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.
The convective heat transfer mechanism in a nano heat exchanger is investigated using molecular dynamics simulation.
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.
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.
With the development of human society, light pollution has gradually become one of the major problems faced by today’s society. In order to improve human awareness of the impact of light pollution ...and reduce the impact of light pollution, we constructed a “light pollution risk assessment” model and proposed relevant strategies to mitigate the impact of light pollution. We established a 3-level Analytical Hierarchy Process (AHP) combined with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model by dividing into three major factors: economic, social, and natural, and selected each index to take data collection and data analysis, then the determination of weights among factors in the model was carried out by entropy weighting method, and the light pollution risk level assessment model was obtained by K-clustering analysis. The model can systematically assess and classify the risk level of light pollution in the investigated area, which is conducive to the establishment of a scientific system for the evaluation and prevention of urban light pollution in the future, and propose effective countermeasures for how to reduce urban light pollution.
We report on the experimental investigation of femtosecond laser filament guided negative coronas. When the coupling between the filament and negative corona was weak, the side fluorescence spectral ...analysis confirmed the existence of impact ionization although less effect on the filament length was observed. When the coupling was strong so that the negative corona was well connected with the filament, the filament guided coronas at the ends of laser filaments were observed. The newly generated negative coronas were confined around the filament axis, and no streamer-type of coronas guided by the filament was observed under conditions similar to those reported in the work of Wang et al. Sci. Rep. 5, 18681 (2015) although both could give rise to an elongation of the filament. A physical picture was proposed to understand the processes of the laser filament guided coronas.
In this paper, the lag exponential synchronization for a class of neural networks with distributed delays and discrete delays (mixed delays) is studied via adaptive semiperiodically intermittent ...control. Using the adaptive control theory, the Lyapunov stability theory combined with the method of intermittent control, the simple but robust adaptive semiperiodically intermittent controller and impulse controller are designed. Via the proposed control methods, the response system can lag synchronize with the drive system, and the less conservative results are obtained. Using the adaptive control approach and giving a rigorous proof for the synchronization scheme, the proposed controllers are obviously little costly and more useful in practice than before. In the original references, the control width should be larger than the time delay and the time delay should be smaller than the noncontrol width. While, in this paper, these strict assumptions can be removed. Moreover, the control time and the control rate may not be constants. This leads to a larger application scope for our method. Last, numerical simulations are exploited to show the effectiveness of the results.
► A novel catalyst of Pt/Fe3O4–CeO2 was prepared. ► The catalyst was tested for methanol electrooxidation in acid solution. ► The results shows enhanced performance of Pt/Fe3O4–CeO2 for methanol ...oxidation.
A novel catalyst of Pt/Fe3O4–CeO2 is prepared by anchoring Pt nanoparticles onto Fe3O4 coated cerium (CeO2) surface. The catalyst is characterized by TEM and tested for methanol electrooxidation in acid solution. The results demonstrated enhanced catalytic performance of Pt/Fe3O4–CeO2 toward methanol oxidation. The catalytic current of Pt/Fe3O4–CeO2 towards methanol is about 4 times higher than that of Pt–CeO2, indicating the introduction of Fe3O4 significantly improved the catalytic activity of the catalyst. The incaresed catalytic activity can be ascribed to the increased conductivity of the catalyst.
•Nanofluids own better heat transfer performance than Ar base fluid.•Nanofluids with higher volume concentration have superior heat transfer capability.•Brownian motion and spinning motion of ...nanoparticles facilitate heat transfer.•Deposited nanoparticles can act as nano fins and extend low potential energy region.
Nanofluids have been proved to be novel work fluids with preeminent thermophysical properties to improve the heat dissipation of the micro/nanochannel. Here we provide atomistic insights into heat transfer and flow behaviors of nanofluids in nanochannels using molecular dynamics simulation. Nanoparticles are found to possess suspension and deposition statements during the flow process in the nanochannel. The temperature development can be accelerated and the temperature jump occurs at the wall-fluid interface in nanofluids. The velocity of the near-wall fluid is disturbed by deposited nanoparticles. In comparison with the base fluid, nanofluids can promote the heat transfer and the nanofluid with a higher nanoparticle volume concentration owns the better convective heat transfer performance. It is discovered that nanoparticles with the irregular Brownian motion and spinning motion disturb the fluid flow and intensify collisions among fluid atoms and thus enhance the heat transfer of nanofluids. Deposited nanoparticles act as nano fins to increase heat transfer areas and disturb the near-wall fluid flow, which ameliorates the convective heat transfer of nanofluids in the nanochannel.
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.
Palladium modified cerium oxide (CeO
2 or ceria) nanoparticles (Pd-CeO
2) were prepared by depositing palladium nanoparticles with average diameters of 3–5
nm on the surface of CeO
2 via chemical ...reduction of the precursor (Pd
2+). The electrocatalytic activity of Pd-CeO
2 toward the detection of different important compounds (i.e. glucose, ascorbic acid (AA), dopamine (DA) and uric acid (UA)) has been investigated. Compared with the CeO
2 or Pd modified electrode, the Pd-CeO
2 modified electrode has higher catalytic activity toward the oxidation of glucose, AA, DA, and UA. A nonenzymatic glucose sensor was developed for the sensitive and selective detection of glucose. Moreover, in the mixture of AA, DA and UA, obvious electrochemical potential separations among the three detected peaks were observed, making the simultaneously determination of AA, DA and UA possible. The attractive features of Pd-CeO
2 provide potential applications in sensor and biosensor design.
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