Graphene Array-Based Anti-fouling Solar Vapour Gap Membrane Distillation with High Energy Efficiency

Photothermal membrane distillation(MD)is a promising technology for desalination and water purification.However,solar-thermal conversion suffers from low energy efficiency(a typical solar-water efficiency of ~50%),while complex modifications are needed to reduce membrane fouling.Here,we demonstrate a new concept of solar vapour gap membrane distillation(SVGMD)synergistically combining self-guided water transport,localized heating,and separation of membrane from feed solution.A free-standing,multifunctional light absorber based on graphene array is custom-designed to locally heat the thin water layer transporting through graphene nanochannels.The as-generated vapour passes through a gap and condenses,while salt/contaminants are rejected before reaching the membrane.The high solar-water efficiency(73.4% at 1 sun),clean water collection ratio(82.3%),excellent anti-fouling performance,and stable permeate flux in continuous operation over 72 h are simultaneously achieved.Meanwhile,SVGMD inherits the advantage of MD in microorganism removal and water collection,enabling the solar-water efficiency 3.5 times higher compared to state-of-the-art solar vapour systems.A scaled system to treat oil/seawater mixtures under natural sunlight is developed with a purified water yield of 92.8 kg m-2 day-1.Our results can be applied for diverse mixed-phase feeds,leading to the next-generation solar-driven MD technology.

ASCFL:Accurate and Speedy Semi-Supervised Clustering Federated Learning

The influence of non-Independent Identically Distribution(non-IID)data on Federated Learning(FL)has been a serious concern.Clustered Federated Learning(CFL)is an emerging approach for reducing the impact of non-IID data,which employs the client similarity calculated by relevant metrics for clustering.Unfortunately,the existing CFL methods only pursue a single accuracy improvement,but ignore the convergence rate.Additionlly,the designed client selection strategy will affect the clustering results.Finally,traditional semi-supervised learning changes the distribution of data on clients,resulting in higher local costs and undesirable performance.In this paper,we propose a novel CFL method named ASCFL,which selects clients to participate in training and can dynamically adjust the balance between accuracy and convergence speed with datasets consisting of labeled and unlabeled data.To deal with unlabeled data,the prediction labels strategy predicts labels by encoders.The client selection strategy is to improve accuracy and reduce overhead by selecting clients with higher losses participating in the current round.What is more,the similarity-based clustering strategy uses a new indicator to measure the similarity between clients.Experimental results show that ASCFL has certain advantages in model accuracy and convergence speed over the three state-of-the-art methods with two popular datasets.

Cost-effective,environmentally-sustainable and scale-up synthesis of vertically oriented graphenes from waste oil and its supercapacitor applications

Vertically oriented graphenes(VGs)have attracted tremendous attention in a variety of energy storage-related applications.However,the high cost of preparing VGs significantly hinders their practical applications.Herein we introduce the Ar-plasma-enhanced chemical vapor deposition to demonstrate the cost-effective,environmentally-sustainable,and scale-up synthesis of VGs from waste oil.In our system,Ar gas can improve the electron energy and ionization rate of plasma,which breaks down the chemical bonding of waste oil into essential species to etch the amorphous carbon,yielding large-area VGs(12×3.5 cm^(2))with highly-oriented structure and superior growth efficiency beyond VGs from hydrocarbon precursors.In the supercapacitor applications,the VG-based electrode exhibits significantly enhanced capacitance(~4 times that from conventional hydrocarbon gases)and efficient AC(alternating current)filtering capability RC(resistor-capacitor)(time constant of of 163μs at 120 Hz),which is obviously superior to the non-oriented counterpart.Besides,MnO_(2)/VGs composite electrode is prepared to deliver a maximum energy density of~33.2 Wh/kg at 1.0 kW/kg and a power density of 10.2 kW/kg at 22.9 Wh/kg.In the end,the economic analysis suggests that the total cost of VGs can be reduced by~32%.This work provides an environment-friendly and low-cost avenue for preparing VGs for advanced energy storage applications.