Elucidating the structure-property relationship is crucial for the design of advanced electrocatalysts towards the production of hydrogen peroxide (H2O2). In this work, we theoretically and experimentally discovered that atomically dispersed Lewis acid sites (octahedral M–O species, M = aluminum (Al), gallium (Ga)) regulate the electronic structure of adjacent carbon catalyst sites. Density functional theory calculation predicts that the octahedral M–O with strong Lewis acidity regulates the electronic distribution of the adjacent carbon site and thus optimizes the adsorption and desorption strength of reaction intermediate (*OOH). Experimentally, the optimal catalyst (oxygen-rich carbon with atomically dispersed Al, denoted as O-C(Al)) with the strongest Lewis acidity exhibited excellent onset potential (0.822 and 0.526 V versus reversible hydrogen electrode at 0.1 mA cm−2 H2O2 current in alkaline and neutral media, respectively) and high H2O2 selectivity over a wide voltage range. This study provides a highly efficient and low-cost electrocatalyst for electrochemical H2O2 production.

Compared to nanomaterials exposing nonpolar facets, polar-faceted nanocrystals often exhibit unexpected and interesting properties. The electrostatic instability arising from the intrinsic dipole moments of polar facets, however, leads to different surface configurations in many cases, making it challenging to extract detailed structural information and develop structure-property relations. The widely used electron microscopy techniques are limited because the volumes sampled may not be representative, and they provide little chemical bonding information with low contrast of light elements. With ceria nanocubes exposing (100) facets as an example, here we show that the polar surface structure of oxide nanocrystals can be investigated by applying 17O and 1H solid-state NMR spectroscopy and dynamic nuclear polarization, combined with DFT calculations. Both CeO4-termination reconstructions and hydroxyls are present for surface polarity compensation and their concentrations can be quantified. These results open up new possibilities for investigating the structure and properties of oxide nanostructures with polar facets.

Many metal organic frameworks (MOFs) incorporate metal oxide clusters as nodes. Node sites where linkers are missing can be catalytic sites. We now show how to dial in the number and occupancy of such sites in MIL-53 and MIL-68, which incorporate aluminum-oxide-like nodes. The methods involve modulators used in synthesis and postsynthesis reactions to control the modulator-derived groups on these sites. We illustrate the methods using formic acid as a modulator, giving formate ligands on the sites, and these can be removed to leave μ2-OH groups and open Lewis acid sites. Methanol dehydration was used as a catalytic reaction to probe these sites, with infrared spectra giving evidence of methoxide ligands as reaction intermediates. Control of node surface chemistry opens the door for placement of a variety of ligands on a wide range of metal oxide cluster nodes for dialing in reactivity and catalytic properties of a potentially immense class of structurally well-defined materials.

Hydrous materials are ubiquitous in the natural environment and efforts have previously been made to investigate the structures and dynamics of hydrated surfaces for their key roles in various chemical and physical applications, with the help of theoretical modelling and microscopy techniques. However, an overall atomic-scale understanding of the water-solid interface, including the effect of water on surface ions, is still lacking. Herein, we employ ceria nanorods with different amounts of water as an example and demonstrate a new approach to explore the water-surface interactions by using solid-state NMR in combination with density functional theory. NMR shifts and relaxation time analysis provide detailed local structure of oxygen ions and the nature of water motion on the surface: the amount of molecularly adsorbed water decreases rapidly with increasing temperature (from room temperature to 150 °C), whereas hydroxyl groups are stable up to 150 °C; dynamic water molecules are found to instantaneously coordinate to the surface oxygen ions. The applicability of dynamic nuclear polarization for selective detection of surface oxygen species is also compared to conventional NMR with surface selective-labeling: the optimal method depends on the feasibility of enrichment and the concentration of protons in the sample. These results provide new insight into the interfacial structure of hydrated oxide nanostructures which is important for relevant applications.

Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.

No abstract

Renal cell carcinoma (RCC) is the most common adult renal epithelial cancer susceptible to metastasis and patients with irresectable RCC always have a poor prognosis. Long noncoding RNAs (lncRNAs) have recently been documented as having critical roles in the etiology of RCC. Nevertheless, the prognostic significance of lncRNA‐based signature for outcome prediction in patients with RCC has not been well investigated. Therefore, it is essential to identify a lncRNA‐based signature for predicting RCC prognosis. In the current study, we comprehensively analyzed the RNA sequencing data of the three main pathological subtypes of RCC (kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell carcinoma [KIRP], and kidney chromophobe carcinoma [KICH]) from The Cancer Genome Atlas (TCGA) database, and identified a 6‐lncRNA prognostic signature with the help of a step‐wise multivariate Cox regression model. The 6‐lncRNA signature stratified the patients into low‐ and high‐risk groups with significantly different prognosis. Multivariate Cox regression analysis showed that predictive value of the 6‐lncRNA signature was independent of other clinical or pathological factors in the entire cohort and in each cohort of RCC subtypes. In addition, the three independent prognostic clinical factors (including age, pathologic stage III, and stage IV) was also stratified into low‐ and high‐risk groups basis on the risk score, and the stratification analyses demonstrated that the high‐risk score was a poor prognostic factor. In conclusion, these findings indicate that the 6‐lncRNA signature is a novel prognostic biomarker for all three subtypes of RCC, and can increase the accuracy of predicting overall survival.

Layered double oxides (LDOs) can restore the parent layered double hydroxides (LDHs) structure under hydrous conditions, and this “memory effect” plays a critical role in the applications of LDHs, yet the detailed mechanism is still under debate. Here, we apply a strategy based on ex situ and in situ solid-state NMR spectroscopy to monitor the Mg/Al-LDO structure changes during recovery at the atomic scale. Despite the common belief that aqueous solution is required, we discover that the structure recovery can occur in a virtually solid-state process. Local structural information obtained with NMR spectroscopy shows that the recovery in aqueous solution follows dissolution-recrystallization mechanism, while the solid-state recovery is retro-topotactic, indicating a true “memory effect”. The amount of water is key in determining the interactions of water with oxides, thus the memory effect mechanism. The results also provide a more environmentally friendly and economically feasible LDHs preparation route.