Search

Here, we demonstrate a photochemical strategy to site-specifically deposit Pd atoms on Au nanoparticles. The high-sensitivity low-energy ion scattering spectra combined with the X-ray photoelectron spectra reveal that the surface electronic structure of Pd can be continuously regulated by tailoring the Pd-to-Au molar ratio and the location of Pd atoms in AuPd nanoparticles. It is revealed that electron-rich Pd atoms are considerably more active than the net Pd atoms in aerobic alcohol oxidation. Remarkably, the catalyst with the most electron-rich Pd sites (binding energy downshift: 1.0 eV) exhibits an extremely high turnover frequency (˜500000 h−1 vs 12000 h−1 for that with net Pd atoms) for solvent-free selective oxidation of benzyl alcohol, which is, to the best of our knowledge, the highest value ever reported. Kinetic studies reveal that electron-rich Pd atoms can accelerate the oxidation of benzyl alcohol by facilitating C-H cleavage, as indicated by the significant reduction in the activation energy as compared to net Pd atoms.

Nanoporous golds (NPG) exhibit excellent performance on CO oxidation due to unique pore structures with huge surface areas. However, it is a challenge for the prediction-assessing of catalytic performance without any experimental data. Herein, deep-learning methods are used to establish the structure–property relationship on NPGs. By comparing different learning algorithms, the deep recursive neural network (DRNN) can be assessed as the best network up to more than 98% accuracy. With several structural parameters, our trained model can be employed as an accurate descriptor to predict the transformation capability of CO molecules on gold catalysts and further to estimate the optimal zone in this system combined with nonlinear programming map. These meaningful findings greatly expand a way to optimize catalysts combining interdisciplinary research means, exhibiting a great application potential in the design of future catalysts.

Accurate delineation of the corneal micro-layers depicted on optical coherence tomography (OCT) plays an important role in computerized detection and diagnosis of various corneal diseases (e.g., keratoconus and dry eye). In this study, we present a novel edge-enhanced convolutional neural network termed EE-Net to automatically delineate three corneal micro-layers from OCT images at the same time, including the epithelium layer, Bowman’s layer, and stroma layer. We innovatively introduced a novel convolutional block and incorporated them with the existing BiO-Net network. Our experiments showed that the developed network achieved a dice similarity coefficient (DSC) of 0.9314, an intersection over union (IOU) of 0.8839, a Matthew's correlation coefficient (MCC) of 0.9314, and a sensitivity of 0.9320 on average respectively for the three different corneal micro-layers, which consistently outperformed available classical networks.

Accurate segmentation of corneal layers depicted on optical coherence tomography (OCT) images is very helpful for quantitatively assessing and diagnosing corneal diseases (e.g., keratoconus and dry eye). In this study, we presented a novel boundary-guided convolutional neural network (CNN) architecture (BG-CNN) to simultaneously extract different corneal layers and delineate their boundaries. The developed BG-CNN architecture used three convolutional blocks to construct two network modules on the basis of the classical U-Net network. We trained and validated the network on a dataset consisting of 1,712 OCT images acquired on 121 subjects using a 10-fold cross-validation method. Our experiments showed an average dice similarity coefficient (DSC) of 0.9691, an intersection over union (IOU) of 0.9411, and a Hausdorff distance (HD) of 7.4423 pixels. Compared with several other classical networks, namely U-Net, Attention U-Net, Asymmetric U-Net, BiO-Net, CE-Net, CPFnte, M-Net, and Deeplabv3, on the same dataset, the developed network demonstrated a promising performance, suggesting its unique strength in segmenting corneal layers depicted on OCT images.

The solvent-free oxidation of benzyl alcohol was studied using supported Pd-Ni bimetallic nanoparticles. Compared with monometallic Pd, the addition of Ni to Pd was found to be effective in suppressing the nondesired product toluene, thereby enhancing the selectivity towards benzaldehyde. This result was attributed to a dual effect of Ni addition: the weakening of dissociative adsorption of benzyl alcohol and the promotion of oxygen species involved in the oxidation pathway.

Early-onset preeclampsia and late-onset preeclampsia have been regarded as two different phenotypes with heterogeneous manifestations; To gain insights into the pathogenesis of the two traits, we analyzed the gene expression profiles in preeclamptic placentas. A whole genome-wide microarray was used to determine the gene expression profiles in placental tissues from patients with early-onset (n = 7;34 weeks), and late-onset (n = 8;36 weeks) preeclampsia and their controls who delivered preterm (n = 5;34 weeks) or at term (n = 5;36 weeks). Genes were termed differentially expressed if they showed a fold-change ≥ 2 and q-value0.05. Quantitative real-time reverse transcriptase PCR was used to verify the results. Western blotting was performed to verify the expressions of secreted genes at the protein level.Six hundred twenty-seven genes were differentially expressed in early-compared with late-onset preeclampsia (177 genes were up-regulated and 450 were down-regulated). Gene ontology analysis identified significant alterations in several biological processes; the top two were immune response and cell surface receptor linked signal transduction. Among the cell surface receptor linked signal transduction-related, differentially expressed genes, those involved in the G-protein coupled receptor protein signaling pathway were significantly enriched. G-protein coupled receptor signaling pathway related genes, such as GPR124 and MRGPRF, were both found to be down-regulated in early-onset preeclampsia. The results were consistent with those of western blotting that the abundance of GPR124 was lower in early-onset compared with late-onset preeclampsia. The different gene expression profiles reflect the different levels of transcription regulation between the two conditions and supported the hypothesis that they are separate disease entities. Moreover, the G-protein coupled receptor signaling pathway related genes may contribute to the mechanism underlying early- and late-onset preeclampsia.