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1. Adaptive kinetic Monte Carlo simulations of surface segregation in PdAu nanoparticles

   https://doi.org/10.1039/C9NR01858A  

   Li, Lei ; Li, Xinyu ; Duan, Zhiyao ; Meyer, Randall J. ; Carr, Rob ; Raman, Sumathy ; Koziol, Lucas ; Henkelman, Graeme ( May 2019 , Nanoscale)

   Surface segregation in bimetallic nanoparticles (NPs) is critically important for their catalytic activity because the activity is largely determined by the surface composition. Little, however, is known about the atomic scale mechanisms and kinetics of surface segregation. One reason is that it is hard to resolve atomic rearrangements experimentally. It is also difficult to model surface segregation at the atomic scale because the atomic rearrangements can take place on time scales of seconds or minutes – much longer than can be modeled with molecular dynamics. Here we use the adaptive kinetic Monte Carlo (AKMC) method to model the segregation dynamics in PdAu NPs over experimentally relevant time scales, and reveal the origin of kinetic stability of the core@shell and random alloy NPs at the atomic level. Our focus on PdAu NPs is motivated by experimental work showing that both core@shell and random alloy PdAu NPs with diameters of less than 2 nm are stable, indicating that one of these structures must be metastable and kinetically trapped. Our simulations show that both the Au@Pd and the PdAu random alloy NPs are metastable and kinetically trapped below 400 K over time scales of hours. These AKMC simulations provide insight into the energymore »landscape of the two NP structures, and the diffusion mechanisms that lead to segregation. In the core–shell NP, surface segregation occurs primarily on the (100) facet through both a vacancy-mediated and a concerted mechanism. The system becomes kinetically trapped when all corner sites in the core of the NP are occupied by Pd atoms. Higher energy barriers are required for further segregation, so that the metastable NP has a partially alloyed shell. In contrast, surface segregation in the random alloy PdAu NP is suppressed because the random alloy NP has reduced strain as compared to the Au@Pd NP, and the segregation mechanisms in the alloy require more elastic energy for exchange of Pd and Au and between the surface and subsurface.« less
2. Controlling the 3-D morphology of Ni–Fe-based nanocatalysts for the oxygen evolution reaction

   https://doi.org/10.1039/c8nr10138h  

   Manso, Ryan H. ; Acharya, Prashant ; Deng, Shiqing ; Crane, Cameron C. ; Reinhart, Benjamin ; Lee, Sungsik ; Tong, Xiao ; Nykypanchuk, Dmytro ; Zhu, Jing ; Zhu, Yimei ; et al ( April 2019 , Nanoscale)

   Controlling the 3-D morphology of nanocatalysts is one of the underexplored but important approaches for improving the sluggish kinetics of the oxygen evolution reaction (OER) in water electrolysis. This work reports a scalable, oil-based method based on thermal decomposition of organometallic complexes to yield highly uniform Ni–Fe-based nanocatalysts with a well-defined morphology ( i.e. Ni–Fe core–shell, Ni/Fe alloy, and Fe–Ni core–shell). Transmission electron microscopy reveals their morphology and composition to be NiO x –FeO x /NiO x core-mixed shell, NiO x /FeO x alloy, and FeO x –NiO x core–shell. X-ray techniques resolve the electronic structures of the bulk and are supported by electron energy loss spectroscopy analysis of individual nanoparticles. These results suggest that the crystal structure of Ni is most likely to contain α-Ni(OH) 2 and that the chemical environment of Fe is variable, depending on the morphology of the nanoparticle. The Ni diffusion from the amorphous Ni-based core to the iron oxide shell makes the NiO x –NiO x /FeO x core-mixed shell structure the most active and the most stable nanocatalyst, which outperforms the comparison NiO x /FeO x alloy nanoparticles expected to be active for the OER. This study suggests that the chemical environment ofmore »the mixed NiO x /FeO x alloy composition is important to achieve high electrocatalytic activity for the OER and that the 3-D morphology plays a key role in the optimization of the electrocatalytic activity and stability of the nanocatalyst for the OER.« less
3. Crystal structure engineering in multimetallic high-index facet nanocatalysts

   https://doi.org/10.1073/pnas.2105722118  

   Shen, Bo ; Huang, Liliang ; Shen, Jiahong ; He, Kun ; Zheng, Cindy Y. ; Dravid, Vinayak P. ; Wolverton, Chris ; Mirkin, Chad A. ( June 2021 , Proceedings of the National Academy of Sciences)

   In the context of metal particle catalysts, composition, shape, exposed facets, crystal structure, and atom distribution dictate activity. While techniques have been developed to control each of these parameters, there is no general method that allows one to optimize all parameters in the context of polyelemental systems. Herein, by combining a solid-state, Bi-influenced, high-index facet shape regulation strategy with thermal annealing, we achieve control over crystal structure and atom distribution on the exposed high-index facets, resulting in an unprecedentedly diverse library of chemically disordered and ordered multimetallic (Pt, Co, Ni, Cu, Fe, and Mn) tetrahexahedral (THH) nanoparticles. Density functional theory calculations show that surface Bi modification stabilizes the {210} high-index facets of the nanoparticles, regardless of their internal atomic ordering. Moreover, we find that the ordering transition temperatures for the nanoparticles are dependent on their composition, and, in the case of Pt₃Fe₁THH nanoparticles, increasing Ni substitution leads to an order-to-disorder transition at 900 °C. Finally, we have discovered that ordered intermetallic THH Pt₁Co₁nanocatalysts exhibit a catalytic performance superior to disordered THH Pt₁Co₁nanoparticles and commercial Pt/C catalysts toward methanol electrooxidation, highlighting the importance of crystal structure and atom distribution control on high-index facets in nanoscale catalysts.
4. Florida mangrove saltmarsh reference surface soils

   https://doi.org/10.6073/pasta/0e08cbe07c84488cb7b9dd16669946d0  

   Lewis, David Bruce ( January 2021 )

   Abstract

   Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens. Field design and sample collection. We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. Plot center elevations (cm above mean sea level, NAVD 88) were estimated by overlaying the plot locations determined with a global positioning system (Garmin GPS 60, Olathe, KS, USA) on a LiDAR-derived bare-earth digital elevation model (Dewberry, Inc., 2019). The digital elevation model had a vertical accuracy of ± 10 cm (95 % CI) and a horizontal accuracy of ± 116 cm (95 % CI). Soil samples were collected via coring at low tide in June 2011. From each plot, we collected a composite soil sample consisting of three discrete 5.1 cm diameter soil cores taken at equidistant points to 7.6 cm depth. Cores were taken by tapping a sleeve into the soil until its top was flush with the soil surface, sliding a hand under the core, and lifting it up. Cores were then capped and transferred on ice to our laboratory at the University of South Florida (Tampa, Florida, USA), where they were combined in plastic zipper bags, and homogenized by hand into plot-level composite samples on the day they were collected. A damp soil subsample was immediately taken from each composite sample to initiate 1 y incubations for determination of active C and N (see below). The remainder of each composite sample was then placed in a drying oven (60 °C) for 1 week with frequent mixing of the soil to prevent aggregation and liberate water. Organic wetland soils are sometimes dried at 70 °C, however high drying temperatures can volatilize non-water liquids and oxidize and decompose organic matter, so 50 °C is also a common drying temperature for organic soils (Gardner 1986, "Methods of Soil Analysis: Part 1", Soil Science Society of America); we accordingly chose 60 °C as a compromise between sufficient water removal and avoidance of non-water mass loss. Bulk density was determined as soil dry mass per core volume (adding back the dry mass equivalent of the damp subsample removed prior to drying). Dried subsamples were obtained for determination of soil organic matter (SOM), mineral texture composition, and extractable and total carbon (C) and nitrogen (N) within the following week. Sample analyses. A dried subsample was apportioned from each composite sample to determine SOM as mass loss on ignition at 550 °C for 4 h. After organic matter was removed from soil via ignition, mineral particle size composition was determined using a combination of wet sieving and density separation in 49 mM (3 %) sodium hexametaphosphate ((NaPO_3)_6) following procedures in Kettler et al. (2001, Soil Science Society of America Journal 65, 849-852). The percentage of dry soil mass composed of silt and clay particles (hereafter, fines) was calculated as the mass lost from dispersed mineral soil after sieving (0.053 mm mesh sieve). Fines could have been slightly underestimated if any clay particles were burned off during the preceding ignition of soil. An additional subsample was taken from each composite sample to determine extractable N and organic C concentrations via 0.5 M potassium sulfate (K_2SO_4) extractions. We combined soil and extractant (ratio of 1 g dry soil:5 mL extractant) in plastic bottles, reciprocally shook the slurry for 1 h at 120 rpm, and then gravity filtered it through Fisher G6 (1.6 μm pore size) glass fiber filters, followed by colorimetric detection of nitrite (NO_2^-) + nitrate (NO_3^-) and ammonium (NH_4^+) in the filtrate (Hood Nowotny et al., 2010,Soil Science Society of America Journal 74, 1018-1027) using a microplate spectrophotometer (Biotek Epoch, Winooski, VT, USA). Filtrate was also analyzed for dissolved organic C (referred to hereafter as extractable organic C) and total dissolved N via combustion and oxidation followed by detection of the evolved CO_2 and N oxide gases on a Formacs HT TOC/TN analyzer (Skalar, Breda, The Netherlands). Extractable organic N was then computed as total dissolved N in filtrate minus extractable mineral N (itself the sum of extractable NH_4-N and NO_2-N + NO_3-N). We determined soil total C and N from dried, milled subsamples subjected to elemental analysis (ECS 4010, Costech, Inc., Valencia, CA, USA) at the University of South Florida Stable Isotope Laboratory. Median concentration of inorganic C in unvegetated surface soil at our sites is 0.5 % of soil mass (Anderson, 2019, Univ. of South Florida M.S. thesis via methods in Wang et al., 2011, Environmental Monitoring and Assessment 174, 241-257). Inorganic C concentrations are likely even lower in our samples from under vegetation, where organic matter would dilute the contribution of inorganic C to soil mass. Nevertheless, the presence of a small inorganic C pool in our soils may be counted in the total C values we report. Extractable organic C is necessarily of organic C origin given the method (sparging with HCl) used in detection. Active C and N represent the fractions of organic C and N that are mineralizable by soil microorganisms under aerobic conditions in long-term soil incubations. To quantify active C and N, 60 g of field-moist soil were apportioned from each composite sample, placed in a filtration apparatus, and incubated in the dark at 25 °C and field capacity moisture for 365 d (as in Lewis et al., 2014, Ecosphere 5, art59). Moisture levels were maintained by frequently weighing incubated soil and wetting them up to target mass. Daily CO_2 flux was quantified on 29 occasions at 0.5-3 week intervals during the incubation period (with shorter intervals earlier in the incubation), and these per day flux rates were integrated over the 365 d period to compute an estimate of active C. Observations of per day flux were made by sealing samples overnight in airtight chambers fitted with septa and quantifying headspace CO_2 accumulation by injecting headspace samples (obtained through the septa via needle and syringe) into an infrared gas analyzer (PP Systems EGM 4, Amesbury, MA, USA). To estimate active N, each incubated sample was leached with a C and N free, 35 psu solution containing micronutrients (Nadelhoffer, 1990, Soil Science Society of America Journal 54, 411-415) on 19 occasions at increasing 1-6 week intervals during the 365 d incubation, and then extracted in 0.5 M K_2SO_4 at the end of the incubation in order to remove any residual mineral N. Active N was then quantified as the total mass of mineral N leached and extracted. Mineral N in leached and extracted solutions was detected as NH_4-N and NO_2-N + NO_3-N via colorimetry as above. This incubation technique precludes new C and N inputs and persistently leaches mineral N, forcing microorganisms to meet demand by mineralizing existing pools, and thereby directly assays the potential activity of soil organic C and N pools present at the time of soil sampling. Because this analysis commences with disrupting soil physical structure, it is biased toward higher estimates of active fractions. Calculations. Non-mobile C and N fractions were computed as total C and N concentrations minus the extractable and active fractions of each element. This data package reports surface-soil constituents (moisture, fines, SOM, and C and N pools and fractions) in both gravimetric units (mass constituent / mass soil) and areal units (mass constituent / soil surface area integrated through 7.6 cm soil depth, the depth of sampling). Areal concentrations were computed as X × D × 7.6, where X is the gravimetric concentration of a soil constituent, D is soil bulk density (g dry soil / cm^3), and 7.6 is the sampling depth in cm.
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5. Spontaneous selective deposition of iron oxide nanoparticles on graphite as model catalysts

   https://doi.org/10.1039/c9na00472f  

   de Alwis, Chathura ; Leftwich, Timothy R. ; Mukherjee, Pinaki ; Denofre, Alex ; Perrine, Kathryn A. ( December 2019 , Nanoscale Advances)

   Iron oxide nanomaterials participate in redox processes that give them ideal properties for their use as earth-abundant catalysts. Fabricating nanocatalysts for such applications requires detailed knowledge of the deposition and growth. We report the spontaneous deposition of iron oxide nanoparticles on HOPG in defect areas and on step edges from a metal precursor solution. To study the nucleation and growth of iron oxide nanoparticles, tailored defects were created on the surface of HOPG using various ion sources that serve as the target sites for iron oxide nucleation. After solution deposition and annealing, the iron oxide nanoparticles were found to nucleate and coalesce at 400 °C. AFM revealed that the particles on the sp 3 carbon sites enabled the nanoparticles to aggregate into larger particles. The iron oxide nanoparticles were characterized as having an Fe 3+ oxidation state and two different oxygen species, Fe–O and Fe–OH/Fe–OOH, as determined by XPS. STEM imaging and EDS mapping confirmed that the majority of the nanoparticles grown were converted to hematite after annealing at 400 °C. A mechanism of spontaneous and selective deposition on the HOPG surface and transformation of the iron oxide nanoparticles is proposed. These results suggest a simple method for growing nanoparticlesmore »as a model catalyst.« less