Unsupervised machine learning reveals eigen reactivity of metal surfaces.

The reactivity of metal surfaces is a cornerstone concept in chemistry, as metals have long been used as catalysts to accelerate chemical reactions. Although fundamentally important, the reactivity of metal surfaces has hitherto not been explicitly defined. For example, in order to compare the activity of two metal surfaces, a particular probe adsorbate, such as O, H, or CO, has to be specified, as comparisons may vary from probe to probe. Here we report that the metal surfaces actually have their own intrinsic/eigen reactivity, independent of any probe adsorbate. By employing unsupervised machine learning algorithms, specifically, principal component analysis (PCA), two dominant eigenvectors emerged from the binding strength dataset formed by 10 commonly used probes on 48 typical metal surfaces. According to their chemical characteristics revealed by vector decomposition, these two eigenvectors can be defined as the covalent reactivity and the ionic reactivity, respectively. Whereas the ionic reactivity turns out to be related to the work function of the metal surface, the covalent reactivity cannot be indexed by simple physical properties, but appears to be roughly connected with the valence-electron number normalized density of states at the Fermi level. Our findings expose that the metal surface reactivity is essentially a two-dimensional vector rather than a scalar, opening new horizons for understanding interactions at the metal surface.

Metal-support interaction boosts the stability of Ni-based electrocatalysts for alkaline hydrogen oxidation.

Ni-based hydrogen oxidation reaction (HOR) electrocatalysts are promising anode materials for the anion exchange membrane fuel cells (AEMFCs), but their application is hindered by their inherent instability for practical operations. Here, we report a TiO2 supported Ni4Mo (Ni4Mo/TiO2) catalyst that can effectively catalyze HOR in alkaline electrolyte with a mass activity of 10.1 ± 0.9 A g-1Ni and remain active even up to 1.2 V. The Ni4Mo/TiO2 anode AEMFC delivers a peak power density of 520 mW cm-2 and durability at 400 mA cm-2 for nearly 100 h. The origin for the enhanced activity and stability is attributed to the down-shifted d band center, caused by the efficient charge transfer from TiO2 to Ni. The modulated electronic structure weakens the binding strength of oxygen species, rendering a high stability. The Ni4Mo/TiO2 has achieved greatly improved stability both in half cell and single AEMFC tests, and made a step forward for feasibility of efficient and durable AEMFCs.

NTR-1's essential contribution to asymmetric mating between two sibling nematode Species: Bursaphelenchus xylophilus and B. Mucronatus.

The pine-wood invasive species nematode Bursaphelenchus xylophilus causes great forestry damage globally, particularly in Eurasia. B. xylophilus can hybridize with its native sibling, Bursaphelenchus mucronatus, with whom it shares an interestingly asymmetric mating behavior. However, the molecular mechanism underlying interspecific asymmetric mating has yet to be clarified. ntr-1, a nematocin receptor gene, is involved in an oxytocin/vasopressin-like signaling system that can regulate reproduction. Structural analysis using bioinformatics revealed that both Bxy- and Bmu-ntr-1 encode 7TM-GPCR, a conserved sequence. In situ hybridization and qPCR showed that both Bxy- and Bmu-ntr-1 were highly expressed in adult nematodes. Specifically, Bxy-ntr-1 was expressed in the vulva of females and caudal gonad of males, whereas Bmu-ntr-1 was expressed in the postal vulva and uterus of females and the whole gonads of males. Furthermore, RNAi of ntr-1 further demonstrated the biological function of interspecific mating: ntr-1 can regulate mating behavior, lead to male-female specificity, and ultimately result in interspecific differences. In B. mucronatus, ntr-1 influenced male mating more than female mating success, while downregulation of ntr-1 in B. xylophilus resulted in a significant decline in the female mating rate. Competitive tests revealed that the mating rate of the cross significantly declined after downregulation of Bxy♀- and Bmu♂-ntr-1, but no obvious change occurred in the reciprocal cross. Thus, we speculate that ntr-1 may be the key factor behind interspecific asymmetric mating. The current study (1) demonstrated the regulatory function of ntr-1 on mating behavior and (2) theoretically revealed the molecular basis of interspecific asymmetric mating.

Modulation of hypoxia and redox in the solid tumor microenvironment with a catalytic nanoplatform to enhance combinational chemodynamic/sonodynamic therapy.

The efficacy of reactive oxygen species-mediated therapy is generally limited by hypoxia and overexpressed glutathione (GSH) in the tumor microenvironment (TME). To address these issues, herein, a smart Mn3O4/OCN-PpIX@BSA nanoplatform is rationally developed to enhance the combinational therapeutic efficacy of chemodynamic therapy (CDT) and sonodynamic therapy (SDT) through TME modulation. For constructing the catalytic nanoplatform (Mn3O4/OCN-PpIX@BSA), Mn3O4 nanoparticles were grown in situ on oxidized g-C3N4 (OCN) nanosheets, and the as-prepared Mn3O4/OCN nano-hybrids were then successively loaded with protoporphyrin (PpIX) and coated with bovine serum albumin (BSA). The catalase-like Mn3O4 nanoparticles are able to effectively catalyze the overexpressed endogenous H2O2 to produce O2, which could relieve hypoxia and improve the therapeutic effect of combinational CDT/SDT. The decomposition of Mn3O4 by GSH enables the release of Mn2+ ions, which not only facilitates good T1/T2 dual-modal magnetic resonance imaging for tumor localization but also results in the depletion of GSH and the Mn2+-driven Fenton-like reaction, thus further amplifying the oxidative stress and achieving improved therapeutic efficacy. It is worth noting that the Mn3O4/OCN-PpIX@BSA nanocomposites exhibit minimal toxicity to normal tissues at therapeutic doses. These positive findings provide a new strategy for the convenient construction of TME-regulating smart theranostic nanoagents to improve the therapeutic outcomes towards malignant tumors effectively.

Functionalized g-C3N4 nanosheets for potential use in magnetic resonance imaging-guided sonodynamic and nitric oxide combination therapy.

The oxygen consumption-induced hypoxia and the high concentration of glutathione in tumor microenvironment limit the treatment outcomes of sonodynamic therapy (SDT). SDT needs to be combined with other treatment modalities to achieve the desired therapeutic efficiency. In this study, an oxidized g-C3N4 (OCN) nanosheet-based theranostic nanoplatform is developed for sonodynamic and nitric oxide (NO) combination therapy of cancer. The OCN nanosheets are successively modified with amino-terminated 6-armed polyethylene glycol, chlorin e6, and Gd3+ ions, and then the as-prepared OCN-PEG-(Ce6-Gd3+) nanosheets are loaded with the NO donor N,N'-di-sec-butyl-N,N'-dinitroso-1,4-phenylenediamine (BNN6). Upon ultrasound (US) irradiation, the OCN-PEG-(Ce6-Gd3+)/BNN6 nanocomposite can induce the generation of reactive oxygen species (ROS) and simultaneously release NO molecules to effectively kill the cancer cells, thereby significantly suppressing the tumor growth. Moreover, a good in vivo T1-weighted magnetic resonance imaging (MRI) contrast effect is achieved after intravenous injection of OCN-PEG-(Ce6-Gd3+)/BNN6 due to remarkably enhanced contrast performance of the nanocomposite. Therefore, the OCN-PEG-(Ce6-Gd3+)/BNN6 formulation can serve as a promising theranostic agent for MRI-guided sonodynamic-NO combination therapy.

Spatial-Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN.

Convolutional neural networks provide an ideal solution for hyperspectral image (HSI) classification. However, the classification effect is not satisfactory when limited training samples are available. Focused on "small sample" hyperspectral classification, we proposed a novel 3D-2D-convolutional neural network (CNN) model named AD-HybridSN (Attention-Dense-HybridSN). In our proposed model, a dense block was used to reuse shallow features and aimed at better exploiting hierarchical spatial-spectral features. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial-spectral features was realized by the channel attention method and spatial attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer, respectively. Experiment results indicate that our proposed model can learn more discriminative spatial-spectral features using very few training data. In Indian Pines, Salinas and the University of Pavia, AD-HybridSN obtain 97.02%, 99.59% and 98.32% overall accuracy using only 5%, 1% and 1% labeled data for training, respectively, which are far better than all the contrast models.