Predicted Binary Compounds of Tin and Sulfur

Three known binary compounds of tin (Sn) and sulfur (S), namely, SnS, SnS₂, and Sn₂S₃, have been extensively studied for potential application in energy generation and conversion applications. Inspired by the existence of many metastable phases of SnS, we explore the chemical space of nine crystalline solids with chemical composition Sn_<i>x</i>S_1–<i>x</i> (<i>x</i> falls between 0.25 and 0.75), predicting that Sn₃S is thermodynamically stable in a metallic <i>Pmn</i>2₁ phase. Due to the layered structure of this phase, Sn₃S is a quasi two-dimensional material, characterized by highly anisotropic electronic-related properties. Moreover, the discovered metastable structures of Sn₃S₂, Sn₂S, and Sn₅S₂ are just about 5 meV/atom above the stability limit, and may potentially be realized. The data set of 369 low-energy structures of nine Sn_<i>x</i>S_1–<i>x</i> crystalline solids reported in this work is a reliable sample of the low-energy sector of the chemical space, and thus being useful for the currently established materials databases, providing a playground for future data-mining works in materials discoveries

Predicted Binary Compounds of Tin and Sulfur

Three known binary compounds of tin (Sn) and sulfur (S), namely, SnS, SnS₂, and Sn₂S₃, have been extensively studied for potential application in energy generation and conversion applications. Inspired by the existence of many metastable phases of SnS, we explore the chemical space of nine crystalline solids with chemical composition Sn_<i>x</i>S_1–<i>x</i> (<i>x</i> falls between 0.25 and 0.75), predicting that Sn₃S is thermodynamically stable in a metallic <i>Pmn</i>2₁ phase. Due to the layered structure of this phase, Sn₃S is a quasi two-dimensional material, characterized by highly anisotropic electronic-related properties. Moreover, the discovered metastable structures of Sn₃S₂, Sn₂S, and Sn₅S₂ are just about 5 meV/atom above the stability limit, and may potentially be realized. The data set of 369 low-energy structures of nine Sn_<i>x</i>S_1–<i>x</i> crystalline solids reported in this work is a reliable sample of the low-energy sector of the chemical space, and thus being useful for the currently established materials databases, providing a playground for future data-mining works in materials discoveries

Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

Mining of currently available and evolving materials databases to discover structure–chemistry–property relationships is critical to developing an accelerated materials design framework. The design of new and advanced polymeric dielectrics for capacitive energy storage has been hampered by the lack of sufficient data encompassing wide enough chemical spaces. Here, data mining and analysis techniques are applied on a recently presented computational data set of around 1100 organic polymers, organometallic polymers, and related molecular crystals, in order to obtain qualitative understanding of the origins of dielectric and electronic properties. By probing the relationships between crucial chemical and structural features of materials and their dielectric constant and band gap, design rules are devised for optimizing either property. Learning from this data set provides guidance to experiments and to future computations, as well as a way of expanding the pool of promising polymer candidates for dielectric applications

Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

Mining of currently available and evolving materials databases to discover structure–chemistry–property relationships is critical to developing an accelerated materials design framework. The design of new and advanced polymeric dielectrics for capacitive energy storage has been hampered by the lack of sufficient data encompassing wide enough chemical spaces. Here, data mining and analysis techniques are applied on a recently presented computational data set of around 1100 organic polymers, organometallic polymers, and related molecular crystals, in order to obtain qualitative understanding of the origins of dielectric and electronic properties. By probing the relationships between crucial chemical and structural features of materials and their dielectric constant and band gap, design rules are devised for optimizing either property. Learning from this data set provides guidance to experiments and to future computations, as well as a way of expanding the pool of promising polymer candidates for dielectric applications

Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

The recent successes of the Materials Genome Initiative have opened up new opportunities for data-centric informatics approaches in several subfields of materials research, including in polymer science and engineering. Polymers, being inexpensive and possessing a broad range of tunable properties, are widespread in many technological applications. The vast chemical and morphological complexity of polymers though gives rise to challenges in the rational discovery of new materials for specific applications. The nascent field of polymer informatics seeks to provide tools and pathways for accelerated property prediction (and materials design) via surrogate machine learning models built on reliable past data. We have carefully accumulated a data set of organic polymers whose properties were obtained either computationally (bandgap, dielectric constant, refractive index, and atomization energy) or experimentally (glass transition temperature, solubility parameter, and density). A fingerprinting scheme that captures atomistic to morphological structural features was developed to numerically represent the polymers. Machine learning models were then trained by mapping the fingerprints (or features) to properties. Once developed, these models can rapidly predict properties of new polymers (within the same chemical class as the parent data set) and can also provide uncertainties underlying the predictions. Since different properties depend on different length-scale features, the prediction models were built on an optimized set of features for each individual property. Furthermore, these models are incorporated in a user-friendly online platform named Polymer Genome (www.polymergenome.org). Systematic and progressive expansion of both chemical and property spaces are planned to extend the applicability of Polymer Genome to a wide range of technological domains

Factors Favoring Ferroelectricity in Hafnia: A First-Principles Computational Study

The surprising ferroelectricity displayed by hafnia thin films has been attributed to a metastable polar orthorhombic (<i>Pca</i>2₁) phase. Nevertheless, the conditions under which this (or another competing) ferroelectric phase may be stabilized remain unresolved. It has been hypothesized that a variety of factors, including strain, grain size, electric field, impurities and dopants, may contribute to the observed ferroelectricity. Here, we use first-principles computations to examine the influence of mechanical and electrical boundary conditions (i.e., strain and electric field) on the relative stability of a variety of relevant nonpolar and polar phases of hafnia. We find that although strain or electric field, independently, do not lead to a ferroelectric phase, the combined influence of in-plane equibiaxial deformation and electric field results in the emergence of the polar <i>Pca</i>2₁ structure as the equilibrium phase. The results provide insights for better controlling the ferroelectric characteristics of hafnia thin films by adjusting the growth conditions and electrical history

Polyelectrolyte-Assisted Oxygen Vacancies: A New Route to Defect Engineering in Molybdenum Oxide

The presence of oxygen vacancy sites fundamentally affects physical and chemical properties of materials. In this study, a dipole-containing interaction between poly­(diallyl­dimethyl­ammonium chloride) PDDA and α-MoO₃ is found to enable high-concentrations of surface oxygen vacancies. Thermal annealing under Ar resulted in negligible reduction of MoO₃ to MoO_3–<i>x</i> with <i>x</i> = 0.03 at 600 °C. In contrast, we show that the thermochemical reaction with PDDA polyelectrolyte under Ar can significantly reduce MoO₃ to MoO_3–<i>x</i> with <i>x</i> = 0.36 (MoO_2.64) at 600 °C. Thermal annealing under H₂ gas enhanced the substoichiometry of MoO_3–<i>x</i> from <i>x</i> = 0.62 to 0.98 by using PDDA at the same conditions. Density functional theory calculations, supported by experimental analysis, suggest that the vacancy sites are created through absorption of terminal site oxygen (O_t) upon decomposition of the N–C bond in the pentagonal ring of PDDA during the thermal treatment. O_t atoms are absorbed as ionic O^– and neutral O^2–, creating Mo⁵⁺-v_O^· and Mo⁴⁺-v_O^·· vacancy bipolarons and polarons, respectively. X-ray photoemission spectroscopy peak analysis indicates the ratio of charged to neutral molybdenum ions in the PDDA-processed samples increased from Mo⁴⁺/Mo⁶⁺ = 1.0 and Mo⁵⁺/Mo⁶⁺ = 3.3 when reduced at 400 °C to Mo⁴⁺/Mo⁶⁺ = 3.7 and Mo⁵⁺/Mo⁶⁺ = 2.6 when reduced at 600 °C. This is consistent with our <i>ab initio</i> calculation where the Mo⁴⁺-v_O^·· formation energy is 0.22 eV higher than that for Mo⁵⁺-v_O^· in the bulk of the material and 0.02 eV higher on the surface. This study reveals a new paradigm for effective enhancement of surface oxygen vacancy concentrations essential for a variety of technologies including advanced energy conversion applications such as electrochemical energy storage, catalysis, and low-temperature thermochemical water splitting

Block Copolymer-Assisted Solvothermal Synthesis of Hollow Bi₂MoO₆ Spheres Substituted with Samarium

Hollow spherical structures of ternary bismuth molybdenum oxide doped with samarium (Bi_2–<i>x</i>Sm_<i>x</i>MoO₆) were successfully synthesized via development of a Pluronic P123 (PEO₂₀-PPO₇₀-PEO₂₀)-assisted solvothermal technique. Density functional theory calculations have been performed to improve our understanding of the effects of Sm doping on the electronic band structure, density of states, and band gap of the material. The calculations for 0 ≤ <i>x</i> ≤ 0.3 revealed a considerably flattened conduction band minimum near the Γ point, suggesting that the material can be considered to possess a quasi-direct band gap. In contrast, for <i>x</i> = 0.5, the conduction band minimum is deflected toward the U point, making it a distinctly indirect band gap material. The effects of a hollow structure as well as Sm substitution on the absorbance and fluorescence properties of the materials produced increased emission intensities at low Sm concentrations (<i>x</i> = 0.1 and 0.3), with <i>x</i> = 0.1 displaying a peak photoluminescence intensity 13.2 times higher than for the undoped bulk sample. Subsequent increases in the Sm concentration resulted in quenching of the emission intensity, indicative of the onset of a quasi-direct-to-indirect electronic band transition. These results indicate that both mesoscale structuring and Sm doping will be promising routes for tuning optoelectronic properties for future applications such as catalysis and photocatalysis

Rational Design of Organotin Polyesters

Large dielectric constant and band gap are essential for insulating materials used in applications such as capacitors, transistors and photovoltaics. Of the most common polymers utilized for these applications, polyvinyldiene fluoride (PVDF) offers a good balance between dielectric constant, >10, and band gap, 6 eV, but suffers from being a ferroelectric material. Herein, we investigate a series of aliphatic organotin polymers, p­[DMT­(CH₂)<i>_n</i>], to increase the dipolar and ionic part of the dielectric constant while maintaining a large band gap. We model these polymers by performing first-principles calculations based on density functional theory (DFT), to predict their structures, electronic and total dielectric constants and energy band gaps. The modeling and experimental values show strong correlation, in which the polymers exhibit both high dielectric constant, ≥5.3, and large band gap, ≥4.7 eV with one polymer displaying a dielectric constant of 6.6 and band gap of 6.7 eV. From our work, we can identify the ideal amount of tin loading within a polymer chain to optimize the material for specific applications. We also suggest that the recently developed modeling methods based on DFT are efficient in studying and designing new generations of polymeric dielectric materials