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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tailoring Molecular Space to Navigate Phase Complexity in Cs-Based Quasi-2D Perovskites via Gated-Gaussian-Driven High-Throughput Discovery

Cesium-based quasi-2D halide perovskites (HPs) offer promising functionalities and low-temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi-2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high-throughput automated experimentation, the phase complexities in the molecular space of quasi-2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two-stage decision-making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary-compositional quasi-2D HP film realizing excellent optoelectronic functionalities is demonstrated. Finally, this work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Direct evidence that tumor cells soften when navigating confined spaces.

The mechanical properties of cells strongly regulate many physiological and pathological processes. For example, in cancer, invasive and metastatic tumor cells have often been reported to be softer than nontumor cells, raising speculation that cancer cells might adaptively soften to facilitate migration through narrow tissue spaces. Despite growing interest in targeting cell softening to impede invasion and metastasis, it remains to be directly demonstrated that tumor cells soften as they migrate through confined spaces. Here, we address this open question by combining topographically patterned substrates with atomic force microscopy (AFM). Using a polydimethylsiloxane open-roof microdevice featuring tapered, fibronectin-coated channels, we followed the migration of U2OS cells through various stages of confinement while simultaneously performing AFM indentation. As cells progress from unconfined migration to fully confined migration, cells soften and exclude Yes-associated protein from the nucleus. Superresolution imaging reveals that confinement induces remodeling of actomyosin stress fiber architecture. Companion studies with flat one-dimensional microlines indicate that the changes in cytoarchitecture and mechanics are intrinsically driven by topographical confinement rather than changes in cellular aspect ratio. Our studies represent among the most direct evidence to date that tumor cells soften during confined migration and support cell softening as a mechanoadaptive mechanism during invasion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Navigating Large Chemical Spaces Using Graph Theory and Integer Programming

Navigating and analyzing large chemical spaces are necessary to accelerate the design and discovery of new molecules and chemical processes. In this work, we introduce a computational framework that integrates graph theory and integer programming to enable the efficient navigation of large chemical spaces. Our framework represents the chemical space as a graph, wherein nodes represent molecules and edges represent the degree of similarity or connectivity based on domain-specific information. Using the graph representation, we identify representative molecules by computing the so-called minimum dominating set (MDS), which in our context is the minimum set of molecules that is connected to all other molecules. We present a suite of solution strategies for the MDS problem including heuristic and rigorous integer programming (IP) approaches. We show that these approaches allow us to capture physicochemical properties and domain-specific logic and constraints, facilitating the identification of molecules with the target properties. We demonstrate the effectiveness of the proposed approach by navigating the chemical space of per- and polyfluoroalkyl substances (PFAS); this comprises approximately 15,000 molecular structures. We compare our framework against traditional dimensionality reduction and clustering methods such as t-SNE and K-means clustering.

Chemical structure↗
DOE OSTI2017

Purpose-Built UAVs for Physical Sampling of Trace Contamination at the Portsmouth Gaseous Diffusion Plant - 17331

The sampling of trace amounts of contamination, particularly of low-energy byproducts such as americium, presents a challenge for non-contact sensing. In many cases, such as determining the need for cleaning inside the exhaust shaft of the Waste Isolation Pilot Plant or sampling the walls of the enormous Gaseous Diffusion Plant at Portsmouth, it may be desirable to take physical swab samples at carefully distributed sample locations for analysis by mass spectrometry. Because of the danger posed to human workers in such scenarios, including risk of falls from lethal heights and exposure to low levels of radiation and the hassle of donning and doffing PPE, a low-cost robotic solution is desired. The inaccessibility of the heights involved suggest Unmanned Aerial Vehicle (UAV) solutions, rather than Unmanned Ground Vehicle (UGV) solutions, but conventional quadrotor UAVs are ill-suited to precise physical interaction with their surroundings. Quadrotors are under-actuated, rendering them non-holonomic and preventing the dexterous application of arbitrary generalized force/torques in interaction with the environment. At Purdue University, the authors have developed a number of novel UAV platforms suitable for dexterous interaction with the environment, making them ideal for such applications. These platforms can independently control forces in all directions, allowing for controlled application of the swab to the surface. Because of the scale of these structures (over 2 km in the case of the Gaseous Diffusion Plant), flight efficiency is of supreme importance. The Purdue team is took a variety of vehicles to the Gaseous Diffusion Plant for field trials on site in August, 2016. (With results available prior to the meeting.) The Purdue Dexterous Hexrotor is a fully-actuated Hexrotor capable of exerting forces in all six directions of force and torque. Operating alone or in swarms, the Dexterous Hexrotor can fly in free space, transition to contact through impact control, and sample the surface through force control. The Purdue I-BoomCopter is a tri-copter with an extra horizontal propeller that enables long-distance flight without pitching and the ability to apply closed-loop forces horizontally. Finally, the Purdue Tiltrotor VTOL is an under-actuated hybrid UAV that combines tilting rotors for vertical takeoff and landing, with a fixed-wing, flying-body design that is ultra-efficient for long-distance flight. The Dexterous Hexrotor provided two demonstrations of physical sampling of walls and crane rails. In sampling the large open sections of the inside of the plant, a small swarm of three UAVs is commanded to sample three distinct points on an internal map of the building. Human-supervised autonomy permits the UAVs to take off by themselves, find a clear elevation at which to fly through uncluttered space, navigate to their designated sampling points, and pause for confirmation by the human supervisor. The supervisor is presented with a video image of the intended sampling spot, which, if confirmed, the UAV will automatically approach and sample. The UAVs then autonomously return to the take-off spot. The second demo involved a single UAV searching upwards, then transitioning into 3-D SLAM mode to wait for a safe sampling spot on the 3-D scan to acquire the sample from the crane rail. The human supervisor, in this case, is responsible for ensuring the absence of cables and other obstacles. I-BoomCopter demonstrated its high-speed, long-distance coverage ability by making a few fast passes before homing in on a mock-up electrical panel with door, which it opened and closed. The dexterity of the I-BoomCopter results from its large propellers, which allow for controlled hovering and the horizontal thruster for applying forces of significant magnitude. Finally, the Tiltrotor VTOL is highly efficient in fixed-wing mode, but the tilt-rotor is under-actuated in hover. The tilt-rotor will take off vertically, then transition to high-speed, long-distance flight to make a number of passes inside the facility. The significance of these demos is the low-cost implementation of physical sampling over prescribable locations in difficult-to-reach areas of potentially contaminate facilities. To make this tractable over very large facilities, the demonstration of combinations of dexterous hovering ability and long-distance/high-speed flight is also in evidence. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Extremized nonlinear and linearized responses in soft metamaterials enabled by gradient-based design and grayscale digital light processing

In this study, we develop a gradient-based design approach that exploits grayscale digital light processing (DLP) 3D printing for extremizing the nonlinear and linearized response of soft metamaterials — materials that harness engineered geometric instabilities to undergo large and programmable changes in configuration. Grayscale DLP approaches modulate local mechanical properties at the pixel scale by tuning the light intensity within a single grayscale image, unlocking an exceptionally large design space. To effectively navigate this space, we develop smooth mappings between local light intensity values and global quantities of interest that characterize the behavior of soft metamaterials. Enabling these smooth mappings are robust and differentiable nonlinear finite element simulations powered by a trust region solver. A PDE-constrained optimization problem is then solved to invert these mappings and produce light intensity distributions that endow the printed part with varying stiffness and flexibility in distinctive regions. It is shown that optimizing the distribution of soft and stiff phases throughout a metamaterial structure results in markedly different buckling and self-contact configurations to drive extremized nonlinear compression and linearized vibration responses. Optimized light intensity distributions are translated to grayscale images and directly used to print soft metamaterial samples, showing remarkable agreement between the buckling and self-contact response in simulated and measured deformed configurations.

Additive manufacturing↗
DOE OSTI2023

Efficient 3D Molecular Design with an E(3) Invariant Transformer VAE

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. Here, we show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗