A Study of the Adsorption Properties of Individual Atoms on the Graphene Surface: Density Functional Theory Calculations Assisted by Machine Learning Techniques.

In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element-graphene interactions.

The Evolution of a Microstructure during Tempering and Its Influence on the Mechanical Properties of AerMet 100 Steel.

In order to provide guidance for furthering the balance of strength and toughness of AerMet 100 steel through tempering treatment, the effects of the tempering time on microstructure and mechanical properties are investigated. The microstructure evolution, especially M2C precipitates and austenite in AerMet 100 tempered at 482 °C for 1~20 h, was characterized, and its influences on the mechanical properties were studied. The tensile strength decreases gradually, the yield strength increases first and then decreases, and the fracture toughness KIC increases gradually with an increasing tempering time. The strength and toughness matching of AerMet 100 steel is achieved by tempering at 482 °C for 5~7 h. Without considering the martensitic size effect, the influence of the dislocation density on the tensile strength is more significant during tempering at 482 °C. The precipitation strengthening mechanism plays a dominant role in the yield strength when tempering for 5 h or less, and the combined influence of carbide coarsening and a sharp decrease in the dislocation density resulted in a significant decrease in tensile strength when tempering for 8 h or more. The fracture toughness KIC is primarily influenced by the reverted austenite, so that KIC increases gradually with the prolongation of the tempering time. However, a significant decrease in the dislocation density resulting from long-term tempering has a certain impact on KIC, giving rise to a decrease in the rising amplitude in KIC after tempering for 8 h or more.

Tissue-derived exosome proteomics identifies promising diagnostic biomarkers for esophageal cancer.

Esophageal cancer (EC) is a fatal digestive disease with a poor prognosis and frequent lymphatic metastases. Nevertheless, reliable biomarkers for EC diagnosis are currently unavailable. Accordingly, we have performed a comparative proteomics analysis on cancer and paracancer tissue-derived exosomes from eight pairs of EC patients using label-free quantification proteomics profiling and have analyzed the differentially expressed proteins through bioinformatics. Furthermore, nano-flow cytometry (NanoFCM) was used to validate the candidate proteins from plasma-derived exosomes in 122 EC patients. Of the 803 differentially expressed proteins discovered in cancer and paracancer tissue-derived exosomes, 686 were up-regulated and 117 were down-regulated. Intercellular adhesion molecule-1 (CD54) was identified as an up-regulated candidate for further investigation, and its high expression in cancer tissues of EC patients was validated using immunohistochemistry, real-time quantitative PCR (RT-qPCR), and western blot analyses. In addition, plasma-derived exosome NanoFCM data from 122 EC patients concurred with our proteomic analysis. The receiver operating characteristic (ROC) analysis demonstrated that the AUC, sensitivity, and specificity values for CD54 were 0.702, 66.13%, and 71.31%, respectively, for EC diagnosis. Small interference (si)RNA was employed to silence the CD54 gene in EC cells. A series of assays, including cell counting kit-8, adhesion, wound healing, and Matrigel invasion, were performed to investigate EC viability, adhesive, migratory, and invasive abilities, respectively. The results showed that CD54 promoted EC proliferation, migration, and invasion. Collectively, tissue-derived exosomal proteomics strongly demonstrates that CD54 is a promising biomarker for EC diagnosis and a key molecule for EC development.

Role of exosomes in the development, diagnosis, prognosis and treatment of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer. It is characterized by occult onset resulting in most patients being diagnosed at advanced stages and with poor prognosis. Exosomes are nanoscale vesicles with a lipid bilayer envelope released by various cells under physiological and pathological conditions, which play an important role in the biological information transfer between cells. There is growing evidence that HCC cell-derived exosomes may contribute to the establishment of a favorable microenvironment that supports cancer cell proliferation, invasion, and metastasis. These exosomes not only provide a versatile platform for diagnosis but also serve as a vehicle for drug delivery. In this paper, we review the role of exosomes involved in the proliferation, migration, and metastasis of HCC and describe their application in HCC diagnosis and treatment. We also discuss the prospects of exosome application in HCC and the research challenges.

Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations.

In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution energy and theoretical stress of atomically doped aluminum substrates were extracted as basic data. By comparing five different algorithms, we found that the Catboost model had the lowest RMSE (0.24) and lowest MAPE (6.34), and this was used as the final prediction model to predict the solid solution strengthening of the aluminum matrix by the elements. Calculations show that alloying atoms such as K, Na, Y and Tl are difficult to dissolve in the aluminum matrix, whereas alloy atoms like Sc, Cu, B, Zr, Ni, Ti, Nb, V, Cr, Mn, Mo, and W exerted a strengthening influence. Theoretical studies on solid solutions and the strengthening effect of various alloy atoms in an aluminum matrix can offer theoretical guidance for the subsequent selection of suitable alloy elements. The theoretical investigation of alloy atoms in an aluminum matrix unveils the fundamental aspects of the solution strengthening effect, contributing significantly to the expedited development of new aluminum alloys.

Accelerating First Principles Calculation of Multi-Component Alloy Steady-State Structure and Elastic Properties in Full Component Space.

The FeNiCrAlCoCuTi alloy system has great advantages in mechanical properties such as high hardness and toughness. It has high performance potential and research value and the key in research is designing alloy compositions with target properties. The traditional method, experimental analysis, is highly inefficient to properly exploit the intrinsic relationship between material characteristics and properties for multi-component alloys, especially in investigating the whole composition space. In this work, we present a research way that uses first principles calculation to obtain the properties of multi-component alloys and uses machine learning to accelerate the research. The FeNiCrAlCoCuTi alloy system with its elastic properties is used as an example to demonstrate this process. We specifically design models for each output, all of which have RMSE values of less than 1.1, and confirm their effectiveness through experimental data in the literature, showing that the relative error is below 5%. Additionally, we perform an interpretable analysis on the models, exposing the underlying relationship between input features and output. By means of spatial transformation, we achieve the prediction of the full-component spatial performance from binary to multiple components. Taking the FeNiCrAlM (M = Co, Cu, Ti) quinary alloy system as an example, we design a single-phase BCC structure composed of an Fe0.23Cr0.23Al0.23Ni0.03Cu0.28 alloy with a Young's modulus of 273.10 GPa, as well as a single-phase BCC structure composed of an Fe0.01Cr0.01Al0.01Ni0.44Co0.53 alloy with a shear modulus of 103.6 GPa. Through this research way, we use machine learning to accelerate the calculation, which greatly shortens research time and costs. This work overcomes the drawbacks of traditional experiments and directly obtains element compositions and composition intervals with excellent performance.

Effects of Cr Content on Microstructure and Mechanical Properties of Co-Free FeCryNiAl0.8 High-Entropy Alloys.

High-entropy alloys have gained widespread concern in response to the increased requirements for future high-temperature structural superalloys. By combining phase-diagram calculations with microhardness, compression behavior measurements at room temperature, and elevated temperature conditions, the very important role of the Cr element on the microstructure and properties is deeply revealed, which provides candidates materials for future high-temperature alloy applications. The increment of Cr favors the regulation of the two-phase fraction and distribution. The thermodynamic calculations illustrate that the density and melting point of the HEAs showed an increasing trend with the increase of the Cr content. The typical worm-like microstructure of the Cr0.6 alloy with a dual BCC structure was detected. Meanwhile, on the one hand, the increment of the Cr elements results in a considerable optimization of the mechanical properties of the alloy in terms of strength and ductility at room temperature. The corresponding compressive strength and plasticity of Cr0.6 alloy at room temperature are 3524 MPa and 43.3%. On the other hand, the high-temperature mechanical properties of the alloy are greatly enhanced. At 1000 °C, the yield strength of the Cr0.6 alloy is about 25 MPa higher than that of the Cr0.4 alloy. The superior mechanical properties are attributed to the pronounced work-hardening response, and the work-hardening behavior of Cr-containing HEAs was systematically analyzed by employing the modified Ludwik model. The higher content of Cr helps the resistance of the local deformation response, improving the nonuniform strain and promoting the balance of strength and ductility of the alloys.

Role of CD147 in the development and diagnosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, and the third leading cause of cancer-related deaths worldwide. HCC is characterized by insidious onset, and most patients are diagnosed at an advanced stage with a poor prognosis. Identification of biomarkers for HCC onset and progression is imperative to development of effective diagnostic and therapeutic strategies. CD147 is a glycoprotein that is involved in tumor cell invasion, metastasis and angiogenesis through multiple mechanisms. In this review, we describe the molecular structure of CD147 and its role in regulating HCC invasion, metastasis and angiogenesis. We highlight its potential as a diagnostic and therapeutic target for HCC.

Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning.

Graphene has attracted significant interest due to its unique properties. Herein, we built an adsorption structure selection workflow based on a density functional theory (DFT) calculation and machine learning to provide a guide for the interfacial properties of graphene. There are two main parts in our workflow. One main part is a DFT calculation routine to generate a dataset automatically. This part includes adatom random selection, modeling adsorption structures automatically, and a calculation of adsorption properties. It provides the dataset for the second main part in our workflow, which is a machine learning model. The inputs are atomic characteristics selected by feature engineering, and the network features are optimized by a genetic algorithm. The mean percentage error of our model was below 35%. Our routine is a general DFT calculation accelerating routine, which could be applied to many other problems. An attempt on graphene/magnesium composites design was carried out. Our predicting results match well with the interfacial properties calculated by DFT. This indicated that our routine presents an option for quick-design graphene-reinforced metal matrix composites.

Effect of Temperatures and Graphene on the Mechanical Properties of the Aluminum Matrix: A Molecular Dynamics Study.

Graphene has become an ideal reinforcement for reinforced metal matrix composites due to its excellent mechanical properties. However, the theory of graphene reinforcement in graphene/aluminum matrix composites is not yet well developed. In this paper, the effect of different temperatures on the mechanical properties of the metal matrix is investigated using a classical molecular dynamics approach, and the effects of the configuration and distribution of graphene in the metal matrix on the mechanical properties of the composites are also described in detail. It is shown that in the case of a monolayer graphene-reinforced aluminum matrix, the simulated stretching process does not break the graphene as the strain increases, but rather, the graphene and the aluminum matrix have a shearing behavior, and thus, the graphene "pulls out" from the aluminum matrix. In the parallel stretching direction, the tensile stress tends to increase with the increase of the graphene area ratio. In the vertical stretching direction, the tensile stress tends to decrease as the percentage of graphene area increases. In the parallel stretching direction, the tensile stress of the system tends to decrease as the angle between graphene and the stretching direction increases. It is important to investigate the effect of a different graphene distribution in the aluminum matrix on the mechanical properties of the composites for the design of high-strength graphene/metal matrix composites.

Recent Advances on Composition-Microstructure-Properties Relationships of Precipitation Hardening Stainless Steel.

Precipitation hardening stainless steels have attracted extensive interest due to their distinguished mechanical properties. However, it is necessary to further uncover the internal quantitative relationship from the traditional standpoint based on the statistical perspective. In this review, we summarize the latest research progress on the relationships among the composition, microstructure, and properties of precipitation hardened stainless steels. First, the influence of general chemical composition and its fluctuation on the microstructure and properties of PHSS are elaborated. Then, the microstructure and properties under a typical heat treatment regime are discussed, including the precipitation of B2-NiAl particles, Cu-rich clusters, Ni3Ti precipitates, and other co-existing precipitates in PHSS and the hierarchical microstructural features are presented. Next, the microstructure and properties after the selective laser melting fabricating process which act as an emerging technology compared to conventional manufacturing techniques are also enlightened. Thereafter, the development of multi-scale simulation and machine learning (ML) in material design is illustrated with typical examples and the great concerns in PHSS research are presented, with a focus on the precipitation techniques, effect of composition, and microstructure. Finally, promising directions for future precipitation hardening stainless steel development combined with multi-scale simulation and ML methods are prospected, offering extensive insight into the innovation of novel precipitation hardening stainless steels.

Metformin exerts anti-tumor effects via Sonic hedgehog signaling pathway by targeting AMPK in HepG2 cells.

Metformin, a traditional first-line pharmacological treatment for type 2 diabetes, has recently been shown to have anti-cancer effects on hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the anti-tumor activity of metformin remains unclear. The Sonic hedgehog (Shh) signaling pathway is closely associated with the initiation and progression of HCC. Therefore, the aim of the current study was to investigate the effects of metformin on the biological behavior of HCC and the underlying functional mechanism of metformin in the Shh pathway. HCC was induced in HepG2 cells using recombinant human Shh (rhShh). The effects of metformin on proliferation and metastasis were evaluated using in vitro proliferation, wound healing, and invasion assays. The mRNA and protein expression levels of proteins related to the Shh pathway were measured using western blotting, quantitative PCR, and immunofluorescence staining. Metformin inhibited rhShh-induced proliferation and metastasis. Furthermore, metformin decreased the mRNA and protein expression of Shh pathway components, including Shh, Ptch, Smo, and Gli-1. Silencing of AMPK in the presence of metformin revealed that metformin exerted its inhibitory effects via AMPK. Our findings demonstrate that metformin suppresses the migration and invasion of HepG2 cells via AMPK-mediated inhibition of the Shh pathway.

Rg1 Promotes the Proliferation and Adipogenic Differentiation of Human Adipose-Derived Stem Cells via FXR1/Lnc-GAS5-AS1 Pathway.

BACKGROUND: Human adipose-derived stem cells (hASCs) play an important role in regenerative medicine. OBJECTIVE: Exploring the mechanism of Rg1 in the promotion of the proliferation and adipogenic differentiation of hASCs is important in regenerative medicine research. METHODS: To observe ginsenoside Rg1 in promoting the proliferation and adipogenic differentiation of hASCs, Rg1 medium at different concentrations was established and tested using the cell counting kit-8 (CCK-8) assay, oil red O staining, alizarin red, and alcian blue. Compared to the control, differentially expressed genes (DEGs) were screened via DEG analysis, which was carried out in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. To explore the relationship among mRNA, long non-coding RNA (lncRNA) and microRNA (miRNA), we constructed a competing endogenous RNA (ceRNA) network. RESULTS: In this study, Rg1 was observed to promote the proliferation and adipogenic differentiation of hASCs. Additionally, enriched BPs and KEGG pathways may be involved in the promotion process, where FXR1 and Lnc-GAS5-AS1 were found to be regulatory factors. The regulatory network suggested that Rg1 could regulate the adipocytokine signaling pathway and IL-17 signaling pathway via FXR1 and Lnc-GAS5-AS1, which served as the mechanism encompassing the promotion of Rg1 on the proliferation and adipogenic differentiation of hASCs. CONCLUSION: A comprehensive transcriptional regulatory network related to the promotion ability of Rg1 was constructed, revealing mechanisms regarding Rg1's promotion of the proliferation and adipogenic differentiation of hASCs. The present study provides a theoretical basis for optimizing the function of hASCs.

The hepatic AMPK-TET1-SIRT1 axis regulates glucose homeostasis.

Ten-eleven translocation methylcytosine dioxygenase 1 (TET1) is involved in multiple biological functions in cell development, differentiation, and transcriptional regulation. Tet1 deficient mice display the defects of murine glucose metabolism. However, the role of TET1 in metabolic homeostasis keeps unknown. Here, our finding demonstrates that hepatic TET1 physically interacts with silent information regulator T1 (SIRT1) via its C-terminal and activates its deacetylase activity, further regulating the acetylation-dependent cellular translocalization of transcriptional factors PGC-1α and FOXO1, resulting in the activation of hepatic gluconeogenic gene expression that includes PPARGC1A, G6PC, and SLC2A4. Importantly, the hepatic gluconeogenic gene activation program induced by fasting is inhibited in Tet1 heterozygous mice livers. The adenosine 5'-monophosphate-activated protein kinase (AMPK) activators metformin or AICAR-two compounds that mimic fasting-elevate hepatic gluconeogenic gene expression dependent on in turn activation of the AMPK-TET1-SIRT1 axis. Collectively, our study identifies TET1 as a SIRT1 coactivator and demonstrates that the AMPK-TET1-SIRT1 axis represents a potential mechanism or therapeutic target for glucose metabolism or metabolic diseases.

Increased room temperature ferromagnetism in Co-doped tetrahedral perovskite niobates.

Dilute magnetic semiconductors (DMSs), such as (In, Mn)As and (Ga, Mn)As prototypes, are limited to III-V semiconductors with Curie temperatures (T c) far from room temperature, thereby hindering their wide application. Here, one kind of DMS based on perovskite niobates is reported. BaM x Nb(1-x)O3-δ (M = Fe, Co) powders are prepared by the composite-hydroxide-mediated method. The addition of M elements endows BaM x Nb(1-x)O3-δ with local ferromagnetism. The tetragonal BaCo x Nb(1-x)O3-δ nanocrystals can be obtained by Co doping, which shows strong saturation magnetization (M sat) of 2.22 emu g-1, a remnant magnetization (M r) of 0.084 emu g-1 and a small coercive field (H c) of 167.02 Oe at room temperature. The ab initio calculations indicate that Co doping could lead to a 64% local spin polarization at the Fermi level (E F) with net spin DOS of 0.89 electrons eV-1, this result shows the possibility of maintaining strong ferromagnetism at room temperature. In addition, the trade-off effect between the defect band absorption and ferromagnetic properties of BaM x Nb(1-x)O3-δ is verified experimentally and theoretically.

Roles of Extracellular Vesicles in Cerebral Protection of Ischemic Stroke.

Stroke is the second-leading cause of death worldwide and exhibits a high disability rate. Ischemic stroke accounts for approximately 80% of all stroke cases. Inflammatory responses induced by innate immunity are involved in all stages of stroke-related injury, including early cerebral-infarction tissue repair and regeneration after ischemia. Toll-like receptors are the main receptors involved in innate immunity. Toll-like receptors specific antagonists inhibit neuroinflammation by reducing overproduction of inflammatory mediators. But there are still some limitations, such as affecting protein clearance and myelination. Extracellular vesicles are widespread and distributed in various body fluids, carry and transmit important signal molecules, affect the physiological state of cells and are closely related to the occurrence and progress of many diseases. In the present review, we summarize recent findings regarding the mechanisms by which extracellular vesicles act as signaling vectors to regulate cellular crosstalk between neurovascular units and further discuss the therapeutic effects of extracellular vesicles derived from mesenchymal stem cells on brain injury. Collectively, our review may provide novel insights into further elucidating pathogenesis and cerebral-protective measures of ischemic stroke.

Topotactic Growth of Free-Standing Two-Dimensional Perovskite Niobates with Low Symmetry Phase.

Here, we report a novel topotactic method to grow 2D free-standing perovskite using KNbO3 (KN) as a model system. Perovskite KN with monoclinic phase, distorted by as large as ∼6 degrees compared with orthorhombic KN, is obtained from 2D KNbO2 after oxygen-assisted annealing at relatively low temperature (530 °C). Piezoresponse force microscopy (PFM) measurements confirm that the 2D KN sheets show strong spontaneous polarization (Ps) along [101̅]pc direction and a weak in-plane polarization, which is consistent with theoretical predictions. Thickness-dependent stripe domains, with increased surface displacement and PFM phase changes, are observed along the monoclinic tilt direction, indicating the preserved strain in KN induces the variation of nanoscale ferroelectric properties. 2D perovskite KN with low symmetry phase stable at room temperature will provide new opportunities in the exploration of nanoscale information storage devices and better understanding of ferroelectric/ferroelastic phenomena in 2D perovskite oxides.

Giant Zeeman-type spin splitting of free electron/hole states on quasi-2D perovskite niobates: a theoretical prediction.

We study the spin-orbit interaction of two-dimensional electron/hole gas (2DEGs/2DHGs) on quasi-2D potassium niobates (KNs) via first-principles calculations. The strong surface polarity changes the free surface states from 2DEGs to 2DHGs. The in-plane dipole maintained on 2D models leads to giant Zeeman-type spin splitting, as high as 566 meV for the (001)c facet KN and 1.21 eV for the (111)c facet KN. The thickness-dependent Zeeman-type spin splitting shows a linear relation with respect to 1/r, while the corresponding in-plane polarization quantum has a linear relation of 1/(2^0.5)with respect to a decrease in thickness. Interestingly, the 2DHGs with molecular-like orbital character is solely constituted by O 2p states, showing logic switchable behavior at extremely thin samples with enormous Zeeman-type splitting that can switch between insulator and conductor by opposite spin polarization.

Quantum confined two-dimensional electron/hole gas switching by facet orientation of perovskite oxides.

The Polar discontinuity at heterointerface and the bare surface reconstructs the electronic phase of perovskite oxides. This gives rise to confined free electrons which intrinsically transit material from band insulator to metal. However, the insulator-metal transition induced by free holes has not been investigated so far due to the challenge in obtaining free hole state in oxides. Here, we propose a simple method whereby free holes can be obtained via polar facet reorientation. In the high polarity case, free holes can be supported by the lift up of O 2p subbands, which split into three independent subbands (one heavy hole subband and two light hole subbands) due to strong quantum confinement. Results show that both of the free electron and hole states are confined in a two dimensional quantum well, subjecting to the confined energy (E - E F) and occupied density of states around the Fermi level, indicating a finite thickness for preserving the metal states.