Reversibility and Improved Hydrogen Release of Magnesium Borohydride

Desorption and subsequent rehydrogenation of Mg(BH_4)_2 with and without 5 mol % TiF_3 and ScCl_3 have been investigated. Temperature programmed desorption (TPD) experiments revealed a significant increase in the rate of desorption as well as the weight percentage of hydrogen released with additives upon heating to 300 °C. Stable Mg(B_xH_y)_n intermediates were formed at 300 °C, whereas MgB_2 was the major product when heated to 600 °C. These samples were then rehydrogenated and subsequently characterized with powder X-ray diffraction (pXRD), Raman, and NMR spectroscopy. We confirmed significant conversion of MgB_2 to fully hydrogenated Mg(BH_4)_2 for the sample with and without additives. TPD and NMR studies revealed that the additives have a significant effect on the reaction pathway during both dehydrogenation and rehydrogenation reactions. This work suggests that the use of additives may provide a valid pathway for improving intrinsic hydrogen storage properties of magnesium borohydride

Explainable Machine Learning for Hydrogen Diffusion in Metals and Random Binary Alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared-errors between 98-119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies.By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importances, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. This framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.Comment: 36 pages, 8 figures, supplemental materia

Design principles for the ultimate gas deliverable capacity material: nonporous to porous deformations without volume change

Understanding the fundamental limits of gas deliverable capacity in porous materials is of critical importance as it informs whether technical targets (e.g., for on-board vehicular storage) are feasible. High-throughput screening studies of rigid materials, for example, have shown they are not able to achieve the original ARPA-E methane storage targets, yet an interesting question remains: what is the upper limit of deliverable capacity in flexible materials? In this work we develop a statistical adsorption model that specifically probes the limit of deliverable capacity in intrinsically flexible materials. The resulting adsorption thermodynamics indicate that a perfectly designed, intrinsically flexible nanoporous material could achieve higher methane deliverable capacity than the best benchmark systems known to date with little to no total volume change. Density functional theory and grand canonical Monte Carlo simulations identify a known metal–organic framework (MOF) that validates key features of the model. Therefore, this work (1) motivates a continued, extensive effort to rationally design a porous material analogous to the adsorption model and (2) calls for continued discovery of additional high deliverable capacity materials that remain hidden from rigid structure screening studies due to nominal non-porosity

Comparison of sugar content for ionic liquid pretreated Douglas-fir woodchips and forestry residues

Background The development of affordable woody biomass feedstocks represents a significant opportunity in the development of cellulosic biofuels. Primary woodchips produced by forest mills are considered an ideal feedstock, but the prices they command on the market are currently too expensive for biorefineries. In comparison, forestry residues represent a potential low-cost input but are considered a more challenging feedstock for sugar production due to complexities in composition and potential contamination arising from soil that may be present. We compare the sugar yields, changes in composition in Douglas-fir woodchips and forestry residues after pretreatment using ionic liquids and enzymatic saccharification in order to determine if this approach can efficiently liberate fermentable sugars. Results These samples were either mechanically milled through a 2 mm mesh or pretreated as received with the ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate [C2mim][OAc] at 120°C and 160°C. IL pretreatment of Douglas-fir woodchips and forestry residues resulted in approximately 71-92% glucose yields after enzymatic saccharification. X-ray diffraction (XRD) showed that the pretreated cellulose was less crystalline after IL pretreatment as compared to untreated control samples. Two-dimensional nuclear magnetic resonance spectroscopy (2D-NMR) revealed changes in lignin and hemicellulose structure and composition as a function of pretreatment. Mass balances of sugar and lignin streams for both the Douglas-fir woodchips and forestry residues throughout the pretreatment and enzymatic saccharification processes are presented. Conclusions While the highest sugar yields were observed with the Douglas-fir woodchips, reasonably high sugar yields were obtained from forestry residues after ionic liquid pretreatment. Structural changes to lignin, cellulose and hemicellulose in the woodchips and forestry residues of Douglas-fir after [C2mim][OAc] pretreatment are analyzed by XRD and 2D-NMR, and indicate that significant changes occurred. Irrespective of the particle sizes used in this study, ionic liquid pretreatment successfully allowed high glucose yields after enzymatic saccharification. These results indicate that forestry residues may be a more viable feedstock than previously thought for the production of biofuels

Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning

An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures and chemistry they can exhibit. While black box machine learning-based algorithms can predict these properties with some success, they do not directly provide the basis on which these predictions are made, therefore complicating the a priori design of novel materials exhibiting a desired property value. In this work we demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H2 pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. Elucidation of this simple structure–property relationship is valid across a range of compositions, metal substitutions, and structural classes exhibited by intermetallic hydrides. This permits rational targeting of novel intermetallics for high-pressure hydrogen storage (low-stability hydrides) by their descriptor values, and we predict a known intermetallic to form a low-stability hydride (as confirmed by density functional theory calculations) that has not yet been experimentally investigated

Probing the unusual anion mobility of LiBH_4 confined in highly ordered nanoporous carbon frameworks via solid state NMR and quasielastic neutron scattering

Particle size and particle–framework interactions have profound effects on the kinetics, reaction pathways, and even thermodynamics of complex hydrides incorporated in frameworks possessing nanoscale features. Tuning these properties may hold the key to the utilization of complex hydrides in practical applications for hydrogen storage. Using carefully synthesized, highly-ordered, nanoporous carbons (NPCs), we have previously shown quantitative differences in the kinetics and reaction pathways of LiBH_4 when incorporated into the frameworks. In this paper, we probe the anion mobility of LiBH_4 confined in NPC frameworks by a combination of solid state NMR and quasielastic neutron scattering (QENS) and present some new insights into the nanoconfinement effect. NMR and QENS spectra of LiBH_4 confined in a 4 nm pore NPC suggest that the BH_4− anions nearer the LiBH_4–carbon pore interface exhibit much more rapid translational and reorientational motions compared to those in the LiBH_4 interior. Moreover, an overly broadened BH_4− torsional vibration band reveals a disorder-induced array of BH_4− rotational potentials. XRD results are consistent with a lack of LiBH_4 long-range order in the pores. Consistent with differential scanning calorimetry measurements, neither NMR nor QENS detects a clear solid–solid phase transition as observed in the bulk, indicating that borohydride–framework interactions and/or nanosize effects have large roles in confined LiBH_4

A Mechanistic Analysis of Phase Evolution and Hydrogen Storage Behavior in Nanocrystalline Mg(BH4)2 within Reduced Graphene Oxide.

Magnesium borohydride (Mg(BH4)2, abbreviated here MBH) has received tremendous attention as a promising onboard hydrogen storage medium due to its excellent gravimetric and volumetric hydrogen storage capacities. While the polymorphs of MBH-alpha (α), beta (β), and gamma (γ)-have distinct properties, their synthetic homogeneity can be difficult to control, mainly due to their structural complexity and similar thermodynamic properties. Here, we describe an effective approach for obtaining pure polymorphic phases of MBH nanomaterials within a reduced graphene oxide support (abbreviated MBHg) under mild conditions (60-190 °C under mild vacuum, 2 Torr), starting from two distinct samples initially dried under Ar and vacuum. Specifically, we selectively synthesize the thermodynamically stable α phase and metastable β phase from the γ-phase within the temperature range of 150-180 °C. The relevant underlying phase evolution mechanism is elucidated by theoretical thermodynamics and kinetic nucleation modeling. The resulting MBHg composites exhibit structural stability, resistance to oxidation, and partially reversible formation of diverse [BH4]- species during de- and rehydrogenation processes, rendering them intriguing candidates for further optimization toward hydrogen storage applications

Selective sorption of oxygen and nitrous oxide by an electron donor-incorporated flexible coordination network

Incorporating strong electron donor functionality into flexible coordination networks is intriguing for sorption applications due to a built-in mechanism for electron-withdrawing guests. Here we report a 2D flexible porous coordination network, [Ni₂(4, 4′-bipyridine)(VTTF)₂]n(1) (where H₂VTTF = 2, 2′-[1, 2-bis(4-benzoic acid)-1, 2ethanediylidene]bis-1, 3-benzodithiole), which exhibits large structural deformation from the as-synthesized or open phase (1α) into the closed phase (1β) after guest removal, as demonstrated by X-ray and electron diffraction. Interestingly, upon exposure to electron-withdrawing species, 1β reversibly undergoes guest accommodation transitions; 1α⊃O₂ (90 K) and 1α⊃N₂O (185 K). Moreover, the 1β phase showed exclusive O₂ sorption over other gases (N₂, Ar, and CO) at 120 K. The phase transformations between the 1α and 1β phases under these gases were carefully investigated by in-situ X-ray diffraction, in-situ spectroscopic studies, and DFT calculations, validating that the unusual sorption was attributed to the combination of flexible frameworks and VTTF (electron-donor) that induces strong interactions with electron-withdrawing species

Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability

Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet, the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial and error. Data-driven materials’ discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here, we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitate a data-driven approach that does not rely on exact crystal structures to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based on not only targeted thermodynamic properties but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70× increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient material selection for various applications, especially in areas such as metal hydride-based hydrogen compressors, actuators, and heat pumps