Self-Healing Liquid Metal Magnetic Hydrogels for Smart Feedback Sensors and High-Performance Electromagnetic Shielding

Hydrogels exhibit potential applications in smart wearable devices because of their exceptional sensitivity to various external stimuli.However,their applications are limited by challenges in terms of issues in biocompatibility,custom shape,and self-healing.Herein,a conductive,stretchable,adaptable,self-healing,and biocompatible liquid metal GaInSn/Ni-based composite hydrogel is developed by incorporating a magnetic liquid metal into the hydrogel framework through crosslinking polyvinyl alcohol(PVA)with sodium tetraborate.The excellent stretchability and fast self-healing capability of the PVA/liquid metal hydrogel are derived from its abundant hydrogen binding sites and liquid metal fusion.Significantly,owing to the magnetic constituent,the PVA/liquid metal hydrogel can be guided remotely using an external magnetic field to a specific position to repair the broken wires with no need for manual operation.The composite hydrogel also exhibits sensitive deformation responses and can be used as a strain sensor to monitor various body motions.Additionally,the multifunctional hydrogel displays absorption-dominated electromagnetic interference(EMI)shielding properties.The total shielding performance of the composite hydrogel increases to~62.5 dB from~31.8 dB of the pure PVA hydrogel at the thickness of 3.0 mm.The proposed bioinspired multifunctional magnetic hydrogel demonstrates substantial application potential in the field of intelligent wearable devices.

Direct field-to-pattern monolithic design of holographic metasurface via residual encoderdecoder convolutional neural network

Complex-amplitude holographic metasurfaces(CAHMs)with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level,leading to higher image-reconstruction quality compared with their natural counterparts.However,prevailing design methods of CAHMs are based on Huygens-Fresnel theory,meta-atom optimization,numerical simulation and experimental verification,which results in a consumption of computing resources.Here,we applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design.A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory,which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images.The training results show that the normalized mean pixel error is about 3%on dataset.As verification,the metasurface prototypes are fabricated,simulated and measured.The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field,which demonstrates the effectiveness of our design.Encouragingly,this work provides a monolithic field-to-pattern design method for CAHMs,which paves a new route for the direct reconstruction of metasurfaces.

Hollow FeCoNiAl microspheres with stabilized magnetic properties for microwave absorption

Development of high-performance microwave absorption materials(MAM)with stabilized magnetic properties at high temperatures is specifically essential but remains challenging.Moreover,the Snoke's limitation restrains the microwave absorption(MA)property of magnetic materials.Modulating alloy components is considered an effective way to solve the aforementioned problems.Herein,a hollow medium-entropy FeCoNiAl alloy with a stable magnetic property is prepared via simple spray-drying and two-step annealing for efficient MA.FeCoNiAl exhibited an ultrabroad effective absorption band(EAB)of 5.84 GHz(12.16–18 GHz)at a thickness of just 1.6 mm,revealing an excellent absorption capability.Furthermore,the MA mechanism of FeCoNiAl is comprehensively investigated via off-axis holography.Finally,the electromagnetic properties,antioxidant properties,and residual magnetism at high temperatures of FeCoNiAl alloys are summarized in detail,providing new insights into the preparation of MAM operating at elevated temperatures.

Highly efficient flexible perovskite solar cells with vacuum-assisted low-temperature annealed SnO2 electron transport layer

The demand for lightweight, flexible, and high-performance portable power sources urgently requires high-efficiency and stable flexible solar cells. In the case of perovskite solar cells(PSCs), most of the common electron transport layer(ETL) needs to be annealed for improving the optoelectronic properties,while conventional flexible substrates could barely stand the high temperature. Herein, a vacuumassisted annealing SnO_(2) ETL at low temperature(100℃) is utilized in flexible PSCs and achieved high efficiency of 20.14%. Meanwhile, the open-circuit voltage(V_(oc)) increases from 1.07 V to 1.14 V. The flexible PSCs also show robust bending stability with 86.8% of the initial efficiency is retained after 1000 bending cycles at a bending radius of 5 mm. X-ray photoelectron spectroscopy(XPS), atomic force microscopy(AFM), and contact angle measurements show that the density of oxygen vacancies, the surface roughness of the SnO_(2) layer, and film hydrophobicity are significantly increased, respectively. These improvements could be due to the oxygen-deficient environment in a vacuum chamber, and the rapid evaporation of solvents. The proposed vacuum-assisted low-temperature annealing method not only improves the efficiency of flexible PSCs but is also compatible and promising in the large-scale commercialization of flexible PSCs.

Stabilizing α-phase FAPbI3 solar cells

Organic–inorganic hybrid perovskite solar cells(PSCs)have been recognized as a promising and cost-effective photovoltaic technology with the power conversion efficiency(PCE) exceeding 25%[1–3]. The high efficiency is attributed to the exceptional optoelectronic properties, such as high absorption coefficient, long carrier diffusion length, low non-radiative recombination rate, and so on[4–7].

Numerical Study on Comparison of Negative and Positive Surface Discharge in c-C_(4)F_(8)/CF_(3)I/CO_(2) Gas Mixture

The dynamics of negative surface discharges in c-C_(4)F_(8)/CF_(3)I/CO_(2) gas mixture is investigated here with a 2D fuid model.The distributions of ion concentration,electric field strength and photon flux during the propagation of the streamer are obtained by solving the drift-diffusion equations of particles and Poisson's equation,and the photon flux variation function during the propagation is also fitted.It is found that the streamer branches occur when the streamer transitions from the upper surface of the insulator to the side surface,and then when the streamer approaches the plane electrode,the photon flux will increase significantly.On this basis,the positive and negative surface discharge models are compared in terms of streamer characteristics,particle characteristics and streamer branches.It is found that the streamer has a higher electron concentration and electric field in the positive model.The streamer develops“floating”in the positive surface discharge,while it is close to the surface of the insulator in the negative model.In addition,the negative streamer branch has a wider width and develops further.

Picoliter droplet array based on bioinspired microholes for in situ single-cell analysis

The division of aqueous samples into microdroplet arrays has many applications in biochemical and medical analysis.Inspired by biological features,we propose a method to produce picoliter droplet arrays for single-cell analysis based on physical structure and interface.A 0.9 pL droplet array with an RSD(relative standard deviation)less than 6.3%and a density of 49,000 droplets/cm^(2) was successfully generated on a PDMS chip(polydimethylsiloxane)from a micromachined glass mold.The droplet generation principle of the wetting behavior in the microholes with splayed sidewalls on the PDMS chip by liquid smearing was exploited.The feasibility of the picoliter droplets for bacterial single-cell analysis was verified by the separation of mixed bacteria into single droplets and isolated in situ bacteria propagation.

Prognostics of radiation power degradation lifetime for ultraviolet light-emitting diodes using stochastic data-driven models

With their advantages of high efficiency,long lifetime,compact size and being free of mercury,ultraviolet light-emitting diodes(UV LEDs)are widely applied in disinfection and purification,photolithography,curing and biomedical devices.However,it is challenging to assess the reliability of UV LEDs based on the traditional life test or even the accelerated life test.In this paper,radiation power degradation modeling is proposed to estimate the lifetime of UV LEDs under both constant stress and step stress degradation tests.Stochastic data-driven predic-tions with both Gamma process and Wiener process methods are implemented,and the degradation mechanisms occurring under different aging conditions are also analyzed.The results show that,compared to least squares regression in the IESNA TM-21 industry standard recommended by the Illuminating Engineering Society of North America(IESNA),the proposed stochastic data-driven methods can predict the lifetime with high accuracy and narrow confidence intervals,which confirms that they provide more reliable information than the IESNA TM-21 standard with greater robustness.

A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children

Background:Axial myopia is the most common type of myopia.However,due to the high incidence of myopia in Chinese children,few studies estimating the physiological elongation of the ocular axial length(AL),which does not cause myopia progression and differs from the non-physiological elongation of AL,have been conducted.The purpose of our study was to construct a machine learning(ML)-based model for estimating the physiological elongation of AL in a sample of Chinese school-aged myopic children.Methods:In total,1011 myopic children aged 6 to 18 years participated in this study.Cross-sectional datasets were used to optimize the ML algorithms.The input variables included age,sex,central corneal thickness(CCT),spherical equivalent refractive error(SER),mean K reading(K-mean),and white-to-white corneal diameter(WTW).The output variable was AL.A 5-fold cross-validation scheme was used to randomly divide all data into 5 groups,including 4 groups used as training data and one group used as validation data.Six types of ML algorithms were implemented in our models.The best-performing algorithm was applied to predict AL,and estimates of the physiological elongation of AL were obtained as the partial derivatives of AL_(predicted)-age curves based on an unchanged SER value with increasing age.Results:Among the six algorithms,the robust linear regression model was the best model for predicting AL,with a R^(2) value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL.Based on the partial derivatives of the AL_(predicted)-age curves,the estimated physiological AL elongation varied from 0.010 to 0.116 mm/year in male subjects and 0.003 to 0.110 mm/year in female subjects and was influenced by age,SER and K-mean.According to the model,the physiological elongation of AL linearly decreased with increasing age and was negatively correlated with the SER and the K-mean.Conclusions:The physiological elongation of the AL is rarely recorded in clinical data in China.In cases of unavailable clinical dat,an ML algorithm could provide practitioners a reasonable model that can be used to estimate the physiological elongation of AL,which is espedally useful when monitoring myopia progression in orthokeratology lens wearers.

利用CRISPR/Cas9技术在活体青年猕猴脑内进行基因编辑诱导其出现孤独症谱系障碍的行为表型

尽管CRISPR/Cas9基因编辑技术被广泛应用于人类疾病建模工作,但此前并无人利用该技术在非人灵长类脑内通过对疾病相关基因的编辑诱导出猕猴的行为表型.本研究利用AAV介导的CRISPR/Cas9技术,对活体猕猴海马脑区中与孤独症谱系障碍(ASD)和瑞特综合症(RTT)发病密切相关的MECP2基因进行编辑,基因编辑组猕猴(n=7)与其自身基因编辑前作为对照,同时与注射空载病毒的对照组猕猴(n=4)相比,其在基因编辑后出现了部分与孤独症临床症状十分相似的行为学改变,包括社交障碍、睡眠节律紊乱、对外界刺激的迟钝和手部精细运动受损等.可是另一些典型的孤独症症状,比如刻板运动,又没有在这些基因编辑猴中被观察到.以上现象提示不同的脑区在该疾病中可能扮演不同的角色.本研究利用上述技术能够快速高效地在猕猴上复制出相关疾病的症状,也为今后探索快速成模的转基因工程猕猴疾病模型奠定了基础.此类猕猴模型将为脑疾病的机理研究以及治疗作出重要贡献.

Fluo-Fluo translation based on deep learning

Fluorescence microscopy technology uses fluorescent dyes to provide highly specific visualization of cell components,which plays an important role in understanding the subcellular structure.However,fluorescence microscopy has some limitations such as the risk of non-specific cross labeling in multi-labeled fluorescent staining and limited number of fluo-rescence labels due to spectral overlap.This paper proposes a deep learning-based fluorescence to fluorescence[Flu0-Fluo]translation method,which uses a conditional generative adversarial network to predict a fluorescence image from another fluorescence image and further realizes the multi-label fluorescent staining.The cell types used include human motor neurons,human breast cancer cells,rat cortical neurons,and rat cardiomyocytes.The effectiveness of the method is verified by successfully generating virtual fluorescence images highly similar to the true fluorescence images.This study shows that a deep neural network can implement Fluo-Fluo translation and describe the localization relationship between subcellular structures labeled with different fluorescent markers.The proposed Fluo-Fluo method can avoid non-specific cross labeling in multi-label fluorescence staining and is free from spectral overlaps.In theory,an unlimited number of fluorescence images can be predicted from a single fluorescence image to characterize cells.