Nucleation and Growth of Cavities in Hydrated Nafion Membranes under Tensile Strain: A Molecular Dynamics Study

Molecular dynamics simulations are performed to investigate the nucleation and growth of cavities in a hydrated Nafion membrane under mechanical deformation. The simulation model used in this study accurately reproduces the experimental values of the elastic modulus of the membrane as a function of water content. The results obtained from triaxial tensile tests reveal a ductile to brittle transition as the water content increases. The nucleation and growth of the cavities have been quantitatively analyzed in terms of the number and size of cavities, illustrating the ductile to brittle transition uncovered by the stress/strain curves. Further local analyses have been carried out to identify the nucleation sites. The analysis of local plasticity indicates that as the water content increases, the membrane accumulates more plastic deformation in the hydrophilic domain than in the hydrophobic domain during the rupture stage of the tensile tests. These results suggest that the water network significantly impacts the nucleation and expansion of cavities induced by mechanical deformation. Furthermore, the local mechanical properties of the Nafion membrane are evaluated. The results show that the mechanical properties are heterogeneous at the nanoscale and that the cavities nucleate in soft regions of the membrane. A statistical analysis of the local water density of nucleation sites indicates that the polymer–water interfaces are more likely to nucleate cavities. The expansion and coalescence of cavities is facilitated by the high molecular reorganization of the water network, which explains the brittle behavior of membranes with high water content

Morphology Evolution and Adsorption Behavior of Ionomers from Solution to Pt/C Substrates

Coarse-grained molecular dynamics simulations were performed to understand the morphological evolution and adsorption mechanism of Nafion ionomers from the aqueous solutions to the Pt/C substrate surface under various solution compositions and substrate properties. We found that the ionomer coverage did not increase with the increasing ionomer-to-carbon ratio but was related to the size and concentration of the ionomer aggregates, following the Langmuir adsorption model that shows a wettability switching behavior due to their changed morphology from solution to the surface. Ionomer aggregates in the solution tended to unfold and spread on the carbon substrate rather than Pt particles, although the cylindrical ionomer aggregates were easily attracted by Pt particles initially due to their hydrophilic ionic shells. The smaller Pt particles had a greater effect on ionomer adsorption. With the increasing number of Pt particles, ionomer coverage increased first and then decreased, depending on whether there was enough carbon surface to anchor the ionomer backbone. A balanced Pt/C ratio and the appropriate distribution of the Pt particles were required for tuning the ionomer coverage and distribution toward the design of the catalyst ink structure to improve the power performance

Molecular Dynamics Study of the Microscopic Mechanical Balance at the Three-Phase Contact Line of Interfacial Nanobubble

This study reveals the microscopic mechanical balance at the three-phase contact line (TPCL) of an interfacial nanobubble on a substrate with a wettability pattern using molecular dynamics simulations. The apparent contact angle was compared to that evaluated using Young’s equation, in which the interfacial tensions were computed using a mechanical route. The comparison was conducted by changing the wettability of the substrate from hydrophilic to neutral while maintaining a hydrophobic region in the center of the substrate. When the wettability pattern pins the TPCL at the wettability boundary, the contact angle computed by Young’s equation is larger than the apparent contact angle because a pinning force exists in the inward direction of the nanobubble. Conversely, on the surfaces where the wettability pattern does not pin the TPCL, the contact angle computed by Young’s equation agrees with the apparent contact angle because the pinning force disappears. The distribution of principal stresses around the TPCL, which was visualized for the first time in this study, indicates that large compressive principal stresses exist between the liquid phase and the solid substrate interface, which pin the TPCL at the surface wettability boundary, and that the maximum principal stress occurs in the inward direction of the nanobubbles at the TPCL. The normalized pinning force estimated from the maximum principal stress is equivalent to that measured experimentally

Reactive Force Field Molecular Dynamics Study of the Effects of Gaseous Species on the Composition and Crystallinity of Silicon–Germanium Thin Films

We simulated the growth of a silicon–germanium (SiGe) film using reactive force field molecular dynamics (ReaxFF MD) in combinations of SiH3, SiH2, GeH3, and GeH2 radicals to evaluate the effects of gaseous species on thin-film composition and crystallinity and to understand the growth mechanisms. The film compositions could be estimated in these combinations because of the linear increase in the Ge content of the films. The average crystallinity grown by SiH3 was higher than that by SiH2 radicals. The crystallinity of the film grown by SiH3 radicals tends to be drastically decreased by GeH2 radicals. The growth mechanisms for XH3 and XH2 (X = Si or Ge) radicals were compared. XH3 radicals abstracted surface H atoms, and then more XH3 radicals chemisorbed onto the formed dangling bonds, resulting in film growth through a two-step reaction known as the Eley–Rideal-type (ER-type) mechanism. The ER-type mechanism grows the film with a low hydrogen content and high crystallinity. In contrast, XH2 radicals displayed not only the ER-type mechanism but also a one-step reaction, the H-capturing mechanism, which incorporates surface H atoms into the gaseous species. The H-capturing mechanism results in film growth with high hydrogen content and low crystallinity. The growth mechanisms are influenced by high/low H-coverage. The surface H atoms thermally move around the bonded atoms and give their kinetic energy to the diffusing gaseous species. Excess surface H atoms promote desorption. Our results from the ReaxFF MD suggested experimental settings and conditions that would enable the growth of high-quality films. Our results also suggested that SiH3 and GeH3 radicals should be mainly generated in the gas phase for high-quality SiGe film growth

Reactive Force Field Molecular Dynamics Studies of the Initial Growth of Boron Nitride Using BCl₃ and NH₃ by Atomic Layer Deposition

A new ReaxFF reactive force field for the atomic layer deposition (ALD) of boron nitride (BN) thin film growth using BCl3 and NH3 has been developed, and the initial stage of the BN growth is numerically demonstrated by ReaxFF reactive force field-based molecular dynamics (ReaxFF MD). Based on density functional theory, the ReaxFF parameters were carefully trained to describe BCl3 geometries and simulate surface reactions with BCl3 and NH3, forming BN films and HCl. The ALD process was simulated by repeating four steps: (1) BCl3 pulse, (2) first purge, (3) NH3 pulse, and (4) second purge. The film growth simulation indicates that BN thin films are grown through five steps: (i) BCl3/NH3 surface diffusion, (ii) BN cluster formation/growth, (iii) HCl formation, (iv) HCl surface diffusion, and (v) HCl desorption. Through the 5 cycles of ALD simulation, we found a mixed growth mechanism of three-dimensional growth in the form of clusters and two-dimensional growth in the form of thin films. The substrate temperature strongly affects the initial growth behavior and the resulting thickness of the BN thin film. A moderate temperature favors the formation and growth of BN clusters, while too high temperature hinders the growth of thin films because of the desorption of gas molecules and BN clusters on the surface. Through our simulation, we show that the ReaxFF MD is capable of approaching nanoscale surface reactions and clarifying the mechanisms of ALD with an atomic scale, which should be a powerful method to realize a wafer-scale ALD simulation by combining with macroscale methods

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

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