With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method–dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method–sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path–dependent and that current physiochemical theories find challenging to predict.

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

A porous liquid containing empty cavities has been successfully fabricated by surface engineering of hollow structures with suitable corona and canopy species. By taking advantage of the liquid-like polymeric matrices as a separation medium and the empty cavities as gas transport pathway, this unique porous liquid can function as a promising candidate for gas separation. Moreover, such a facile synthetic strategy can be further extended to the fabrication of other types of nanostructure-based porous liquid, opening up new opportunities for preparation of porous liquids with attractive properties for specific tasks.

Porous Liquids: A Promising Class of Media for Gas Separation

In this study it has been aimed to enhance the thermal resistance of epoxy coating through incorporation of SiO2-GO nanohybrids. SiO2-GO nanohybrids were synthesized through one-step sol-gel route using a mixture of Tetraethylorthosilane (TEOS) and 3-Aminopropyl triethoxysilane (APTES) silanes. The SiO2-GO nanohybrids were prepared at various hydrolysis times of 24, 48 and 72h. Then 0.2wt.% of GO and SiO2-GO nanohybrids were separately incorporated into the epoxy coating. Results revealed that amino functionalized SiO2 nanoparticles with particle size around 20-30nm successfully synthesized on the basal plane of GO. Results showed significant improvement of dispersion and interfacial interactions between nanohybrids and epoxy composite arising from covalent bonding between the SiO2-GO and the epoxy matrix. It was found that the thermal resistance of SiO2-GO nanohybrids and SiO2-GO/Epoxy nanocomposite was noticeably higher than GO and epoxy matrix, respectively.

A novel fabrication of a high performance SiO(2)-graphene oxide (GO) nanohybrids: Characterization of thermal properties of epoxy nanocomposites filled with SiO(2)-GO nanohybrids.

Effect of alkali dosage and silicate modulus on carbonation of alkali-activated slag mortars

Arsenic and fluoride removal from groundwater by electrocoagulation using a continuous filter-press reactor

The experimental conditions were optimized for the synthesis of amorphous SiO2 particles by the reaction of neutralization of sodium silicate solution with H2SO4 solution Amorphous SiO2 particles were characterized by XRD, FT-IR, FE-SEM, EDS and microelectrophoresis The amorphous peak was located at 2θ = 218o in the XRD pattern Primary SiO2 particles were ~ 15 to ~ 30 nm in size and they aggregated into bigger particles Amorphous SiO2 particles showed a specific surface area up to 130 m2g-1, dependent on the parameters of the precipitation process The EDS spectrum of amorphous SiO2 particles did not show contamination with sulfate or other ions, which cannot be excluded in traces pHzpc =17 was obtained by microelectrophoresis

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Nada Filipović-Vinceković

Papers

Precipitation of amorphous SiO2 particles and their properties

The stability of zeolite/surfactant systems