Heterogeneous CNF/MoO3 nanofluidic membranes with tunable surface plasmon resonances for solar-osmotic energy conversion.

Two-dimensional (2D) nanofluidic membranes are competitive candidates for osmotic energy harvesting and have been greatly developed. However, the use of diverse inherent characteristics of 2D nanosheets, such as electronic or optoelectronic properties, to achieve intelligent ion transport, still lacks sufficient exploration. Here, a cellulose nanofiber/molybdenum oxide (CNF/MoO3) heterogeneous nanofluidic membrane with high performance solar-osmotic energy conversion is reported, and how surface plasmon resonances (SPR) regulate selective cation transport is revealed. The SPR of amorphous MoO3 endows the heterogeneous nanofluidic membranes with tunable surface charge and good photothermal conversion. Through DFT calculations and finite element modeling, the regulation of electronic and optoelectronic properties on the surface of materials by SPR and the influence of surface charge density and temperature gradient on ion transport in nanofluidic membranes are demonstrated. By mixing 0.01/0.5 M NaCl solutions using SPR and photothermal effects, the power density can achieve a remarkable value of ≈13.24 W m-2, outperforming state-of-the-art 2D-based nanofluidic membranes. This work first reveals the regulation and mechanism of SPR on ion transport in nanofluidic membranes and systematically studies photon-electron-ion interactions in nanofluidic membranes, which could also provide a new viewpoint for promoting osmotic energy conversion.

A marine bacteria-inspired electrochemical regulation for continuous uranium extraction from seawater and salt lake brine.

Oceans and salt lakes contain vast amounts of uranium. Uranium recovery from natural water not only copes with radioactive pollution in water but also can sustain the fuel supply for nuclear power. The adsorption-assisted electrochemical processes offer a promising route for efficient uranium extraction. However, competitive hydrogen evolution greatly reduces the extraction capacity and the stability of electrode materials with electrocatalytic activity. In this study, we got inspiration from the biomineralisation of marine bacteria under high salinity and biomimetically regulated the electrochemical process to avoid the undesired deposition of metal hydroxides. The uranium uptake capacity can be increased by more than 20% without extra energy input. In natural seawater, the designed membrane electrode exhibits an impressive extraction capacity of 48.04 mg-U per g-COF within 21 days (2.29 mg-U per g-COF per day). Furthermore, in salt lake brine with much higher salinity, the membrane can extract as much uranium as 75.72 mg-U per g-COF after 32 days (2.37 mg-U per g-COF per day). This study provides a general basis for the performance optimisation of uranium capture electrodes, which is beneficial for sustainable access to nuclear energy sources from natural water systems.

Skeleton engineering of rigid covalent organic frameworks to alter the number of binding sites for improved radionuclide extraction.

Crystalline porous covalent frameworks (COFs) have been considered as a platform for uranium extraction from seawater and nuclear waste. However, the role of rigid skeleton and atomically precise structures of COFs is often ignored in the design of defined binding configuration. Here, a COF with an optimized relative position of two bidentate ligands realizes full potential in uranium extraction. Compared with the para-chelating groups, the optimized ortho-chelating groups with oriented adjacent phenolic hydroxyl groups on the rigid skeleton endow an additional uranyl binding site, thereby increasing the total number of binding sites up to 150%. Experimental and theoretical results indicate that the uranyl capture is greatly improved via the energetically favored multi-site configuration and the adsorption capacity reaches up to 640 mg g-1, which exceeds that of most reported COF-based adsorbents with chemical coordination mechanism in uranium aqueous solution. This ligand engineering strategy can efficiently advance the fundamental understanding of designing the sorbent systems for extraction and remediation technology.

Engineering Multi-field-coupled Synergistic Ion Transport System Based on the Heterogeneous Nanofluidic Membrane for High-Efficient Lithium Extraction.

The global carbon neutrality strategy brings a wave of rechargeable lithium-ion batteries technique development and induces an ever-growing consumption and demand for lithium (Li). Among all the Li exploitation, extracting Li from spent LIBs would be a strategic and perspective approach, especially with the low energy consumption and eco-friendly membrane separation method. However, current membrane separation systems mainly focus on monotonous membrane design and structure optimization, and rarely further consider the coordination of inherent structure and applied external field, resulting in limited ion transport. Here, we propose a heterogeneous nanofluidic membrane as a platform for coupling multi-external fields (i.e., light-induced heat, electrical, and concentration gradient fields) to construct the multi-field-coupled synergistic ion transport system (MSITS) for Li-ion extraction from spent LIBs. The Li flux of the MSITS reaches 367.4 mmol m-2 h-1, even higher than the sum flux of those applied individual fields, reflecting synergistic enhancement for ion transport of the multi-field-coupled effect. Benefiting from the adaptation of membrane structure and multi-external fields, the proposed system exhibits ultrahigh selectivity with a Li+/Co2+ factor of 216,412, outperforming previous reports. MSITS based on nanofluidic membrane proves to be a promising ion transport strategy, as it could accelerate ion transmembrane transport and alleviate the ion concentration polarization effect. This work demonstrated a collaborative system equipped with an optimized membrane for high-efficient Li extraction, providing an expanded strategy to investigate the other membrane-based applications of their common similarities in core concepts.

Aberrant Static and Dynamic Functional Brain Network in Depression Based on EEG Source Localization.

OBJECTIVE: Depression is accompanied by abnormalities in large-scale functional brain networks. This paper combined static and dynamic methods to analyze the abnormal topology and changes of functional connectivity network (FCN) of depression. METHODS: We collected resting-state EEG recordings from 27 depressed subjects and 28 normal subjects, then obtained 68 regions of interests (ROIs) by source localization. We took ROIs as the nodes and correlations as the edges to build FCNs and analyzed static network based on graph theory. We used a sliding window method followed by k-means clustering, states analyses and trend analysis of network metrics over time to study dynamic connectivity. RESULTS: The clustering coefficient (CC) and local efficiency in depression were increased, the characteristic path length and global efficiency were decreased, and local metrics had different manifestations in different resting state networks (RSNs); Depression had reduced connectivity in most RSNs, but increased connectivity in the default mode network, and there was a decoupling phenomenon between different RSNs; Depressed patients spent more time in sparsely connected states, their FCN's flexibility was less than normal subjects; The trend of CC over time was opposite between two groups. Most metrics in normal showed a relatively stronger correlation with time. SIGNIFICANCE: Our research may provide a deeper understanding of neurophysiological mechanisms of depression and new biomarkers for clinical diagnosis of depression.

Surfactant-Assisted Sulfonated Covalent Organic Nanosheets: Extrinsic Charge for Improved Ion Transport and Salinity-Gradient Energy Harvesting.

Charge-governed ion transport is the vital property of nanofluidic channels for salinity-gradient energy harvesting and other electrochemical energy conversion technologies. 2D nanofluidic channels constructed by nanosheets exhibit great superiority in ion selectivity, but a high ion transport rate remains challenging due to the insufficiency of intrinsic surface charge density in nanoconfinement. Herein, extrinsic surface charge into nanofluidic channels composed of surfactant-assisted sulfonated covalent organic nanosheets (SCONs), which enable tunable ion transport behaviors, is demonstrated. The polar moiety of surfactant is embedded in SCONs to adjust in-plane surface charges, and the aggregation of nonpolar moiety results in the sol-to-gel transformation of SCON solution for membrane fabrication. The combination endows SCON/surfactant membranes with considerable water-resistance, and the designable extrinsic charges promise fast ion transport and high ion selectivity. Additionally, the SCON/surfactant membrane, serving as a power generator, exhibits huge potential in harvesting salinity-gradient energy where corresponding output power density can reach up to 9.08 W m-2 under a 50-fold salinity gradient (0.5 m NaCl|0.01 m NaCl). The approach to extrinsic surface charge provides new and promising insight into regulating ion transport behaviors.

EEG based depression recognition using improved graph convolutional neural network.

Depression is a global psychological disease that does serious harm to people. Traditional diagnostic method of the doctor-patient communication, is not objective and accurate enough. Thus, a more accurate and objective method for depression detection is urgently needed. Resting-state electroencephalography (EEG) can effectively reflect brain function, which have been used to study the difference of the brain between the depression patients and normal controls. In this work, the Resting-state EEG data of 27 depression patients and 28 normal controls was used in this study. We constructed the brain functional network using correlation, and extracted four linear features of EEG (activity, mobility complexity and power spectral density). We utilized a learnable weight matrix in the input layer of graph convolution neural network, creatively took the brain function network as the adjacency matrix input and the linear feature as the node feature input. We proposed our model Graph Input layer attention Convolutional Network (GICN), and it provided a good performance, showing the accuracy of 96.50% for recognition of depression and normal with 10-fold cross-validation, which indicated that our model could be used as an effective auxiliary tool for depression recognition. Besides, our method significantly outperformed other method. Additionally, the learnable weight matrix in the input layer was also used to find some edges and nodes that played an important role in depression recognition. Our findings showed that temporal lobe and parietal-occipital lobe had great effect in depression identification.

Wetting-Induced Water Promoted Flow on Tunable Liquid-Liquid Interface-Based Nanopore Membrane System.

Membrane separation provides effective methods for solving the global water crisis. Contemporary membrane systems depend on interfacial interactions between liquid and solid membrane matrixes. However, it may lead to a limiting permeate flux due to the large flow resistance at hydrophobic liquid-solid interfaces. Herein, the liquid-liquid interface with improved interface energy is reversibly introduced in membrane systems to boost wetting and reduce transport resistance. A series of interfaces were systematically explored to reveal mechanisms of wetting and boosted flow performances, which are further supported by simulations. Findings of this study highlight that interfacial liquids with lower surface energies, lower viscosities, and higher solubilities can effectively improve water flow without sacrificing rejection performance, achieving by transforming a solid-liquid interface into liquid-liquid interface interaction. It provides a concept to design advanced membrane systems for water purification (e.g., desalination and oil-water separation) and energy conversion processes.

Surface Charge Regulated Asymmetric Ion Transport in Nanoconfined Space.

The asymmetric ion transport in the nanoconfined space, similar to that of natural ion channels, has attracted broad interest in sensor, energy conversion, and other related fields. Among these systems, the surface charge plays an important role in regulating ion transport behaviors. Herein, this surface charge-regulated asymmetric ion transport behavior is systematically explored in the nanoconfined space and the influence on the performance of nanofluidic energy conversion system. The ion transport behaviors in the nanoconfined space are classified into pure diffusion, electrical double layer, and the polarization controlled state. The asymmetric solution environment or surface charge distribution induces asymmetric ion transport behavior which is largely controlled by the low concentration side. The ion-selectivity and the energy conversion performance can be effectively enhanced by improving the local apparent surface charge (more active sites and higher charge strength) or introducing a selective layer with dense surface charge on the low concentration side. These material design concepts for asymmetric ion transport are further supported by both simulation and experiment. The results provide a significant comprehension for ion behaviors in nanoconfined space and the development of high-performance energy storage and conversion systems.