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Flexible reduced graphene oxide (rGO) sheets are being considered for applications in portable electrical devices and flexible energy storage systems. However, the poor mechanical properties and electrical conductivities of rGO sheets are limiting factors for the development of such devices. Here we use MXene (M) nanosheets to functionalize graphene oxide platelets through Ti-O-C covalent bonding to obtain MrGO sheets. A MrGO sheet was crosslinked by a conjugated molecule (1-aminopyrene-disuccinimidyl suberate, AD). The incorporation of MXene nanosheets and AD molecules reduces the voids within the graphene sheet and improves the alignment of graphene platelets, resulting in much higher compactness and high toughness. In situ Raman spectroscopy and molecular dynamics simulations reveal the synergistic interfacial interaction mechanisms of Ti-O-C covalent bonding, sliding of MXene nanosheets, and π-π bridging. Furthermore, a supercapacitor based on our super-tough MXene-functionalized graphene sheets provides a combination of energy and power densities that are high for flexible supercapacitors. Poor mechanical properties of reduced graphene oxide sheets hinder development of flexible energy storage systems. MXene functionalised graphene oxide with Ti-O-C bonding and additional crosslinking is here reported to dramatically increase toughness for flexible supercapacitors.

In the energy sector, accurate forecasting of natural gas production and liquid level detection is crucial for efficient resource management and operational planning. This study proposes an integrated deep learning model by incorporating bidirectional long short-term memory and Informer, for predicting these critical parameters. The bidirectional long short-term memory model, a type of recurrent neural network, is renowned for its ability to capture temporal dependencies in sequential data, making it a strong candidate for time series forecasting. On the other hand, Informer, a recent advancement in the field, offers an innovative self-attention mechanism that can handle long-term dependencies with reduced computational complexity. In addition, these models are implemented by using a comprehensive dataset of natural gas production and liquid level detection, applying rigorous preprocessing and feature engineering techniques to enhance model performance. The proposed deep learning models are evaluated on the dataset comparing with the state-of-the-art algorithms. Experimental results demonstrate the effectiveness of both models for gas production and liquid level detection, simultaneously. This study contributes to the body of knowledge by providing insights into the application of advanced deep learning techniques in the energy sector and offers a benchmark for future research in this domain.

Unidirectional fiber-reinforced composites of “plain” carbon fiber–reinforced polymer laminates and carbon nanofibers modified carbon fiber–reinforced polymer laminates were prepared based on the manufacture of the epoxy resin modified with various contents of carbon nanofibers. The carbon nanofibers–modified epoxy matrix and carbon fiber–reinforced polymer laminates specimens were subject to constant amplitude cyclic tensile loading, quasi-static tension loading, and incremental cyclic tension loading while the values of their electrical resistance were monitored through electrical resistance technique. Resistance-change curves of carbon nanofibers/carbon fiber–reinforced polymer laminates indicated the changes in conductive percolation networks formed by carbon fibers or carbon nanofibers. These changes can identify the complex damage modes and the loss of mechanical integrity in laminates. The changes in resistance of specimens showed a nearly linear correlation with the strain, so the damage process of the carbon fiber–reinforced polymer laminates can be self-sensed according to the resistance-change curves. In addition, uniformly dispersed carbon nanofibers formed a network that spans the whole insulation area, which improved their self-sensing property of strain sensitivity without compromising the mechanical properties of the carbon fiber–reinforced polymer laminates. This technology can achieve the quantitative strain and damage self-sensing properties of nano-reinforced composites without any additional sensor, and it is bound to be a promising method for in situ health monitoring.

Graphene-based films with high toughness have many promising applications, especially for flexible energy storage and portable electrical devices. Achieving such high-toughness films, however, remains a challenge. The conventional mechanisms for improving toughness are crack arrest or plastic deformation. Herein we demonstrate black phosphorus (BP) functionalized graphene films with record toughness by combining crack arrest and plastic deformation. The formation of covalent bonding P-O-C between BP and graphene oxide (GO) nanosheets not only reduces the voids of GO film but also improves the alignment degree of GO nanosheets, resulting in high compactness of the GO film. After further chemical reduction and π-π stacking interactions by conjugated molecules, the alignment degree of rGO nanosheets was further improved, and the voids in lamellar graphene film were also further reduced. Then, the compactness of the resultant graphene films and the alignment degree of reduced graphene oxide nanosheets are further improved. The toughness of the graphene film reaches as high as ∼51.8 MJ m−3, the highest recorded to date. In situ Raman spectra and molecular dynamics simulations reveal that the record toughness is due to synergistic interactions of lubrication of BP nanosheets, P-O-C covalent bonding, and π-π stacking interactions in the resultant graphene films. Our tough black phosphorus functionalized graphene films with high tensile strength and excellent conductivity also exhibit high ambient stability and electromagnetic shielding performance. Furthermore, a supercapacitor based on the tough films demonstrated high performance and remarkable flexibility.

Robotic surgery (RS) has been increasingly used for the resection of rectal cancer, and its advantages over laparoscopic surgery (LS) have been demonstrated. However, few studies focused on the severity of postoperative complications. This study aimed to compared the postoperative complications within 30 days after RS over LS according to the Clavien-Dindo (C-D) classification. A literature research of PubMed, Embase, Cochrane Library and Web of Science were systematically performed. The studies comparing the complications of RS and LS for rectal cancer based on the C-D classification were enrolled. Primary outcomes were C-D grade III, IV, V, III-V (severe complications). Seventeen studies (3193 patients) were included in the final analysis: 1554 underwent RS and 1639 underwent LS. The RS group was associated with significantly lower rates of severe complications (OR = 0.69, 95% CI 0.53-0.90, P = 0.005), C-D grade IV (OR = 0.69, 95% CI 0.53-0.90, P = 0.005), and anastomotic leak (OR = 0.66, 95% CI 0.48-0.91, P = 0.01). There was no significant difference in C-D grade III, C-D grade I, II, I-II (minor complications), overall complications, bleeding, wound complications, postoperative ileus, urinary retention, readmission, reoperation between two groups. Robotic surgery is safe for rectal cancer and may be an effective alternative to laparoscopic surgery, with lower rates of severe complications, C-D grade IV, and anastomotic leak. Further large randomized controlled trials are necessary to confirm this conclusion.

Hollow multishelled structures (HoMSs), with relatively isolated cavities and hierarchal pores in the shells, are structurally similar to cells. Functionally inspired by the different transmission forms in living cells, we studied the mass transport process in HoMSs in detail. In the present work, after introducing the antibacterial agent methylisothiazolinone (MIT) as model molecules into HoMSs, we discover three sequential release stages, i.e., burst release, sustained release and stimulus-responsive release, in one system. The triple-shelled structure can provide a long sterility period in a bacteria-rich environment that is nearly 8 times longer than that of the pure antimicrobial agent under the same conditions. More importantly, the HoMS system provides a smart responsive release mechanism that can be triggered by environmental changes. All these advantages could be attributed to chemical diffusion- and physical barrier-driven temporally-spatially ordered drug release, providing a route for the design of intelligent nanomaterials. Hollow multishell structures (HoMSs) consist of multiple shells with voids between them that provide separate reaction environments within the same assembly. Here, the authors used TiO2-HoMS to deliver a broad-spectrum antibacterial agent, in three-stages and in response to environmental changes.

Diabetes remains a significant global health challenge, marked by increasing incidence and a complex pathophysiological mechanism involving dysregulated lipid metabolism, impaired autophagy, and chronic inflammatory responses. Lipophagy, an autophagic process that involves the targeting of lipid droplets, is crucial for metabolic homeostasis. Therefore, investigating lipophagy-associated molecules may facilitate the discovery of novel biomarkers and potential therapeutic targets for diabetes. In this study, two GEO datasets, GSE33440 and GSE9006, were combined to identify differentially expressed genes (DEGs) linked to diabetes. By integrating weighted gene coexpression network analysis (WGCNA) with machine learning algorithms, this study identified EGR2 and CCR1 as key hub genes related to lipophagy. The results of the rank sum test revealed a strong positive correlation between these two genes, both of which were significantly upregulated in diabetic samples. Functional analyses, such as gene set enrichment analysis (GSEA), gene ontology (GO) enrichment, and protein‒protein interaction (PPI) network analysis, were used to validate their coherence. Diagnostic models and receiver operating characteristic (ROC) curve analysis further underscore the potential of CCR1 and EGR2 as biomarkers. Importantly, experimental validation demonstrated that the expressions of both genes were significantly elevated in the serum of diabetic patients and in the liver tissues of BKS-db diabetic mice. Notably, compared with control mice, CCR1 knockout mice ( CCR1 -/-) exhibited improved glucose homeostasis under high-fat diet conditions. Collectively, these findings suggest that EGR2 and CCR1 may be potential biomarkers associated with lipophagy in diabetes.

Converting the lignin into value-added chemicals and fuels represents a promising way to upgrade lignin. Here, we present an effective electrocatalytic approach that simultaneously modulates the depolymerization and hydrogenation pathways of lignin model compounds within a single reaction system. By fine-tuning the pH of the electrolyte, we achieve a remarkable shift in product selectivity, from acetophenone (with selectivity >99%) to 1-phenylethanol (with selectivity >99%), while effectively preventing over-hydrogenation. The robust metallic glass (MG) catalyst, endowed with an amorphous structure, demonstrates high stability, activity, and full recyclability across over 100 consecutive cycles in ionic liquid electrolytes. The relatively strong affinity of the MG catalyst for the substrate during the initial reaction stage, in conjunction with its weaker binding to the phenolic product, as the reaction progresses, creates a delicate balance that optimizes substrate adsorption and product desorption, which is pivotal in driving the cascade hydrogenation process of acetophenone. This work opens versatile pathways for lignin upgrading through integrated tandem reactions and expands the scope of catalyst design with amorphous structures. Lignin valorization has long been hindered by the challenge of precisely controlling product selectivity. Here, the authors construct a robust electrocatalytic system that enables pH-driven switching between depolymerization and hydrogenation pathways with high selectivity.

Circadian humidity fluctuation is an important factor that affects human life all over the world. Here we show that spherical cap-shaped ionic liquid drops sitting on nanowire array are able to continuously output electricity when exposed to outdoor air, which we attribute to the daily humidity fluctuation induced directional capillary flow. Specifically, ionic liquid drops could absorb/desorb water around the liquid/vapor interface and swell/shrink depending on air humidity fluctuation. While pinning of the drop by nanowire array suppresses advancing/receding of triple-phase contact line. To maintain the surface tension-regulated spherical cap profile, inward/outward flow arises for removing excess fluid from the edge or filling the perimeter with fluid from center. This moisture absorption/desorption-caused capillary flow is confirmed by in-situ microscope imaging. We conduct further research to reveal how environmental humidity affects flow rate and power generation performance. To further illustrate feasibility of our strategy, we combine the generators to light up a red diode and LCD screen. All these results present the great potential of tiny humidity fluctuation as an easily accessible anytime-and-anywhere small-scale green energy resource. Droplet generators convert mechanical movements of droplets into small-scale electricity. Here, Tang et al . report a humidity-driven power generator by utilizing daily humidity fluctuation in atmosphere enabling continuous generation of electricity upon moisture absorption and desorption cycles.

Seven cyclic depsipeptides, including two new cyclic pentadepsipeptides avenamides A (1) and B (2), were isolated from a plant-derived fungus Fusarium avenaceum W8 by using the bioassay-guided fractionation method. The planar structures were elucidated by using comprehensive spectroscopic analyses, including 1D and 2D NMR, as well as MS/MS spectrometry. The absolute configuration of the amino acid and hydroxy acid residues was confirmed by using the advanced Marfey’s method and chiral HPLC analysis, respectively. Compounds 1–7 were evaluated for their cytotoxic activities against A549 and NCI-H1944 human lung adenocarcinoma cell lines and their antimicrobial activities against Staphylococcus aureus and Saccharomyces cerevisiae. As a result, compounds 1–4 showed moderate cytotoxicity, with IC50 values of 6.52~45.20 µM. Compounds 1 and 3 exhibited significant antimicrobial activities against S. aureus and S. cerevisiae, with an MIC80 of 11.1~30.0 µg/mL.