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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.

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.

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.

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.