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Summary Platinum (Pt) alloys are expected to overcome long-standing issues of Pt/C electrocatalysts for oxygen reduction reaction (ORR). Entangled with serious uncertainty in configurational and compositional information, the design of a promising multi-component electrocatalyst, however, has been delayed. Here, we demonstrate that a first-principle database-driven machine-learning approach is extremely useful for the purpose via exploring materials beyond the regime of pure quantum mechanical calculations. Guided by a computational ternary phase diagram we indeed experimentally synthesized a PtFeCu nanocatalyst with 2 g per batch capacity and measured its catalytic performance for ORR. Both our computation and experiment consistently demonstrate that PtFeCu is highly active due to the atomic distribution of Cu leading to beneficial modulation of surface strain and segregation. Strikingly, PtFehighCulow (776 μA cm−2Pt and 0.67 A mg−1Pt) exhibits not only 3-fold better specific and mass activities than Pt/C but also little performance degradation over the accelerated stress test.

Precipitation hardening of Al alloys is an essential aging process for industrial applications demanding lightweight and high-mechanical strength materials. However, this process drastically reduces the thermal conductivity of the Al alloy owing to the formation of intermetallic precipitates. Because the growth of the intermetallic precipitates is a complicated process involving the diffusion of alloying elements as well as phase transformation, the prediction of the thermal conductivity during the precipitation process has not been accomplished. Herein, an accurate prediction method for the effective thermal conductivity of Al alloys during the precipitation process is presented. Interestingly, the study finds that the inhomogeneous distribution of the composition in the Al solid solution generates a considerably inhomogeneous distribution of the thermal conductivity around the precipitates; consequently, the accuracy of our prediction is 30% higher than that in previous studies.

The practical application of thermally enhanced nanofluids in automotive industries for higher energy efficiency requires that nanoparticles be buoyant, leading to Brownian motion in the base fluid without aggregation. Numerous studies have reported the long-term stability of dispersions resulting from steric hindrance and an affinity for the surrounding base fluid via modification of nanoparticles with functional groups. In this study, we performed molecular dynamics simulations of nanofluids containing a single graphene sheet with various functional groups to investigate the influence of affinity, as well as particle-size, on the behavior of the graphene sheet. Using the concept of the speed of nanoparticle, we quantitatively evaluated the dependence of behavior on affinity to investigate whether having more functional groups with more attractive interactions has an impact on the stable dispersion in nanofluid. In addition, the simulation results for the particle-size effect revealed that the larger nanoparticles produced more stable dispersion in nanofluid. We concluded that the behavior of the graphene sheet depends on a combination of two factors: different charge assigned to atoms due to a nitrogen atom, and the difference in mass, which influences the speed of the nanoparticles.

Nanofluids are candidate materials for thermal management of heat transfer equipment. Practical applications of thermally enhanced nanofluids contribute to the reduction of weight of systems, leading to improved energy efficiency. Microsize particles sink into the systems because of gravity, therefore rendering the addition meaningless in terms of improving thermal properties. However, nanoparticles can be buoyant, leading to Brownian motion in the fluid, when they do not aggregate with each other. The most important factor in nanofluids is long-term stability of the dispersion in the fluid. Numerous studies have reported the dispersion stability; functional groups attached to nanoparticles play a role in causing steric hindrance and have an affinity for the surrounding fluid, resulting in preserving the dispersion. We investigate the structural effects on dispersion by molecular dynamics simulations of nanofluid containing graphene sheets with functional groups of varying lengths at the surface. ...

A hierarchical multiscale modeling approach to characterize the elastic and plastic behavior of carbon nanotube (CNT)-reinforced polymer nanocomposites is proposed via molecular dynamics simulations and a continuum nonlinear micromechanics based on the secant moduli method. Even though the importance of the densified interphase zone formed between the CNT and polymer matrix has been demonstrated by many related studies for elastic properties, studies on how to identify the behavior and contribution of the interfacial condition and interphase zone in the overall elastoplastic behavior of nanocomposites is still an open issue. Different from conventional micromechanics approaches that homogenize overall elastoplastic behavior of heterogeneous structures from known behaviors of its constituent phases, the present study focuses on the identification of local elastoplastic behavior of the interphase region from the known elastoplastic behavior of nanocomposites through a hierarchical domain decomposition method. Firstly, the overall elastoplastic behavior of the CNT-reinforced nanocomposites is obtained from molecular dynamics (MD) simulations which are based on an ab initio force field. Due to a weak van der Waals interaction between the pristine CNT and the matrix polymer, the elastoplastic behavior of the nanocomposites clearly shows a weakened interface condition, while the matrix molecular structure in the vicinity of the CNT confirms the existence of the interphase zone. In upper level analysis, an effective matrix concept is adopted, and its elastoplastic behavior is inversely identified by equating the MD simulation result to a two-phase nonlinear micromechanics model that can consider imperfect interfacial condition. Then, the effective matrix domain is again decomposed into the interphase and pure matrix polymer regions in lower level analysis, and the elastoplastic behavior of the interphase is again identified through the same method. Using the constitutive relation of the interphase obtained from the proposed multiscale model, the overall elastoplastic behavior of the nanocomposites is obtained and compared with some available experimental results and an additional MD simulation result to validate the applicability and physical rigorousness of the proposed nonlinear multiscale approach.

We developed an efficient and extensible multiscale analysis to consider the carbon nanotube (CNT) size effect and weakened bonding effect at the interface on the effective elastic stiffness of CNT/polymer nanocomposites using molecular dynamics (MD) simulations and continuum micromechanics. Under the assumption that the CNT molecular structure is an equivalent solid cylinder, molecular mechanics calculation results for transversely isotropic elastic stiffness were found to decrease as the radius of the CNT increased. Similarly, the transversely isotropic elastic moduli of aligned pristine CNT-reinforced polypropylene composites obtained from molecular dynamics simulations exhibited the same CNT size dependency. However, a weakened interface effect was observed from the transverse Young’s modulus and two shear moduli. To account for the size effect and the weakened interface in the micromechanics-based multiscale model, a modified multi-inclusion model is derived with an effective particle scheme. Also, an effective matrix concept is suggested to account for the formation of an interphase near the surface of the CNT, and the elastic stiffness of the CNT and the effective matrix is defined as a function of the CNT radius to describe size-dependent elastic stiffness in the micromechanics regime.

Nanofluids with enhanced thermal properties are candidates for thermal management in automotive systems, with scope for improving energy efficiency. In particular, many studies have reported on dispersions of nanoparticles with long-term stability in the base fluid, with qualitative evaluations of the dispersion stability via either the naked eye or optical instruments. Additives such as surfactants can be used to enhance the dispersion of nanoparticles; however, this may diminish their intrinsic thermal properties. Here, we describe molecular dynamics simulations of nanofluids containing graphene sheets dispersed in ethylene glycol and water. We go on to suggest a quantitative evaluation method for the degree of dispersion, based on the ratio of the total number of nanoparticles to the number of clustered nanoparticles. Moreover, we investigate the effects of functional groups on the surface of graphene, which are expected to improve the dispersion without requiring additives such as surfactants due to steric hindrance and chemical affinity for the surrounding fluid. We find that, for pure graphene, the degree of dispersion decreased as the quantity of graphene sheets increased, which is attributed to an increased probability of aggregation at higher loadings; however, the presence of functional groups inhibited the graphene sheets from forming aggregates.