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Metal boride nanostructures have shown significant promise for hydrogen storage applications. However, the synthesis of nanoscale metal boride particles is challenging because of their high surface energy, strong inter- and intraplanar bonding, and difficult-to-control surface termination. Here, it is demonstrated that mechanochemical exfoliation of magnesium diboride in zirconia produces 3-4 nm ultrathin MgB2 nanosheets (multilayers) in high yield. High-pressure hydrogenation of these multilayers at 70 MPa and 330 °C followed by dehydrogenation at 390 °C reveals a hydrogen capacity of 5.1 wt%, which is ≈50 times larger than the capacity of bulk MgB2 under the same conditions. This enhancement is attributed to the creation of defective sites by ball-milling and incomplete Mg surface coverage in MgB2 multilayers, which disrupts the stable boron-boron ring structure. The density functional theory calculations indicate that the balance of Mg on the MgB2 nanosheet surface changes as the material hydrogenates, as it is energetically favorable to trade a small number of Mg vacancies in Mg(BH4 )2 for greater Mg coverage on the MgB2 surface. The exfoliation and creation of ultrathin layers is a promising new direction for 2D metal boride/borohydride research with the potential to achieve high-capacity reversible hydrogen storage at more moderate pressures and temperatures.
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Batteries based on solid-state electrolytes, including Li7La3Zr2O12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. These examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.
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Lithium-rich oxychloride antiperovskites are promising solid electrolytes for enabling next-generation batteries. Here, we report a comprehensive study varying Li+ concentrations in [Formula: see text] using ab initio molecular dynamics simulations. The simulations accurately capture the complex interactions between Li+ vacancies ([Formula: see text]), the dominant mobile species in [Formula: see text]. The [Formula: see text] polarize and distort the host lattice, inducing additional non-vacancy-mediated diffusion mechanisms and correlated diffusion events that reduce the activation energy barrier at concentrations as low as 1.5% [Formula: see text]. Our analyses of discretized diffusion events in both space and time illustrate the critical interplay between correlated dynamics, polarization and local distortion in promoting ionic conductivity in [Formula: see text]. This article is part of the Theo Murphy meeting issue 'Understanding fast-ion conduction in solid electrolytes'.
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Electrólitos , Litio , Suministros de Energía Eléctrica , Simulación de Dinámica MolecularRESUMEN
Superionic solid electrolytes have widespread use in energy devices, but the fundamental motivations for fast ion conduction are often elusive. In this Perspective, we draw upon atomistic simulations of a wide range of superionic conductors to illustrate some ways frustration can lower diffusion cation barriers in solids. Based on our studies of halides, oxides, sulfides and hydroborates and a survey of published reports, we classify three types of frustration that create competition between different local atomic preferences, thereby flattening the diffusive energy landscape. These include chemical frustration, which derives from competing factors in the anion-cation interaction; structural frustration, which arises from lattice arrangements that induce site distortion or prevent cation ordering; and dynamical frustration, which is associated with temporary fluctuations in the energy landscape due to anion reorientation or cation reconfiguration. For each class of frustration, we provide detailed simulation analyses of various materials to show how ion mobility is facilitated, resulting in stabilizing factors that are both entropic and enthalpic in origin. We propose the use of these categories as a general construct for classifying frustration in superionic conductors and discuss implications for future development of suitable descriptors and improvement strategies. This article is part of the Theo Murphy meeting issue 'Understanding fast-ion conduction in solid electrolytes'.
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Cu-based catalysts currently offer the most promising route to actively and selectively produce value-added chemicals via electrochemical reduction of CO2 (eCO2R); yet further improvements are required for their wide-scale deployment in carbon mitigation efforts. Here, we systematically investigate a family of dilute Cu-based alloys to explore their viability as active and selective catalysts for eCO2R through a combined theoretical-experimental approach. Using a quantum-classical modeling approach that accounts for dynamic solvation effects, we assess the stability and activity of model single-atom catalysts under eCO2R conditions. Our calculations identify that the presence of eCO2R intermediates, such as CO*, H*, and OH*, may dynamically influence the local catalyst surface composition. Additionally, we identify through binding energy descriptors of the CO*, CHO*, and OCCO* dimer intermediates that certain elements, such as group 13 elements (B, Al, and Ga), enhance the selectivity of C2+ species relative to pure Cu by facilitating CO dimerization. The theoretical work is corroborated by preliminary testing of eCO2R activity and selectivity of candidate dilute Cu-based alloy catalyst films prepared by electron beam evaporation in a zero-gap gas diffusion electrode-based reactor. Of all studied alloys, dilute CuAl was found to be the most active and selective toward C2+ products like ethylene, consistent with the theoretical predictions. We attribute the improved performance of dilute CuAl alloys to more favorable dimerization reaction energetics of bound CO species relative to that on pure Cu. In a broader context, the results presented here demonstrate the power of our simulation framework in terms of rational catalyst design.
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The highly unfavorable thermodynamics of direct aluminum hydrogenation can be overcome by stabilizing alane within a nanoporous bipyridine-functionalized covalent triazine framework (AlH3 @CTF-bipyridine). This material and the counterpart AlH3 @CTF-biphenyl rapidly desorb H2 between 95 and 154 °C, with desorption complete at 250 °C. Sieverts measurements, 27 Al MAS NMR and 27 Al{1 H} REDOR experiments, and computational spectroscopy reveal that AlH3 @CTF-bipyridine dehydrogenation is reversible at 60 °C under 700â bar hydrogen, >10 times lower pressure than that required to hydrogenate bulk aluminum. DFT calculations and EPR measurements support an unconventional mechanism whereby strong AlH3 binding to bipyridine results in single-electron transfer to form AlH2 (AlH3 )n clusters. The resulting size-dependent charge redistribution alters the dehydrogenation/rehydrogenation thermochemistry, suggesting a novel strategy to enable reversibility in high-capacity metal hydrides.
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Knowledge and foundational understanding of phenomena associated with the behavior of materials at the nanoscale is one of the key scientific challenges toward a sustainable energy future. Size reduction from bulk to the nanoscale leads to a variety of exciting and anomalous phenomena due to enhanced surface-to-volume ratio, reduced transport length, and tunable nanointerfaces. Nanostructured metal hydrides are an important class of materials with significant potential for energy storage applications. Hydrogen storage in nanoscale metal hydrides has been recognized as a potentially transformative technology, and the field is now growing steadily due to the ability to tune the material properties more independently and drastically compared to those of their bulk counterparts. The numerous advantages of nanostructured metal hydrides compared to bulk include improved reversibility, altered heats of hydrogen absorption/desorption, nanointerfacial reaction pathways with faster rates, and new surface states capable of activating chemical bonds. This review aims to summarize the progress to date in the area of nanostructured metal hydrides and intends to understand and explain the underpinnings of the innovative concepts and strategies developed over the past decade to tune the thermodynamics and kinetics of hydrogen storage reactions. These recent achievements have the potential to propel further the prospects of tuning the hydride properties at nanoscale, with several promising directions and strategies that could lead to the next generation of solid-state materials for hydrogen storage applications.
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In this article, we present a systematic study on developing machine learning force fields (MLFFs) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training set from molecular dynamics simulations, it is unlikely to cover the global features of the potential energy surface. To remedy this issue, we used randomly generated symmetrical crystal structures to train a more general Si-MLFF. Furthermore, we performed substantial benchmarks among different choices of material descriptors and regression techniques on two different sets of silicon data. Our results show that neural network potential fitting with bispectrum coefficients as descriptors is a feasible method for obtaining accurate and transferable MLFFs.
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Complex light metal hydrides are promising candidates for efficient, compact solid-state hydrogen storage. (De)hydrogenation of these materials often proceeds via multiple reaction intermediates, the energetics of which determine reversibility and kinetics. At the solid-state reaction front, molecular-level chemistry eventually drives the formation of bulk product phases. Therefore, a better understanding of realistic (de)hydrogenation behavior requires considering possible reaction products along all stages of morphological evolution, from molecular to bulk crystalline. Here, we use first-principles calculations to explore the interplay between intermediate morphology and reaction pathways. Employing representative complex metal hydride systems, we investigate the relative energetics of three distinct morphological stages that can be expressed by intermediates during solid-state reactions: i) dispersed molecules; ii) clustered molecular chains; and iii) condensed-phase crystals. Our results verify that the effective reaction energy landscape strongly depends on the morphological features and associated chemical environment, offering a possible explanation for observed discrepancies between X-ray diffraction and nuclear magnetic resonance measurements. Our theoretical understanding also provides physical and chemical insight into phase nucleation kinetics upon (de)hydrogenation of complex metal hydrides.
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Magnesium-based materials provide some of the highest capacities for solid-state hydrogen storage. However, efforts to improve their performance rely on a comprehensive understanding of thermodynamic and kinetic limitations at various stages of (de)hydrogenation. Part of the complexity arises from the fact that unlike interstitial metal hydrides that retain the same crystal structures of the underlying metals, MgH2 and other magnesium-based hydrides typically undergo dehydrogenation reactions that are coupled to a structural phase transformation. As a first step towards enabling molecular dynamics studies of thermodynamics, kinetics, and (de)hydrogenation mechanisms of Mg-based solid-state hydrogen storage materials with changing crystal structures, we have developed an analytical bond order potential for Mg-H systems. We demonstrate that our potential accurately reproduces property trends of a variety of elemental and compound configurations with different coordinations, including small clusters and bulk lattices. More importantly, we show that our potential captures the relevant (de)hydrogenation chemical reactions 2H (gas)âH2 (gas) and 2H (gas)+Mg (hcp)âMgH2 (rutile) within molecular dynamics simulations. This verifies that our potential correctly prescribes the lowest Gibbs free energies to the equilibrium H2 and MgH2 phases as compared to other configurations. It also indicates that our molecular dynamics methods can directly reveal atomic processes of (de)hydrogenation of the Mg-H systems.
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The contamination of water resources with nitrate is a growing and significant problem. Here we report the use of ultramicroporous carbon as a capacitive deionization (CDI) electrode for selectively removing nitrate from an anion mixture. Through moderate activation, we achieve a micropore-size distribution consisting almost exclusively of narrow (<1 nm) pores that are well suited for adsorbing the planar, weakly hydrated nitrate molecule. Cyclic voltammetry measurements reveal an enhanced capacitance for nitrate when compared to chloride as well as significant ion sieving effects when sulfate is the only anion present. We measure high selectivities (S) of both nitrate over sulfate (SNO3/SO4 = 17.8 ± 3.6 at 0.6 V) and nitrate over chloride (SNO3/Cl = 6.1 ± 0.4 at 0.6 V) when performing a constant voltage CDI separation on 3.33 mM/3.33 mM/1.67 mM Cl/NO3/SO4 feedwater. These results are particularly encouraging considering that a divalent interferant was present in the feed. Using molecular dynamics simulations, we examine the solvation characteristics of these ions to better understand why nitrate is preferentially electrosorbed over sulfate and chloride.
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Carbono , Purificación del Agua , Adsorción , Capacidad Eléctrica , Electrodos , NitratosRESUMEN
Using density functional theory, we study the effect of injected electrons (simulating photoexcited electrons) on the energetics, structures, and binding sites available to CO2 molecules on subnanometer Pt clusters decorated onto anatase TiO2(101) surfaces, shedding light on the first and key step of CO2 photoreduction. Upon the addition of one, two, or three electrons, the O-C-O angles of adsorbed CO2 become progressively smaller in binding sites that directly contact Pt clusters, while no significant change is found in the intra bond length of the adsorbed CO2 and in the bonding distances between the adsorbed CO2 and supported clusters. The extra electrons lead to the stabilization of adsorption sites identified on neutral slabs, including previously metastable configurations, suggesting the enhancement of accessible CO2 binding sites. Furthermore, supported clusters are able to populate the electronic states of adsorbed CO2 species, facilitating the formation of the CO2- anion. To help interpret experimentally observed frequencies, conversion factors are proposed to gain insight into the charge state and O-C-O angle of the adsorbed CO2. Interestingly, upon electron addition, cluster reconstruction may exist due to the bonding inclination between CO2 and atoms in the Pt cluster, further stabilizing the intermediate complexes. Finally, the rate-limiting step (C-O bond cleavage) in the CO2 dissociation to CO is slightly reduced by the introduction of an extra electron. Our results show that subnanometer metal cluster based photocatalysts are good candidates for CO2 photoreduction.
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Mg(BH4)2 is a promising solid-state hydrogen storage material, releasing 14.9 wt% hydrogen upon conversion to MgB2. Although several dehydrogenation pathways have been proposed, the hydrogenation process is less well understood. Here, we present a joint experimental-theoretical study that elucidates the key atomistic mechanisms associated with the initial stages of hydrogen uptake within MgB2. Fourier transform infrared, X-ray absorption, and X-ray emission spectroscopies are integrated with spectroscopic simulations to show that hydrogenation can initially proceed via direct conversion of MgB2 to Mg(BH4)2 complexes. The associated energy landscape is mapped by combining ab initio calculations with barriers extracted from the experimental uptake curves, from which a kinetic model is constructed. The results from the kinetic model suggest that initial hydrogenation takes place via a multi-step process: molecular H2 dissociation, likely at Mg-terminated MgB2 surfaces, is followed by migration of atomic hydrogen to defective boron sites, where the formation of stable B-H bonds ultimately leads to the direct creation of Mg(BH4)2 complexes without persistent BxHy intermediates. Implications for understanding the chemical, structural, and electronic changes upon hydrogenation of MgB2 are discussed.
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Using density functional theory calculations, we investigate the influence of size-dependent cluster morphology on the synergistic catalytic properties of anatase TiO2(101) surfaces decorated with subnanometer Pt clusters. Focusing on the formation of the key precursor in the CO2 photoreduction reaction (bent CO2(-)), we find that flatter (2D-like) Pt clusters that "wet" the TiO2 surface offer significantly less benefit than 3D-like Pt clusters. We attribute the differences to three factors. First, the 3D clusters provide a greater number of accessible Pt-TiO2 interfacial sites with geometries that can aid CO2 bond bending and charge transfer processes. Second, binding competition among each Pt-CO2 bonding interaction mitigates maximum orbital overlaps, leading to insufficient CO2 binding. Third and also most interestingly, the 3D clusters tend to possess higher structural fluxionality than the flatter clusters, which is shown to correlate positively with CO2 binding strength. The preferred morphology adopted by the clusters depends on several factors, including the cluster size and the presence of oxygen vacancies on the TiO2 surface; this suggests a strategy for optimizing the synergistic effect between Pt clusters and TiO2 surfaces for CO2 photocatalysis. Clusters of â¼6-8 atoms should provide the largest benefit, since they retain the desired 3D morphology, yet are small enough to exhibit high structural fluxionality. Electronic structure analysis provides additional insight into the electronic motivations for the enhanced binding of CO2 on TiO2-supported 3D Pt clusters, as well as suppressed binding on flattened, 2D-like clusters.
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Dióxido de Carbono/química , Adsorción , Catálisis , Oxidación-Reducción , Platino (Metal)/química , Propiedades de Superficie , Titanio/químicaRESUMEN
Although crystalline silicon (c-Si) anodes promise very high energy densities in Li-ion batteries, their practical use is complicated by amorphization, large volume expansion and severe plastic deformation upon lithium insertion. Recent experiments have revealed the existence of a sharp interface between crystalline Si (c-Si) and the amorphous LixSi alloy during lithiation, which propagates with a velocity that is orientation dependent; the resulting anisotropic swelling generates substantial strain concentrations that initiate cracks even in nanostructured Si. Here we describe a novel strategy to mitigate lithiation-induced fracture by using pristine c-Si structures with engineered anisometric morphologies that are deliberately designed to counteract the anisotropy in the crystalline/amorphous interface velocity. This produces a much more uniform volume expansion, significantly reducing strain concentration. Based on a new, validated methodology that improves previous models of anisotropic swelling of c-Si, we propose optimal morphological designs for c-Si pillars and particles. The advantages of the new morphologies are clearly demonstrated by mesoscale simulations and verified by experiments on engineered c-Si micropillars. The results of this study illustrate that morphological design is effective in improving the fracture resistance of micron-sized Si electrodes, which will facilitate their practical application in next-generation Li-ion batteries. The model and design approach present in this paper also have general implications for the study and mitigation of mechanical failure of electrode materials that undergo large anisotropic volume change upon ion insertion and extraction.
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Many transition metal complexes exhibit infrared or visible optical absorption arising from d-d transitions that are the key to functionality in technological applications and biological processes. The observed spectral characteristics of the absorption spectra depend on several underlying physical parameters whose relative contributions are still not fully understood. Although conventional arguments based on ligand-field theory can be invoked to rationalize the peak absorption energy, they cannot describe the detailed features of the observed spectral profile such as the spectral width and shape, or unexpected correlations between the oscillator strength and absorption peak position. Here, we combine experimental observations with first-principles simulations to investigate origins of the absorption spectral profile in model systems of aqueous Cu(2+) ions with Cl(-), Br(-), NO2(-) and CH3CO2(-) ligands. The ligand identity and concentration, fine structure in the electronic d-orbitals of Cu(2+), complex geometry, and solvation environment are all found to play key roles in determining the spectral profile. Moreover, similar physiochemical origins of these factors lead to interesting and unexpected correlations in spectral features. The results provide important insights into the underlying mechanisms of the observed spectral features and offer a framework for advancing the ability of theoretical models to predict and interpret the behavior of such systems.
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Many key performance characteristics of carbon-based lithium-ion battery anodes are largely determined by the strength of binding between lithium (Li) and sp(2) carbon (C), which can vary significantly with subtle changes in substrate structure, chemistry, and morphology. Here, we use density functional theory calculations to investigate the interactions of Li with a wide variety of sp(2) C substrates, including pristine, defective, and strained graphene, planar C clusters, nanotubes, C edges, and multilayer stacks. In almost all cases, we find a universal linear relation between the Li-C binding energy and the work required to fill previously unoccupied electronic states within the substrate. This suggests that Li capacity is predominantly determined by two key factors-namely, intrinsic quantum capacitance limitations and the absolute placement of the Fermi level. This simple descriptor allows for straightforward prediction of the Li-C binding energy and related battery characteristics in candidate C materials based solely on the substrate electronic structure. It further suggests specific guidelines for designing more effective C-based anodes. The method should be broadly applicable to charge-transfer adsorption on planar substrates, and provides a phenomenological connection to established principles in supercapacitor and catalyst design.
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We have performed ab initio density functional theory calculations, incorporating London dispersion corrections, to study the absorption of molecular hydrogen on zigzag graphene nanoribbons whose edges have been functionalized by OH, NH2, COOH, NO2, or H2PO3. We find that hydrogen molecules always preferentially bind at or near the functionalized edge, and display induced dipole moments. Binding is generally enhanced by the presence of polar functional groups. The largest gains are observed for groups with oxygen lone pairs that can facilitate local charge reorganization, with the biggest single enhancement in adsorption energy found for "strong functionalization" by H2PO3 (115 meV/H2 versus 52 meV/H2 on bare graphene). We show that for binding on the "outer edge" near the functional group, the presence of the group can introduce appreciable contributions from Debye interactions and higher-order multipole electrostatic terms, in addition to the dominant London dispersion interactions. For those functional groups that contain the OH moiety, the adsorption energy is linearly proportional to the number of lone pairs on oxygen atoms. Mixed functionalization with two different functional groups on a graphene edge can also have a synergistic effect, particularly when electron-donating and electron-withdrawing groups are combined. For binding on the "inner edge" somewhat farther from the functional group, most of the binding again arises from London interactions; however, there is also significant charge redistribution in the π manifold, which directly reflects the electron donating or withdrawing capacity of the functional group. Our results offer insight into the specific origins of weak binding of gas molecules on graphene, and suggest that edge functionalization could perhaps be used in combination with other strategies to increase the uptake of hydrogen in graphene. They also have relevance for the storage of hydrogen in porous carbon materials, such as activated carbons.
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Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.
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Pitting corrosion in seawater is one of the most difficult forms of corrosion to identify and control. A workhorse material for marine applications, 316L stainless steel (316L SS) is known to balance resistance to pitting with good mechanical properties. The advent of additive manufacturing (AM), particularly laser powder bed fusion (LPBF), has prompted numerous microstructural and mechanical investigations of LPBF 316L SS; however, the origins of pitting corrosion on as-built surfaces is unknown, despite their utmost importance for certification of LPBF 316L SS prior to fielding. Here, we show that Mn-rich silicate slags are responsible for pitting of the as-built LPBF material in sodium chloride due to their introduction of deleterious defects such as cracks or surface oxide heterogeneities. In addition, we explain how slags are formed in the liquid metal and deposited at the as-built surfaces using high-fidelity melt pool simulations. Our work uncovers how LPBF changes surface oxides due to rapid solidification and high-temperature oxidation, leading to fundamentally different pitting corrosion mechanisms.