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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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Abstract Structured light beams offer promising properties for a variety of applications, but the generation of broadband structured light remains a challenge. New opportunities are emerging in the terahertz frequency range owing to recent progress in light-driven ultrafast vectorial currents through spatially patterning spintronic and optoelectronic systems.
Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol-1 (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.
Abstract Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m 2 /g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm 2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H 2 SO 4 . This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.
Abstract Kagome-lattice materials possess attractive properties for quantum computing applications, but their synthesis remains challenging. Herein, based on the compelling identification of the two cleavable surfaces of Co 3 Sn 2 S 2 , we show surface kagome electronic states (SKESs) on a Sn-terminated triangular Co 3 Sn 2 S 2 surface. Such SKESs are imprinted by vertical p-d electronic hybridization between the surface Sn (subsurface S) atoms and the buried Co kagome-lattice network in the Co 3 Sn layer under the surface. Owing to the subsequent lateral hybridization of the Sn and S atoms in a corner-sharing manner, the kagome symmetry and topological electronic properties of the Co 3 Sn layer is proximate to the Sn surface. The SKESs and both hybridizations were verified via qPlus non-contact atomic force microscopy (nc-AFM) and density functional theory calculations. The construction of SKESs with tunable properties can be achieved by the atomic substitution of surface Sn (subsurface S) with other group III-V elements (Se or Te), which was demonstrated theoretically. This work exhibits the powerful capacity of nc-AFM in characterizing localized topological states and reveals the strategy for synthesis of large-area transition-metal-based kagome-lattice materials using conventional surface deposition techniques.
Abstract Complex multi-element alloys are gaining prominence for structural applications, supplementing steels, and superalloys. Understanding the impact of each element on alloy surfaces due to oxidation is vital in maintaining material integrity. This study investigates oxidation mechanisms in these alloys using a model five-element equiatomic CoCrFeNiMn alloy, in a controlled oxygen environment. The oxidation-induced surface changes correlate with each element’s interactive tendencies with the environment, guided by thermodynamics. Initial oxidation stages follow atomic size and redox potential, with the latter becoming dominant over time, causing composition inversion. The study employs in-situ atom probe tomography, transmission electron microscopy, and X-ray absorption near-edge structure techniques to elucidate the oxidation process and surface oxide structure evolution. Our findings deconvolute the mechanism for compositional and structural changes in the oxide film and will pave the way for a predictive design of complex alloys with improved resistance to oxidation under extreme conditions.
Abstract Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs.
Abstract Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.
Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations ( R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.
Abstract Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO $$_2$$ 2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO $$_2$$ 2 fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model’s accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400 000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.
Abstract Single-layer membranes have emerged as promising candidates for applications requiring high transport rates due to their low resistance to molecular transport. Owing to their atomically thin structure, these membranes experience significant microscopic fluctuations, emphasizing the need to explore their impact on ion transport processes. In this study, we investigate the effects of membrane fluctuations on the elementary scaling behavior of ion conductance $$G$$ G as a function of ion concentration $$c_{0}$$ c 0 , represented as $$G = \beta c_{0}^{\alpha }$$ G = β c 0 α , using molecular dynamics simulations. Our findings reveal that membrane fluctuations not only alter the conductance coefficient $$\beta$$ β but also the power-law exponent $$\alpha$$ α . We identify two distinct frequency regimes of membrane fluctuations, GHz-scale and THz-scale fluctuations, and examine their roles in conductance scaling. Furthermore, we demonstrate that the alteration of conductance scaling arises from the non-linearity between ion conductance and membrane shape. This work provides a fundamental understanding of ion transport in fluctuating membranes.
Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.
Abstract Plasma catalysis is gaining increasing interest for various gas conversion applications, such as CO 2 conversion into value-added chemicals and fuels, CH 4 activation into hydrogen, higher hydrocarbons or oxygenates, and NH 3 synthesis. Other applications are already more established, such as for air pollution control, e.g. volatile organic compound remediation, particulate matter and NO x removal. In addition, plasma is also very promising for catalyst synthesis and treatment. Plasma catalysis clearly has benefits over ‘conventional’ catalysis, as outlined in the Introduction. However, a better insight into the underlying physical and chemical processes is crucial. This can be obtained by experiments applying diagnostics, studying both the chemical processes at the catalyst surface and the physicochemical mechanisms of plasma-catalyst interactions, as well as by computer modeling. The key challenge is to design cost-effective, highly active and stable catalysts tailored to the plasma environment. Therefore, insight from thermal catalysis as well as electro- and photocatalysis is crucial. All these aspects are covered in this Roadmap paper, written by specialists in their field, presenting the state-of-the-art, the current and future challenges, as well as the advances in science and technology needed to meet these challenges.
Abstract Fluorite oxides are attractive ionic compounds for a range of applications with critical thermal management requirements. In view of recent reports alluding to anisotropic thermal conductivity in this face-centered cubic crystalline systems, we perform a detailed analysis of the impact of direction-dependent phonon group velocities and lifetimes on the thermal transport of fluorite oxides. We demonstrate that the bulk thermal conductivity of this class of materials remains isotropic despite notable anisotropy in phonon lifetime and group velocity. However, breaking the symmetry of the phonon lifetime under external stimuli including boundary scattering present in nonequilibrium molecular dynamics simulations of finite size simulation cell gives rise to apparent thermal conductivity anisotropy. We observe that for accurate determination of thermal conductivity, it is important to consider phonon properties not only along high symmetry directions commonly measured in inelastic neutron or x-ray scattering experiments but also of those along lower symmetry. Our results suggests that certain low symmetry directions have a larger contribution to thermal conductivity compared to high symmetry ones.
Abstract Advances in timing detector technology require new specialized readout electronics. Applications demand below 10 ps time of arrival resolution and low power for a low repetition rate. A possible path to achieve O(10 ps) time resolution is an integrated chip using Silicon Germanium (SiGe) technology. Using DoE SBIR funding, Anadyne, Inc., in collaboration with UC Santa Cruz, has developed a prototype SiGe front-end readout chip optimized for low power and timing resolution. Two versions of the chip were produced with performance in simulation: a more power version with 10 ps resolution at 5 fC with 1.1 mW/channel, and a less power version with 10 ps resolution at 8 fC with 0.6 mW/channel.The chip was produced at Tower Semiconductor with 350 nm technology. The ASIC from the prototype run shows good performance: a rise time of 0.7–1 ns and 25 mV per fC response with RMS noise <1 mV. Simulation and results from the prototype will be reported in this paper.
Abstract Strong nonlinear materials have been sought after for decades for applications in telecommunications, sensing, and quantum optics. Gallium-doped zinc oxide is a II–VI transparent conducting oxide that shows promising nonlinearities similar to indium tin oxide and aluminum-doped zinc oxide for the telecommunications band. Here we explore its nonlinearities in the epsilon near zero (ENZ) region and show n 2,eff values on the order of 4.5 × 10 −3 cm 2 GW −1 for IR pumping on 200–300 nm thin films. Measuring nonlinear changes in transmission and reflection with a white light source probe in the near-IR while exciting in the near-IR provides data in both time and wavelength. Three films varying in thickness, optical loss, and ENZ crossover wavelength are numerically modeled and compared to experimental data showing agreement for both dispersion and temporal relaxation. In addition, we discuss optimal excitation and probing wavelengths occur around ENZ for thick films but are red-shifted for thin films where our model provides an additional degree of freedom to explore. Obtaining accurate nonlinear measurements is a difficult and time-consuming task where our method in this paper provides experimental and modeled data to the community for an ENZ material of interest.
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).
Abstract Cross-linked polymers play an important role in structural applications as adhesives. In this article, we performed an experimental study to understand the effects of high temperature i.e. thermo-oxidative aging on the mechanical behavior of polymers. Subsequently, the constitutive behavior, i.e. stress-strain relation of these adhesives before and after the accelerated aging tests were studied on samples aged at different temperature and different time durations at zero humidity. The aging conditions and periods selected for the tests have been specifically designed to simulate the effects of aging under the working conditions that the materials will be subjected to in their standard applications. The focus of this study is on the investigation of the effects of aging on the polymeric adhesives used in automotive applications. Consequently, four different polymeric adhesives (one silicone-based, two polyurethane-based, and one acrylic type), which are being used extensively in these applications, have been chosen. The quasi-static tensile loading was used on these four adhesives and the changes in the constitutive behavior were measured. The failure tensile tests as well as the cyclic tensile tests were performed in our lab using a Test-Resources tensile testing machine. The temperatures that were used in this work are 60, 80 and 95°C for varying aging periods of 24hrs, 240hrs, and 720hrs. Then, all the data was collected and consequently analyzed, and the resulting behavior was compared to that of the unaged material, showing varying results depending on the material, temperature, and aging period. Different behaviors have been observed after aging. The polymers’ behavior was expanding from no effect to significant damage caused by aging conditions. This work provides the core understanding required for modeling the material behavior during aging and lays down the groundwork for further data-infused model-free approaches.
Abstract Resonant optical cavities are essential components in mid-infrared applications. However, typical film-type cavities require multilayer stacks with a micron-thick spacer due to mid-infrared wavelengths, and their performance is limited by narrow frequency tunability and angular sensitivity. We propose and experimentally demonstrate the subwavelength-scale (≈ λ 0 /150) resonant nanocavity arrays that enhance the absorption spectrum of the device in the mid-infrared (10–12 microns) via excitation of coupled surface plasmon–phonon polaritons. The proposed metal–insulator–polar dielectric (gold–silicon–silicon carbide) structure supports a guided mode of the coupled surface polaritons in the lateral direction while vertically confining the mid-infrared wave within the 80 nm thick dielectric spacer. In particular, the subwavelength-scale (≈ λ 0 /10) gratings are imposed to form Fabry–Pérot cavity arrays displaying angle-insensitive and frequency-tunable absorption of up to 80% of the optical power in the mid-infrared. Our work should benefit diverse mid-infrared applications and novel designs of polariton-based photonic devices.