Ultrasmall Ru nanoparticles-decorated nickel/nickel oxide three phase heterojunctions to boost alkaline hydrogen evolution.

The rational design and optimization of heterogeneous interface for low loading noble metal HER eletrocatalysts to facilitate the upscaling of alkaline water/seawater electrolysis is highly challenging. Herein, we present a facile deep corrosion strategy induced by NaBH4 to precisely construct an ultrasmall Ru nanoparticle-decorated Ni/NiO hybrid (r-Ru-Ni/NiO) with highly dispersed triple-phase heterostructures. Remarkably, it exhibits superior activity with only 53 mV and 70 mV at 100 mA cm-2 for hydrogen evolution reaction (HER) in alkaline water and seawater, respectively, surpassing the performance of Pt/C (109.7 mV, 100 mA cm-2, 1 M KOH). It is attributed to collaborative optimization of electroactive interfaces between well-distributed ultrasmall Ru nanoparticles and Ni/NiO hybrid. Moreover, the assembled r-Ru-Ni/NiO system just require 2.03 V at 1000 mA cm-2 in anion exchange membrane (AEM) electrolyzer, outperforming a RuO2/NF || Pt/C system, while exhibiting outstanding stability at high current densities. This study offers a logical design for accurate construction of interfacial engineering, showing promise for large-scale hydrogen production via electrochemical water splitting.

LncRNA MCM3AP-AS1 promotes chemoresistance in triple-negative breast cancer through the miR-524-5p/RBM39 axis.

Triple-negative breast cancer (TNBC) is the most lethal subtype of BC, with unfavorable treatment outcomes. Evidence suggests the engagement of lncRNA MCM3AP-AS1 in BC development. This study investigated the action of MCM3AP-AS1 in chemoresistance of TNBC cells. Drug-resistant TNBC cell lines SUM159PTR and MDA-MB-231R were constructed by exposure to increasing concentrations of doxorubicin/docetaxel (DOX/DXL). MCM3AP-AS1 and miR-524-5p expression levels were determined by RT-qPCR. RNA binding motif 39 (RBM39) level was measured using Western blot. Cell viability and apoptosis were assessed by CCK-8 assay and flow cytometry. The targeted binding of miR-524-5p with MCM3AP-AS1 or RBM39 was predicted by ECORI database and validated by dual-luciferase assays. The gain-and-loss of function assays were conducted in cells to investigate the interactions among MCM3AP-AS1, miR-524-5p, and RBM39. TNBC xenograft mouse models were established through subcutaneous injection of MCM3AP-AS1-silencing MDA-MB-231R cells and intraperitoneally administrated with DOX/DXL to verify the role of MCM3AP-AS1 in vivo. MCM3AP-AS1 was upregulated in drug-resistant TNBC cells, and MCM3AP-AS1 silencing could sensitize drug-resistant TNBC cells to chemotherapeutic drugs by promoting apoptosis. MCM3AP-AS1 targeted miR-524-5p. After DOX/DXL treatment, miR-524-5p inhibition partially reversed the effect of MCM3AP-AS1 silencing on inhibiting chemoresistance and promoting apoptosis of drug-resistant TNBC cells. miR-524-5p targeted RBM39. Silencing MCM3AP-AS1 promoted apoptosis via the miR-524-5p/RBM39 axis, thereby enhancing chemosensitivity of drug-resistant TNBC cells. MCM3AP-AS1 knockdown upregulated miR-524-5p, downregulated RBM39, and restrained tumor development in vivo. MCM3AP-AS1 silencing potentiates apoptosis of drug-resistant TNBC cells by upregulating miR-524-5p and downregulating RBM39, thereby suppressing chemoresistance in TNBC.

Microbiome and its relevance to indigenous inflammatory bowel diseases in China.

BACKGROUND: Inflammatory Bowel Disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract with an unknown etiology. Although dysbiosis is implicated in its pathogenesis, deep sequencing and oral microbiota study in Chinese IBD patients is absent. AIM: To explore the role of oral / intestinal microbiota in patients with IBD and the potential associations therein. METHODS: Clinical data, fecal and saliva samples were harvested from 80 patients with IBD (Crohn's disease, CD, n = 69; Ulcerative colitis, UC, n = 11) and 24 normal controls. Microbiomics (16S rRNA sequencing and 16S rRNA full-length sequencing) were used to detect and analyze the difference between IBD patients and normal control. RESULTS: Compared with normal controls, a higher abundance of the intestinal Shigella spp. (Shigella flexneri and Shigella sonnei, which were positively relate to the severity of IBD), lower abundance of intestinal probiotics (Prevotella, Faecalibacterium and Roseburia), and higher abundance of oral Neisseria were present in IBD patients with microbiome. The higher inflammation-related markers, impaired hepatic and renal function, and dyslipidaemia were present in patients with IBD. A higher intake of red meat and increased abundance of Clostridium in the gut were found in CD patients, while the elevated abundance of Ruminococcus in the gut was showed in UC ones. The bacterial composition of saliva and fecal samples was completely different, yet there was some correlation in the distribution of dominant probiotics. CONCLUSION: Enteric dysbacteriosis and the infections of pathogenic bacteria (Shigella) may associate with the occurrence or development of IBD.

Injectable Hydrogels Including Magnetic Nanosheets for Multidisciplinary Treatment of Hepatocellular Carcinoma via Magnetic Hyperthermia.

Relapse and unresectability have become the main obstacle for further improving hepatocellular carcinoma (HCC) treatment effect. Currently, single therapy for HCC in clinical practice is limited by postoperative recurrence, intraoperative blood loss and poor patient outcomes. Multidisciplinary therapy has been recognized as the key to improving the long-term survival rate for HCC. However, the clinical application of HCC synthetic therapy is restricted by single functional biomaterials. In this study, a magnetic nanocomposite hydrogel (CG-IM) with iron oxide nanoparticle-loaded mica nanosheets (Iron oxide nanoparticles@Mica, IM) is reported. This biocompatible magnetic hydrogel integrated high injectability, magnetocaloric property, mechanical robustness, wet adhesion, and hemostasis, leading to efficient HCC multidisciplinary therapies including postoperative tumor margin treatment and percutaneous locoregional ablation. After minimally invasive hepatectomy of HCC, the CG-IM hydrogel can facilely seal the bleeding hepatic margin, followed by magnetic hyperthermia ablation to effectively prevent recurrence. In addition, CG-IM hydrogel can inhibit unresectable HCC by magnetic hyperthermia through the percutaneous intervention under ultrasound guidance.

Establishment of a Mathematical Model for Parkinson's Disease Based on Complex Neural Networks and Design of Deep Brain Electrical Stimulation Treatment Plans.

This article introduces the modeling idea of complex neural networks based on the analysis of Parkinson's disease(PD) seizures. According to the Hodgkin-Huxley model of neurons and the synaptic connection theory between neurons, a theoretical model of the basal ganglia circuit was established. To reveal the working mechanism of the brain during the attack of nervous system disease. On this basis, the neural system model is combined with the control theory. Finally, an intermittent adaptive feedback controller is proposed to effectively suppress the onset of Parkinson's disease by stimulating the neuronal system accordingly.

Prediction of water transport properties on an anisotropic wetting surface via deep learning.

Understanding the water flow behavior on an anisotropic wetting surface is of practical significance in nanofluidic devices for their performance improvement. However, current methods of experiments and simulations face challenges in measuring water transportation in real time and visually displaying it. Here, molecular dynamics simulation was integrated with our developed multi-attribute point cloud dataset and a customized network of deep learning to achieve mapping from an anisotropic wetting surface to the static and dynamic behaviors of water molecules and realize the high-performance prediction of water transport behavior. More importantly, for the chaotic phenomenon of water molecule flow caused by thermal fluctuation and limited sampling, we proposed a nanoparticle tracking optimization strategy to improve the prediction performance of the velocity field. The prediction results proved that the deep learning framework proposed in this work had superior performance in terms of accuracy, computational cost and visualization, and had the potential for generality to model the transport behavior of different molecules. Our framework can be expected to motivate the development of real-time water flow prediction at an interface and contribute to the optimization and design of surface structures in nanofluidic devices.

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers.

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.

Flow Condensation Heat Transfer Characteristics of Nanochannels with Nanopillars: A Molecular Dynamics Study.

Flow condensation in nanochannels is a high-efficiency method to deal with increasingly higher heat flux from micro/nanoelectronic devices. Here, we study the flow condensation heat transfer characteristics of nanochannels with different nanopillar cross-sectional areas and heights using molecular dynamics simulation. Results show that two phases containing vapor in the middle of the channel and liquid near walls can be distinguished by obvious interfaces when the fluid is at a stable state. The condensation performance can be promoted by adding nanopillars. With the increase in nanopillar cross-sectional areas or heights, the time that the fluid spends to reach stability will be put off, while the condensation performance enhances. Different from the small enhancement of nanopillar cross-sectional areas, the condensation heat transfer performance improves significantly at a higher nanopillar height, which increases the heat transfer rates by 11.6 and 35.8% when heights are 6a and 8a, respectively. The preeminent condensation heat transfer performance is ascribed to the fact that nanopillars with a higher height disturb the vapor-liquid interface and vapor region, which not only allows vapor atoms with strong Brownian motion to collide with nanopillar atoms directly but also increases deviations of vapor-liquid potential energy to facilitate condensation heat transfer in nanochannels.

Laser ellipticity-dependent supercontinuum generation by femtosecond laser filamentation in air.

We experimentally investigate the laser polarization effect on the supercontinuum (SC) generation through femtosecond laser filamentation in air. By tuning filamenting laser ellipticity from linear polarization to circular polarization, the spectral intensity of the SC after filamentation gradually increases, while the spectral bandwidth of the SC continuously decreases. The laser ellipticity-dependent spectral intensity modulation of the SC is stronger at higher filamenting pulse energy. Laser energy deposits more in linearly polarized laser filaments than in circularly polarized laser filaments. The experimental results are supported by numerical simulations. A physical picture based on the laser ellipticity-dependent clamped intensity inside the filament, together with the Kerr nonlinearity and plasma related self-phase modulations, is proposed to explain the observation.

Direct measurement of radial fluence distribution inside a femtosecond laser filament core.

Modulation and direct measurement of the radial fluence distribution inside a single filament core (especially less than 100 µm in diameter) is crucial to filament-based applications. We report direct measurements of the radial fluence distribution inside a femtosecond laser filament core and its evolution via the filament-induced ablation method. The radial fluence distributions were modulated by manipulating the input pulse diffraction through an iris. Compared with using a traditionally circular iris, a stellate iris substantially suppressed the diffraction effect, and laser fluence, intensity and plasma density inside the filament core were considerably increased. The radial fluence inside filament cores was also quantitatively measured via the filament drilling diaphragms approach. Furthermore, numerical simulations were performed to support the experimental results by solving nonlinear Schrödinger equations. The effects of the tooth size of the stellate iris were numerically investigated, which indicated that bigger tooth favors higher fluence and longer filament. In addition to being beneficial in understanding the filamentation process and its control, the results of this study can also be valuable for filament-based applications.

Molecular dynamics study of convective heat transfer mechanism in a nano heat exchanger.

With the rapid development of micro/nano electro-mechanical systems, the convective heat transfer at the micro/nanoscale has been widely studied for the thermal management of micro/nano devices. Here we investigate the convective heat transfer mechanism of a nano heat exchanger by the employment of molecular dynamics simulation with a modified thermal pump method. First, the temperature jump and velocity slip are observed at the wall-fluid interfaces of the nano heat exchanger. Moreover, the larger Kapitza resistance in the entrance region weakens the convective heat transfer. Second, the heat transfer performance of the nano heat exchanger can be improved by increasing the surface wettability of the solid walls owing to more fluid atoms being involved in heat transport at the walls when the wall-fluid interaction is enhanced. Meanwhile, the strong surface wettability results in the appearance of the quasi-solid fluid layers, which improves the heat transfer between walls and fluids. Finally, we point out that when the surface wettability of the nano heat exchanger is weak, the heat transfer of the hot fluid side is better than that of the cold fluid side, while the convective heat transfer performances of the cold and hot fluid sides are reversed when the surface wettability is strong. This is because of the feebler temperature jump of the hot fluid side when wall-fluid interaction is small and the greater velocity slip of the cold fluid side for walls with large wall-fluid interaction.

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer.

One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS2), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS2 and t-WS2 monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young's modulus. We find that the mechanical properties of h-WS2 surpass those of t-WS2 due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young's modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS2 (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS2 type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model.

Aluminum-target-assisted femtosecond-laser-filament-induced water condensation and snow formation in a cloud chamber.

We compare the water condensation and snow formation induced by a femtosecond laser filament with that when the filament is assisted by an aluminum target located at different positions along the filament. We reveal that the laser-filament-induced water condensation and snow formation assisted by the aluminum target are more efficient compared with those obtained without the assistance of the aluminum target. We find that the mass of the snow induced by the laser filament is the largest when the aluminum target is located at the end of the filament, smaller when it is at the middle of the filament, and the smallest at the beginning of the filament. These findings indicate that a higher plasma density and the generation of vortex pairs below the filament are important for enhancing the efficiency and yield of the laser-induced water condensation and precipitation. The higher plasma density provides more cloud condensation nuclei and facilitates the water condensation; vortex pairs below the filament are favourable to the growth of particles up to larger sizes.

Femtosecond laser filament-assisted AgI-type pyrotechnic nucleant-induced water condensation in cloud chamber.

AgI-type pyrotechnics are widely used in the field of weather modification, as a kind of artificial ice nuclei. However, their precipitation yield remains an intensively studied area. In this paper, we present a study of AgI-type pyrotechnic nucleant-induced water condensation promoted by femtosecond laser filaments in a cloud chamber. It is found that when 50-ml sample was irradiated by the laser filaments, the particles condensed on the glass slide are more soluble and slightly larger (5-15 µm). The irradiation of the laser filament on the nucleant rarely induces the generation of particles of sizes larger than 1 µm; however, it increases the decay time of particles from 13 to 18 min by the creation of numerous small particles. The amount of snow on the cold bottom plate increases by 4.2-13.1% in 2 h, compared to that without the irradiation of the laser filament. These results are associated with the production of high-concentration HNO3 by the laser filament. The concentration of HNO3 in the melt water increases by more than ten times when the sample was irradiated by the laser filaments.

Laser guided ionic wind.

We report on a method to experimentally generate ionic wind by coupling an external large electric field with an intense femtosecond laser induced air plasma channel. The measured ionic wind velocity could be as strong as >4 m/s. It could be optimized by increasing the strength of the applied electric field and the volume of the laser induced plasma channel. The experimental observation was qualitatively confirmed by a numerical simulation of spatial distribution of the electric field. The ionic wind can be generated outside a high-voltage geometry, even at remote distances.

Temporal evolution of condensation and precipitation induced by a 22-TW laser.

Water condensation and precipitation induced by 22-TW 800-nm laser pulses at 1 Hz in an open cloud chamber were investigated in a time-resolved manner. Two parts of precipitation in two independent periods of time were observed directly following each laser shot. One part started around the filament zone at t < 500 µs and ended at t ≅ 1.5 ms after the arrival of the femtosecond laser pulse. The other following the laser-induced energetic air motion (turbulence), started at t ≅ 20 ms and ended at t ≅ 120 ms. Meanwhile, the phase transitions of large-size condensation droplets with diameters of 400-500 µm from liquid to solid (ice) in a cold area (T < -30 °C) were captured at t ≅ 20 ms.

Self-Organized Kilotesla Magnetic-Tube Array in an Expanding Spherical Plasma Irradiated by kHz Femtosecond Laser Pulses.

By using a millijoule kHz femtosecond laser pulse to irradiate a preformed expanding spherical plasma, which is driven by a prepulse with intensity of 1×10^{14} W/cm^{2}, we observe fast-electron-mediated filamentary structures and an accompanying self-organized magnetic-tube array with 2000 T via time-resolved magneto-optical polarization rotation measurements. We reveal that these periodical filamentary structures predominantly originate from ejected energetic electron flows from the inner denser region of the spherical plasma, which will induce the electron Weibel instability and magnetic field organization and amplification in the expanding plasma in 2 ps. These results open new paths to investigate amplification of intense magnetic fields and the radiation signature from gamma-ray bursts just by means of a much smaller and robust experimental platform.

Corona discharge induced snow formation in a cloud chamber.

Artificial rainmaking is in strong demand especially in arid regions. Traditional methods of seeding various Cloud Condensation Nuclei (CCN) into the clouds are costly and not environment friendly. Possible solutions based on ionization were proposed more than 100 years ago but there is still a lack of convincing verification or evidence. In this report, we demonstrated for the first time the condensation and precipitation (or snowfall) induced by a corona discharge inside a cloud chamber. Ionic wind was found to have played a more significant role than ions as extra CCN. In comparison with another newly emerging femtosecond laser filamentation ionization method, the snow precipitation induced by the corona discharge has about 4 orders of magnitude higher wall-plug efficiency under similar conditions.

Probing the effective length of plasma inside a filament.

We present a novel method based on plasma-guided corona discharges to probe the plasma density longitudinal distribution, which is particularly good for the weakly ionized plasmas (~1014 cm-3). With this method, plasma density longitudinal distribution inside both a weakly ionized plasma and a filament were characterized. When a high voltage electric field was applied onto a plasma channel, the original ionization created by a laser pulse would be enhanced and streamer coronas formed along the channel. By measuring the fluorescence of enhanced ionization, in particular, on both ends of a filament, the weak otherwise invisible plasma regions created by the laser pulse were identified. The observed plasma guided coronas were qualitatively understood by solving a 3D Maxwell equation through finite element analysis. The technique paves a new way to probe low density plasma and to precisely measure the effective length of plasma inside a filament.

Picosecond laser-induced water condensation in a cloud chamber.

We investigated water condensation in a laboratory cloud chamber induced by picosecond (ps) laser pulses at ~350 ps (800 nm/1-1000 Hz) with a maximum peak power of ~25 MW. The peak power was much lower than the critical power for self-focusing in air (~3-10 GW depending on the pulse duration). Sparks, airflow and snow formation were observed under different laser energies or repetition rates. It was found that weaker ps laser pulses can also induce water condensation by exploding and breaking down ice crystals and/or water droplets into tiny particles although there was no formation of laser filament. These tiny particles would grow until precipitation in a super-saturation zone due to laser-induced airflow in a cold region with a large temperature gradient.