Adaptive Thermal Management Radiative Cooling Smart Window with Perfect Near-Infrared Shielding.

The architectural window with spectrally selective features and radiative cooling is an effective way to save building energy consumption. However, architectural windows that combine both functions are currently based on micro-nano photonic structures, which undoubtedly hinder their commercial application due to the complexity of manufacture. Herein, a novel tunable visible light transmittance radiative cooling smart window (TTRC smart window) with perfect near-infrared (NIR) shielding ability is manufactured via a mass-producible scraping method. TTRC smart window presents high luminous transmittance (Tlum = 56.8%), perfect NIR shielding (TNIR = 3.4%), bidirectional transparency adjustment ability unavailable in other transparent radiative coolers based on photonic structures (ΔTlum = 54.2%), and high emittance in the atmospheric window (over 94%). Outdoor measurements confirm that smart window can reduce 8.2 and 6.6 °C, respectively, compared to ordinary glass and indium tin oxide (ITO) glass. Moreover, TTRC smart window can save over 20% of annual energy in the tropics compared to ITO and ordinary glass. The simple preparation method employed in this work and the superior optical properties of the smart window have significantly broadened the scope of application of architectural windows and advanced the commercialization of architectural windows.

Combining ab initio and ab initio molecular dynamics simulations to predict the complex refractive indices of organic polymers.

Organic polymers have attracted widespread interest in various fields ranging from optic and optoelectronic devices to optical system design owing to their light weight, high machinability, excellent thermal performance and reasonable costs. The complex refractive index is an inherent property of organic polymers and directly affects the accuracy of optical system simulation. This study introduces a theoretical protocol to accurately predict the complex refractive indices of organic polymers in the 0-5000 cm-1 region for guiding the discovery and design of high-refractive index materials. In the proposed protocol, we computed the refractive indices of polymers with different monomer units using ab initio calculated static polarizability and mass density obtained by classical isothermal-isobaric ensemble simulations based on the Lorentz-Lorenz equation; we proposed a "Polymer Polarizability Fragment Segmentation" method to extrapolate the polarizabilities of polymers with longer chain lengths; meanwhile, the imaginary part of the dielectric functions of the polymers was calculated using the ab initio molecular dynamics (AIMD) method, and the real part of the dielectric functions was obtained using the Kramers-Kronig relation. We calculated the complex refractive indices of four commonly used organic polymers, i.e. polyethylene, polyvinyl chloride, polyvinyl alcohol and polylactic acid, to demonstrate the performance of the theoretical protocol. The approach combining ab initio and AIMD simulations is effective and economical to predict the complex refractive indices of organic polymers and other organic materials.

A novel theoretical method to determine the effective optical properties of high refractive index nanocomposites.

The continuous development of advanced optical devices towards high performance, miniaturization and integration has led to an increasing demand for high refractive index optical materials. Nanocomposites - made from high refractive index inorganic nanoparticles and good processability polymers - combine the advantages of both materials to create a synergistic effect. However, the diversity and complexity of the composites make laboratory preparation less efficient. Therefore, to prepare composites that meet the refractive index requirements, it is essential to predict the effective optical properties at different wavelengths. This study proposes a finite element parametric retrieval (FEPR) method to calculate the effective complex refractive index of nanocomposites (meff). The effects of the ratio of film thickness to particle diameter, particle arrangement, particle volume fraction (fv) and particle diameter (d) on meff are considered. The results demonstrate that changing the spatial arrangement, volume fraction and diameter of the particles can cause changes in the scattering effect of particles or the interaction between the electromagnetic waves and the particles, resulting in changes in the meff. Compared with effective medium theory (EMT), the FEPR method can be used to characterise the meff values in complex cases through finite element parametric modelling. The FEPR method is an efficient and accurate method for predicting the effective optical properties of nanocomposites, and can also be applied to the design and development of materials to discover the factors influencing the properties and variation patterns from large amounts of data, and to obtain predictive models that can guide the design of new materials.

Polarized radiative transfer in seawater-in-oil emulsions floated on seawater considering the impact of oil absorption on seawater droplet scattering.

Among various remote sensing approaches, optical polarization remote sensing shows great advantages in identifying oil-water emulsions in seawater and has become one of the most promising detection technologies. Herein, we focus on exploring the sensitivity of polarized radiative transfer properties for oil emulsion polarization detection to the influence factors of viewing angle, droplet volume fraction and radius, incident wavelength, and emulsion thickness. The radiative properties of seawater droplets dispersed in crude oil are calculated using the improved Lorenz-Mie theory considering the absorption of crude oil as the host medium, after which the reflected Stokes vector and the degree of linear polarization (DOLP) of seawater-in-oil emulsions floating on seawater are obtained using the spectral element method. By analyzing the calculation results of a 0° viewing azimuth angle, the detection wavelength and viewing zenith angles corresponding to the highest sensitivity of the DOLP to the above factors are significantly different; thus, quantitative remote sensing detection of the droplet volume fraction, droplet diameter, and emulsion thickness is possible. Exploring the sensitivity of polarized remote sensing signals for oil emulsion polarization detection to the above factors is a prerequisite for quantitative polarization detection of oil emulsions.

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM.

Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model's efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model's effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model's generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model's commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance.

Machine learning-assisted design of polarization-controlled dynamically switchable full-color metasurfaces.

Dynamic color tuning has significant application prospects in the fields of color display, steganography, and information encryption. However, most methods for color switching require external stimuli, which increases the structural complexity and hinders the applicability of front-end dynamic display technology. In this study, we propose polarization-controlled hybrid metal-dielectric metasurfaces to realize full-color display and dynamic color tuning by altering the polarization angle of incident light without changing the structure and properties of the material. A bidirectional neural network is trained to predict the colors of mixed metasurfaces and inversely design the geometric parameters for the desired colors, which is less dependent on design experience and reduces the computational cost. According to the color recognition ability of human eyes, the accuracy of color prediction realized in our study is 93.18% and that of inverse parameter design is 92.37%. This study presents a simple method for dynamic structural color tuning and accelerating the design of full-color metasurfaces, which can offer further insight into the design of color filters and promote photonics research.

Monte Carlo simulation of spectral reflectance and BRDF of the bubble layer in the upper ocean.

The presence of bubbles can significantly change the radiative properties of seawater and these changes will affect remote sensing and underwater target detection. In this work, the spectral reflectance and bidirectional reflectance characteristics of the bubble layer in the upper ocean are investigated using the Monte Carlo method. The Hall-Novarini (HN) bubble population model, which considers the effect of wind speed and depth on the bubble size distribution, is used. The scattering coefficients and the scattering phase functions of bubbles in seawater are calculated using Mie theory, and the inherent optical properties of seawater for wavelengths between 300 nm and 800 nm are related to chlorophyll concentration (Chl). The effects of bubble coating, Chl, and bubble number density on the spectral reflectance of the bubble layer are studied. The bidirectional reflectance distribution function (BRDF) of the bubble layer for both normal and oblique incidence is also investigated. The results show that bubble populations in clear waters under high wind speed conditions significantly influence the reflection characteristics of the bubble layer. Furthermore, the contribution of bubble populations to the reflection characteristics is mainly due to the strong backscattering of bubbles that are coated with an organic film.

Expression of the laminin genes family and its relationship to prognosis in pancreatic carcinoma.

BACKGROUND AND STUDY AIMS: Laminin is an extracellular matrix molecule that is the major component of the basement membrane and plays a key role in regulating various processes. However, the association between the laminin gene family and the prognosis of pancreatic carcinoma has not been systematically investigated. PATIENTS AND METHODS: The role of the laminin gene family in pancreatic cancer was evaluated using data from the TCGA database. The effects of different expressions of members of the laminin gene family on pancreatic cancer survival were compared, and their primary cellular roles were examined. The effects of different expressions of positive family genes on proliferation, metastasis, and invasion, as well as EMT and ferroptosis in pancreatic cancer, were also examined. RESULTS: Based on univariate and multifactorial analysis of pancreatic cancer patients, LAMA3 was identified as an independent prognostic factor for overall survival in pancreatic cancer. LAMA3 was found to be enriched in the actin cytoskeleton, P53 signaling pathway, adhesion molecule junctions, pentose phosphate pathway, and regulatory differences in the cell cycle and focal adhesion. Additionally, high expression of LAMA3 was found to promote cancer proliferation, invasion, and metastasis, facilitate the EMT process, and inhibit ferroptosis. CONCLUSIONS: Our results identified LAMA3 was associated with the prognosis of patients with pancreatic cancer and may serve as a prognostic biomarker for pancreatic cancer.

STFuse: Infrared and Visible Image Fusion via Semisupervised Transfer Learning.

Infrared and visible image fusion (IVIF) aims to obtain an image that contains complementary information about the source images. However, it is challenging to define complementary information between source images in the lack of ground truth and without borrowing prior knowledge. Therefore, we propose a semisupervised transfer learning-based method for IVIF, termed STFuse, which aims to transfer knowledge from an informative source domain to a target domain, thus breaking the above limitations. The critical aspect of our method is to borrow supervised knowledge from the multifocus image fusion (MFIF) task and to filter out task-specific attribute knowledge by using a guidance loss Lg , which motivates its cross-task use in IVIF tasks. Using this cross-task knowledge effectively alleviates the limitation of the lack of ground truth on fusion performance, and the complementary expression ability under the constraint of supervised knowledge is more instructive than prior knowledge. Moreover, we designed a cross-feature enhancement module (CEM) that utilizes self-attention and mutual-attention features to guide each branch to refine features and then facilitate the integration of cross-modal complementary features. Extensive experiments demonstrate that our method has good advantages in terms of visual quality and statistical metrics, as well as the docking of high-level vision tasks, compared with other state-of-the-art methods.

Landscape changes and livelihood outcomes in rural tea farming communities: A case study in Fuding City, Fujian Province, Southeast China.

Landscape changes driven by cash crop plantations have been prevalent in tropical and subtropical regions worldwide in recent decades. Investigating the landscape changes and concluding livelihood outcomes are fundamental to figure out the solutions for rural sustainability. This paper examined the landscape changes which was caused by land use changes in tea plantations as well as investigated the resultant livelihood impacts, based on a case study in Fuding City, Southeast China. A questionnaire survey of 114 rural households in four sampled villages was conducted. Results demonstrated that expansion and intensification of tea plantations were two major proximate causes of landscape changes in recent decade. Our survey indicated that some existing intensively-managed tea plantations had derived from intensification and expansion of tea plantations, respectively. We identified four underlying driving forces of landscape changes, including economic benefit, governmental policies, wildlife destruction on grain crops, and rural return migration. Our study confirmed that landscape changes have significant positive effects on farmers' livelihoods, including increasing employment and incomes, raising living standards, enhancing livelihood assets and livelihood sustainability. Especially, the aged rural populations could have a relatively decent living standard. Meanwhile, the excessive expansion of tea plantations may impair livelihood resilience. Lastly, three policy suggestions based on different time scales have been put forward to promote rural households' livelihood sustainability and resilience.

The relationship between cancer and biomechanics.

The onset, development, diagnosis, and treatment of cancer involve intricate interactions among various factors, spanning the realms of mechanics, physics, chemistry, and biology. Within our bodies, cells are subject to a variety of forces such as gravity, magnetism, tension, compression, shear stress, and biological static force/hydrostatic pressure. These forces are perceived by mechanoreceptors as mechanical signals, which are then transmitted to cells through a process known as mechanical transduction. During tumor development, invasion and metastasis, there are significant biomechanical influences on various aspects such as tumor angiogenesis, interactions between tumor cells and the extracellular matrix (ECM), interactions between tumor cells and other cells, and interactions between tumor cells and the circulatory system and vasculature. The tumor microenvironment comprises a complex interplay of cells, ECM and vasculature, with the ECM, comprising collagen, fibronectins, integrins, laminins and matrix metalloproteinases, acting as a critical mediator of mechanical properties and a key component within the mechanical signaling pathway. The vasculature exerts appropriate shear forces on tumor cells, enabling their escape from immune surveillance, facilitating their dissemination in the bloodstream, dictating the trajectory of circulating tumor cells (CTCs) and playing a pivotal role in regulating adhesion to the vessel wall. Tumor biomechanics plays a critical role in tumor progression and metastasis, as alterations in biomechanical properties throughout the malignant transformation process trigger a cascade of changes in cellular behavior and the tumor microenvironment, ultimately culminating in the malignant biological behavior of the tumor.

Evaluation and Design of Colored Silicon Nanoparticle Systems Using a Bidirectional Deep Neural Network.

Silicon nanoparticles (SiNPs) with lowest-order Mie resonance produce non-iridescent and non-fading vivid structural colors in the visible range. However, the strong wavelength dependence of the radiation pattern and dielectric function makes it very difficult to design nanoparticle systems with the desired colors. Most existing studies focus on monodisperse nanoparticle systems, which are unsuitable for practical applications. This study combined the Lorentz-Mie theory, Monte Carlo, and deep neural networks to evaluate and design colored SiNP systems. The effects of the host medium and particle size distribution on the optical and color properties of the SiNP systems were investigated. A bidirectional deep neural network achieved accurate prediction and inverse design of structural colors. The results demonstrated that the particle size distribution flattened the Mie resonance peak and influenced the reflectance and brightness of the SiNP system. The SiNPs generated vivid colors in all three of the host media. Meanwhile, our proposed neural network model achieved a near-perfect prediction of colors with high accuracy of the designed geometric parameters. This work accurately and efficiently evaluates and designs the optical and color properties of SiNP systems, thus accelerating the design process and contributing to the practical production design of color inks, decoration, and printing.

Reduced Mo-doped NiCo2O4 with rich oxygen vacancies as an advanced electrode material in supercapacitors.

Reduced Mo-doped NiCo2O4 (R-Mo-NiCo2O4) was facilely prepared through a dual-defect strategy. Mo-doped NiCo layered double hydroxide (Mo-NiCo-LDH) was used as the precursor and calcined in an air atmosphere, and the resultant Mo-doped NiCo2O4 (Mo-NiCo2O4) was further reduced by NaBH4. The number of oxygen vacancies in the obtained R-Mo-NiCo2O4 is significantly increased by both Mo doping and NaBH4 reduction, resulting in greatly enhanced electrical conductivity and facilitated charge transfer. Finally, the R-Mo-NiCo2O4 was used as the electrode material in supercapacitors, which displayed greatly improved electrochemical performance, such as higher specific capacity (285.8 mA h g-1 at 1 A g-1), rate capability (86.1%) and cycling stability (87.4% retention after 5000 cycles).

Dynamic livelihood impacts of COVID-19 on different rural households in mountainous areas of China.

BACKGROUND: The outbreak of COVID-19 pandemic has brought about severe negative livelihood consequences for rural households worldwide. However, the heterogeneity and dynamics of livelihood impacts have been under-researched. There is also lacking a livelihood assessment of the pandemic based on a whole pandemic cycle. This study aimed to investigate the dynamic and heterogeneous livelihood impacts of COVID-19 pandemic for rural households in 2020 based on a case study of Southeast China. METHODS: The pandemic in China had experienced a complete cycle from initial outbreak, to intermediate recovery and finally new normal stage in 2020. We conducted face-to-face interviews with 95 rural households randomly drawn from 2 rural villages in Xunwu County, Jiangxi Province, Southeast China. The sampled households are interviewed with a questionnaire through face-to-face surveys in February and March, 2021 to evaluate the overall livelihood impacts of the pandemic during 2020. The survey collected data on demographic and economic characteristics, governmental control measures, and effects of the COVID-19 on agricultural production, employment, income, education, and daily life. In-depth interviews are also conducted to clarify the livelihood impacts of COVID-19 on villages. RESULTS: Results showed that the pandemic tremendously caused substantially negative livelihood impacts, including decreasing household income, and disorders in daily lives. The average income loss of all survey households is 6,842 RMB, accounting for 13.01% of the total household income in 2020. Containment measures also resulted in a series of disturbances in daily lives, such as rising food price additional expenditures, travel restrictions, party restrictions, closure of schools and deceasing living standards. There is remarkable household heterogeneity in the livelihood impacts. Results also revealed that the livelihood strategies of rural households to cope with the threat of COVID-19 were different in various pandemic stages. CONCLUSION: Our findings have illustrated the severity and heterogeneity of livelihood impacts on rural households induced by COVID-19 pandemic. The dynamics of livelihood impacts is also highlighted in the study. Several policy suggestion was proposed to mitigate these negative consequences of the pandemic.

A Systematic Review and Meta-Analysis of Tai Chi Training in Cardiorespiratory Fitness of Elderly People.

Objectives: The purpose of this study was to investigate the influence of Tai Chi on cardiorespiratory fitness (CRF) in elderly people using meta-analysis. Methods: This study used seven electronic databases and data retrieved from randomized controlled trials (RCTs) investigating the role of Tai Chi on CRF in the elderly. All these 24 RCTs were screened and selected from 7 literature databases. The Stata 11.2 software (StataCorp, USA) was used for the meta-analysis, subgroup analysis, and bias test, while the Cochrane Collaboration's tool was used for the assessment of the risk of bias (RoB). 4 researchers independently participated in sample selection, data extraction, and RoB assessment. Results: Following the inclusion criteria, 24 eligible studies were included in our analysis. The meta-analysis indicated that Tai Chi practice significantly increased the maximum rate of oxygen consumption (VO2 max) (weighted mean difference (WMD) = 3.76, 95% CI: 1.25 to 6.26, P < 0.1), leading to an overall reduction in the heart rate (HR) (WMD = -1.84, 95% CI: -2.04 to -1.63, P ≤ 0.001) and an increase in the O2 pulse (WMD = 0.94, 95% CI: 0.60 to 1.28, P ≤ 0.001) in individuals who practiced Tai Chi regularly compared with those who did not. The subgroup analysis suggested that overall in those who practiced Tai Chi, males (WMD = 1.48, 95% CI: 0.85 to 2.12, P ≤ 0.001) had higher O2 pulse than females (WMD = 0.73, 95% CI: 0.33 to 1.12, P ≤ 0.001). The subgroup analysis also showed an increase in the vital capacity (VC) (WMD = 316.05, 95% CI: 239.74 to 392.35, P ≤ 0.001) in individuals practicing Tai Chi. When the samples were further stratified by Tai Chi practicing time, the subgroup analysis suggested that individuals practicing Tai Chi over a period of 24 weeks showed no significant difference in VC (WMD = 82.95, 95% CI: -98.34 to 264.23, P=0.370), while those practicing Tai Chi over a period of 48 weeks showed a significant increase (WMD = 416.62, 95% CI: 280.68 to 552.56, P ≤ 0.001). Furthermore, the subgroup analysis demonstrated that the increase in VC is significantly correlated with the Tai Chi practicing time (WMD = 344.97, 95% CI: 227.88 to 442.06, P ≤ 0.001). Conclusion: Regular Tai Chi practice could improve the CRF in the elderly, as indicated by significant improvement in indicators including VO2 max, O2 pulse, VC, and HR. However, gender and practice time might influence the overall beneficial outcomes.

Economic development, rural livelihoods, and ecological restoration: evidence from China.

This article uses a case study in Southeast China to demonstrate how the substantial changes in rural livelihoods have been driven by a combination of "pull" forces from external economic development, and "push" forces from local areas, leading to a shift in rural household economic activities: household outmigration and de-population of the countryside, changes in energy consumption, and most importantly, changes in land uses and eventually, ecological restoration. Such dramatic changes are becoming common across the Chinese countryside. It is pointed out that economic development has generally caused a deterioration of the environment at least at the early period of economic growth, but the positive impacts, especially in some ecosystem in rural areas, have become more apparent.

Nanowired NiMoO4/NiSe2/MoSe2 prepared through in situ selenylation as a high performance supercapacitor electrode.

After in situ selenylation, hydrothermally synthesized NiMoO4 was partially converted to NiSe2 and MoSe2, and the obtained NiMoO4/NiSe2/MoSe2 nanowires were used as a supercapacitor electrode material. The specific capacitance of the nanowired NiMoO4/NiSe2/MoSe2 was significantly higher than that of NiMoO4 at a current density of 1 A g-1 (955 vs. 489 F g-1). NiMoO4/NiSe2/MoSe2 also exhibited a high capacitance retention of 86.1% after 5000 cycles at 10 A g-1. The high supercapacitive performance of NiMoO4/NiSe2/MoSe2 can be ascribed to the obvious heterointerfaces, rich defects and remarkably increased electrical conductivity after in situ selenylation of NiMoO4.

Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network.

Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles' structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials.

The novel effectiveness of Tai Chi on cardiopulmonary fitness among stroke patients in the recovery phase: a study protocol for a randomized controlled trial.

BACKGROUND: Stroke is the leading cause of death worldwide. China faces a similar risk of stroke as developed countries because of considerable changes in lifestyle, such as overeating and smoking. Tai Chi is a traditional form of mind-body exercise that has been widely practiced in China for thousands of years. However, there are few studies on the effect of Tai Chi on the cardiopulmonary function of stroke patients in the recovery phase. Therefore, it is necessary to observe the effect of Tai Chi on the cardiorespiratory fitness of patients after stroke. METHODS: This is a parallel-design, two-arm, analyst assessor-blinded, randomized controlled trial. A total of 226 stroke patients in the recovery phase will be recruited and assigned randomly to a control group or Tai Chi group at a 1:1 ratio. The patients in the Tai Chi group will perform the Tai Chi exercise. The patients in the control group will perform walking exercises. Patients in both groups will receive conventional treatments and healthy education. The primary outcomes will be VO2peak and scores on the MOS item short form health survey (SF-36) scale. Secondary outcomes will include vital capacity (VC), ejection fractions (EF), and cardiac output (CO). The assessments of the tests will be performed at three time points (before exercise, at the end of exercise, and 6 weeks after exercise). Adverse events will be recorded faithfully during the study. DISCUSSION: If the results are positive, this study will contribute to the establishment of further guided Tai Chi rehabilitation programs. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000034719 . Registered on 16 July 2020.

Polyaniline functionalized reduced graphene oxide/carbon nanotube ternary nanocomposite as a supercapacitor electrode.

Aniline was chemically bonded to the surface of graphene oxide (GO) via a nucleophilic ring-opening reaction. The aniline-bonded GO was then reduced to reduced graphene oxide (RGO) in the presence of multi-walled carbon nanotubes (MWCNTs), which could prevent the RGO from aggregating. The bonded aniline was further responsible for the grafting of polyaniline (PANI) onto the surface of RGO via chemical oxidative polymerization of aniline, and the resultant PANI-RGO/MWCNT ternary nanocomposite could be used as a supercapacitor electrode with high performance.