The pace of soft material formulation (re)-development and design is rapidly increasing as both consumers and new legislation demand products that do less harm to the environment while maintaining high standards of performance. To meet this need, we have developed the Autonomous Formulation Lab (AFL), a platform that can automatically prepare and measure the microstructure of liquid formulations using small-angle neutron and X-ray scattering and, soon, a variety of other techniques. Here, we describe the design, philosophy, tuning, and validation of our active learning agent that guides the course of AFL experiments. We show how our extensive in silico tuning results in an efficient agent that is robust to both the number of measurements and signal-to-noise variation. Finally, we experimentally validate our virtually tuned agent by addressing a model formulation problem: replacing a petroleum-derived component with a natural analog. We show that the agent efficiently maps both formulations and how post hoc analysis of the measured data reveals the opportunity for further specialization of the agent. With the tuned and proven active learning agent, our autonomously guided AFL platform will accelerate the pace of discovery of liquid formulations and help speed us toward a greener future.

Solution-based soft matter self-assembly (SA) promises unique material structures and properties from approaches including additive manufacturing/three-dimensional (3D) printing. The 3D printing of periodically ordered porous functional inorganic materials through SA unfolding during printing remains a major challenge, however, due to the often vastly different ordering kinetics of separate processes at different length scales. Here, we report a "one-pot" direct ink writing process to produce hierarchically porous transition metal nitrides and precursor oxides from block copolymer (BCP) SA. Heat treatment protocols identified in various environments enable mesostructure retention in the final crystalline materials with periodic lattices on three distinct length scales. Moreover, embedded printing enables the first BCP directed mesoporous non-self-supporting helical oxides and nitrides. Resulting nitrides are superconducting, with record nanoconfinement-induced upper critical fields correlated with BCP molar mass and record surface areas for compound superconductors. Results suggest scalable porous functional inorganic material formation approaches for applications including catalysis, sensing, and microelectronics.

Block copolymer structure direction has been demonstrated as a technique to impart nanostructure and mesoporosity with enhanced properties to a variety of metal oxides for applications including catalysis, energy conversion and storage, as well as superconductivity. Such approaches require polymer-compatible solution synthesis routes toward oxide nanoclusters, which are not generally available for a broad range of functional materials. Here, we report an acetic acid-based sol-gel-derived method for the synthesis of mesoporous ternary strontium titanate with a morphology consistent with alternating gyroid. In-depth structural characterization suggests a periodic gyroidal structure and phase purity of the resultant perovskite. Magnetometry reveals that these normally diamagnetic oxide materials are ferromagnetic at room temperature. This magnetism is significantly enhanced by mild vacuum annealing, suggesting oxygen vacancies as the source of ferromagnetism. Block copolymer self-assembly-directed mesoporous ternary perovskites may provide a rich platform for studying surface and interfacial effects in surface-dominated systems by enhancing normally dilute surface phenomena.

Polyolefins (POs) are the largest class of polymers produced worldwide. Despite the intrinsic chemical similarities within this class of polymers, they are often physically incompatible. This combination presents a significant hurdle for high-throughput recycling systems that strive to sort various types of plastics from one another. Some research has been done to show that near-infrared spectroscopy (NIR) can sort POs from other plastics, but they generally fall short of sorting POs from one another. In this work, we enhance NIR spectroscopy-based sortation by screening over 12 000 machine-learning pipelines to enable sorting of PO species beyond what is possible using current NIR databases. These pipelines include a series of scattering corrections, filtering and differentiation, data scaling, dimensionality reduction, and machine learning classifiers. Common scattering corrections and preprocessing steps include scatter correction, linear detrending, and Savitzky-Golay filtering. Dimensionality reduction techniques such as principal component analysis (PCA), functional principal component analysis (fPCA) and uniform manifold approximation and projection (UMAP) were also investigated for classification enhancements. This analysis of preprocessing steps and classification algorithm combinations identified multiple data pipelines capable of successfully sorting PO materials with over 95% accuracy. Through rigorous testing, this study provides recommendations for consistently applying preprocessing and classification techniques without over-complicating the data analysis. This work also provides a set of preprocessing steps, a chosen classifier, and tuned hyperparameters that may be useful for benchmarking new models and data sets. Finally, the approach outlined here is ready to be applied by the developers of materials sortation equipment so that we can improve the value and purity of recycled plastic waste streams. Large cross-validation campaigns produce classification models from NIR spectroscopy measurements of polyolefins (the most common class of plastic produced), which may improve sortation at recycling facilities.

Solution-based soft matter self-assembly (SA) promises unique materials properties from approaches including additive manufacturing/three-dimensional (3D) printing. We report direct ink writing derived, hierarchically porous transition metal nitride superconductors (SCs) and precursor oxides, structure-directed by Pluronics-family block copolymer (BCP) SA and heat treated in various environments. SCs with periodic lattices on three length scales show record nanoconfinement-induced upper critical field enhancements correlated with BCP molar mass, attaining values of 50 T for NbN and 8.1 T for non-optimized TiN samples, the first mapping of a tailorable SC property onto a macromolecular parameter. They reach surface areas above 120 m^2/g, the highest reported for compound SCs to date. Embedded printing enables the first BCP directed mesoporous non-self-supporting helical SCs. Results suggest that additive manufacturing may open pathways to mesoporous SCs with not only a variety of macroscopic form factors but enhanced properties from intrinsic, SA-derived mesostructures with substantial academic and technological promise.

Metal-organic frameworks (MOFs) are renowned for their tunable structure, porosity, and internal chemistry, with demonstrated applications in molecular separations, storage, and conversion. While they are widely usable, the powdery characteristics of MOF materials can be limiting for large-scale processing and implementation in devices. Incorporating MOF particles into polymer supports affords engineering solutions to overcome these issues, yet the nature of the resulting composites is difficult to assess. In this work, we present spectroscopic and calorimetric methods that we believe help establish a holistic physicochemical picture of the composite structure using a series of Zr MOFs with different pore sizes as a testbed. Power law decays are observed in X-ray scattering profiles in low q-space ranging between 2.4 and 3.3, which we interpret as changes in scattering due to polymer infiltrating MOF particles. This interpretation is supported by solid-state nuclear magnetic resonance spectroscopy and differential scanning calorimetry measurements that identify populations of the MOF-associated polymer. Additionally, positron annihilation lifetime spectroscopy measurements collected on a series of composites with different MOF-polymer ratios show multiple decay constants, each correlated to a different free volume elements. In combination with the spectroscopic, calorimetric, and scattering results, we utilize the trends in decay constants as a function of polymer mass fraction to hypothesize a polymer infiltration mechanism whereby large pores are preferentially filled, followed by small pores and, later still, interstitial spaces between particles. Even with vigorous investigation of polymer, MOF, and interface characteristics, the complex and heterogeneous nature of the composites makes absolute structural assertions difficult. We envision that the approaches demonstrated here will be a useful foundation to assess and ultimately guide the design of future MOF-polymer composites.

Blending block copolymers (BCP) with additives is a useful approach for controlling BCP morphology and properties. In athermal systems, blends of BCPs with polymer additives having very high molecular (M-n) mass generally result in macrophase separation. Bottlebrush polymers, which consist of a linear backbone and grafted side chains, present an interesting alternative where the overall M-n of the system can be very large but the low M-n side chains may drive miscibility with the BCP. In this study, a bottlebrush with a polynorbornene backbone and polystyrene (PS) side chains is blended with PS-b-poly(methyl methacrylate) (PS-b-PMMA) of varying M-n, and the resulting morphologies are examined in both the bulk and thin films. Two different M-n of PS-b-PMMA were used in the bulk study, and the analysis of small-angle X-ray scattering data shows that the blends were miscible and lamellar at all concentrations. This deviates from reference series of both low and high M-n linear polymer additives, which either showed morphological transitions from lamellae to cylinders (low M-n) or were immiscible at all mass fractions studied (high M-n). The relative molecular mass of the side chain (N SC) and the corresponding component in the BCP (N A) dictate the distribution of the bottlebrush throughout the BCP, analogous to BCP/linear blends or grafted nanoparticles in a homopolymer matrix. The studies on thin films show a thickness dependence for bottlebrush mass fractions at or above 0.17, a behavior which may be driven by conformational changes of the bottlebrush upon confinement.

The industry standard for sorting plastic wastes is near-infrared (NIR) spectroscopy, which offers rapid and nondestructive identification of various plastics. However, NIR does not provide insights into the chain composition, conformation, and topology of polyolefins. Molar mass, branching distribution, thermal properties, and comonomer content are important variables that affect final recyclate properties and compatibility with virgin resins. Heterogeneous mixtures arise through sorting errors, multicomponent materials, or limits on differentiation of polyolefin subclasses leading to poor thermal and mechanical properties. Classic polymer measurement methods can quantify physical properties, which would enable better sorting; however, they are generally too slow for application in commercial recycling facilities. Herein, we leverage the limited chemistry of polyolefins and correlate the structural information from slower measurement methods to NIR spectra through machine learning models. We discuss the success of NIR-property correlations to delineate between polyolefins based on topology.

Art and materials innovation have always been intertwined, dating back to the earliest human creations. In modern times, however, the increasing specialization of materials science often restricts artists' access to cutting-edge materials. Here, the materials science aspects of an art-science collaboration between artist Kimsooja and the Wiesner Lab at Cornell University, are detailed. The project involves the development of a custom-made iridescent block copolymer coating by means of self-assembly, originally applied to transparent window panels of a façade for the ≈14 m tall art installation: A Needle Woman: Galaxy Is a Memory, Earth is a Souvenir by artist Kimsooja. After several exhibitions in the US and Europe, the installation is now part of the permanent museum collection at Yorkshire Sculpture Park in Wakefield, UK. Full characterization of the solution blade-cast coatings show shear aligned, standing up lamellar morphologies that behave as volume-phase gratings with periodicities between 300 and 400 nm. Coatings are also applied to foldable (origami) paper and converted into iridescent porous ceramic materials. It is hoped this work inspires and informs communities across materials science, the arts, and architecture.

Industrial liquid formulations from nanoparticle coatings to drug delivery vehicles are often strikingly complex, with large numbers of components (10 - 100), complex multistep processing, and a wide variety of design requirements for a functional product. This complexity often precludes physics - informed mapping between component fractions, processing, structure, leaving most formulation des ign to empirical trial-and-error or design of experiments strategies. I will discuss recent efforts by the Autonomous Formulation Laboratory (AFL) team at NIST to use machine learning driven, highly automated characterization to rapidly and intelligently ma p formulation phase space using structural characterization tools such as small -angle x-ray and neutron scattering (SAXS/SANS) together with secondary measurements, e.g. optical imaging, UV - vis -NIR and capillary viscometry. Our initial studies using the AFL at the Materials Support Network at CHESS (MSN - C) beamline and the NIST Center for Neutron Research 10m SANS have resulted in an order of magnitude reduction in the time needed to map a model phase diagram, and extensive mapping studies in an industrial coating formulation, a block copolymer - additive system, and other systems of interest from our personal care, biopharmaceutical, and alternative energy industrial collaborators. Future direct ions in algorithms and instrumentation to study the far - from - equilibrium self -assembly and processing steps that underlie many real products will also be discussed.

Crystalline and 3D continuous mesoporous quaternary CsTaWO6 semiconductors are prepared with different degrees of long‐range periodic order and local order, respectively, to investigate the influence of periodic pore order on the photocatalytic performance in hydrogen evolution of mesoporous photocatalysts. The degree of long‐range order of the mesopores is changed by modifying the ratio between metal precursors and soft polymer template poly(isoprene‐b‐styrene‐b‐ethylene oxide) (PI‐b‐PS‐b‐PEO; ISO) in the sol–gel synthesis. Long‐range periodic order is found to have no appreciable advantage compared with an only locally ordered continuous pore system. On the contrary, nonperiodically ordered mesopores result in higher activity toward photocatalytic hydrogen evolution, even with slightly smaller pore diameter and lower cumulative pore volume. Most importantly, it is shown that pore connectivity and heterogeneous pore systems in mesoporous photocatalysts play a major role for hydrogen evolution when other parameters are confirmed to be not rate limiting.

Polarized resonant soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines the principles of X-ray scattering and X-ray spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P-RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy-dependent three-dimensional tensors with heterogeneities at nanometre to sub-nanometre length scales. This challenge is overcome here by developing an open-source virtual instrument that uses graphical processing units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. This computational framework - called CyRSoXS (https://github.com/usnistgov/cyrsoxs) - is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co-simulation with the physical instrument for operando analytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi-modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposing CyRSoXS to Python using Pybind. This eliminates input/output requirements for large-scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches.

The application of machine learning techniques to X-ray scattering experiments has been of significant recent interest, offering advances in areas such as the study of complex oxides. Despite these success stories, few applications of these techniques into soft materials have been reported, likely due in part to the highly nonequilibrium nature of soft materials phase spaces and the complexities associated with autonomous formulation preparation. Here, we report the design of the Autonomous Formulation Laboratory, a robust platform for the automated synthesis and measurement of complex liquid mixtures using X-ray and neutron scattering, readily extensible to system-specific complementary techniques such as spectroscopy and rheometry. We describe the application of the platform to generate dense, highly reproducible data sets on material systems ranging from silica nanoparticles to block copolymer micelles. We expect the platform to prove revolutionary to the understanding of the stability of complex liquid formulations and the resulting data sets to provide fertile ground for the development of machine learning techniques for complex soft materials phase spaces.

We systematically reduce the cross-link density of a PA network based on m-phenylene diamine by substituting a fraction of the trifunctional trimesoyl chloride cross-linking agent with a difunctional isophthaloyl analog that promotes chain extension, in order to elucidate robust design cues for improving the polyamide (PA) separation layer in reverse osmosis (RO) membranes for desalination. Thin films of these model PA networks are fully integrated into a composite membrane and evaluated in terms of their water flux and salt rejection. By incorporating 15 mol % of the difunctional chain extender, we reduce the cross-link density of the network by a factor of two, which leads to an 80 % increase in the free or unreacted amine content. The resulting swelling of the PA network in liquid water increases by a factor of two accompanied by a 30 % increase in the salt passage through the membrane. Surprisingly, this leads to a 30 % decrease in the overall permeance of water through the membrane. This conundrum is resolved by quantifying the microscopic diffusion coefficient of water inside the PA network with quasi-elastic neutron scattering. In the highest and lowest cross-link density networks, water shows strong signatures of confined diffusion. At short length scales, the water exhibits a translational diffusion that is consistent with the jump-diffusion mechanism. This translational diffusion coefficient is approximately five times slower in the lowest cross-linked density network, consistent with the reduced water permeance. This is interpreted as water molecules interacting more strongly with the increased free amine content. Over longer length scales the water diffusion is confined, exhibiting mobility that is independent of length scale. The length scales of confinement from the quasi-elastic neutron scattering experiments at which this transition from confined to translational diffusion occurs is on the order of (5 to 6) Å, consistent with complementary X-ray scattering, small angle neutron scattering, and positron annihilation lifetime spectroscopy measurements. The confinement appears to come from heterogeneities in the average inter-atomic distances, suggesting that diffusion occurs by water bouncing between chains and occasionally sticking to the polar functional groups. The results obtained here are compared with similar studies of water diffusion through both rigid porous silicates and ion exchange membranes, revealing robust design cues for engineering high-performance RO membranes.

Materials combining an asymmetric pore structure with mesopores everywhere enable high surface area accessibility and fast transport, making them attractive for e.g., energy conversion and storage applications. Block copolymer (BCP)/inorganic precursor co-assembly combined with non-solvent induced phase separation (NIPS) provides a route to materials in which a mesoporous top surface layer merges into an asymmetric support with graded porosity along the film normal and mesopores throughout. Here, the co-assembly and non-solvent-induced phase separation (CNIPS) of poly(isoprene)-b-poly(styrene)-b-poly(4-vinylpyridine) (ISV) triblock terpolymer and titanium dioxide (TiO2) sol-gel nanoparticlesare reported. Heat-treatment in air results in free-standing asymmetric porous TiO2. Further thermal processing in ammonia results in free-standing asymmetric porous titanium nitride (TiN). processing changes alter structural membrane characteristics is demonstrated. Changing the CNIPS evaporation time results in various membrane cross-sections ( finger-like to sponge-like). Oxide and nitride material composition, crystallinity, and porosity are tuned by varying thermal processing conditions. Finally, thermal processing condition effects are probed on phase-pure asymmetric nitride membrane behavior using cyclic voltammetry to elucidate their influence, e.g., on specific capacitance. Results provide further insights into improving asymmetric and porous materials for applications including energy conversion and storage, separation, and catalysis and motivate a further expansion of CNIPS to other (in)organic materials.