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Treating hazardous waste Ni from the electroplating industry is mandated world-wide, is exceptionally expensive, and carries a very high CO 2 footprint. Rather than regarding Ni as a disposable waste, the chemicals and petrochemicals industries could instead consider it a huge resource. In the work described herein, we present a strategy for upcycling waste Ni from electroplating wastewater into a photothermal catalyst for converting CO 2 to CO. Specifically, magnetic nanoparticles encapsulated in amine functionalized porous SiO 2 , is demonstrated to efficiently scavenge Ni from electroplating wastewater for utilization in photothermal CO 2 catalysis. The core-shell catalyst architecture produces CO at a rate of 1.9 mol·g Ni −1 ·h −1 (44.1 mmol·g cat −1 ·h −1 ), a selectivity close to 100%, and notable long-term stability. This strategy of upcycling metal waste into functional, catalytic materials offers a multi-pronged approach for clean and renewable energy technologies. The valorization of undesirable substrates offers a two-fold strategy for pollution remediation and renewable energy conversion. Here, authors demonstrate the scavenging of Ni from electroplating wastewater and subsequent utilization as materials for photothermal CO 2 reduction.

Hoop-shaped or belt-like molecules have been fascinating not only due to their challenging synthesis, but also unique physical and chemical properties. The incorporation of heteroatoms (N, O, S, etc.) into these belts could alter both molecular structures and electronic properties which will lead to versatile applications, from advanced host-guest systems to functional materials. Despite numerous computational studies, the synthesis and characterization of heteroatom-bridged double-stranded molecular belts remains scarce. Here we report the synthesis, crystal structure, and host-guest chemistry of two novel heteroatom-bridged belt-like macrocycles composed of phenoxathiin. The bowl-shaped belt demonstrates a strong binding affinity ( K a  = 3.6 × 10 9  M ‒2 ) towards fullerene C 60 and forms a 2:1 capsule-like complex with the aid of C‒H···S hydrogen bonds. The column-like belt can bind the cyclic guest [2,2]paracyclophane to form a ring-in-ring complex. The modular synthesis, structural specificity, and diverse host-guest chemistry of cyclophenoxathiins markedly expands the known chemistry of molecular belts. Heteroatom-bearing molecular loops and belts are fascinating but generally difficult to synthesize. Here, the authors demonstrate that O,S-bridged double-stranded molecular belts—cyclophenoxathiins—can be successfully constructed and employed as versatile supramolecular hosts.

Due to their unique physical and chemical properties, two-dimensional (2D) boron nanosheets have received tremendous research attention and demonstrated substantial value in electronic devices, biomedicine, and energy conversion. In the preparation of boron nanosheets, compared with the bottom-up synthesis predominantly employed for electronics, the top-down synthesis route offers more facile and scalable production. In this mini-review, we mainly discuss the recent advances in the synthesis of boron nanosheets using the top-down strategy and the relevant applications in energy catalysis. Finally, inspired by our recent works on the novel applications of 2D silicon, we put forward prospects for designing boron nanosheets, providing insights into developing viable techniques for high-performance heterogeneous catalysis.

In recent years, blockchain technology has attracted substantial interest for its capability to transform supply chain management and finance. This paper employs evolutionary game theory to investigate the application of blockchain in mitigating financial risks within supply chains, taking into account the technology’s maturity and the risk preferences of financial institutions. By modeling interactions among financial institutions, small and medium enterprises (SMEs), and core enterprises within the accounts receivable financing framework, this study evaluates blockchain’s impact on their decision-making and its efficacy in risk reduction. Our findings suggest the transformative potential of blockchain in mitigating financial risks, solving information asymmetry, and enhancing collaboration between financial entities and SMEs. Additionally, we integrate smart contracts into supply chain finance, proposing pragmatic procedures for their deployment in real-world contexts. Via a detailed examination of blockchain’s maturity and financial institutions’ risk preferences, this research demonstrates the primary determinants of strategic decisions in supply chain finance and underscores how blockchain technology fosters system stability using risk mitigation. Our innovative contribution lies in the design of smart contracts for the ARF process, rooted in blockchain’s core attributes of security, transparency, and immutability, thereby ensuring efficient operation and cost reduction in supply chain finance.

Fingerprinting-based Wi-Fi indoor positioning has great potential for positioning in GPS-denied areas. However, establishing a fingerprinting map (also called a radio map) prior to positioning (site survey) is normally a labor-intensive task. This paper proposes a method for easy site survey without need for any extra hardware. The user can conduct the site survey adopting only a smart phone. The collected inertial-based readings are processed using the pedestrian dead-reckoning algorithms to generate a raw trajectory. Then a factor graph optimization method is proposed to re-estimate the trajectory by adding constraints originated from collected Wi-Fi fingerprints and landmark positions. The proposed method is verified through an experiment in a mall. The mean positioning error is 1.10 m and the maximum error is 2.25 m. This level of positioning accuracy is considered sufficient for radio map generation purposes. A classical baseline algorithm, the k-Nearest Neighbor (kNN) algorithm, is adopted to test the positioning performance of the radio map (RM), which also validates the quality of the constructed RM from the proposed method.

Background Sepsis-induced acute lung injury (ALI) is a severe clinical condition accompanied with high mortality. Tangeretin, which is widely found in citrus fruits, has been reported to exert antioxidant and anti-inflammatory properties. However, whether tangeretin protects against sepsis-induced ALI and the potential mechanisms remain unclear. Methods We established an ALI model via intraperitoneally injected with 5 mg/kg lipopolysaccharides (LPS) for 12 h. Tangeretin was applied intraperitoneally 30 min before LPS treatment. Dexamethasone (Dex) was used as a positive control. Hematoxylin and eosin (HE) staining and protein content in bronchoalveolar lavage fluid (BALF) were determined to detect the degree of lung injury. RNA-seq was also applied to explore the effect of tangeretin on ALI. In vitro, RAW264.7 were treated with Nrf2 siRNA, the expression of ferroptosis-associated biomarkers, including glutathione peroxidase 4 (GPX4) and prostaglandin-endoperoxide synthase 2 (PTGS2) were assessed. Glutathione (GSH), malondialdehyde (MDA) levels, reactive oxygen species (ROS) and inflammatory factors were also determined both in vivo and in vitro. Furthermore, mice were treated with an Nrf2 inhibitor (ML385) to verify the mechanism of tangeretin in inhibiting sepsis-induced lung injury and ferroptosis. Data were analyzed using one way analysis of variance or two-tailed unpaired t tests. Results Our study demonstrated that tangeretin significantly alleviated lung injury, reversed the LPS-induced reduction in GPX4 and GSH, and mitigates the elevation of PTGS2 and MDA levels. Tangeretin also reduced 4-HNE and iron levels. Besides, the levels of LPS-stimulated inflammatory factors IL-6, IL-1β and TNF-α were also decreased by tangeretin. RNA-seq and bioinformatics analysis demonstrated that tangeretin inhibited inflammatory response. Mechanistically, we identified that tangeretin inhibited the GPX4-dependent lipid peroxidation through activation of Nrf2. The silence of Nrf2 abolished the inhibitory effect of tangeretin on oxidative stress, inflammatory response and ferroptosis in RAW264.7 cells. Additionally, all the protective effects of tangeretin on ALI were abolished in Nrf2 inhibitor-treated mice. Conclusion We identified that ferroptosis as a critical mechanism contributing to sepsis-induced ALI. Tangeretin, a promising therapeutic candidate, effectively mitigates ALI through inhibiting ferroptosis via upregulating Nrf2 signaling pathway.

Bovine viral diarrhea virus (BVDV) causes a severe threat to the cattle industry due to ineffective control measures. Gypenoside is the primary component of Gynostemma pentaphyllum, which has potential medicinal value and has been widely applied as a food additive and herbal supplement. However, little is known about the antiviral effects of gypenoside. The present study aimed to explore the antiviral activities of gypenoside against BVDV infection. The inhibitory activity of gypenoside against BVDV was assessed by using virus titration and performing Western blotting, quantitative reverse transcription PCR (RT-qPCR), and immunofluorescence assays in MDBK cells. We found that gypenoside exhibited high anti-BVDV activity by interfering with the viral attachment to and internalization in cells. The study showed that BVDV infection inhibits apoptosis of infected cells from escaping the innate defense of host cells. Our data further demonstrated that gypenoside inhibited BVDV infection by electively activating the apoptosis of BVDV-infected cells for execution, as evidenced by the regulation of the expression of the apoptosis-related protein, promotion of caspase-3 activation, and display of positive TUNEL staining; no toxicity was observed in non-infected cells. Collectively, the data identified that gypenoside exerts an anti-BVDV-infection role by inhibiting viral attachment and internalization and selectively purging virally infected cells. Therefore, our study will contribute to the development of a novel prophylactic and therapeutic strategy against BVDV infection.

Drought stress is one of the major limiting factors for plant growth and agricultural production worldwide. This study seeks to investigate the role and mechanism of CYP71AJ49, a key enzyme gene in the coumarin biosynthesis pathway, under drought stress, and to elucidate its dual functions in drought tolerance and coumarin synthesis. CYP71AJ49 was isolated and cloned from Peucedanum praeruptorum Dunn, and heterologously overexpressed in Arabidopsis thaliana. Drought stress was mimicked with 20% PEG, after which transcriptome sequencing, analysis of physiological and biochemical parameters (including root length, fresh weight, antioxidant enzyme activities, proline, and malondialdehyde), and qRT-PCR validation were performed. Furthermore, coumarin content was quantified using ultra-performance liquid chromatography. Our findings demonstrated that overexpression of CYP7IAJ49 remarkably improved the drought tolerance of Arabidopsis thaliana. Under drought stress, transgenic plants exhibited significant increases in root length (22.84%), fresh weight (164.91%), and water content (294.41%). Simultaneously, their carbohydrate metabolism pathway was activated, the transcription levels of stress-responsive genes were upregulated, antioxidant enzyme activities and proline content were significantly enhanced, while malondialdehyde content was substantially decreased. Furthermore, CYP7IAJ49 also significantly promoted the accumulation of bergapten (52.42%) in Arabidopsis thaliana. Our findings demonstrate that CYP71AJ49 improves plant drought resistance through multiple pathways while regulating the accumulation of coumarin secondary metabolites. This discovery not only expands our understanding of the functions of CYP450 family genes but also provides critical theoretical support and genetic resources for breeding new crop varieties with high drought tolerance and high coumarin content.

Cu‐based catalysts exhibit excellent performance in hydrogenation reactions. However, the poor stability of Cu catalysts under high temperatures has restricted their practical applications. The preparation of stable Cu catalysts supported by SiO2 with strong metal–support interaction (SMSI) has thus aroused great interest due to the high abundance, low toxicity, feasible processability, and low cost of SiO2. The challenge in the construction of such SMSI remains to be the inertness of SiO2. Herein, a simple and scalable method is developed to prepare 2D silica (2DSiO2) supported Cu catalysts with SMSI by carefully manipulating the topological exfoliation of CaSi2 with CuCl2 and thereafter calcination. The prepared Cu‐2DSiO2 catalysts with the unique encapsulated Cu nanoparticles exhibit excellent activity and long‐term stability in high‐temperature CO2 hydrogenation reactions. This feasible and low‐cost solution for stabilizing Cu catalysts might shed light on their realistic applications. Achieving long‐term stability of Cu/SiO2 catalysts at high temperatures has been a long‐sought‐after goal for many catalytic reactions that demand low cost and high throughput. Herein, a cost‐effective and feasible route is developed to prepare a new Cu/2DSiO2 catalyst with strong metal–support interaction that exhibits ultrahigh stability at high temperatures, which is not enjoyed by the traditional impregnation method.

A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion.