An integrative method for evaluating the biological effects of nanoparticle-protein corona.

BACKGROUND: Nanoplastics in the environment can enter the human body through gastrointestinal intake, dermal contact, and pulmonary inhalation, posing a threat to human health. Protein molecules in body fluids will quickly adsorb on the surfaces of the nanoplastics, forming a protein corona, which has implications for the interaction of the nanoplastics with cells and the metabolic pathways of the nanoplastic within cells. For years, practical tools such as dynamic light scattering, transmission electron microscopy, and liquid chromatography have been developed to understand the protein corona of nanoparticles (NPs), either in vitro or in cellular or molecular level. However, an integrated approach to understand the nanoparticles-protein corona is still lacking. METHODS: Using the most frequently observed environmental nanoplastics, polystyrene nanoplastics (PS), as a standard, we established an integrative structural characterization platform, a biophysical and biochemical evaluation method to investigate the effect of surface charge on protein corona composition. The cellular and molecular mechanisms were also explored through in vitro cellular experiments. RESULTS: The first integrative method for characterizing biological properties of NPs-protein corona has been established. This method comprehensively covers the critical aspects to understand NPs-protein corona interactions, from structure to function. CONCLUSIONS: The integrative method for nanoplastics microstructure characterization can be applied to the structural characterization of nanoparticles in nanoscale, which is of universal significance from in vitro characterization to cellular experiments and then to molecular mechanism studies. GENERAL SIGNIFICANCE: This strategy has high reliability and repeatability and can be applied both in environment and nanomedicine safety assessment.

Polydopamine functionalized graphene oxide membrane with the sandwich structure for osmotic energy conversion.

Ion-selective membrane is the key component for osmotic energy conversion. Nanofluid channels based on two-dimensional materials have advantages of facile preparation, tunable channel size, and easy upscaling, which is promising for efficient osmotic energy harvesting. However, further improvement of the output power is hindered by the low ion sensitivity for the limited charge density. Herein, we demonstrate the preparation of a cation-selective polydopamine-coated graphene oxide composite membrane with the sandwich structure by a simple interfacial polymerization technique, which greatly improves the surface charge density and further generates a power density of 3.4 W/m2 under river water and seawater. The GO membrane is firstly fabricated to function as the supporting layer and provide the reaction sites. And the ultrathin selective layer of the polydopamine membrane is chemically bonded with the GO layer by the in-situ polymerization on both sides of the GO membrane. The sandwiched nanofluidic membrane with ultrahigh charge density exhibits both high cation selectivity and ionic conductivity, benefiting the performance of osmotic energy conversion. The economic, easy-prepared method of the sandwiched nanofluidic membrane provides a promising strategy for high-performance osmotic energy conversion.

Well-Defined Nanostructures by Block Copolymers and Mass Transport Applications in Energy Conversion.

With the speedy progress in the research of nanomaterials, self-assembly technology has captured the high-profile interest of researchers because of its simplicity and ease of spontaneous formation of a stable ordered aggregation system. The self-assembly of block copolymers can be precisely regulated at the nanoscale to overcome the physical limits of conventional processing techniques. This bottom-up assembly strategy is simple, easy to control, and associated with high density and high order, which is of great significance for mass transportation through membrane materials. In this review, to investigate the regulation of block copolymer self-assembly structures, we systematically explored the factors that affect the self-assembly nanostructure. After discussing the formation of nanostructures of diverse block copolymers, this review highlights block copolymer-based mass transport membranes, which play the role of "energy enhancers" in concentration cells, fuel cells, and rechargeable batteries. We firmly believe that the introduction of block copolymers can facilitate the novel energy conversion to an entirely new plateau, and the research can inform a new generation of block copolymers for more promotion and improvement in new energy applications.

Construction and Ion Transport-Related Applications of the Hydrogel-Based Membrane with 3D Nanochannels.

Hydrogel is a type of crosslinked three-dimensional polymer network structure gel. It can swell and hold a large amount of water but does not dissolve. It is an excellent membrane material for ion transportation. As transport channels, the chemical structure of hydrogel can be regulated by molecular design, and its three-dimensional structure can be controlled according to the degree of crosslinking. In this review, our prime focus has been on ion transport-related applications based on hydrogel materials. We have briefly elaborated the origin and source of hydrogel materials and summarized the crosslinking mechanisms involved in matrix network construction and the different spatial network structures. Hydrogel structure and the remarkable performance features such as microporosity, ion carrying capability, water holding capacity, and responsiveness to stimuli such as pH, light, temperature, electricity, and magnetic field are discussed. Moreover, emphasis has been made on the application of hydrogels in water purification, energy storage, sensing, and salinity gradient energy conversion. Finally, the prospects and challenges related to hydrogel fabrication and applications are summarized.

Parallel information processing by a reservoir computing system based on a VCSEL subject to double optical feedback and optical injection.

In this work, we propose a scheme of reservoir computing (RC) for processing a Santa-Fe time series prediction task and a signal classification task in parallel, and the performances of the RC have been numerically investigated. For this scheme, a vertical-cavity surface-emitting laser (VCSEL) simultaneously subject to double optical feedback and optical injection is utilized as a nonlinear node, and the parallel information processing of the RC system is implemented based on the dynamical responses of X polarization component (X-PC) and Y polarization component (Y-PC) in the VCSEL. Considering that two different feedback frames (polarization-preserved optical feedback (PP-OF) or polarization-rotated optical feedback (PR-OF)) may be adopted in two feedback loops, four feedback combination cases are numerically analyzed. The simulated results show that the parallel processing ability of the proposed RC system depends on the feedback frames adopted in two loops. After comprehensively evaluating the parallel processing performances of the two tasks under different feedback combinations, the best parallel processing performance can be achieved by adopting PP-OFs in both two feedback loops. Under some optimized operation parameters, this proposed RC system can realize the lowest prediction error of 0.0289 and the lowest signal classification error of 2.78 × 10-5.

Performance optimization research of reservoir computing system based on an optical feedback semiconductor laser under electrical information injection.

Via Santa Fe time series prediction and nonlinear channel equalization tasks, the performances of a reservoir computing (RC) system based on an optical feedback semiconductor laser (SL) under electrical information injection are numerically investigated. The simulated results show that the feedback delay time and strength seriously affect the performances of this RC system. By adopting a current-related optimized feedback delay time and strength, the RC can achieve a good performance for an SL biased within a wide region of 1.1-3.5 times its threshold. The prediction errors are smaller than 0.01 when implementing the Santa Fe tests, and the symbol error rates (SERs) are very low on the order of 10-5 for accomplishing nonlinear channel equalization tests under a signal-to-noise ratio (SNR) of 32 dB. Moreover, under a given RC performance level, the information processing rate of the RC can be improved by increasing the SL current.

Experimental investigation on the nonlinear dynamics of two mutually coupled 1550 nm multi-transverse-mode vertical-cavity surface-emitting lasers.

We experimentally investigate the nonlinear dynamics of two mutually coupled 1550 nm multi-transverse-mode vertical-cavity surface-emitting lasers (VCSELs). The results show that, through continuously varying the coupling coefficient, the Y-polarization fundamental transverse mode and the Y-polarization first-order transverse mode in both VCSELs can be driven into period one, period doubling, multi-period, and chaos states. When the two mutually coupled VCSELs are simultaneously operating in the periodic state, localized synchronizations between the corresponding modes are observed. Moreover, mappings of dynamical states for typical transverse modes of the two mutually coupled VCSELs in the parameter space of the frequency detuning and coupling coefficient are specified.

Prediction performance of reservoir computing system based on a semiconductor laser subject to double optical feedback and optical injection.

A reservoir computing (RC) system based on a semiconductor laser (SL) with double optical feedback and optical injection is proposed, and the prediction performance of such a system is numerically investigated via Santa Fe Time-Series Prediction task. The simulation results indicate that the RC system can yield a good prediction performance. Through optimizing some relevant operating parameters, ultra-fast information processing rates up to Gb/s level can be realized for the prediction error is below 3%.

[The effects of nitrogenous fertilizer on carbon metabolism characteristics of Glehnia littoralis].

The effects of different nitrogenous fertilizer on carbon metabolism in Glehnia littoralis were studied under the field condition. The results showed that the Sucrose Phosphat Synthase (SPS) activities and the content of soluble sugar in leaves showed the pattern of single peak curve during the growth period, and both highest level were similary appeared in the middle stage. The suitable rate of nitrogenous fertilizer can improve the SPS activities, the content of soluble sugar, the root Sucrose Synthase (SS) activities, and also kept low level of leaves soluble sugar in harvest. So it can be supply sufficient for assimilation of polysaccharide in the root as well as to increase the yield.