VMD. Then, the SSA is used to optimize the key parameters of the LSTM to establish a GVSL prediction model. The model predicts each component and finally superimposes the prediction results for each component to obtain the final gas emission result. The results show that the accuracy of the evaluation indexes of the GVSL model and VMD-LSTM model, as well as the SSA-LSTM model and Gaussian process regression (GPR) model, are compared and analyzed horizontally and vertically under three scenarios with prediction sets of 121,94 and 57 groups. The GVSL model has the best prediction effect, and its fitting degree R2 values are 0.95, 0.96, and 0.99, which confirms the effectiveness of the proposed GVSL model for the time series prediction of gas emission in the mining face.

In Situ Reconstruction of Active Catalysis Sites Triggered by Chromium Immobilization for Sulfite Oxidation.

Hexavalent chromium (Cr(VI)) is a highly toxic substance in wastewater, triggering grievous detriment to aquatic life and human health. Magnesium sulfite is spawned along with the desulfurization process in coal-fired power plants, which is usually disposed of as solid waste. Here, a "waste control by waste" method was proposed upon the redox of Cr(VI)-sulfite, in which highly toxic Cr(VI) is detoxicated and sequent enriched on a novel biochar-induced cobalt-based silica composite (BISC) due to the forced electron transfer from chromium to surface hydroxyl. The immobilized Cr on BISC gave rise to the reconstruction of catalytic active sites "Cr-O-Co", which further enhance its performance in sulfite oxidation by elevating O2 adsorption. As a result, the sulfite oxidation rate increased by 10 times compared with the non-catalysis benchmark together with the maximum chromium adsorption capacity being 120.3 mg/g. Therefore, this study provides a promising strategy to simultaneously control highly toxic Cr(VI) and sulfite, achieving high-grade sulfur resource recovery for wet magnesia desulfurization.

Near-infrared plasmonic sensing and digital metasurface via double Fano resonances.

Plasmonic sensing that enables the detection of minute events, when the incident light field interacts with the nanostructure interface, has been widely applied to optical and biological detection. Implementation of the controllable plasmonic double Fano resonances (DFRs) offers a flexible and efficient way for plasmonic sensing. However, plasmonic sensing and digital metasurface induced by tailorable plasmonic DFRs require further study. In this work, we numerically and theoretically investigate the near-infrared plasmonic DFRs for plasmonic sensing and digital metasurface in a hybrid metasurface with concentric ϕ-shaped-hole and circular-ring-aperture unit cells. We show that a plasmonic Fano resonance, resulting from the interaction between a narrow and a wide effective dipolar modes, can be realized in the ϕ-shaped hybrid metasurface. In particular, we demonstrate that the tailoring plasmonic DFRs with distinct mechanisms of actions can be accomplished in three different ϕ-shaped hybrid metasurfaces. Moreover, the resonance mode-broadening and mode-shifting plasmonic sensing can be fulfilled by modulating the polarization orientation and the related geometric parameters of the unit cells in the near-infrared waveband, respectively. In addition, the plasmonic switch with a high ON/OFF ratio can not only be achieved but also be exploited to establish a single-bit digital metasurface, even empower to implement two- and three-bit digital metasurface characterized by the plasmonic DFRs in the telecom L-band. Our results offer a new perspective toward realizing polarization-sensitive optical sensing, passive optical switches, and programmable metasurface devices, which also broaden the landscape of subwavelength nanostructures for biosensors and optical communications.

Total synthesis of wewakazole B.

Wewakazole B is a novel cyclodecapeptide with highly potent cytotoxic activity isolated from a sample of M. producens collected from the Red Sea. It contains nine common and three modified amino acid residues. The first total synthesis of Wewakazole B was successfully achieved on a gram scale, unambiguously confirming its structure. Notable features include the careful choice of amino acid-protecting groups and the construction of three different substituted oxazoles present in this natural product.

The fibrosis-4 index is a prognostic factor for cholangiocarcinoma patients who received immunotherapy.

Background: Research of immunotherapy for cholangiocarcinoma has yielded some results, but more clinical data are needed to prove its efficacy and safety. Moreover, there is a need to identify accessible indexes for selecting patients who may benefit from such treatments. Methods: The medical records of 66 cholangiocarcinoma patients who underwent immunotherapy were retrospectively collected. The effectiveness of immunotherapy was assessed by tumor response, progression-free survival (PFS), and overall survival (OS), while safety was evaluated by adverse events during treatment. Univariate and multivariate Cox regression analyses were performed to identify prognostic risk factors for PFS and OS, and Kaplan-Meier curves of potential prognostic factors were drawn. Results: Overall, in this study, immunotherapy achieved an objective response rate of 24.2% and a disease control rate of 89.4% for the included patients. The median PFS was 445 days, and the median OS was 772.5 days. Of the 66 patients, 65 experienced adverse events during treatment, but none had severe consequences. Multivariate Cox analysis indicated that tumor number is a prognostic risk factor for disease progression following immunotherapy in cholangiocarcinoma patients, while tumor differentiation and the fibrosis-4 (FIB-4) index are independent risk factors for OS. Conclusion: In general, immunotherapy for cholangiocarcinoma is safe, with adverse events remaining within manageable limits, and it can effectively control disease progression in most patients. The FIB-4 index may reflect the potential benefit of immunotherapy for patients with cholangiocarcinoma.

Prediction of malignant intraductal papillary mucinous neoplasm: A nomogram based on clinical information and radiological outcomes.

OBJECTIVE: Clinical practitioners face a significant challenge in maintaining a healthy balance between overtreatment and missed diagnosis in the management of intraductal papillary mucinous neoplasm (IPMN). The current study aimed to identify significant risk factors of malignant IPMN from a series of clinical and radiological parameters that are widely available and noninvasive and develop a method to individually predict the risk of malignant IPMN to improve its management. METHODS: We retrospectively investigated 168 patients who were pathologically diagnosed with IPMN after individualized pancreatic resection between June, 2012 and December, 2020. Independent predictors determined using both univariate and multivariate analyses to construct a predictive model. The discriminatory power of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed to demonstrate the clinical usefulness of the nomogram. Internal cross validation was performed to assess the validity of the predictive model. RESULTS: In the multivariate analysis, five significant independent risk factors were identified: increased serum CA19-9 level, low prognostic nutritional index (PNI), cyst size, enhancing mural nodule, and main pancreatic duct diameter. The nomogram based on the parameters mentioned above had outstanding performance in distinguishing malignancy, with an AUC of 0.907 (95% confidence interval: 0.859-0.956, p < 0.05), which remained 0.875 after internal cross-validation, and showed good clinical usefulness. CONCLUSION: A novel nomogram for predicting malignant IPMN first introducing PNI was developed, which may aid in improving IPMN management. Nevertheless, external validation is required to confirm its efficacy.

Realistic fault detection of li-ion battery via dynamical deep learning.

Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.

An S-scheme α-Fe2O3/Cu2O photocatalyst for an enhanced primary amine oxidative coupling reaction under visible light.

Photoredox catalysis under visible light has been recorded as a potential and reassuring recipe for organic synthesis. More and more heterojunction catalysts have appeared in people's fields of vision, especially those with S-scheme heterojunction structures. The S-scheme heterojunction structure of a photocatalyst can remarkably improve its photocatalytic efficiency. Here, an S-scheme α-Fe2O3/Cu2O heterojunction photocatalyst was designed and fabricated for the primary amine oxidative coupling reaction. On account of that, the S-scheme structure effectively separated the photogenerated electron-hole pairs and enhanced the photoredox ability of the photocatalytic system. The α-Fe2O3/Cu2O composite could also enhance the reactivity to a large extent. This work will provide new insight into the design of photocatalysts with a more reasonable structure and higher performance.

Missing links towards understanding the equilibrium shapes of hexagonal boron nitride: algorithm, hydrogen passivation, and temperature effects.

There is a large discrepancy between the experimental observations and the theoretical predictions in the morphology of hexagonal boron nitride (h-BN) nanosheets. Theoretically predicted hexagons terminated by armchair edges are not observed in experiments; and experimentally observed triangles terminated by zigzag edges are found theoretically unstable. There are two key issues in theoretical investigations, namely, an efficient and accurate algorithm of the absolute formation energy of h-BN edges, and a good understanding of the role of hydrogen passivation during h-BN growth. Here, we first proposed an efficient algorithm to calculate asymmetric edges with a self-consistent accuracy of about 0.0014 eV Å-1. This method can also serve as a standard approach for other two-dimensional (2D) compound materials. Then, by using this method, we discovered that only when edges are passivated by hydrogen atoms and temperature effects are taken into account can experimental morphology be explained. We further employed the Wulff construction to obtain the equilibrium shapes of H-passivated h-BN nanosheets under their typical growth conditions at T = 1300 K and p = 1 bar, and found out that the equilibrium shapes are sensitive to hydrogen passivation and the growth conditions. Our results resolved long-standing discrepancies between experimental observations and theoretical analysis, explaining the thermodynamic driving force of triangular, truncated triangular, and hexagonal shapes, and revealing the key role of hydrogen in h-BN growth. These discoveries and the advancement in the algorithm may open the gateway towards the realization of 2D electronic and spintronic devices based on h-BN.

Pseudo-Hydrogen Passivation: A Novel Way to Calculate Absolute Surface Energy of Zinc Blende (111)/(͞1 ͞1 ͞1) Surface.

Determining accurate absolute surface energies for polar surfaces of semiconductors has been a great challenge in decades. Here, we propose pseudo-hydrogen passivation to calculate them, using density functional theory approaches. By calculating the energy contribution from pseudo-hydrogen using either a pseudo molecule method or a tetrahedral cluster method, we obtained (111)/(111) surfaces energies of Si, GaP, GaAs, and ZnS with high self-consistency. This method quantitatively confirms that surface energy is determined by the number and the energy of dangling bonds of surface atoms. Our findings may greatly enhance the basic understandings of different surfaces and lead to novel strategies in the crystal growth.

Direct and simultaneous detection of organic and inorganic ingredients in herbal powder preparations by Fourier transform infrared microspectroscopic imaging.

Herbal powder preparation is a kind of widely-used herbal product in the form of powder mixture of herbal ingredients. Identification of herbal ingredients is the first and foremost step in assuring the quality, safety and efficacy of herbal powder preparations. In this research, Fourier transform infrared (FT-IR) microspectroscopic identification method is proposed for the direct and simultaneous recognition of multiple organic and inorganic ingredients in herbal powder preparations. First, the reference spectrum of characteristic particles of each herbal ingredient is assigned according to FT-IR results and other available information. Next, a statistical correlation threshold is determined as the lower limit of correlation coefficients between the reference spectrum and a larger number of calibration characteristic particles. After validation, the reference spectrum and correlation threshold can be used to identify herbal ingredient in mixture preparations. A herbal ingredient is supposed to be present if correlation coefficients between the reference spectrum and some sample particles are above the threshold. Using this method, all kinds of herbal materials in powder preparation Kouqiang Kuiyang San are identified successfully. This research shows the potential of FT-IR microspectroscopic identification method for the accurate and quick identification of ingredients in herbal powder preparations.

Antimicrobial and cytotoxic phenolics and phenolic glycosides from Sargentodoxa cuneata.

Five new phenolic glycosides, Sargentodosides A-E, and two new dihydronaphthalene lignans, Sargentodognans F-G, together with thirty-two known phenolic compounds were isolated from the 60% ethanol extracts of Sargentodoxa cuneata. Their structures including absolute configurations were determined by spectroscopic analysis and electronic circular dichroism experiments. In bioscreening experiments, twelve compounds (22-26, 29, 33-34, 36, 38) exhibited antibacterial activities against S. aureus ATCC 29213 with minimum inhibitory concentration (MIC) values of 2-516µg/mL. And compound 29 showed the highest antibacterial activity against S. aureus ATCC 29213 with MIC values of 2µg/mL, while the MIC values of levofloxacin was 8µg/mL. Three compounds (29, 33, 36) exhibited antibacterial activities against S. aureus ATCC 25923 with MIC values of 256-516µg/mL. Two compounds (29, 33) exhibited antibacterial activities against A. baumanii ATCC 19606 with MIC values of 128-516µg/mL. However, no compound exhibited antimicrobial activities against C. albicans ATCC 10231. Moreover, three compounds (10, 25, 36) exhibited significant inhibition of proliferation in the two cell lines Hela and Siha, and showed stronger inhibitive activity of these two selected cell lines than cisplatin in the cytotoxic assay. Thus, S. cuneata is a potential plant source for further research targeting bacteria and cancer diseases.

Anti-Obesity effects of protopanaxdiol types of Ginsenosides isolated from the leaves of American ginseng (Panax quinquefolius L.) in mice fed with a high-fat diet.

Effects of protopanaxdiol (PDG) and protopanaxatriol (PTG) types of ginsenosides isolated from the leaves of American ginseng on porcine pancreatic lipase activity were determined in vitro. PDG inhibited the pancreatic lipase activity in a dose-dependent manner at the concentrations of 0.25-1mg/ml. It inhibited hydrolysis of about 83.2% of triolein at about 1mg/ml of PDG. However, PTG showed no inhibitory activity. Therefore, anti-obesity activity of PDG was evaluated in mice fed a high-fat diet. The results demonstrated that PDG was effective in preventing and healing obesity, fatty liver and hypertriglyceridemia in mice fed with a high-fat diet.