Autonomous x-ray scattering.

Autonomous experimentation (AE) is an emerging paradigm that seeks to automate the entire workflow of an experiment, including-crucially-the decision-making step. Beyond mere automation and efficiency, AE aims to liberate scientists to tackle more challenging and complex problems. We describe our recent progress in the application of this concept at synchrotron x-ray scattering beamlines. We automate the measurement instrument, data analysis, and decision-making, and couple them into an autonomous loop. We exploit Gaussian process modeling to compute a surrogate model and associated uncertainty for the experimental problem, and define an objective function exploiting these. We provide example applications of AE to x-ray scattering, including imaging of samples, exploration of physical spaces through combinatorial methods, and coupling toin situprocessing platforms These uses demonstrate how autonomous x-ray scattering can enhance efficiency, and discover new materials.

Surface enrichment dictates block copolymer orientation.

Orientation of block copolymer (BCP) morphology in thin films is critical to applications as nanostructured coatings. Despite being well-studied, the ability to control BCP orientation across all possible block constituents remains challenging. Here, we deploy coarse-grained molecular dynamics simulations to study diblock copolymer ordering in thin films, focusing on chain makeup, substrate surface energy, and surface tension disparity between the two constituent blocks. We explore the multi-dimensional parameter space of ordering using a machine-learning approach, where an autonomous loop using a Gaussian process (GP) control algorithm iteratively selects high-value simulations to compute. The GP kernel was engineered to capture known symmetries. The trained GP model serves as both a complete map of system response, and a robust means of extracting material knowledge. We demonstrate that the vertical orientation of BCP phases depends on several counter-balancing energetic contributions, including entropic and enthalpic material enrichment at interfaces, distortion of morphological objects through the film depth, and of course interfacial energies. BCP lamellae are found more resistant to these effects, and thus more robustly form vertical orientations across a broad range of conditions; while BCP cylinders are found to be highly sensitive to surface tension disparity.

Responsive blends of block copolymers stabilize the hexagonally perforated lamellae morphology.

Blends of block copolymers can form phases and exhibit features distinct from the constituent materials. We study thin film blends of cylinder-forming and lamellar-forming block copolymers across a range of substrate surface energies. Blend materials are responsive to interfacial energy, allowing selection of pure or coexisting phases based on surface chemistry. Blending stabilizes certain motifs that are typically metastable, and can be used to generate pure hexagonally perforated lamellar thin films across a range of film thicknesses and surface energies. This tolerant behavior is ascribed to the ability of blend materials to redistribute chains to stabilize otherwise high-energy defect structures. The blend responsiveness allows the morphology to be spatially defined through multi-tone chemical surface patterns.

Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends.

The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to fabricate complex nanostructure arrays, but finding morphologies that emerge with changes in polymer architecture, composition, or assembly constraints remains daunting because of the increased dimensionality of the DSA design space. Here, we demonstrate machine-guided discovery of emergent morphologies from a cylinder/lamellae BCP blend directed by a chemical grating template, conducted without direct human intervention on a synchrotron x-ray scattering beamline. This approach maps the morphology-template phase space in a fraction of the time required by manual characterization and highlights regions deserving more detailed investigation. These studies reveal localized, template-directed partitioning of coexisting lamella- and cylinder-like subdomains at the template period length scale, manifesting as previously unknown morphologies such as aligned alternating subdomains, bilayers, or a "ladder" morphology. This work underscores the pivotal role that autonomous characterization can play in advancing the paradigm of DSA.

Enhancing Crystallization in Hybrid Perovskite Solar Cells Using Thermally Conductive 2D Boron Nitride Nanosheet Additive.

Controlling crystallization and grain growth is crucial for realizing highly efficient hybrid perovskite solar cells (PSCs). In this work, enhanced PSC photovoltaic performance and stability by accelerating perovskite crystallization and grain growth via 2D hexagonal boron nitride (hBN) nanosheet additives incorporated into the active perovskite layer are demonstrated. In situ X-ray scattering and infrared thermal imaging during the perovskite annealing process revealed the highly thermally conductive hBN nanosheets promoted the phase conversion and grain growth in the perovskite layer by facilitating a more rapid and spatially uniform temperature rise within the perovskite film. Complementary structural, physicochemical, and electrical characterizations further showed that the hBN nanosheets formed a physical barrier at the perovskite grain boundaries and the interfaces with charge transport layers, passivating defects, and retarding ion migration. As a result, the power conversion efficiency of the PSC is improved from 17.4% to 19.8%, along with enhanced device stability, retaining ≈90% of the initial efficiency even after 500 h ambient air storage. The results not only highlight 2D hBN as an effective additive for PSCs but also suggest enhanced thermal transport as one of the pathways for improved PSC performance by 2D material additives in general.

Priming self-assembly pathways by stacking block copolymers.

Block copolymers spontaneously self-assemble into well-defined nanoscale morphologies. Yet equilibrium assembly gives rise to a limited set of structures. Non-equilibrium strategies can, in principle, expand diversity by exploiting self-assembly's responsive nature. In this vein, we developed a pathway priming strategy combining control of thin film initial configurations and ordering history. We sequentially coat distinct materials to form prescribed initial states, and use thermal annealing to evolve these manifestly non-equilibrium states through the assembly landscape, traversing normally inaccessible transient structures. We explore the enormous associated hyperspace, spanning processing (annealing temperature and time), material (composition and molecular weight), and layering (thickness and order) dimensions. We demonstrate a library of exotic non-native morphologies, including vertically-oriented perforated lamellae, aqueduct structures (vertical lamellar walls with substrate-pinned perforations), parapets (crenellated lamellae), and networks of crisscrossing lamellae. This enhanced structural control can be used to modify functional properties, including accessing regimes that surpass their equilibrium analogs.

Chain Redistribution Stabilizes Coexistence Phases in Block Copolymer Blends.

The nanoscale morphologies of block copolymer (BCP) thin films are determined by chain architecture. Experimental studies of thin film blends of different BCP chain types have demonstrated that blending can stabilize new motifs, such as coexistence phases. Here, we deploy coarse-grained molecular dynamics (MD) simulations in order to better understand the self-assembly behavior of BCP blend thin films. We consider blends of lamella- and cylinder-forming BCP chains, studying their morphological makeup, the chain distribution within the morphology, and the underlying polymer chain conformations. Our simulations show that there are local concentration deviations at the scale of the morphological objects that dictate the local structure, and that BCP chains redistribute within the morphology so as to stabilize the structure. Underlying these effects are measurable distortions in the BCP chain conformations. The conformational freedom afforded by BCP blending stabilizes defects and allows coexistence phases to appear, while also leading to kinetic trapping effects. These results highlight the power of blending in designing the morphology that forms.

Mechanisms of Interface Cleaning in Heterostructures Made from Polymer-Contaminated Graphene.

Heterostructures obtained from layered assembly of 2D materials such as graphene and hexagonal boron nitride have potential in the development of new electronic devices. Whereas various materials techniques can now produce macroscopic scale graphene, the construction of similar size heterostructures with atomically clean interfaces is still unrealized. A primary barrier has been the inability to remove polymeric residues from the interfaces that arise between layers when fabricating heterostructures. Here, the interface cleaning problem of polymer-contaminated heterostructures is experimentally studied from an energy viewpoint. With this approach, it is established that the interface cleaning mechanism involves a combination of thermally activated polymer residue mobilization and their mechanical actuation. This framework allows a systematic approach for fabricating record large-area clean heterostructures from polymer-contaminated graphene. These heterostructures provide state-of-the-art electronic performance. This study opens new strategies for the scalable production of layered materials heterostructures.

Molecular Weight Dependence of Block Copolymer Micelle Fragmentation Kinetics.

The effect of molecular weight (M) on the fragmentation kinetics of micelles formed by 1,2-polybutadiene-block-poly(ethylene oxide) (PB-PEO) copolymers was studied in the ionic liquid 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide. A series of six samples, with total M ranging from 104 to 105 g mol-1 and nearly constant composition (fPEO ≈ 0.4), were examined; all six formed spherical micelles with PEO coronas. Nonequilibrium PB-PEO micelles were prepared by direct dissolution, a process that systematically produces nanoparticles with mean aggregation numbers more than twice the equilibrium values. When subjected to high temperature annealing (170 °C), the average micelle radius was found to decrease substantially, as determined by temperature-jump dynamic light scattering (T-jump DLS) and time-resolved small-angle X-ray scattering (TR-SAXS). The characteristic fragmentation times (τ) were found to increase strongly with increasing degree of polymerization N, as τ ∼ N1.8. This result compares favorably with the prediction of a previously untested model.

Resilient three-dimensional ordered architectures assembled from nanoparticles by DNA.

Rapid developments of DNA-based assembly methods provide versatile capabilities in organizing nanoparticles (NPs) in three-dimensional (3D) organized nanomaterials, which is important for optics, catalysis, mechanics, and beyond. However, the use of these nanomaterials is often limited by the narrow range of conditions in which DNA lattices are stable. We demonstrate here an approach to creating an inorganic, silica-based replica of 3D periodic DNA-NP structures with different lattice symmetries. The created ordered nanomaterials, through the precise 3D mineralization, maintain the spatial topology of connections between NPs by DNA struts and exhibit a controllable degree of the porosity. The formed silicated DNA-NP lattices exhibit excellent resiliency. They are stable when exposed to extreme temperatures (>1000°C), pressures (8 GPa), and harsh radiation conditions and can be processed by the conventional nanolithography methods. The presented approach allows the use of a DNA assembly strategy to create organized nanomaterials for a broad range of operational conditions.

Stable Thermotropic 3D and 2D Double Gyroid Nanostructures with Sub-2-nm Feature Size from Scalable Sugar-Polyolefin Conjugates.

Ultra-low molecular weight disaccharide-polyolefin conjugates with cellobiose, lactose and maltose head groups and atactic polypropene tails, such as 1, undergo a series of irreversible thermotropic order-order transitions with increasing temperature to provide nanostructured phases in the sequence: lamellar (L), hexagonal perforated lamellar (HPL), double gyroid (DG) and hexagonal cylindrical (C). The DG phase displays exceptional stability at ambient temperature and features two interpenetrating sugar domain networks having a sub-2-nm strut width and a lattice parameter, aDG , of 13.1 nm. The unique stability of this DG phase extends further within ultrathin films all the way down to the two-dimensional limit of 15 nm in which film thickness, l, is now less than the surface-oriented unit cell height, hDG . In addition to raising the fundamental question of what minimally constitutes a Schoen triply periodic minimal surface and DG lattice, these results serve to establish the class of sugar-polyolefin conjugates as a new material platform for nanoscience and nanotechnology.

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels.

A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

Future trends in synchrotron science at NSLS-II.

In this paper, we summarize briefly some of the future trends in synchrotron science as seen at the National Synchrotron Light Source II, a new, low emittance source recently commissioned at Brookhaven National Laboratory. We touch upon imaging techniques, the study of dynamics, the increasing use of multimodal approaches, the vital importance of data science, and other enabling technologies. Each are presently undergoing a time of rapid change, driving the field of synchrotron science forward at an ever increasing pace. It is truly an exciting time and one in which Roger Cowley, to whom this journal issue is dedicated, would surely be both invigorated by, and at the heart of.

Vertical Lamellae Formed by Two-Step Annealing of a Rod-Coil Liquid Crystalline Block Copolymer Thin Film.

Silicon-containing block copolymer thin films with high interaction parameter and etch contrast are ideal candidates to generate robust nanotemplates for advanced nanofabrication, but they typically form in-plane oriented microdomains as a result of the dissimilar surface energies of the blocks. Here, we describe a two-step annealing method to produce vertically aligned lamellar structures in thin film of a silicon-containing rod-coil thermotropic liquid crystalline block copolymer. The rod-coil block copolymer with the volume fraction of the Si-containing block of 0.22 presents an asymmetrical lamellar structure in which the rod block forms a hexatic columnar nematic liquid crystalline phase. A solvent vapor annealing step first produces well-ordered in-plane cylinders of the Si-containing block, then a subsequent thermal annealing promotes the phase transition from in-plane cylinders to vertical lamellae. The pathways of the order-order transition were examined by microscopy and in situ using grazing incidence small-angle X-ray scattering and wide-angle X-ray scattering.

Electrospray deposition tool: Creating compositionally gradient libraries of nanomaterials.

Electrospray deposition (ESD) enables the growth of solution deposited thin films in a precise and continuous manner by the delivery of submicron droplets of dilute solutions to a heated substrate. By combining ESD with programmable motor control and gradient solution pumping in a first-of-its-kind user tool at the Center for Functional Nanomaterials at Brookhaven National Laboratory, we show the ability to create one or two dimensional compositional gradient nanoscale films via ESD. These capabilities make it possible to construct thin film multicomponent "libraries" on a single substrate to rapidly and systematically characterize composition-dependent properties in a variety of material systems such as thin films involving homopolymer and block copolymer blends. We report the design, construction, and validation of a gradient ESD tool that allows users to carefully control the jet stability, flow composition, spray position, and substrate temperature. Calibrated thin films range in thickness from tens to hundreds of nanometers. We demonstrate gradient thin films using a ternary dye triangle as well as a gradual blending of polystyrene homopolymer with poly(styrene-block-methyl methacrylate) on a single substrate. Paired with the rapid measurement capabilities of synchrotron small angle X-ray scattering, this tool forms an integral part of a new platform for high-throughput, autonomous characterization and design of nanomaterial thin films and soft materials more generally.

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments.

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.

Ordered three-dimensional nanomaterials using DNA-prescribed and valence-controlled material voxels.

The ability to organize nanoscale objects into well-defined three-dimensional (3D) arrays can translate advances in nanoscale synthesis into targeted material fabrication. Despite successes in nanoparticle assembly, most extant methods are system specific and not fully compatible with biomolecules. Here, we report a platform for creating distinct 3D ordered arrays from different nanomaterials using DNA-prescribed and valence-controlled material voxels. These material voxels consist of 3D DNA frames that integrate nano-objects within their scaffold, thus enabling the object's valence and coordination to be determined by the frame's vertices, which can bind to each other through hybridization. Such DNA material voxels define the lattice symmetry through the spatially prescribed valence decoupling the 3D assembly process from the nature of the nanocomponents, such as their intrinsic properties and shapes. We show this by assembling metallic and semiconductor nanoparticles and also protein superlattices. We support the technological potential of such an assembly approach by fabricating light-emitting 3D arrays with diffraction-limited spectral purity and 3D enzymatic arrays with increased activity.

High-throughput morphology mapping of self-assembling ternary polymer blends.

Multicomponent blending is a convenient yet powerful approach to rationally control the material structure, morphology, and functional properties in solution-deposited films of block copolymers and other self-assembling nanomaterials. However, progress in understanding the structural and morphological dependencies on blend composition is hampered by the time and labor required to synthesize and characterize a large number of discrete samples. Here, we report a new method to systematically explore a wide composition space in ternary blends. Specifically, the blend composition space is divided into gradient segments deposited sequentially on a single wafer by a new gradient electrospray deposition tool, and characterized using high-throughput grazing-incidence small-angle X-ray scattering. This method is applied to the creation of a ternary morphology diagram for a cylinder-forming polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) block copolymer blended with PS and PMMA homopolymers. Using "wet brush" homopolymers of very low molecular weight (∼1 kg mol-1), we identify well-demarcated composition regions comprising highly ordered cylinder, lamellae, and sphere morphologies, as well as a disordered phase at high homopolymer mass fractions. The exquisite granularity afforded by this approach also helps to uncover systematic dependencies among self-assembled morphology, topological grain size, and domain period as functions of homopolymer mass fraction and PS : PMMA ratio. These results highlight the significant advantages afforded by blending low molecular weight homopolymers for block copolymer self-assembly. Meanwhile, the high-throughput, combinatorial approach to investigating nanomaterial blends introduced here dramatically reduces the time required to explore complex process parameter spaces and is a natural complement to recent advances in autonomous X-ray characterization.