Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance.

Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domain-informed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, the Formulation Graph Convolution Network (F-GCN), that can map the structure-composition relationship of the formulation constituents to the property of liquid formulation as a whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on the respective constituent's molar percentage in the formulation, followed by integration into a combined formulation descriptor that represents the complete formulation to an external learning architecture. The use case of the proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary data sets representing electrolyte formulations vs battery performance: one data set is sourced from the literature about Li/Cu half-cells, while the other is obtained by lab experiments related to lithium-iodide full-cell chemistry. The model is shown to predict performance metrics such as Coulombic efficiency (CE) and specific capacity of new electrolyte formulations with the lowest reported errors. The best-performing F-GCN model uses molecular descriptors derived from molecular graphs (GCNs) that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.

In situ AFM visualization of Li-O2 battery discharge products during redox cycling in an atmospherically controlled sample cell.

The in situ observation of electrochemical reactions is challenging due to a constantly changing electrode surface under highly sensitive conditions. This study reports the development of an in situ atomic force microscopy (AFM) technique for electrochemical systems, including the design, fabrication, and successful performance of a sealed AFM cell operating in a controlled atmosphere. Documentation of reversible physical processes on the cathode surface was performed on the example of a highly reactive lithium-oxygen battery system at different water concentrations in the solvent. The AFM data collected during the discharge-recharge cycles correlated well with the simultaneously recorded electrochemical data. We were able to capture the formation of discharge products from correlated electrical and topographical channels and measure the impact of the presence of water. The cell design permitted acquisition of electrochemical impedance spectroscopy, contributing information about electrical double layers under the system's controlled environment. This characterization method can be applied to a wide range of reactive surfaces undergoing transformations under carefully controlled conditions.

Amine-functionalized, multi-arm star polymers: A novel platform for removing glyphosate from aqueous media.

We describe a novel method for efficiently removing glyphosate from aqueous media via adsorption onto highly functionalized star-shaped polymeric particles. These particles have a polystyrene core with more than 35 attached methacrylate polymer arms, each containing a plurality of pendant amines (poly(dimethylamino ethyl methacrylate): PDMAEMA) that are partially protonated in water. Kinetic studies demonstrate that these star-polymers successfully remove up to 93% of glyphosate present in aqueous solution (feed concentration: 5 ppm), within 10 min contact time, outperforming activated carbon, which removed 33% after 20 min. On these star-polymers, glyphosate adsorption closely follows the Langmuir model indicating monolayer coverage at most. Ionic interaction between the protonated amines and glyphosate's dissociated carboxylic and phosphoric acid groups lead to effective glyphosate capture even at feed concentrations below 1 ppm. Surface charge of these star polymers and dissociation of glyphosate are both influenced by pH, thus glyphosate removal efficiency increases from 63% to 93% when pH increases from 4.2 to 7.7. NMR studies conducted with butylamine as a proxy for these polymeric particles confirm that the amine group binds with both glyphosate's carboxylic and phosphoric acid groups when its concentrations are in a 2:1 or higher molar ratio with glyphosate.

Assembly of nanocrystal arrays by block-copolymer-directed nucleation.

Growing in line: The surface chemistry of self-assembled nanostructured block copolymers is used to control the sites at which semiconducting metal sulfide nanocrystals nucleate and grow on a surface directly from aqueous solutions. This process is a new and general strategy for the bottom-up assembly of functional nanocrystalline materials for a variety of applications.

Combinatorial generation and replication-directed assembly of complex and varied geometries with thin films of diblock copolymers.

Directed assembly of fine-scale, very complex patterns with a variety of features, including terminations, jogs, disclinations, acute and obtuse bends, and sharp radii of curvature, was achieved with a symmetric poly(styrene-block-methylmethacrylate) (PS-b-PMMA) copolymer. The complex pattern was generated spontaneously by spin coating and annealing a thin film of a lamellae-forming block copolymer on a chemically neutral surface. The resulting "fingerprint" pattern had a domain spacing of 47.5 nm. Oxygen plasma treatment of the block copolymer converted it into an insoluble chemical nanopattern that was quantified by XPS, goniometry, and the wetting behavior of the block copolymer. Spin coating a second thin film of the block copolymer and annealing resulted in directed assembly that replicated the fingerprint pattern, including the most complicated defect structures. A computer vision algorithm was developed and implemented to compare the patterns quantitatively, taking into account inherent differences in image contrast, scale, rotation, and translation.

Side-chain-grafted random copolymer brushes as neutral surfaces for controlling the orientation of block copolymer microdomains in thin films.

Random copolymers of P(S-r-MMA-r-HEMA)s with a distribution of surface reactive hydroxyl groups were synthesized to formulate neutral surface layers on a SiO2 substrate. The layers were designed to drive vertical orientation of lamellar microdomains in a top P(S-b-MMA) thin film. Copolymers with a styrene weight fraction (f(St)) of 0.58 and a HEMA fraction (f(HEMA)) ranging from 0.01 to 0.03, with a corresponding MMA fraction (f(MMA)) ranging from 0.41 to 0.39, in the P(S-r-MMA-r-HEMA) copolymer showed neutral surface characteristics. The morphology of block copolymer thin films was studied by scanning electron microscopy (SEM). P(S-r-MMA-r-HEMA) copolymers prepared by both living and classical free-radical polymerizations were equally effective in demonstrating the neutrality of the surface. These side-chain-grafted random copolymer brushes showed faster grafting kinetics than the end-chain-grafted P(S-r-MMA) because of multipoint attachment to the surface. The modified surfaces had a very thin layer of random copolymer brush (5-7 nm), which is desirable for effective pattern transfer. Furthermore, neutral surfaces could be obtained even when the grafting time was reduced to 3 h. These results indicate that the composition of the random copolymer brush, rather than its PDI or molecular weights, is the most important factor in controlling the neutrality of the surface. These results also demonstrate the feasibility of using a third comonomer (C) in the random copolymer brush P(A-r-B-r-C) to alter the interfacial and surface energies of a diblock copolymer (A-b-B).

Directed assembly of cylinder-forming block copolymer films and thermochemically induced cylinder to sphere transition: a hierarchical route to linear arrays of nanodots.

A morphological transition from cylinders to spheres was induced in an asymmetric diblock copolymer, poly(styrene)-block-poly(tert-butyl acrylate) (PS-b-PtBA). The periodic arrays of the poly(tert-butyl acrylate) (PtBA) domains were transformed to the ordered poly(acrylic anhydride) (PAA) spheres via the thermal deprotection of tert-butyl acrylate linkages and the subsequent volume change of the minority block. Coupled with techniques to direct the assembly of cylinder-forming block copolymers, this finding provides new routes to fabricate ordered geometries of nanodot arrays.

Graphoepitaxy of cylinder-forming block copolymers for use as templates to pattern magnetic metal dot arrays.

We report a method to fabricate high-quality patterned magnetic dot arrays using block copolymer lithography, metal deposition, and a dry lift-off technique. Long-range order of cylindrical domains oriented perpendicular to the substrate and in hexagonal arrays was induced in the block copolymer films by prepatterning the substrate with topographic features and chemically modifying the surface to exhibit neutral wetting behaviour towards the blocks of the copolymer. The uniformity of the domain size and row spacing of block copolymer templates created in this way was improved compared to those reported in previous studies that used graphoepitaxy of sphere-forming block copolymers. The pattern of block copolymer domains was transferred to a pattern of magnetic metal dots, demonstrating the potential of this technology for the fabrication of patterned magnetic recording media.