Photothermal superhydrophobic copper nanowire assemblies: fabrication and deicing/defrosting applications

Ice and frost buildup continuously pose significant challenges to multiple fields.As a promising de-icing/defrosting alternative,designing photothermal coatings that leverage on the abundant sunlight source on the earth to facilitate ice/frost melting has attracted tremendous attention recently.However,previous designs suffered from either localized surface heating owing to the limited thermal conductivity or unsatisfied meltwater removal rate due to strong water/substrate interaction.Herein,we developed a facile approach to fabricate surfaces that combine photothermal,heat-conducting,and superhydrophobic properties into one to achieve efficient de-icing and defrosting.Featuring copper nanowire assemblies,such surfaces were fabricated via the simple template-assisted electrodeposition method,allowing us to tune the nanowire assembly geometry by adjusting the template dimensions and electrodeposition time.The highly ordered copper nanowire assemblies facilitated efficient sunlight absorption and lateral heat spreading,resulting in a fast overall temperature rise to enable the thawing of ice and frost.Further promoted by the excellent water repellency of the surface,the thawed ice and frost could be spontaneously and promptly removed.In this way,the all-in-one design enabled highly enhanced de-icing and defrosting performance compared to other nanostructured surfaces merely with superhydrophobicity,photothermal effect,or the combination of both.In particular,the defrosting efficiency could approach∼100%,which was the highest compared to previous studies.Overall,our approach demonstrates a promising path toward designing highly effective artificial deicing/defrosting surfaces.

Different types of drug abusers prefrontal cortex activation patterns and based on machine-learning classification

Drug addiction can cause abnormal brain activation changes,which are the root cause of drug craving and brain function errors.This study enrolled drug abusers to determine the effects of different drugs on brain activation.A functional near-infrared spectroscopy(fNIRS)device was used for the research.This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings.We collected the fNIRS data of 30 drug users,including 10 who used heroin,10 who used Methamphetamine,and 10 who used mixed drugs.First,using Statistical Analysis,the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin,Methamphetamine,and mixed drugs,including Left/Right-Dorsolateral prefrontal cortex(L/R-DLPFC),Left/Right-Ventrolateral prefrontal cortex(L/R-VLPFC),Left/Right-Fronto-polar prefrontal cortex(L/R-FPC),and Left/Right Orbitofrontal Cortex(L/R-OFC).Second,referencing the degrees of activation of oxyhaemoglobin concentration(HbO2),the study made an analysis and got the specific activation patterns of each group of the addicts.Finally,after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts,and which had the same channel numbers,the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks(CNNs).The average three-class accuracy is 67.13%.It is of great significance for the analysis of brain function errors and personalized rehabilitation.

BCI+VR Rehabilitation Design of Closed-Loop Motor Imagery Based on the Degree of Drug Addiction

After abusing drugs for long,drug users will experience deteriorated self-control cognitive ability,and poor emotional regulation.This paper designs a closed-loop virtual-reality(VR),motorimagery(MI)rehabilitation training system based on brain-computer interface(BCI)(MI-BCI+VR),aiming to enhance the self-control,cognition,and emotional regulation of drug addicts via personalized rehabilitation schemes.This paper is composed of two parts.In the first part,data of 45 drug addicts(mild:15;moderate:15;and severe:15)is tested with electroencephalogram(EEG)and near-infrared spectroscopy(NIRS)equipment(EEG-NIRS)under the dual-mode,synchronous signal collection paradigm.Using these data sets,a dual-modal signal convolutional neural network(CNN)algorithm is then designed based on decision fusion to detect and classify the addiction degree.In the second part,the MIBCI+VR rehabilitation system is designed,optimizing the Filter Bank Common Spatial Pattern(FBCSP)algorithm used in MI,and realizing MI-EEG intention recognition.Eight VR rehabilitation scenes are devised,achieving the communication between MI-BCI and VR scene models.Ten subjects are selected to test the rehabilitation system offline and online,and the test accuracy verifies the feasibility of the system.In future,it is suggested to develop personalized rehabilitation programs and treatment cycles based on the addiction degree.

Namib desert beetle inspired special patterned fabric with programmable and gradient wettability for efficient fog harvesting

Efficient collection of water from fog provides a potential solution to solve the global freshwater shortage problem, particularly in the desert or arid regions. In this work, a flexible and highly efficient fog collector was prepared by mimicking the back exoskeleton structure of the Namib desert beetle. The improved fog collector was constructed by a superhydrophobic-superhydrophilic patterned fabric via a simple weaving method, followed by in-situ deposition of copper particles. Compared with the conventional fog collector with a plane structure, the fabric has shown a higher water-harvesting rate at 1432.7 mg/h/cm2,owing to the biomimetic three-dimensional structure, its enhanced condensation performance enabled by the copper coating and the rational distribution of wetting units. The device construction makes use of the widely available textile materials through mature manufacturing technology, which makes it highly suitable for large-scale industrial production.