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In this work, silver nanoparticles (AgNPs)/reduced graphene oxide (rGO) nanocomposites were electrodeposited on glassy carbon electrodes (GCE) to construct electrochemical sensors for the detection of hydrogen peroxide (H2O2) and dopamine (DA). The AgNPs were synthesized on graphene oxide (GO) by the hydrothermal method, followed by the reduction of the GO during the electrodeposition process, resulting in the formation of the nanocomposites on the surface of the electrodes. The generation of AgNPs on the graphene sheets was verified by scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS). The AgNPs/rGO/GCE showed a linear response to H2O2 in the range of 5 μM to 620 μM, with a sensitivity of 49 μA mM−1cm−2 and a limit of detection (LOD) of 3.19 μA. The linear response of the AgNPs/rGO/GCE to DA ranged from 1 μM to 276 μM, the sensitivity was 7.86 μA mM−1cm−2, and the LOD was 0.18 μM. Furthermore, DA and H2O2 were detected simultaneously in the same solution without interferences, and the sensors displayed good stability over time. The preparation method for the sensors is relatively eco-friendly, convenient, and efficient, exhibiting great potential for sensitive detection of DA and H2O2.

Membrane technology using well-defined pore structure can achieve high ion purity and recovery. However, fine-tuning the inner pore structure of the separation nanofilm to be uniform and enhance the effective pore area is still challenging. Here, we report dendrimers with different peripheral groups that preferentially self-assemble in aqueous-phase amine solution to facilitate the formation of polyamide nanofilms with a well-defined effective pore range and uniform pore structure. The high permeabilities are maintained by forming asymmetric hollow nanostripe nanofilms, and their well-designed ion effective separation pore ranges show an enhancement, rationalized by molecular simulation. The self-assembled dendrimer polyamide membrane provides Cl – /SO 4 2– selectivity more than 17 times that of its pristine polyamide counterparts, increasing from 167.9 to 2883.0. Furthermore, the designed membranes achieve higher Li purity and Li recovery compared to current state-of-the-art membranes. Such an approach provides a scalable strategy to fine-tune subnanometre structures in ion separation nanofilms. Membrane technology using well-defined pore structure enables high ion purity and recovery but achieving uniform pore structure and effective pore area is challenging. Here the authors introduce dendrimers that self-assemble, facilitating the formation of polyamide nanofilms with well-defined effective pore ranges and uniform pore structures.

With intensifying market competition, the characteristics of knowledge elements and collaboration networks under the deep integration patterns of industry, universities, and research institutes have significant impacts on radical innovation performance, which has profound implications for enterprises striving to establish a sustainable competitive advantage.This study used Specialized, Refined, Differentiated, and Innovative enterprises as the research object, combining machine learning methods such as the classification and regression tree algorithm and Bayesian network analysis to explore the interactive mechanism between the structural characteristics of internal knowledge elements and external collaboration networks of enterprises adopting different collaboration patterns, such as industry–university, industry–research institute, and industry–university–research institute, to provide an in-depth analysis of the multiple pathways for enterprises to improve their radical innovation performance. The research results verified and found the following: (1) Enterprises adopting industry-university-research institute collaboration pattern are more likely to achieve high radical innovation performance than those adopting industry-university or industry-research institute collaboration patterns. (2) The structural characteristics of different knowledge elements and collaboration networks have a complex nonlinear impact on enterprises’ radical innovation performance, with knowledge diversity being the most important factor. (3) The influence of the characteristic variables of enterprises adopting heterogeneous collaboration patterns on radical innovation performance shows significant differences, and appropriately utilizing structural holes can facilitate enterprises to conduct innovative activities. Enterprises of heterogeneous collaboration patterns can identify the optimal development pathways tailored to their specific conditions and strategic goals, as well as strategically adjusting their resource allocation from the perspective of interaction between knowledge elements and collaboration networks. In this way, the goal of enhancing radical innovation performance can ultimately be achieved through “different paths leading to the same destination”.

Tumor cells reside in a highly complex and heterogeneous tumor microenvironment (TME), which is composed of a myriad of genetically stable non-cancer cells, including fibroblasts, immune cells, endothelial cells, and epithelial cells, and a tumor-specific extracellular matrix (ECM). Cancer-associated fibroblasts (CAFs), as an abundant and active stromal cell population in the TME, function as the signaling center and remodeling machine to aid the creation of a desmoplastic tumor niche. Although there is no denial that the TME and CAFs may have anti-tumor effects as well, a great deal of findings reported in recent years have convincingly revealed the tumor-promoting effects of CAFs and CAF-derived ECM proteins, enzymes, chemical factors and other downstream effectors. While there is growing enthusiasm for the development of CAF-targeting therapies, a better understanding of the complexities of CAF-ECM and CAF-cancer cell interactions is necessary before novel therapeutic strategies targeting the malignant tumor \"soil\" can be successfully implemented in the clinic.

Raising photoelectric conversion efficiency and enhancing heat management are two critical concerns for silicon-based solar cells. In this work, efficient Yb3+ infrared emissions from both quantum cutting and upconversion were demonstrated by adjusting Er3+ and Yb3+ concentrations, and thermo-manage-applicable temperature sensing based on the luminescence intensity ratio of two super-low thermal quenching levels was discovered in an Er3+/Yb3+ co-doped tungstate system. The quantum cutting mechanism was clearly decrypted as a two-step energy transfer process from Er3+ to Yb3+. The two-step energy transfer efficiencies, the radiative and nonradiative transition rates of all interested 4 f levels of Er3+ in NaY(WO4)2 were confirmed in the framework of Föster-Dexter theory, Judd-Ofelt theory, and energy gap law, and based on these obtained efficiencies and rates the quantum cutting efficiency was furthermore determined to be as high as 173% in NaY(WO4)2: 5 mol% Er3+/50 mol% Yb3+ sample. Strong and nearly pure infrared upconversion emission of Yb3+ under 1550 nm excitation was achieved in Er3+/Yb3+ co-doped NaY(WO4)2 by adjusting Yb3+ doping concentrations. The Yb3+ induced infrared upconversion emission enhancement was attributed to the efficient energy transfer 4I11/2 (Er3+) + 2F7/2 (Yb3+) → 4I15/2 (Er3+) + 2F5/2 (Yb3+) and large nonradiative relaxation rate of 4I9/2. Analysis on the temperature sensing indicated that the NaY(WO4)2:Er3+/Yb3+ serves well the solar cells as thermos-managing material. Moreover, it was confirmed that the fluorescence thermal quenching of 2H11/2/4S3/2 was caused by the nonradiative relaxation of 4S3/2. All the obtained results suggest that NaY(WO4)2:Er3+/Yb3+ is an excellent material for silicon-based solar cells to improve photoelectric conversion efficiency and thermal management.The Yb3+ emissions from both the quantum cutting and nearly-pure infrared upconversion and excellent temperature detection were realized in Er3+/Yb3+ co-doped NaY(WO4)2 phosphors.

In this study, a cationic amphiphilic self-assembling peptide (SAP) Z23 was designed, and a simple bisphenol a (BPA) sensor, based on SAP Z23/multiwalled carbon nanotubes (Z23/MWCNTs) composite, was successfully fabricated on the surface of a glassy carbon electrode (GCE). The composite material was formed by π-π stacking interaction between the aromatic group on the hydrophobic side of Z23 and the side-wall of MWCNTs, with the charged hydrophilic group of Z23 exposed. During the electrocatalytic process of BPA, a synergistic effect was observed between Z23 and MWCNTs. The current response of the sensor based on composite material was 3.24 times that of the MWCNTs-modified electrode, which was much higher than that of the peptide-based electrode. Differential pulse voltammetry (DPV) was used to optimize the experimental conditions affecting the analytical performance of the modified electrode. Under optimal conditions, the linear range of the sensor was from 10 nM to 100 μM by amperometric measurement with sensitivity and limit of detection (LOD) at 6.569 μAμM−1cm−2 and 1.28 nM (S/N = 3), respectively. Consequently, the sensor has excellent electrochemical performance and is easy to fabricate, making it a good prospect in the field of electrochemical detection in the future.

Maize/soybean intercropping is a common cropping practice in Chinese agriculture, known to boost crop yield and enhance soil fertility. However, the role of below-ground interactions, particularly root exudates, in maintaining intercropping advantages in soybean/maize intercropping systems remains unclear. This study aimed to investigate the differences in root exudates between intercropping and monocropping systems through two pot experiments using metabolomics methods. Multiple omics analyses were conducted to explore correlations between differential metabolites and the community of Arbuscular Mycorrhizal Fungi (AMF), shedding light on the mechanisms underlying the dominance of intercropping from the perspective of root exudates-soil microorganism interactions. The study revealed that intercropping significantly increased the types and contents of root exudates, lowered soil pH, increased the availability of nutrients like available nitrogen (AN) and available phosphorus (AP), and enhanced AMF colonization, resulting in improving the community composition of AMF. Besides, root exudates in intercropping systems differed significantly from those in monocropping, with 41 and 39 differential metabolites identified in the root exudates of soybean/maize, predominantly amino acids and organic acids. The total amount of amino acids in the root exudates of soybean intercropping was 3.61 times higher than in monocropping. Additionally, the addition of root exudates significantly improved the growth of soybean/maize and AMF colonization, with the mycorrhizal colonization rate in intercropping increased by 105.99% and 111.18% compared to monocropping, respectively. The identified metabolic pathways associated with root exudates were closely linked to plant growth, soil fertility improvement, and the formation of AMF. Correlation analysis revealed a significant relationship (P < 0.05) between certain metabolites such as tartaric acid, oxalic acid, malic acid, aspartic acid, alanine, and the AMF community. Notably, the photosynthetic carbon fixation pathway involving aspartic acid showed a strong association with the function of Glomus_f_Glomerace, the dominant genus of AMF. A combined analysis of metabolomics and high throughput sequencing revealed that the root exudates of soybean/maize intercropping have direct or indirect connections with AMF and soil nutrients. This suggests that the increased root exudates of the soybean/maize intercropping system mediate an improvement in AMF community composition, thereby influencing soil fertility and maintaining the advantage of intercropping.

Distributed generation and demand-side management are expected to play a more prominent role in future power systems. However, the increased number of generations and load demands pose new challenges to optimal energy management in a microgrid. In this paper, an economic dispatch model for microgrids considering Traditional Generators (TGs), energy storage units, wind turbines (WTs), and flexible loads is established. To tackle the Economic Dispatch Problem (EDP) over directed communication networks, a fully distributed algorithm developed by leveraging a two-step state information exchange mechanism is proposed. In addition, by employing a fixed stepsize, the proposed algorithm demonstrates rapid convergence. Furthermore, our algorithm is well-suited for nonquadratic convex cost functions. Subsequently, we extend our algorithm to address imperfect communication scenarios. Even in the presence of arbitrarily large yet bounded time delays, our algorithm exhibits robustness. Finally, several numerical examples are given to verify the correctness and effectiveness of the developed results.

Soybean ( Glycine max ) serves as a major source of protein and edible oils worldwide. The genetic and genomic bases of the adaptation of soybean to tropical regions remain largely unclear. Here, we identify the novel locus Time of Flowering 16 ( Tof16 ), which confers delay flowering and improve yield at low latitudes and determines that it harbors the soybean homolog of LATE ELONGATED HYPOCOTYL ( LHY ). Tof16 and the previously identified J locus genetically additively but independently control yield under short-day conditions. More than 80% accessions in low latitude harbor the mutations of tof16 and j , which suggests that loss of functions of Tof16 and J are the major genetic basis of soybean adaptation into tropics. We suggest that maturity and yield traits can be quantitatively improved by modulating the genetic complexity of various alleles of the LHY homologs, J and E1 . Our findings uncover the adaptation trajectory of soybean from its temperate origin to the tropics. How soybean, a temperate origin crop, adapted to a tropical environment remains unclear. Here, the authors report Tof16 , an ortholog of LHY , and the previously identified J locus, control soybean yield under short-day condition and loss of function of these two genes contributes to the adaptation to tropics.

Nutrition affects plant growth and development, including flowering. Flowering represents the transition from the vegetative period to the reproduction period and requires the consumption of nutrients. Moreover, nutrients (e.g., nitrate) act as signals that affect flowering. Regulation of flowering time is therefore intimately associated with both nutrient-use efficiency and crop yield. Here, we review current knowledge of the relationships between nutrients (primarily nitrogen, phosphorus, and potassium) and flowering, with the goal of deepening our understanding of how plant nutrition affects flowering.