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Photothermoelectric materials have important applications in many fields. Here, we joined a silver nanostructure film and a carbon nanotube film by van der Waals force to form a heterojunction, which shows excellent photothermal and photoelectric conversion properties. The local temperature difference and the output photovoltage increase rapidly when the heterojunction is irradiated by lasers with wavelengths ranging from ultraviolet to terahertz. The maximum temperature difference reaches 215.9 K, which is significantly higher than that of other photothermoelectric materials reported in the literature. The photothermal and photoelectric responsivity depend on the wavelength of lasers, which are 175~601 K W -1 and 9.35~40.4 mV W -1 , respectively. We demonstrate that light absorption of the carbon nanotube is enhanced by local surface plasmons, and the output photovoltage is dominated by Seebeck effect. The proposed heterostructure can be used as high-efficiency sensitive photothermal materials or as ultra-wideband fast-response photoelectric materials. Finding efficient photothermoelectric materials remains critical to the development of clean and renewable energy conversion technologies. Here, authors prepare a silver nanostructure film/carbon nanotube film heterojunction with excellent photothermal and photoelectric conversion performance.

In the paper, we compute the correlation functions in 2D N = (1 , 1) and N = (2 , 2) superconformal field theories with T T ¯ deformation up to the first order of the deformation in terms of perturbation theory. With the help of superconformal Ward identity in N = (1 , 1) and N = (2 , 2) theories and careful regularization, the correlation functions in the deformed theory can be obtained up to the first order perturbation. This study is the extension from previous bosonic T T ¯ deformation to the supersymmetric one.

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel's ALFF by the global mean ALFF, named mALFF. Although fractional ALFF (fALFF), a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. The current study borrowed the percent signal change in task fMRI studies and proposed percent amplitude of fluctuation (PerAF) for RS-fMRI. We firstly applied PerAF and mPerAF (i.e., divided by global mean PerAF) to eyes open (EO) vs. eyes closed (EC) RS-fMRI data. PerAF and mPerAF yielded prominently difference between EO and EC, being well consistent with previous studies. We secondly performed test-retest reliability analysis and found that (PerAF ≈ mPerAF ≈ mALFF) > (fALFF ≈ mfALFF). Head motion regression (Friston-24) increased the reliability of PerAF, but decreased all other metrics (e.g. mPerAF, mALFF, fALFF, and mfALFF). The above results suggest that mPerAF is a valid, more reliable, more straightforward, and hence a promising metric for voxel-level RS-fMRI studies. Future study could use both PerAF and mPerAF metrics. For prompting future application of PerAF, we implemented PerAF in a new version of REST package named RESTplus.

A bstract In the holographic framework, we argue that the partial entanglement entropy (PEE) can be explicitly interpreted as the component flow flux in a locking bit thread configuration. By applying the locking theorem of bit threads, and constructing a concrete locking scheme, we obtain a set of uniquely determined component flow fluxes from this viewpoint, and successfully derive the PEE proposal and its generalized version in the multipartite cases. Moreover, from this perspective of bit threads, we also present a coherent explanation for the coincidence between the BPE (balanced partial entanglement)/EWCS (entanglement wedge cross section) duality proposed recently and the EoP (entanglement of purification)/EWCS duality. We also discuss the issues implied by this coincident between the idea of the PEE and the picture of locking thread configuration.

Hepatic fibrosis is a disease characterized by excessive deposition of extracellular matrix (ECM) in the liver. Activation of hepatic stellate cells (HSCs) is responsible for most of ECM production. Oxidative stress and reactive oxygen species (ROS) may be important factors leading to liver fibrosis. NADPH oxidase 4 (NOX4) is the main source of ROS in hepatic fibrosis, but the mechanism by which NOX4 regulates oxidative stress is not fully understood. β-Arrestin2 is a multifunctional scaffold protein that regulates receptor endocytosis, signaling and trafficking. In this study, we investigated whether β-arrestin2 regulated oxidative stress in hepatic fibrosis. Both β-arrestin2 knockout ( Arrb 2 KO) mice and wild-type mice were intraperitoneally injected with carbon tetrachloride (CCl 4 ) to induce hepatic fibrosis. Arrb 2 KO mice showed significantly attenuated liver fibrosis, decreased ROS levels and NOX4 expression, and reduced collagen levels in their livers. In vitro, NOX4 knockdown significantly inhibited ROS production, and decreased expression of alpha-smooth muscle actin in angiotensin II-stimulated human HSC cell line LX-2. Through overexpression or depletion of β-arrestin2 in LX-2 cells, we revealed that decreased β-arrestin2 inhibited ROS levels and NOX4 expression, and reduced collagen production; it also inhibited activation of ERK and JNK signaling pathways. These results demonstrate that β-arrestin2 deficiency protects against liver fibrosis by downregulating ROS production through NOX4. This effect appears to be mediated by ERK and JNK signaling pathways. Thus, targeted inhibition of β-arrestin2 might reduce oxidative stress and inhibit the progression of liver fibrosis.

Point cloud is a widely used 3D data form, which can be produced by depth sensors, such as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and irregular, many researchers focused on the feature engineering of the point cloud. Being able to learn complex hierarchical structures, deep learning has achieved great success with images from cameras. Recently, many researchers have adapted it into the applications of the point cloud. In this paper, the recent existing point cloud feature learning methods are classified as point-based and tree-based. The former directly takes the raw point cloud as the input for deep learning. The latter first employs a k-dimensional tree (Kd-tree) structure to represent the point cloud with a regular representation and then feeds these representations into deep learning models. Their advantages and disadvantages are analyzed. The applications related to point cloud feature learning, including 3D object classification, semantic segmentation, and 3D object detection, are introduced, and the datasets and evaluation metrics are also collected. Finally, the future research trend is predicted.

HighlightsMultidiscipline application of triboelectric nanogenerators (TENGs) for intelligent Internet of Things (IoTs) are summarized from the aspects of agriculture, industry, city, emergency monitoring, and artificial intelligence.Perspectives on the challenges and future research directions of TENGs in IoTs have been proposed.In the era of 5G and the Internet of things (IoTs), various human–computer interaction systems based on the integration of triboelectric nanogenerators (TENGs) and IoTs technologies demonstrate the feasibility of sustainable and self-powered functional systems. The rapid development of intelligent applications of IoTs based on TENGs mainly relies on supplying the harvested mechanical energy from surroundings and implementing active sensing, which have greatly changed the way of human production and daily life. This review mainly introduced the TENG applications in multidiscipline scenarios of IoTs, including smart agriculture, smart industry, smart city, emergency monitoring, and machine learning-assisted artificial intelligence applications. The challenges and future research directions of TENG toward IoTs have also been proposed. The extensive developments and applications of TENG will push forward the IoTs into an energy autonomy fashion.

Metal borides/borates have been considered promising as oxygen evolution reaction catalysts; however, to date, there is a dearth of evidence of long-term stability at practical current densities. Here we report a phase composition modulation approach to fabricate effective borides/borates-based catalysts. We find that metal borides in-situ formed metal borates are responsible for their high activity. This knowledge prompts us to synthesize NiFe-Boride, and to use it as a templating precursor to form an active NiFe-Borate catalyst. This boride-derived oxide catalyzes oxygen evolution with an overpotential of 167 mV at 10 mA/cm 2 in 1 M KOH electrolyte and requires a record-low overpotential of 460 mV to maintain water splitting performance for over 400 h at current density of 1 A/cm 2 . We couple the catalyst with CO reduction in an alkaline membrane electrode assembly electrolyser, reporting stable C 2 H 4 electrosynthesis at current density 200 mA/cm 2 for over 80 h. Metal borides/borates are promising candidates to become high-performance alkaline oxygen evolution reaction catalysts. This study reports an in-situ phase composition modulation approach to fabricate boride/borate-based catalysts.

Metal halide perovskites have emerged as promising candidates for solution-processed blue light-emitting diodes (LEDs). However, halide phase segregation – and the resultant spectral shift – at LED operating voltages hinders their application. Here we report true-blue LEDs employing quasi-two-dimensional cesium lead bromide with a narrow size distribution of quantum wells, achieved through the incorporation of a chelating additive. Ultrafast transient absorption spectroscopy measurements reveal that the chelating agent helps to control the quantum well thickness distribution. Density functional theory calculations show that the chelating molecule destabilizes the lead species on the quantum well surface and that this in turn suppresses the growth of thicker quantum wells. Treatment with γ-aminobutyric acid passivates electronic traps and enables films to withstand 100 °C for 24 h without changes to their emission spectrum. LEDs incorporating γ-aminobutyric acid-treated perovskites exhibit blue emission with Commission Internationale de l'Éclairage coordinates of (0.12, 0.14) at an external quantum efficiency of 6.3%. Metal halide perovskites have been studied as promising materials for blue light-emitting diodes (LEDs) but the stability is still a bottleneck. Here Wang et al. develop a chelating additive strategy to increase efficiency, operational stability and color stability of blue perovskite LEDs.

Real-time monitoring of chemical reactions is still challenging as well as important to study reaction mechanisms and reaction kinetics. Herein, we demonstrated the real-time monitoring of o-phenylenediamine (OPD) oxidation on the surface of gold nanoparticles by surface-enhanced Raman spectroscopy (SERS). The oxidation mechanism and the reaction kinetics were investigated on the basis of the SERS spectrum variation and the related density functionalized theory calculation. It was shown that the oxidation of OPD in the presence of copper ions was a two-step process of the deprotonation of the amino group on the aromatic rings and the rearrangement of the electron cloud to a π-conjugated system, which may open a new door to comprehensively understand the reaction process.