H-Glass Supported Hybrid Gold Nano-Islands for Visible-Light-Driven Hydrogen Evolution.

Flat panel reactors, coated with photocatalytic materials, offer a sustainable approach for the commercial production of hydrogen (H2) with zero carbon footprint. Despite this, achieving high solar-to-hydrogen (STH) conversion efficiency with these reactors is still a significant challenge due to the low utilization efficiency of solar light and rapid charge recombination. Herein, hybrid gold nano-islands (HGNIs) are developed on transparent glass support to improve the STH efficiency. Plasmonic HGNIs are grown on an in-house developed active glass sheet composed of sodium aluminum phosphosilicate oxide glass (H-glass) using the thermal dewetting method at 550 °C under an ambient atmosphere. HGNIs with various oxidation states (Au0, Au+, and Au-) and multiple interfaces are obtained due to the diffusion of the elements from the glass structure, which also facilitates the lifetime of the hot electron to be ≈2.94 ps. H-glass-supported HGNIs demonstrate significant STH conversion efficiency of 0.6%, without any sacrificial agents, via water dissociation. This study unveils the specific role of H-glass-supported HGNIs in facilitating light-driven chemical conversions, offering new avenues for the development of high-performance photocatalysts in various chemical conversion reactions for large-scale commercial applications.

Multi-Functional Applications of H-Glass Embedded with Stable Plasmonic Gold Nanoislands.

Metal nanoparticles (MNPs) are synthesized using various techniques on diverse substrates that significantly impact their properties. However, among the substrate materials investigated, the major challenge is the stability of MNPs due to their poor adhesion to the substrate. Herein, it is demonstrated how a newly developed H-glass can concurrently stabilize plasmonic gold nanoislands (GNIs) and offer multifunctional applications. The GNIs on the H-glass are synthesized using a simple yet, robust thermal dewetting process. The H-glass embedded with GNIs demonstrates versatility in its applications, such as i) acting as a room temperature chemiresistive gas sensor (70% response for NO2 gas); ii) serving as substrates for surface-enhanced Raman spectroscopy for the identifications of Nile blue (dye) and picric acid (explosive) analytes down to nanomolar concentrations with enhancement factors of 4.8 × 106 and 6.1 × 105 , respectively; and iii) functioning as a nonlinear optical saturable absorber with a saturation intensity of 18.36 × 1015 W m-2 at 600 nm, and the performance characteristics are on par with those of materials reported in the existing literature. This work establishes a facile strategy to develop advanced materials by depositing metal nanoislands on glass for various functional applications.

Superlubricity and Stress-Shielding of Graphene Enables Ultra Scratch-Resistant Glasses.

Glasses, when subjected to scratch loading, incur damages affecting their optical and mechanical integrity. Here, it is demonstrated that silica glasses protected with mechanically exfoliated few-layer graphene sheets can exhibit remarkable improvement in scratch resistance. To this extent, the friction and wear characteristics of silica glasses with exfoliated graphene using atomic force microscopy (AFM) are explored. The friction forces recorded during AFM scratch tests of the graphene-glass surfaces at multiple loads exhibit ∼98% reduction compared to that of the bare silica glass, with the friction coefficient falling in the superlubricity regime. This dramatic reduction in friction achieved by the graphene sheets results in significantly lower wear of the graphene-glass surfaces postscratching. Further investigations employing atomistic simulations reveal that the stress-shielding mechanism is due to the reduced deformation of graphene-glass surfaces, thereby curtailing the overall damage. Altogether, the present work provides a new fillip toward the development of glasses with enhanced scratch resistance exploiting two-dimensional coatings.

Learning the stress-strain fields in digital composites using Fourier neural operator.

Increased demands for high-performance materials have led to advanced composite materials with complex hierarchical designs. However, designing a tailored material microstructure with targeted properties and performance is extremely challenging due to the innumerable design combinations and prohibitive computational costs for physics-based solvers. In this study, we employ a neural operator-based framework, namely Fourier neural operator (FNO), to learn the mechanical response of 2D composites. We show that the FNO exhibits high-fidelity predictions of the complete stress and strain tensor fields for geometrically complex composite microstructures with very few training data and purely based on the microstructure. The model also exhibits zero-shot generalization on unseen arbitrary geometries with high accuracy. Furthermore, the model exhibits zero-shot super-resolution capabilities by predicting high-resolution stress and strain fields directly from low-resolution input configurations. Finally, the model also provides high-accuracy predictions of equivalent measures for stress-strain fields, allowing realistic upscaling of the results.

Role of steric repulsions on the precipitation kinetics and the structure of calcium-silicate-hydrate gels.

The microstructure and properties of calcium-silicate-hydrate (C-S-H) gels are largely controlled by the physicochemical environment during their precipitation. However, the role of the steric repulsive environment induced by the pore solution chemistry on the kinetics, structure, and properties of C-S-H gels remains unclear. Here, we develop two potential formalisms, namely sinusoidal and polynomial, to simulate the role of steric repulsions in C-S-H. The results show excellent agreement with experimental observations of precipitation kinetics and elastic properties. We demonstrate that the repulsive interactions result in delayed precipitation and percolation, and an open and branched microstructure. Interestingly, the elastic properties (which are equilibrium properties) are also significantly affected by these second-neighbor interactions. Overall, the present study demonstrates that the kinetics, structure, and equilibrium properties of colloidal gels are controlled by the steric repulsions induced by the chemical environment.

Ionic Conductivity of Na3Al2P3O12 Glass Electrolytes-Role of Charge Compensators.

In glasses, a sodium ion (Na+) is a significant mobile cation that takes up a dual role, that is, as a charge compensator and also as a network modifier. As a network modifier, Na+ cations modify the structural distributions and create nonbridging oxygens. As a charge compensator, Na+ cations provide imbalanced charge for oxygen that is linked between two network-forming tetrahedra. However, the factors controlling the mobility of Na+ ions in glasses, which in turn affects the ionic conductivity, remain unclear. In the current work, using high-fidelity experiments and atomistic simulations, we demonstrate that the ionic conductivity of the Na3Al2P3O12 (Si0) glass material is dependent not only on the concentration of Na+ charge carriers but also on the number of charge-compensated oxygens within its first coordination sphere. To investigate, we chose a series of glasses formulated by the substitution of Si for P in Si0 glass based on the hypothesis that Si substitution in the presence of Na+ cations increases the number of Si-O-Al bonds, which enhances the role of Na as a charge compensator. The structural and conductivity properties of bulk glass materials are evaluated by molecular dynamics (MD) simulations, magic angle spinning-nuclear magnetic resonance, Raman spectroscopy, and impedance spectroscopy. We observe that the increasing number of charge-imbalanced bridging oxygens (BOs) with the substitution of Si for P in Si0 glass enhances the ionic conductivity by an order of magnitude-from 3.7 × 10-8 S.cm-1 to 3.3 × 10-7 S.cm-1 at 100 °C. By rigorously quantifying the channel regions in the glass structure, using MD simulations, we demonstrate that the enhanced ionic conductivity can be attributed to the increased connectivity of Na-rich channels because of the increased charge-compensated BOs around the Na atoms. Overall, this study provides new insights for designing next-generation glass-based electrolytes with superior ionic conductivity for Na-ion batteries.

Realizing cool and warm white-LEDs based on color controllable (Sr,Ba)2Al3O6F:Eu2+ phosphors obtained via a microwave-assisted diffusion method.

Globally, phosphor converted white-LEDs (W-LEDs) are among the most suitable sources to reduce energy consumption. Nevertheless, modernization of efficient broadband emitting phosphors is most crucial to improve the W-LED performance. Herein, we synthesized a series of novel broadband emitting Sr2-xAl3O6F:xEu2+ phosphors via a new microwave-assisted diffusion method. Rietveld refinement of the obtained X-ray diffraction results was performed to recognize the exact crystal phase and the various cationic sites. Oxygen vacancies (VO) formed under synthetic reducing conditions enabled Sr2Al3O6F to demonstrate bright self-activated bluish emission. Doping of Eu2+ ions unlocked the energy transfer process from the host to the activator ions, owing to which, the self-activated emission diminished and the Eu2+-doped sample showed amplified bluish-green emission. The gradual increase in Eu2+ concentrations regulated the controllable emissions from the bluish (0.34, 0.42) to the greenish (0.38, 0.43) zone under UV excitation. Because of the different absorption preferences of Eu2+ ions located at the different Sr2+ sites, Sr2-xAl3O6F:xEu2+ exhibited bluish-white emission under blue irradiation. A further enhancement in PL intensity had been observed by the cation substitution of Ba2+ for Sr2+ sites in the optimum Sr1.95Al3O6F:0.05Eu2+ phosphor. The as-fabricated W-LEDs utilizing the optimized Sr1.75Ba0.2Al3O6F:0.05Eu2+ phosphor exhibited a cool-white light emission along with a 372 nm NUV-LED and a 420 nm blue-LED with a moderate CRI of 70 and a CCT above 6000 K. Such cool white emission was controlled to natural white with the CCT close to 5000 K, and the CRI above 80 via utilizing a suitable red emitting phosphor. The W-LED performances of the optimized phosphor justified its applicability to produce white light for lighting applications.

Looking through glass: Knowledge discovery from materials science literature using natural language processing.

Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA) to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots is presented using the caption cluster plot (CCP), providing direct access to images buried in the papers. Finally, we combine the LDA and CCP with chemical elements to present an elemental map, a topical and image-wise distribution of elements occurring in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition-structure-processing-property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery.

An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19.

The SARS-CoV-2 driven disease COVID-19 is pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. To launch both containment and mitigation measures, each country requires estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and to design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory of COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India. We show that our approach predicts state-wise COVID-19 spread for each country with reasonable accuracy. We show that Rt, as the effective reproduction number, exhibits significant spatial variations in these countries. However, by accounting for the spatial variation of Rt in an adaptive fashion, the predictive model provides estimates of the possible asymptomatic and undetected COVID-19 cases, both of which are key contributors in COVID-19 transmission. We have applied our methodology to make detailed predictions for COVID19 incidences at the district and state level in India. Finally, to make the models available to the public at large, we have developed a web-based dashboard, namely "Predictions and Assessment of Corona Infections and Transmission in India" (PRACRITI, see http://pracriti.iitd.ac.in), which provides the detailed Rt values and a three-week forecast of COVID cases.

Elucidating the constitutive relationship of calcium-silicate-hydrate gel using high throughput reactive molecular simulations and machine learning.

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium-silicate-hydrate (C-S-H) gel-the primary binding phase in concrete formed via the hydration of ordinary portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C-S-H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excellent predictions. To interpret the predicted results from NN, we employ SHapley Additive exPlanations (SHAP), which reveals that the influence of silicate network on all the elastic constants of C-S-H is significantly higher than that of water and CaO content. Additionally, the water content is found to have a more prominent influence on the shear components than the normal components along the direction of the interlayer spaces within C-S-H. This result suggests that the in-plane elastic response is controlled by water molecules whereas the transverse response is mainly governed by the silicate network. Overall, by seamlessly integrating MD simulations with ML, this paper can be used as a starting point toward accelerated optimization of C-S-H nanostructures to design efficient cementitious binders with targeted properties.

Dynamics of confined water and its interplay with alkali cations in sodium aluminosilicate hydrate gel: insights from reactive force field molecular dynamics.

This paper presents the dynamics of confined water and its interplay with alkali cations in disordered sodium aluminosilicate hydrate (N-A-S-H) gel using reactive force field molecular dynamics. N-A-S-H gel is the primary binding phase in geopolymers formed via alkaline activation of fly ash. Despite attractive mechanical properties, geopolymers suffer from durability issues, particularly the alkali leaching problem which has motivated this study. Here, the dynamics of confined water and the mobility of alkali cations in N-A-S-H is evaluated by obtaining the evolution of mean squared displacements and Van Hove correlation function. To evaluate the influence of the composition of N-A-S-H on the water dynamics and diffusion of alkali cations, atomistic structures of N-A-S-H with Si/Al ratio ranging from 1 to 3 are constructed. It is observed that the diffusion of confined water and sodium is significantly influenced by the Si/Al ratio. The confined water molecules in N-A-S-H exhibit a multistage dynamic behavior where they can be classified as mobile and immobile water molecules. While the mobility of water molecules gets progressively restricted with an increase in Si/Al ratio, the diffusion coefficient of sodium also decreases as the Si/Al ratio increases. The diffusion coefficient of water molecules in the N-A-S-H structure exhibits a lower value than those of the calcium-silicate-hydrate (C-S-H) structure. This is mainly due to the random disordered structure of N-A-S-H as compared to the layered C-S-H structure. To further evaluate the influence of water content in N-A-S-H, atomistic structures of N-A-S-H with water contents ranging from 5-20% are constructed. Qn distribution of the structures indicates significant depolymerization of N-A-S-H structure with increasing water content. Increased conversion of Si-O-Na network to Si-O-H and Na-OH components with an increase in water content helps explain the alkali-leaching issue in fly ash-based geopolymers observed macroscopically. Overall, the results in this study can be used as a starting point towards multiscale simulation-based design and development of durable geopolymers.

A Peridynamics-Based Micromechanical Modeling Approach for Random Heterogeneous Structural Materials.

This paper presents a peridynamics-based micromechanical analysis framework that can efficiently handle material failure for random heterogeneous structural materials. In contrast to conventional continuum-based approaches, this method can handle discontinuities such as fracture without requiring supplemental mathematical relations. The framework presented here generates representative unit cells based on microstructural information on the material and assigns distinct material behavior to the constituent phases in the random heterogenous microstructures. The framework incorporates spontaneous failure initiation/propagation based on the critical stretch criterion in peridynamics and predicts effective constitutive response of the material. The current framework is applied to a metallic particulate-reinforced cementitious composite. The simulated mechanical responses show excellent match with experimental observations signifying efficacy of the peridynamics-based micromechanical framework for heterogenous composites. Thus, the multiscale peridynamics-based framework can efficiently facilitate microstructure guided material design for a large class of inclusion-modified random heterogenous materials.

Elucidating the formation of Al-NBO bonds, Al-O-Al linkages and clusters in alkaline-earth aluminosilicate glasses based on molecular dynamics simulations.

Exploring the reasons for the initiation of Al-O-Al bond formation in alkali-earth alumino silicate glasses is a key topic in the glass-science community. Evidence for the formation of Al-O-Al and Al-NBO bonds in the glass composition 38.7CaO-9.7MgO-12.9Al2O3-38.7SiO2 (CMAS, mol%) has been provided based on Molecular Dynamics (MD) simulations. Analyses in the short-range order confirm that silicon and the majority of aluminium cations form regular tetrahedra. Well-separated homonuclear (Si-O-Si) and heteronuclear (Si-O-Al) cluster regions have been identified. In addition, a channel region (C-Region), separated from the network region, enriched with both NBO and non-framework modifier cations, has also been identified. These findings are in support of the previously proposed extended modified random network (EMRN) model for aluminosilicate glasses. A detailed analysis of the structural distributions revealed that a majority of Al, 51.6%, is found in Si-O-Al links. Although the formation of Al-O-Al and Al-NBO bonds is energetically less favourable, a significant amount of Al is found in Al-O-Al links (33.5%), violating Lowenstein's rule, and the remainder is bonded with non-bridging oxygen (NBO) in the form of Al-NBO (Al-O-(Ca, Mg)). The conditions necessary for the formation of less favourable bonds are attributed to the presence of a high amount of modifier cations in current CMAS glass and their preferable coordination.

Redox Sensitive Self-Assembling Dipeptide for Sustained Intracellular Drug Delivery.

The rational design and synthesis of molecules with functional supramolecular assemblies continues to be a challenging endeavor. Self-assembled nano- and microstructures from natural building blocks are considered more appropriate for medical applications due to their biocompatible nature. We report for the first time a simple redox-responsive dipeptide that self-assembles to form vesicles in aqueous medium. The experimental results based on the control compound and all-atom molecular dynamics (MD) simulations support the mechanism of association through intermolecular π-π interactions between the indole rings of tryptophan residues. These peptide vesicles showed a DOX loading capacity of ∼16% (w/w) and redox-triggered controlled release of the packaged drug. The drug-loaded vesicles were able to penetrate into MDA-MB-231 and HeLa cells, and release payload, suggesting their putative use as chemotherapeutic delivery vehicles. These natural peptide-based carriers disassemble inside cells due to the high cytosolic GSH concentration, and the resultant Cys-Trp dipeptide is degradable. The minimalistic peptide design presented here, coupled with the propensity to form vesicles that can encapsulate the chemotherapeutic drug, opens up unlimited potential for engineering targeted sustained-release drug delivery vehicles.

Predicting the Young's Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning.

The application of machine learning to predict materials' properties usually requires a large number of consistent data for training. However, experimental datasets of high quality are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young's modulus of silicate glasses. We demonstrate that this combined approach offers good and reliable predictions over the entire compositional domain. By comparing the performances of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning.

Evidence of a two-dimensional glass transition in graphene: Insights from molecular simulations.

Liquids exhibit a sudden increase in viscosity when cooled fast enough, avoiding thermodynamically predicted route of crystallization. This phenomenon, known as glass transition, leads to the formation of non-periodic structures known as glasses. Extensive studies have been conducted on model materials to understand glass transition in two dimensions. However, despite the synthesis of disordered/amorphous single-atom thick structures of carbon, little attention has been given to glass transition in realistic two-dimensional materials such as graphene. Herein, using molecular dynamics simulation, we demonstrate the existence of glass transition in graphene leading to a realistic two-dimensional glassy structure, namely glassy graphene. We show that the resulting glassy structure exhibits excellent agreement with experimentally realized disordered graphene. Interestingly, this glassy graphene exhibits a wrinkled but stable structure, with reduced thermal vibration in comparison to its crystalline counterpart. We suggest that the topological disorder induced by glass transition governs the unique properties of this structure.

New insights into the atomic structure of amorphous TiO2 using tight-binding molecular dynamics.

Amorphous TiO2 (a-TiO2) could offer an attractive alternative to conventional crystalline TiO2 phases for photocatalytic applications. However, the atomic structure of a-TiO2 remains poorly understood with respect to that of its crystalline counterparts. Here, we conduct some classical molecular dynamics simulations of a-TiO2 based on a selection of empirical potentials. We show that, on account of its ability to dynamically assign the charge of each atom based on its local environment, the second-moment tight-binding charge equilibration potential yields an unprecedented agreement with available experimental data. Based on these simulations, we investigate the degree of order and disorder in a-TiO2. Overall, the results suggest that a-TiO2 features a large flexibility in its local topology, which may explain the high sensitivity of its structure to the synthesis method being used.

The hydrophilic-to-hydrophobic transition in glassy silica is driven by the atomic topology of its surface.

The surface reactivity and hydrophilicity of silicate materials are key properties for various industrial applications. However, the structural origin of their affinity for water remains unclear. Here, based on reactive molecular dynamics simulations of a series of artificial glassy silica surfaces annealed at various temperatures and subsequently exposed to water, we show that silica exhibits a hydrophilic-to-hydrophobic transition driven by its silanol surface density. By applying topological constraint theory, we show that the surface reactivity and hydrophilic/hydrophobic character of silica are controlled by the atomic topology of its surface. This suggests that novel silicate materials with tailored reactivity and hydrophilicity could be developed through the topological nanoengineering of their surface.

Effects of Irradiation on Albite's Chemical Durability.

Albite (NaAlSi3O8), a framework silicate of the plagioclase feldspar family and a common constituent of felsic rocks, is often present in the siliceous mineral aggregates that compose concrete. When exposed to radiation (e.g., in the form of neutrons) in nuclear power plants, the crystal structure of albite can undergo significant alterations. These alterations may degrade its chemical durability. Indeed, careful examinations of Ar+-implanted albite carried out using Fourier transform infrared spectroscopy (FTIR) and molecular dynamics simulations show that albite's crystal structure, upon irradiation, undergoes progressive disordering, resulting in an expansion in its molar volume (i.e., a reduction of density) and a reduction in the connectivity of its atomic network. This loss of network connectivity (i.e., rigidity) results in an enhancement of the aqueous dissolution rate of albite-measured using vertical scanning interferometry (VSI) in alkaline environments-by a factor of 20. This enhancement in the dissolution rate (i.e., reduction in chemical durability) of albite following irradiation has significant impacts on the durability of felsic rocks and of concrete containing them upon their exposure to radiation in nuclear power plant (NPP) environments.

Revealing the Effect of Irradiation on Cement Hydrates: Evidence of a Topological Self-Organization.

Despite the crucial role of concrete in the construction of nuclear power plants, the effects of radiation exposure (i.e., in the form of neutrons) on the calcium-silicate-hydrate (C-S-H, i.e., the glue of concrete) remain largely unknown. Using molecular dynamics simulations, we systematically investigate the effects of irradiation on the structure of C-S-H across a range of compositions. Expectedly, although C-S-H is more resistant to irradiation than typical crystalline silicates, such as quartz, we observe that radiation exposure affects C-S-H's structural order, silicate mean chain length, and the amount of molecular water that is present in the atomic network. By topological analysis, we show that these "structural effects" arise from a self-organization of the atomic network of C-S-H upon irradiation. This topological self-organization is driven by the (initial) presence of atomic eigenstress in the C-S-H network and is facilitated by the presence of water in the network. Overall, we show that C-S-H exhibits an optimal resistance to radiation damage when its atomic network is isostatic (at Ca/Si = 1.5). Such an improved understanding of the response of C-S-H to irradiation can pave the way to the design of durable concrete for radiation applications.