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Molecular sieving membranes with sufficient and uniform nanochannels that break the permeability-selectivity trade-off are desirable for energy-efficient gas separation, and the arising two-dimensional (2D) materials provide new routes for membrane development. However, for 2D lamellar membranes, disordered interlayer nanochannels for mass transport are usually formed between randomly stacked neighboring nanosheets, which is obstructive for highly efficient separation. Therefore, manufacturing lamellar membranes with highly ordered nanochannel structures for fast and precise molecular sieving is still challenging. Here, we report on lamellar stacked MXene membranes with aligned and regular subnanometer channels, taking advantage of the abundant surface-terminating groups on the MXene nanosheets, which exhibit excellent gas separation performance with H 2 permeability >2200 Barrer and H 2 /CO 2 selectivity >160, superior to the state-of-the-art membranes. The results of molecular dynamics simulations quantitatively support the experiments, confirming the subnanometer interlayer spacing between the neighboring MXene nanosheets as molecular sieving channels for gas separation. Two-dimensional materials show great potential for membrane technologies, but their disordered channels hinder their molecular sieving performance. Here, Wang, Gogotsi and colleagues design a MXene membrane with ordered nanochannels that exhibits an excellent H 2 /CO 2 gas separation performance.

Ammonia (NH 3 ) is essential for modern agriculture and industry and is a potential energy carrier. NH 3 is traditionally synthesized by the Haber–Bosch process at high temperature and pressure. The high-energy input of this process has motivated research into electrochemical NH 3 synthesis via nitrogen (N 2 )–water reactions under ambient conditions. However, the future of this low-cost process is compromised by the low yield rate and poor selectivity, ascribed to the inert N≡N bond and ultralow solubility of N 2 . Obtaining NH 3 directly from non-N 2 sources could circumvent these challenges. Here we report the eight-electron direct electroreduction of nitrate to NH 3 catalysed by copper-incorporated crystalline 3,4,9,10-perylenetetracarboxylic dianhydride. The catalyst exhibits an NH 3 production rate of 436 ± 85 μg h −1  cm −2 and a maximum Faradaic efficiency of 85.9% at −0.4 V versus a reversible hydrogen electrode. This notable performance is achieved by the catalyst regulating the transfer of protons and/or electrons to the copper centres and suppressing hydrogen production. Electrochemically reducing nitrogen-containing molecules could provide less energy-intense routes to produce ammonia than the traditional Haber–Bosh process. Here the authors use a catalyst comprising Cu embedded in an organic molecular solid to synthesize ammonia from nitrate ions.

Transformers have recently lead to encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by adding three designs: (i) a linear complexity attention layer, (ii) an overlapping patch embedding, and (iii) a convolutional feed-forward network. With these modifications, PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification, detection, and segmentation. In particular, PVT v2 achieves comparable or better performance than recent work such as the Swin transformer. We hope this work will facilitate state-of-the-art transformer research in computer vision. Code is available at https://github.com/whai362/PVT .

Exploring highly efficient Pt-free catalysts for hydrogen evolution reaction (HER) is of great importance for hydrogen (H 2 ) production. Herein, a novel HER electrocatalyst having abundant ultra-small (2–3 nm) Ru electronically confined by a B, N co-doped polar carbon surface (Ru/(B-N)-PC) was constructed. The Ru/(B-N)-PC catalyst exhibits a low overpotential of 15 mV at the current density of 10 mA·cm −2 , a low Tafel slope of 22.6 mV·dec −1 , and superior durability, which outperforms the benchmark Pt/C catalyst. Both experimental characterizations and theory calculations suggest that an electron communication established between B, N co-doped carbon surface and ultra-small Ru nanoparticles with electrons transferred from N atoms to Ru and back-transferred from Ru to B atoms, which exerts a moderate electronic modification of Ru. This, in turn, affords a modest H adsorption energy and a lower H 2 O dissociation barrier, leading to the high-performance hydrogen evolution reaction. The work provides meaningful insight into the size control and electronic modulation of Ru catalyst for intrinsic HER activity improvement.

In this study, we investigate the influence of global innovation networks (GINs) on the innovation output of semiconductor firms. Utilizing negative binomial regression and network analysis, we assess how network positions, specifically degree, betweenness, and closeness centrality, affect firms’ innovation performance, revealing significant positive impacts. Moreover, our results identify a positive U-shaped relationship between structural holes in GINs and innovation performance, suggesting that while moderate network engagement aids innovation, too much can be detrimental. This research provides key insights into optimizing GIN participation for better innovation results in the competitive semiconductor sector.

Real-time representation of the current performance of structures is an important task for perceiving potential danger in in-service bridges. Methods driven by the multisource sensing data of structural health monitoring systems are an effective way to achieve this goal. Due to the explicit zero-point of signals, the live load-induced response has an inherent advantage for quantitatively representing the performance of bridges. Taking a long-span cable-stayed railway–highway combined bridge as the case study, this paper presents a representation method of in-service performance. First, the non-stationary sections of train-induced response are automatically extracted by wavelet transform and window with threshold. Then, the data of the feature parameter of each non-stationary section are automatically divided into four cases of train load according to the calculational theory of bridge vibration under train effect and clustering analysis. Finally, the performance indexes for structural deformation and dynamics are determined separately, based on hierarchical clustering and statistical modeling. Fusing the real variability of massive data from monitoring and the knowledge of mechanics of theoretical calculations, accurate and robust indexes of bridge deflection distribution and forced vibration frequency are obtained in real time. The whole process verifies the feasibility of the representation of bridge in-service performance from massive multisource sensing data. The presented method, framework, and analysis results can be used as a reference for the design, operation, and maintenance works of long-span railway bridges.

Digital trade, as one of the vanguards of the global technological revolution and industrial transformation, empowers low-carbon economic cooperation on a global scale. This study based on panel data from 2007 to 2021 of 46 countries with varied development levels, constructed a multi-dimensional indicator system from six aspects: trade potential, digital infrastructure, digital innovation, digital skills and security, scale of digital trade, and digital trade environment, aim to measure the development level of digital trade and explore its impact on carbon emissions, followed by a heterogeneity analysis. The research results indicate that there are significant differences in the development levels of digital trade among countries with different economic strengths. Countries with stronger economic power and larger trade scales have a higher level of digital trade development, while less developed regions lag in digital trade. As the level of digital trade development increases, the impact of digital trade on carbon emissions will also change. Overall, digital trade exhibits a pronounced low-carbon effect, while its impact varies among countries with different development levels.

The long R&D process, the high risk, and the externalities of technological innovation are challenges that enterprises have to meet when making decisions on R&D investment. Governments share this risk with enterprises through preferential tax policies. We summarized China’s preferential tax policies related to enterprises and R&D innovation, and used panel data of listed enterprises from 2013 to 2018 in the Growth Enterprises Market (GEM) of the Shenzhen Stock Exchange to explore the incentive effects of current tax policies on the R&D innovation of enterprises. Through empirical analysis, we found that tax incentives significantly motivate R&D innovation input and promote output. In addition, we found that the income tax incentives are greater than that of the circulation tax, since the profitability of enterprise has a positive correlation with R&D investment. Meanwhile, the size of the enterprise is negatively correlated with the intensity of R&D investment.

Accumulating evidences have indicated that lncRNAs play an important role in various human complex diseases. However, known disease-related lncRNAs are still comparatively small in number, and experimental identification is time-consuming and labor-intensive. Therefore, developing a useful computational method for inferring potential associations between lncRNAs and diseases has become a hot topic, which can significantly help people to explore complex human diseases at the molecular level and effectively advance the quality of disease diagnostics, therapy, prognosis and prevention. In this paper, we propose a novel prediction of lncRNA-disease associations via lncRNA-disease-gene tripartite graph (TPGLDA), which integrates gene-disease associations with lncRNA-disease associations. Compared to previous studies, TPGLDA can be used to better delineate the heterogeneity of coding-non-coding genes-disease association and can effectively identify potential lncRNA-disease associations. After implementing the leave-one-out cross validation, TPGLDA achieves an AUC value of 93.9% which demonstrates its good predictive performance. Moreover, the top 5 predicted rankings of lung cancer, hepatocellular carcinoma and ovarian cancer are manually confirmed by different relevant databases and literatures, affording convincing evidence of the good performance as well as potential value of TPGLDA in identifying potential lncRNA-disease associations. Matlab and R codes of TPGLDA can be found at following: https://github.com/USTC-HIlab/TPGLDA .

Aging is an independent risk factor for vascular diseases. Perivascular adipose tissue (PVAT), an active component of the vasculature, contributes to vascular dysfunction during aging. Identification of underlying cell types and their changes during aging may provide meaningful insights regarding the clinical relevance of aging‐related vascular diseases. Here, we take advantage of single‐cell RNA sequence to characterize the resident stromal cells in the PVAT (PVASCs) and identified different clusters between young and aged PVASCs. Bioinformatics analysis revealed decreased endothelial and brown adipogenic differentiation capacities of PVASCs during aging, which contributed to neointimal hyperplasia after perivascular delivery to ligated carotid arteries. Mechanistically, in vitro and in vivo studies both suggested that aging‐induced loss of peroxisome proliferator‐activated receptor‐γ coactivator‐1 α (PGC1α) was a key regulator of decreased brown adipogenic differentiation in senescent PVASCs. We further demonstrated the existence of human PVASCs (hPVASCs) and overexpression of PGC1α improved hPVASC delivery‐induced vascular remodeling. Our finding emphasizes that differentiation capacities of PVASCs alter during aging and loss of PGC1α in aged PVASCs contributes to vascular remodeling via decreased brown adipogenic differentiation. Loss of peroxisome proliferator‐activated receptor‐γ coactivator‐1 α (PGC1α) in aged PVASCs showed decreased endothelial and adipogenic differentiation, especially brown adipocyte generation, which contribute to neointimal hyperplasia after injured arteries.