The simulation and prediction of fluid flow in porous media play a profoundly significant role in today's scientific and engineering domains, particularly in gaining a deeper understanding of phenomena such as the...
The simulation and prediction of fluid flow in porous media play a profoundly significant role in today's scientific and engineering domains, particularly in gaining a deeper understanding of phenomena such as the migration and fluid flow in underground rock formations and the enhancement of oil recovery rates. The flow of fluids in nanoscale porous media requires consideration of the effects of microscale phenomena, which are challenging to accurately describe using traditional physical models. Currently, research in deep learning for porous media predominantly focuses on conventional porous media, and there is an urgent need for investigations into heterogeneous nanoporous media. Simultaneously, there is a necessity to overcome the limitations of traditional data-driven models lacking physical prior knowledge. Therefore, the integration of physics-informed neural networks, which combine deep learning with physical principles, becomes essential for inferring relatively accurate results from sparse data. In this work, based on the heterogeneity of porous media in shale, we have introduced a deep learning model that couples physical information to predict the flow in heterogeneous nanoscale porous media. In the Physical Information Neural Network model, we utilize point clouds and couple them with deep residual networks. Discrete sampling points are used as inputs, and a multi-level residual connection, along with dimension concatenation, is employed to fuse feature information. The network, through backpropagation, takes into account the Navier-Stokes equations and wall conditions in heterogeneous nanoscale porous media. The results indicate that the apparent permeability and pressure field accuracy are over 90% and 95%, respectively. The Physical Information Neural Network demonstrates promising prospects for predicting flow in nanoscale porous media. Future work will extend to the multiphase complex flow in three-dimensional porous media.
Microalgae have great potential in producing energy-dense and valuable products via thermochemical processes. Therefore, producing alternative bio-oil to fossil fuel from microalgae has rapidly gained popularity due t...
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Oxide-derived Cu (OD−Cu) featured with surface located sub-20 nm nanoparticles (NPs) created via surface structure reconstruction was developed for electrochemical CO 2 reduction (ECO 2 RR). With surface adsorbed hydr...
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Oxide-derived Cu (OD−Cu) featured with surface located sub-20 nm nanoparticles (NPs) created via surface structure reconstruction was developed for electrochemical CO 2 reduction (ECO 2 RR). With surface adsorbed hydroxyls (OH ad ) identified during ECO 2 RR, it is realized that OH ad , sterically confined and adsorbed at OD−Cu by surface located sub-20 nm NPs, should be determinative to the multi-carbon (C 2 ) product selectivity. In situ spectral investigations and theoretical calculations reveal that OH ad favors the adsorption of low-frequency *CO with weak C≡O bonds and strengthens the *CO binding at OD−Cu surface, promoting *CO dimerization and then selective C 2 production. However, excessive OH ad would inhibit selective C 2 production by occupying active sites and facilitating competitive H 2 evolution. In a flow cell, stable C 2 production with high selectivity of ∼60 % at −200 mA cm −2 could be achieved over OD−Cu, with adsorption of OH ad well steered in the fast flowing electrolyte.
The recombination of photogenerated charge carriers severely limits the performance of photoelectrochemical (PEC) H 2 production. Here, we demonstrate that this limitation can be overcome by optimizing the charge tran...
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The recombination of photogenerated charge carriers severely limits the performance of photoelectrochemical (PEC) H 2 production. Here, we demonstrate that this limitation can be overcome by optimizing the charge transfer dynamics at the solid–liquid interface via molecular catalyst design. Specifically, the surface of a p-Si photocathode is modulated using molecular catalysts with different metal atoms and organic ligands to improve H 2 production performance. Co(pda-SO 3 H) 2 is identified as an efficient and durable catalyst for H 2 production through the rational design of metal centers and first/second coordination spheres. The modulation with Co(pda-SO 3 H) 2 , which contains an electron-withdrawing −SO 3 H group in the second coordination sphere, elevates the flat-band potential of the polished p-Si photocathode and nanoporous p-Si photocathode by 81 mV and 124 mV, respectively, leading to the maximized energy band bending and the minimized interfacial carrier transport resistance. Consequently, both the two photocathodes achieve the Faradaic efficiency of more than 95 % for H 2 production, which is well maintained during 18 h and 21 h reaction, respectively. This work highlights that the band-edge engineering by molecular catalysts could be an important design consideration for semiconductor–catalyst hybrids toward PEC H 2 production.
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