Display omitted
•The natural GO-Ag-L.l. substrate was fabricated with a facile and low-cost approach.•Microstructures of the Lotus leaf were replicated using magnetron sputtering system.•The SERS ...substrate obtains the well antioxidant stability with the assist of GO.•Ag NPs were assembled in a island-like pattern to create electromagnetic hot spots.•The SERS substrate obtains the excellent reproducibility (RSD < 7.09%).
A stable biomimetic surface enhanced Raman scattering (SERS) system was fabricated by covering graphene oxide (GO) on the Ag micro-islands substrate using micro/nanostructured Lotus leaf (L.l.) as a template via the simple magnetron sputtering system and dip-coated method. The Raman signals of crystal violet (CV) on the GO-Ag-L.l.-30 substrate (the sputtering time was 30 min) showed far better SERS performances than Ag-L.l.-30 hybrids in terms of signal enhancement, sensitivity and stability. The results suggested that the SERS activity of the bio-inspired GO-Ag-L.l.-30 substrate using CV as probe molecules showed an enhancement factor (EF) of ∼1.52 × 106. Sensitivity tests indicated that the limit of detection (LOD) for CV was as low as 5 × 10−10 M, which was two orders of magnitude lower than Ag-L.l.-30 substrate. Time-stability for 30 days was also reported which revealed that the Raman intensity of CV on GO-Ag-L.l.-30 substrate only reduced by 18.2% after aging for 30 days. Moreover, the maximum relative standard deviations (RSD) of Raman intensities was less than 7.09%, demonstrating outstanding reproducibility and uniformity of GO-Ag-L.l.-30 substrate. Therefore, the cost-efficient and high-performance SERS system shows great application prospects in biochemical sensing and attracts broad attention to many other natural materials which can be prepared as multifarious novel SERS systems.
Separation is one of the most energy-intensive processes in the chemical industry, and membrane-based separation technology helps to reduce the energy consumption dramatically. Supported ...metal–organic framework (MOF) layers hold great promise as a molecular sieve membrane, yet only a few MOF membranes showed the expected separation performance. The main reasons include e.g. nonselective grain boundary transport or the flexible MOF framework, especially the inevitable linker rotation. Here, we propose a crystal engineering strategy that balances the grain boundary structure and framework flexibility in Co–Zn bimetallic zeolitic imidazolate framework (ZIF) membranes and exploit their contributions to the improvement of membrane quality and separation performance. It reveals that a good balance between the two trade-off factors enabled a “sweet spot” that offers the best C3H6/C3H8 separation factor up to 200.
Metal–organic framework (MOF) membranes have enormous potential in separation applications. There are several MOF membranes grown on polymer substrates aimed for scale-up, but their brittleness ...hampers any industrial application. Herein, intergrown continuous polypropylene (PP)-supported ZIF-8 membranes have been successfully synthesized via fast current-driven synthesis (FCDS) within 1 h. The PP-supported ZIF-8 membranes exhibit a promising separation factor of 122 ± 13 for binary C3H6–C3H8 mixtures combined with excellent flexibility behavior. The C3H6/C3H8 separation performance of the PP-supported ZIF-8 membrane was found to be constant after bending the supported ZIF-8 film with a curvature of 92 m–1. This outstanding mechanical property is crucial for practical applications. Moreover, we further synthesized ZIF-8 membranes on various polymer substrates and even polymer hollow fibers to demonstrate the production scalability.
Cancer expression of PD-L1 suppresses anti-tumor immunity. PD-L1 has emerged as a remarkable therapeutic target. However, the regulation of PD-L1 degradation is not understood. Here, we identify ...several compounds as inducers of PD-L1 degradation using a high-throughput drug screen. We find EGFR inhibitors promote PD-L1 ubiquitination and proteasomal degradation following GSK3α-mediated phosphorylation of Ser279/Ser283. We identify ARIH1 as the E3 ubiquitin ligase responsible for targeting PD-L1 to degradation. Overexpression of ARIH1 suppresses tumor growth and promotes cytotoxic T cell activation in wild-type, but not in immunocompromised mice, highlighting the role of ARIH1 in anti-tumor immunity. Moreover, combining EGFR inhibitor ES-072 with anti-CTLA4 immunotherapy results in an additive effect on both tumor growth and cytotoxic T cell activation. Our results delineate a mechanism of PD-L1 degradation and cancer escape from immunity via EGFR-GSK3α-ARIH1 signaling and suggest GSK3α and ARIH1 might be potential drug targets to boost anti-tumor immunity and enhance immunotherapies.
In order to solve the problem of frequency instability of power system due to strong random disturbance caused by large-scale electric vehicles and wind power grid connection, an improved ...reinforcement learning algorithm, namely, optimistic initialized double Q, is proposed in this article from the perspective of automatic generation control. The proposed algorithm uses the optimistic initialization principle to expand the agent action exploration space, so as to prevent Q-learning from falling into local optimum by greedy strategy; meanwhile, it integrates double Q-learning to solve the problem of overestimation of action value in traditional reinforcement learning based on Q-learning. In the algorithm, the hyperparameter <inline-formula> <tex-math notation="LaTeX">\alpha _{\tau } </tex-math></inline-formula> is introduced to improve the learning efficiency, and the reward <inline-formula> <tex-math notation="LaTeX">b_{\tau } </tex-math></inline-formula> based on exploration times is introduced to increase the <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> value estimation to drive the exploration of the algorithm, so as to obtain the optimal solution. By simulating the two-area load frequency control model integrated with large-scale electric vehicles and the four-area interconnected power grid model integrated with large-scale wind power generation, it is verified that the proposed algorithm can obtain the global optimal solution, thus effectively solvinng the frequency instability caused by strong random disturbance in the grid-connected mode of large-scale wind power generation, and compared with many reinforcement learning algorithms, the proposed algorithm has better control performance.
In order to enhance the drilling sealing effect and improve the gas drainage efficiency of coal mines, this paper combines material composite mechanism, microcapsule technology, and expansion ...mechanism to produce efficient drilling sealing materials. Aluminum powder is the main driving force for the expansion of the sealing material and the expansion effect reached a maximum at an addition amount of 0.15%. The use of microcapsule technology to treat the expansive agent can delay the time during which the expansive agent works, so that it expands after the material is at a suitable strength, reduces the loss of expansion energy caused by premature expansion, and achieves coordinated development of expansion-strength. Polymer has good flexibility and cohesiveness. It can fully adapt to the change of particles between cement and mortar drying process. It exists in the cement stone space in a continuous network structure.
Display omitted
•Water has an effect on the colloidal concentration and bond strength of the material.•Polymers exist in the material in a continuous network structure to block the voids and improve the compactness.•Microencapsulation reduces the negative effect of aluminum expansion agent.
In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect ...of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect ...of traditional Chinese medicine during Corona Virus Disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
Display omitted
•The sources of multi-source data of medicinal plants and the strategies for processing multi-source data are summarized.•This paper summarizes several machine learning algorithms commonly used to analyze multi-source data of medicinal plants.•This paper summarizes the application of machine learning combined with multi-source data in medicinal plants in recent years, and prospects the development of this field in the future.
Ecological risks of heavy metals in urban soils were evaluated using Beijing, China as an example. Cadmium, Cu, Zn, Pb, Cr and Ni contents of 233 surface soils sampled by 1 min latitude × 1 min ...longitude grid were used to identify their spatial distribution patterns and potential emission sources. Throughout the city, longer the duration of urbanization greater was the accumulations of heavy metals especially, Cd, Cu, Pb, and Zn. The soil Zn mainly came from the wears of vehicular tires. Point source emissions of heavy metals were few and far in the downwind south–east quadrant of Beijing. The calculated risk indices showed potential median eco-risks in the ancient central city. No potential high eco-risk due to soil-borne heavy metals was found. The potential medium eco-risk areas in Beijing would expand from the initial 24 to 110 km
2 if soil pH were to reduce by 0.5 units in anticipation.
► Longer the time of urbanization, greater heavy metal accumulations were in the soils. ► Point source emissions of heavy metals are few in Beijing urban areas. ► The Zn enrichments in urban soils were caused by vehicle tires wearing. ► No high eco-risk areas were observed in Beijing. ► The decrease of soil pH will cause the expansion of medium eco-risk areas in Beijing.
Spatial distributions and potential eco-risks of soil-borne heavy metals in Beijing.
PDF
