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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Multi-response maps show that atomically dispersed iron on alumina provides relatively high activity and good stability in low temperature CO oxidation.
Significance The kinetic assessment of catalytic reactions is essential to the design and application of thermochemical processes useful in petrochemical synthesis, energy conversion, and environmental remediation. We demonstrate that the ubiquitously used Langmuir–Hinshelwood formalism is incomplete and fails to correctly describe even simple catalytic reactions (e.g., A + A → A 2 ). An accurate kinetic description requires explicit rate equations for multisite configurations (e.g., A*–A* pairs) to account for the nonrandom clustering/isolation of surface species. We rigorously describe clustering/isolation phenomena by derivation of coverage-dependent stoichiometric coefficients, which capture the distinct influence each elementary step exerts on each catalytic site ensemble—thereby overcoming limitations of the mean-field (i.e., Hinshelwood) assumption in describing reactions of mobile Langmuirian adsorbates on catalytic surfaces.
This paper describes the software design patterns and vehicle interfaces that were employed to transition vehicle controllers from simulation environments to open-road field experiments. The approach relies on a life cycle that utilizes model-based design and code generation, along with agile software development, and both software and hardware-in-the-loop testing, with additional safety margins. Autonomous designs should consider the dynamics of mixed autonomy in traffic to safely operate among humans. The software that provides a vehicle’s behavior intelligence is often developed through simulation, which may have a mismatch between dynamics, or as a result of a reinforcement learning workflow, which may be a black box with challenges to analyze. In each of these cases, it is important to have research interfaces that provide strongly typed data streams accessible to researchers who are not software experts while continuing to satisfy safety and liveness constraints. This paper describes how we design the hardware platform interfaces and software design process for a mixed autonomy traffic experiment with a leader-follower scenario. Controller synthesis for these vehicles requires clearly articulated vehicle interfaces and software design patterns for successful onboard deployment. Testing strategies for such controllers are also described before algorithms are transitioned to full-scale field experiments with safety operators for the vehicles. Testing strategies include software-in-the-loop simulation testing, hardware-in-the-loop simulation, ghost-car testing, and read-only testing in live traffic. With our approach, we were not only able to validate our controller synthesized in scripts and simulation, but also able to scale deployment to multiple vehicles.
In designing membrane systems, the synergy between membrane materials and the process design is often overlooked. In this paper, we present a mixed-integer nonlinear programming (MINLP) model for synthesizing membrane systems while simultaneously designing the respective membrane materials for multicomponent gas separation. The approach considers superstructure representations for systems with: (1) same, (2) potentially different, and (3) property-targeting membrane materials. In the first two systems, the selection of membrane material is a decision, while in the final type, membrane permeances are subject to optimization. Physics-based surrogate models are used to describe permeation in crossflow and countercurrent flow permeators. We show that, through a case study of biogas upgrading, our approach obtains high quality solutions. Furthermore, we use the proposed approach while considering permeance-based production cost to find the optimal membrane.
‘Green ambition towards sustainability’ is one of the hot research topics of the 21st century. With the sharp steering of the energy infrastructure toward fulfilling this radical expectation, the last decade has seen a global trend toward diversified sustainable renewable energy sources (such as solar, wind, and hydroelectric power) and electrification. Lithium-ion batteries (LIBs) are playing a significant role in this energy revolution. However, with the massive scale of the production of LIBs and their eventual retirement, attention has been turned to the erratic supply of raw materials used to manufacture these batteries and their disposal. Cathode regeneration technology is likely to be an optimal solution for the disposal of waste cathodes, aimed at non-destructively repairing and straightforwardly reusing degraded cathode materials for re-manufacturing LIBs with a shortened circular process, reasonable profits, and excellent efficiency. Additionally, cathode upcycling technology, aimed at upgrading the electrochemical performance of degraded materials, can be well adapted to the upgrading of battery chemistry and show excellent flexibility and potential for transforming the LIB industry from a resource-based to a circular economy. Thus, a holistic perspective based on the complete life cycle of LIBs is encouraged, and sustainability must be at the forefront regarding battery and pack design, raw material acquisition, materials synthesis, manufacturing, operating, retiring, and recycling processes of LIBs.
Cool GTL is a new process under development for conversion of biogas from digestors or CO2 +H2 to high quality drop in jet fuel. Cool GTL is designed to be a streamlined simplified low cost process which can be cost effective for small scale biogas applications. Cool GTL has the advantage that it uses a high activity bi-reforming catalyst, in a streamlined electric reformer design, to make the synthesis gas for a high conversion slurry fischer Tropsch process followed by a trailing wax cracking reactor. In this way high quality jet fuel can be directly produced. Recent experimental test data will be presented showing product quality. The life cycle analysis LCA will also be presented as well.
One of the most challenging aspects of developing high-energy lithium-based batteries is the structural and (electro)chemical stability of Ni-rich active cathode materials at thermally-abused and prolonged cell cycling conditions. Here, we report in situ physicochemical characterizations to improve the fundamental understanding of the degradation mechanism of charged polycrystalline Ni-rich cathodes at elevated temperatures (e.g., ≥ 40 °C). Using multiple microscopy, scattering, thermal, and electrochemical probes, we decouple the major contributors for the thermal instability from intertwined factors. Our research work demonstrates that the grain microstructures play an essential role in the thermal stability of polycrystalline lithium-based positive battery electrodes. We also show that the oxygen release, a crucial process during battery thermal runaway, can be regulated by engineering grain arrangements. Furthermore, the grain arrangements can also modulate the macroscopic crystallographic transformation pattern and oxygen diffusion length in layered oxide cathode materials.
Heating at the nanoscale is the basis of several biomedical applications, including magnetic hyperthermia therapies and heat-triggered drug delivery. The combination of multiple inorganic materials in hybrid magnetic nanoparticles provides versatile platforms to achieve an efficient heat delivery upon different external stimuli or to get an optical feedback during the process. However, the successful design and application of these nanomaterials usually require intricate synthesis routes and their magnetic response is still not fully understood. In this review we give an overview of the novel systems reported in the last few years, which have been mostly obtained by organic phase-based synthesis and epitaxial growth processes. Since the heating efficiency of hybrid magnetic nanoparticles often relies on the exchange-interaction between their components, we discuss various interface-phenomena that are responsible for their magnetic properties. Finally, followed by a brief comment on future directions in the field, we outline recent advances on multifunctional nanoparticles that can boost the heating power with light and combine heating and temperature sensing in a single nanomaterial.
Stable and redox active metal–organic frameworks were constructed by incorporating Zr 6 clusters and redox-active TTF-based linkers, which allow in situ generation and stabilization of ultra-small noble metal nanoparticles for heterogenous catalysis.
Here, in this work, we resolve how proton dynamics and halide mixing enhance or impede ionic conduction in protonated lithium antiperovskites (pLiAP) at compositions near the eutectic points of the halide salts. As a material class, pLiAPs of the form Li 3-x OH x X, (X = Cl, Br) show vast compositional design freedom; however, the resulting properties are susceptible to synthesis and processing methodologies. Proton incorporation and halide mixing stabilize the perovskite cubic phase at low temperatures (<50 °C) and using halide mixtures near the eutectic points (~250 to 300 °C) offer possibilities of lower temperature and faster synthesis and processing conditions (<1 h). Mixed-halide compositions such as Li 2 OHCl 0.37 Br 0.63 lead to a 30-fold improvement in room temperature ionic conductivity of a single halide structure, 1.5 × 10 -6 vs. 4.9 × 10 -8 S cm -1 (Li 2 OHCl). We combine infrared spectroscopy and nuclear magnetic resonance with first-principles density functional theory calculations to deconvolute halide mixing effects from local proton dynamics on Li-ion transport. In contrast to what has been supposed, our findings suggest that the halide sublattice dynamics, besides the OH rotation, correlate strongly with the fast-ion conduction at high temperatures.
Quantum computers are growing in size, and design decisions are being made now that attempt to squeeze more computation out of these machines. In this spirit, we design a method to boost the computational power of near-term quantum computers by adapting protocols used in quantum error correction to implement "Approximate Quantum Error Correction (AQEC)." By approximating fully-fledged error correction mechanisms, we can increase the compute volume (qubits × gates, or "Simple Quantum Volume (SQV)") of near-term machines. The crux of our design is a fast hardware decoder that can approximately decode detected error syndromes rapidly. Specifically, we demonstrate a proof-of-concept that approximate error decoding can be accomplished online in near-term quantum systems by designing and implementing a novel algorithm in Single-Flux Quantum (SFQ) superconducting logic technology. This avoids a critical decoding backlog, hidden in all offline decoding schemes, that leads to idle time exponential in the number of T gates in a program. Our design utilizes one SFQ processing module per physical qubit. Employing state-of-the-art SFQ synthesis tools, we show that the circuit area, power, and latency are within the constraints of contemporary quantum system designs. Under pure dephasing error models, the proposed accelerator and AQEC solution is able to expand SQV by factors between 3,402 and 11,163 on expected near-term machines. The decoder achieves a 5% accuracy-threshold and pseudo-thresholds of ~ 5%,4.75%,4.5%, and 3.5% physical error-rates for code distances 3,5,7, and 9. Decoding solutions are achieved in a maximum of ~20 nanoseconds on the largest code distances studied. By avoiding the exponential idle time in offline decoders, we achieve a 10x reduction in required code distances to achieve the same logical performance as alternative designs.
The PFvSM cluster at the University of Illinois greatly deepened the science of soft materials and expanded the means for embedding within them unprecedented functionalities. We pioneered a mesoscopic approach to materials design that combined leading competencies in fundamental experimental and theoretical soft matter science and fabrication that powerfully establishes equilibrium and nonequilibrium assembly of novel and complex building blocks as a preeminent contributor to progress in materials chemistry. Our cluster’s vision was an integrated program of work centered on soft matter science with the ultimate goal of impacting important energy technologies. As elementary units we employed function-encoded colloids, nanoparticles, large mesh fibrillar gels, deterministically fabricated networks and hybrid objects. These materials were employed as building blocks for diverse model systems that allowed fundamental soft matter principles of wide relevance to be established. Their assembly into materials with useful functional behavior provided new approaches to challenging problems in basic energy science including energy harvesting, transport and storage. To achieve these goals the cluster brought together a team of recognized world-leaders in materials synthesis and processing, in-situ characterization, theory and modeling, and materials design, integration and fabrication
The commercialization of the lithium-ion battery (LIB) in 1991 was responsible for the explosion in portable electronic technologies that has been seen over the past 30 years. With the advent of electric vehicles and other high-powered technologies, there is tremendous demand for LIBs with higher energy density and high safety. To achieve this, new electrode materials must be explored. The obvious choice of anode material would be pure metal lithium, which has a theoretical specific capacity of 3860 mAh g-1 . Unfortunately, metal lithium anodes have not been widely commercialized due to their tendency to react violently with the flammable liquid electrolytes used in today’s batteries. Battery safety can best be achieved by adopting solid electrolytes in place of liquid electrolytes. Solid electrolytes are nonvolatile and nonflammable, safely allowing for the combination of high-capacity cathode materials with a Li metal anode. Argyrodite sulfide solid electrolytes such as halogen-doped Li6PS5X (X = Cl, Br, I) are noted for their high ionic conductivity. But before sulfides can be commercially adopted, they possess several disadvantages which must be addressed, including time- and energy-consuming synthesis processes, poor electrochemical stability, and intrinsically poor air stability. This dissertation seeks to address each of these challenges through materials design an synthesis strategies. In this work, we pioneer a solvent-based approach for the synthesis of argyrodite solid electrolytes Li7PS6 and Li6PS5Xinstead of a stringent solid-state synthesis. Nontoxic ethanol is employed as the solvent, enabling a rapid synthetic approach to produce argyrodite solid electrolytes with high phase purity and compositional flexibility. Compared with Li7PS6, halogen doping (i.e. X = F, Cl, Br, I) not only increases the ionic conductivity, but also enhances the electrochemical stability at the interface towards Li metal. Specifically, F-doped argyrodites produce a robust SEI layer containing LiF, contributing to enhanced interfacial stability. Finally, to address the air instability challenge, argyrodite-incorporated composite solid electrolytes (CSEs) are designed and prepared to produce stable and flexible membranes that are demonstrated in solid-state Li metal batteries. These advances push argyrodite sulfide solid electrolyte research further and pave the way for the proliferation of next generation lithium metal batteries.
Here, the design and synthesis of functional polymers, aimed at targeted properties through specific structures, have long been challenged by their complex and often nonlinear structure–property relationships. Key processes, including knowledge accumulation for predictive design and experimental refinement and validation, are traditionally labor-insensitive and time-consuming, making it difficult to balance accuracy and efficiency. Here, we introduce an accelerated, autonomous system for the on-demand synthesis of electronic polymers that achieves the desired electrochromic functionality with high accuracy and efficiency. Our approach leverages large language model-assisted data mining, a physics-informed copolymer machine learning model, and an AI-driven autonomous robotic workflow in the Polybot lab. Within 72 h, Polybot autonomously synthesized electrochromic polymers (ECPs) with targeted, previously-unreported color values, including green polymers with specific absorption profiles, precisely fine-tuning copolymer structures with a 5% step size in comonomer composition within a three-monomer system. A publicly accessible ECP informatics database has also been created to foster knowledge exchange.
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multipleefforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of synthesized ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680 000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.
Ammonia (NH 3 ) is a key commodity chemical for the agricultural, textile and pharmaceutical industries, but its production via the Haber–Bosch process is carbon-intensive and centralized. Alternatively, an electrochemical method could enable decentralized, ambient NH 3 production that can be paired with renewable energy. The first verified electrochemical method for NH 3 synthesis was a process mediated by lithium (Li) in organic electrolytes. So far, however, elements other than Li remain unexplored in this process for potential benefits in efficiency, reaction rates, device design, abundance and stability. In our demonstration of a Li-free system, we found that calcium can mediate the reduction of nitrogen for NH 3 synthesis. Here we verified the calcium-mediated process using a rigorous protocol and achieved an NH 3 Faradaic efficiency of 40 ± 2% using calcium tetrakis(hexafluoroisopropyloxy)borate (Ca[B(hfip) 4 ] 2 ) as the electrolyte. Our results offer the possibility of using abundant materials for the electrochemical production of NH 3 , a critical chemical precursor and promising energy vector
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.