Randy Jalem

Synthesis of Porous Single-Crystalline Platinum Nanocubes Composed of Nanoparticles

Single-crystalline platinum nanocubes with porous morphology were synthesized for the first time by using ethylene glycol, HCl, and polyvinylpyrrolidone as the reducing agents of H2PtCl6. The morphology and size distribution of the Pt particles formed were studied with a high-resolution transmission electron microscope and selected-area electron diffraction pattern. By controlling the material concentrations and reaction temperature and period, Pt single crystals about 5 nm in size were formed in the first stage of the reduction process that had {100} facets, which were stacked one on top of the other, forming porous nanocubes 20-80 nm in length. The synthesized Pt nanocubes exhibited enhanced catalytic activity for methanol oxidation.

Synthesis and characterization of polyhedral Pt nanoparticles: Their catalytic property, surface attachment, self-aggregation and assembly

In this paper, we presented the preparation procedure of Pt nanoparticles with the well-controlled polyhedral morphology and size by a modified polyol method using AgNO(3) in accordance with the reduction of H(2)PtCl(6) in EG at high temperature around 160°C. The methods of UV-vis spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), and high resolution (HR) TEM measurements were used to characterize their surface morphology, size, and crystal structure. We have observed that the polyhedral Pt nanoparticles of sharp edges and corners were produced in the preferential homogenous growth as well as the formation of porous and large Pt particles by self-aggregation and assembly originating from as-prepared polyhedral Pt nanoparticles. It is most impressive to find that the arrangement of Pt nanoparticles was observed in their surface attachments, self-aggregation, random and directed surface self-assembly by the bottom-up approach. Their high electrocatalytic activity for methanol oxidation was predicted. The findings and results showed that the polyhedral Pt nanoparticle-based catalysts exhibited the high electrocatalytic activity for their potential applications in developing the efficient Pt-based catalysts for direct methanol fuel cells.

An efficient rule-based screening approach for discovering fast lithium ion conductors using density functional theory and artificial neural networks

The density functional theory (DFT) method is a widely used tool that can guide targeted searches exploring numerous possible chemistries according to any property of interest. However, acquiring accurate DFT results from a large chemical search space is still a major challenge for computationalists because it requires considerable time and resources. Therefore, advances in this field are urgently needed. In particular, the development of new materials for Li ion batteries would benefit greatly because of the increasing demand for power, energy density, stability during operation, and safety. We have previously demonstrated the use of multivariate partial least squares (PLS) regression to augment DFT calculations and accelerate material screening. However, the linear function scheme in the PLS method does not accurately reproduce the material values near the extreme ends of the attribute dataset. In this study, we examined neural network (NN) modeling, which offers a more flexible framework, to improve the accuracy of the values. We used the compositional space for LiMXO4 (M – main group elements, X – group XIV and group XV), which is a candidate solid electrolyte material. The evolved NN models were substantially more accurate and could generalize better than the PLS models for values inside and outside the dataset that they were trained on. We also explored NN modeling with common literature data as descriptors for target properties and found that the predictive capability was comparable with that of DFT data-based modeling. The relevance of the input variables was then identified using two of the most common techniques: the causal index method and sensitivity analysis. Our method offers a simple, practical approach for merging theoretical and experimental databases to accelerate the screening of a wider variety of materials.

Informatics-Aided Density Functional Theory Study on the Li Ion Transport of Tavorite-Type LiMTO4F (M3+–T5+, M2+–T6+)

The ongoing search for fast Li-ion conducting solid electrolytes has driven the deployment surge on density functional theory (DFT) computation and materials informatics for exploring novel chemistries before actual experimental testing. Existing structure prototypes can now be readily evaluated beforehand not only to map out trends on target properties or for candidate composition selection but also for gaining insights on structure-property relationships. Recently, the tavorite structure has been determined to be capable of a fast Li ion insertion rate for battery cathode applications. Taking this inspiration, we surveyed the LiMTO4F tavorite system (M(3+)-T(5+) and M(2+)-T(6+) pairs; M is nontransition metals) for solid electrolyte use, identifying promising compositions with enormously low Li migration energy (ME) and understanding how structure parameters affect or modulate ME. We employed a combination of DFT computation, variable interaction analysis, graph theory, and a neural network for building a crystal structure-based ME prediction model. Candidate compositions that were predicted include LiGaPO4F (0.25 eV), LiGdPO4F (0.30 eV), LiDyPO4F (0.30 eV), LiMgSO4F (0.21 eV), and LiMgSeO4F (0.11 eV). With chemical substitutions at M and T sites, competing effects among Li pathway bottleneck size, polyanion covalency, and local lattice distortion were determined to be crucial for controlling ME. A way to predict ME for multiple structure types within the neural network framework was also explored.

Global minimum structure search in LixCoO2 composition using a hybrid evolutionary algorithm

The global minimum structures for Li(x)CoO(2) compositions where 0 ≤ x ≤ 1 were probed by using a hybrid evolutionary algorithm with an underlying ab initio structural relaxation scheme. The method successfully predicted experimentally observed variants of layered configurations at various degrees of lithiation and the spinel (Fd3[combining macron]m) phase at x = 1/2. New low-energy non-layered host structures at x < 1/2 were also revealed. These structures can be formed from the usual layered configuration through coherent stacking faults along the c-axis and the migration of Co ions into the Li-poor intercalation layer.

Experimental and first-principles DFT study on the electrochemical reactivity of garnet-type solid electrolytes with carbon

The operating stability of the solid electrolyte component is one of paramount importance in the design of all-solid-state Li ion batteries. In this work, we investigated the origin of capacity fading during the charge process of an air-isolated Li ion battery with a garnet Li6.625La3Zr1.625Ta0.375O12 (LLZrTaO) solid electrolyte, a Li metal anode, and a LiFePO4 + carbon (LFP + C) active cathode material. Cyclic voltammetry measurements of the fabricated Li/LLZrTaO/(LLZrTaO + C) and Li/LLZrTaO/Al cells revealed a rise and an absence, respectively, of oxidation current which points to the garnet oxide electrolyte being decomposed via a reaction with carbon. XRD patterns of the solid electrolyte at post-charging showed no detectable impurity phases, suggesting that the decomposition product(s) is (are) likely made up of light elements. Based on first-principles calculations, the decomposition route may involve the formation of defective garnet by Li removal, oxygen release, and formation of products such as Li2CO3 and possibly of CO2; formation of the latter product is facilitated at elevated operating temperatures. Among the evaluated base garnet compounds (Li5+xLa3M2O12, with M: Nb Ta for x = 0 and M: Zr, Ti, Hf for x = 2), Li7La3Hf2O12 is predicted to be the most stable against Li2CO3 formation. A strong correlation has been determined between stability at charging and the electronegativity of the M cation, that is, the smaller the M electronegativity, the more stable is the garnet compound against carbon reactions.

Efficient automatic screening for Li ion conductive inorganic oxides with bond valence pathway models and percolation algorithm

Fast ion conductive solid oxide electrolytes are urgently needed because of the development of batteries, fuel cells, and sensors. Ab initio density functional theory can predict ionic conductivities with high accuracy, although it often requires large computational resources and time. In this paper, we use empirical bond valence relations [Adams et al., Phys. Status Solidi A 208, 1746 (2011)] and a percolation algorithm for fast, efficient, fully automated evaluation of migration energies for Li ion conduction in 14 olivine-type LiMXO4 compounds. The results showed a high correlation coefficient with the ab initio density functional theory (DFT) approach, indicating that our method could be attractive for identifying fast ion conductors in databases of numerous candidates.

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data

Abstract Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density (d), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction.