Energy Sciences

Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate an end-to-end attempt towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis. Our approach initializes materials discovery campaigns by autonomously mapping the synthetic feasibility of a chemical system using density functional theory with AI feedback. Following expert-driven down-selection of newly generated phases, we use solid-state synthesis and in situ characterization via hot-stage X-ray diffraction in order to realize new ternary oxide phases experimentally. We applied this strategy in six ternary transition-metal oxide chemistries previously considered well-explored, one of which culminated in the discovery of two novel phases of calcium ruthenates. Detailed characterization using room temperature X-ray powder diffraction, 4D-STEM and SQUID measurements identifies the structure and composition and confirms distinct properties, including distinct defect concentrations, of one of the new phases formed in our experimental campaigns. While the discovery of a new material guided by AI and DFT theory represents a milestone, our procedure and results also highlight a number of critical gaps in the process that can inform future efforts towards the improvement of AI-coupled methodologies.

Moving toward a circular plastics economy is a vital aspect of global resource management. Chemical recycling of plastics ensures that high-value monomers can be recovered from depolymerized plastic waste, thus enabling circular manufacturing. However, to increase chemical recycling throughput in materials recovery facilities, the present understanding of polymer transport, diffusion, swelling, and heterogeneous deconstruction kinetics must be systematized to allow industrial-scale process design, spanning molecular to macroscopic regimes. To develop a framework for designing depolymerization processes, we examined acidolysis of circular polydiketoenamine elastomers. We used magnetic resonance to monitor spatially resolved observables in situ and then evaluated these data with a fractal method that treats nonlinear depolymerization kinetics. This approach delineated the roles played by network architecture and reaction medium on depolymerization outcomes, yielding parameters that facilitate comparisons between bulk processes. These streamlined methods to investigate polymer hydrolysis kinetics portend a general strategy for implementing chemical recycling on an industrial scale.

Isolated platinum(II) ions anchored at acid sites in the pores of zeolite HZSM-5, initially introduced by aqueous ion exchange, were reduced to form platinum nanoparticles that are stably dispersed with a narrow size distribution (1.3 ± 0.4 nm in average diameter). The nanoparticles were confined in reservoirs within the porous zeolite particles, as shown by electron beam tomography and the shape-selective catalysis of alkene hydrogenation. When the nanoparticles were oxidatively fragmented in dry air at elevated temperature, platinum returned to its initial in-pore atomically dispersed state with a charge of +2, as shown previously by X-ray absorption spectroscopy. The results determine the conditions under which platinum is retained within the pores of HZSM-5 particles during redox cycles that are characteristic of the reductive conditions of catalyst operation and the oxidative conditions of catalyst regeneration.

The L_2,3-edge X-ray absorption spectra of late transition metals such as Cu, Ag, and Au exhibit absorption onsets lower in energy for higher oxidation states, which is at odds with the measured spectra of earlier transition metals. Time-dependent density functional theory calculations for Cu²⁺/Cu⁺ reveal a larger 2p core-exciton binding energy for Cu²⁺, overshadowing shifts in single-particle excitation energies with respect to Cu⁺. We explore this phenomenon in a Cu⁺ metal-organic framework with ∼12% Cu²⁺ defects and find that corrections with self-consistent excited-state total energy differences provide accurate XAS peak alignment.

A composite structure was developed for use in all-solid-state batteries that consists of a conductive 3D reduced graphene oxide framework embedded beneath cathode active material particles. This unique structure offers significant advantages when combined with a sulfide solid electrolyte as the heterogeneous distribution of the conductive carbon in the composite cathode ensures good contact between the carbon and cathode particles for facile electron transfer while a direct contact between the carbon and sulfide solid electrolyte is avoided or minimized. This approach assists in preventing or reducing unwanted irreversible faradaic reactions. As a result, the newly developed composite of cathode particles decorated on a 3D reduced graphene oxide framework delivers higher specific capacity with improved cycling stability compared with a typical composite cathode consisting of a homogenous mixture of the cathode active material, carbon nanofibers, and sulfide solid electrolyte.

Metal halide perovskite solar cells (PSCs) are infamous for their batch-to-batch and lab-to-lab irreproducibility in terms of stability and performance. Reproducible fabrication of PSCs is a critical requirement for market viability and practical commercialization. PSC irreproducibility plagues all levels of the community; from institutional research laboratories, start-up companies, to large established corporations. In this work, the critical function of atmospheric humidity to regulate the crystallization and stabilization of formamidinium lead triiodide (FAPbI₃) perovskites is unraveled. It is demonstrated that the humidity content during processing induces profound variations in perovskite stoichiometry, thermodynamic stability, and optoelectronic quality. Almost counterintuitively, it is shown that the presence of humidity is perhaps indispensable to reproduce phase-stable and efficient FAPbI₃-based PSCs.

Abstract: Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Efforts to study intricate, higher-order cellular functions have called for fluorescence imaging under physiologically relevant conditions such as tissue systems in simulated native buffers. This endeavor has presented novel challenges for fluorescent probes initially designed for use in simple buffers and monolayer cell culture. Among current fluorescent probes, semiconductor nanocrystals, or quantum dots (QDs), offer superior photophysical properties that are the products of their nanoscale architectures and chemical formulations. While their high brightness and photostability are ideal for these biological environments, even state of the art QDs can struggle under certain physiological conditions. A recent method correlating electron microscopy ultrastructure with single-QD fluorescence has begun to highlight subtle structural defects in QDs once believed to have no significant impact on photoluminescence (PL). Specific defects, such as exposed core facets, have been shown to quench QD PL in physiologically accurate conditions. For QD-based imaging in complex cellular systems to be fully realized, mechanistic insight and structural optimization of size and PL should be established. Insight from single QD resolution atomic structure and photophysical correlative studies provides a direct course to synthetically tune QDs to match these challenging environments.

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is "out-of-distribution" from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

In 2019, the United States consumed over 57 million metric tons (MMT) of plastic with less than 7% recovered for reuse. This study provides an updated material flow analysis at national and regional scales for all durable and single-use plastics in the United States. From this material flow analysis, we develop a series of alternative future national plastic flow scenarios that envision a scale-up of recycling technologies, incorporating technical limitations and sorting infrastructure constraints. The results suggest that a maximum of 68% (24 MMT) of plastic waste could be diverted from landfills by scaling up existing commercial recycling technologies. Based on the current technological landscape, reaching near-zero waste is only possible if processes that are operating at pilot and laboratory scales can be effectively scaled and coupled with improved sorting infrastructure. Through these scenarios with increased recycling, the availability of postconsumer resin stocks could increase by 22–43 MMT.