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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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Enzymes can be optimized to accelerate chemical transformations via a range of methods. In this review, we showcase how protein engineering and computational design techniques can be interfaced to develop highly efficient and selective biocatalysts.
Microfluidics has revolutionized high-throughput miniaturized biological assays. However, co-culture of mammalian cells and bacteria remains challenging in microfluidic systems due to incompatible growth requirements, limited spatial control, and the requirement for a mammalian cell adhesion matrix. Here, we present a microfluidic platform that generates multiphasic droplets which encapsulate mammalian and bacterial cells, enabling their direct and indirect co-culture. By combining photopolymerizable hydrogels with polymer phase separation, we generate core–shell droplets composed of a liquid and a hydrogel compartment. The hydrogel compartment supports mammalian cell adhesion and culture, while the liquid compartment sustains bacterial growth. We demonstrate two droplet architectures that allow physical bacteria–mammalian cell contacts or enforce complete physical separation, representing direct and indirect co-culture. Our multiphasic droplets are stable, customizable, able to sustain co-culture for over 24 hours, and compatible with fluorescence-based cell sorting technologies. Overall, our multiphasic droplet microfluidic platform provides a scalable and versatile tool for high-throughput co-culture and screening of host–microbe interactions.
As computational capabilities continue to grow, exploring the limits of computational fluid dynamics to capture complex and elusive phenomena, which are otherwise difficult to study by experimental techniques, is one of the main targets for the research community. This paper presents a detailed analysis of the physical processes that lead to combustion noise emissions in internal combustion engines. In particular, diesel combustion in a compression-ignition (CI) engine is studied in order to understand the singular behavior of the in-cylinder flow field responsible for the acoustic emissions. The main objective is, therefore, to improve the understanding of the phenomena involved in CI engine noise using large eddy simulations. Several visualization methods are employed to investigate the connection between combustion behavior and its effects on the pressure field. In addition, proper orthogonal decomposition is used to analyze the modal energy distribution among all the acoustic modes. The results show that the acoustic signature is fundamentally conditioned by the intensity of the premixed combustion rather than by the pressure oscillations generated by turbulent fluctuations in the flame surface established during the diffusion stage.
The residual stress within the nose of a worn manganese steel railway crossing was investigated. The macroscopic residual stress at the running surface was determined in areas with and without visual faults by laboratory X-rays. To get information about the penetration depth of the residual stress, synchrotron X-ray diffraction was used to map the distribution of local (within grain) residual strains below the running surface. The laboratory X-ray measurements revealed significant compressive stress on the rail running surface. The synchrotron measurements documented that local strains exist through the entire depth investigated. Relations between residual stress, plastic deformation, and crack formation are analysed, leading to a discussion on potentials/limitations of the two experimental methods for investigations of residual stress in large engineering components.
SUMMARY Microseismic observations during unconventional reservoir stimulation are typically seen as a proxy for clusters of hydraulic fractures and the extent of the stimulated reservoir. Such straightforward interpretation is often misleading and fails to provide a physically reasonable image of the fracturing process. This paper demonstrates the application of a physics-based machine learning algorithm which enables a rapid and accurate fracture mapping from the microseismic data. Our training and validation data set relies on a history-matched geomechanical modelling workflow implemented in GEOS software for the Hydraulic Fracturing Test Site 1 (HFTS-1) project. For this study we augmented the simulated fracture growth through geostatistical modelling of induced seismicity, so that the synthetic microseismic catalogue matches the main statistical properties of the field observations. We formulated the problem of mapping the actual fracture in the clutter of events to parallel common video segmentation workflows: several past video frames (microseismic density snapshots) are passed through a deep convolutional network to classify whether a given voxel is associated with a fracture or intact rock. We found that for accurate fracture mapping, the network’s input and architecture must be augmented to incorporate the fluid injection parameters (pressure, rate, concentration of proppant, and location of the perforation within the cluster). The error rate for the network reached as little as 10 per cent of the fracture area, while a conventional microseismic interpretation approach yielded ∼300 per cent. Our approach also yields must faster predictions than conventional methods (minutes instead of weeks), and could enable engineers to make rapid decisions regarding engineering parameters (pumping rate, viscosity) in real time during stimulation.
Biofertilizers are critical for sustainable agriculture because they can replace ecologically disruptive chemical fertilizers while improving the trajectory of soil and plant health. However, for improving deployment, the persistence of biofertilizers within native soil consortia must be elucidated and enhanced. In this study we characterized a high-throughput, modular, and automation-friendly in vitro approach to screen for biofertilizer persistence within soil-derived consortia after co-cultivation with stable synthetic soil microbial communities (SynComs) obtained through a top-down cultivation process. Here, we profiled ~1200 SynComs isolated from various soil sources and cultivated in divergent media types, and we detected significant phylogenetic diversity (e.g. Shannon index >4) and richness (observed richness >400) across these communities. We observed high reproducibility in SynCom community structure from common soil and media types, which provided a testbed for assessing biofertilizer persistence within representative native consortia. Furthermore, we demonstrated that the screening method described herein can be coupled with microbial engineering to efficiently identify soil-derived SynComs in which an engineered biofertilizer organism (i.e. Bacillus subtilis) persists. Accordingly, we discovered that B. subtilis persisted in ~10% of SynComs that generally followed the diversity–invasion principle. Additionally, our approach enabled analysis of the ecological impact of B. subtilis inoculation on SynCom structure and profile alterations in community diversity and richness associated with the presence of a genetically modified model bacterium. Ultimately, this work has established a modular pipeline that could be integrated into a variety of microbiology/microbiome-relevant workflows or related applications that would benefit from assessment of the persistence of a specific organism of interest and its interaction with native consortia.
A lattice Boltzmann (LB) model with an efficient and accurate interface treatment for conjugate heat transfer across a thin wall between two different media is developed. The proposed interface treatment avoids fine meshing and computation within the thin layer; instead, the energy balance within the thin layer and the conjugate conditions on each interface are utilized to construct explicit updating schemes for the microscopic distribution functions of the LB model at the interior lattice nodes of the two media next to the thin layer. The proposed interface schemes reduce to the standard interface scheme for conjugate conditions in the literature in the limit of zero thickness of the thin layer, and thus it can be considered a more general interface treatment. A simplified version of the interface treatment is also proposed when the heat flux variation along the tangential direction of the thin layer is negligible. Three representative numerical tests are conducted to verify the applicability and accuracy of the proposed interface schemes. The results demonstrate that the intrinsic second-order accuracy of the LB model is preserved with the proposed interface schemes for thin layers with constant tangential fluxes, while for general situations with varying tangential fluxes, first-order accuracy is obtained. Finally, this interface treatment within the LB framework is attractive in conjugate heat transfer modeling involving thin layers for its simplicity, accuracy, and significant reduction in computational resources.
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Characteristic modes on infinite periodic structures are studied using spectral dyadic Green’s functions. This formulation demonstrates that, in contrast to the modal analysis of finite structures, the number of radiating characteristic modes is limited by unit cell size and incident wave vector (i.e., scan angle or phase shift per unit cell). Here, the reflection tensor is decomposed into modal contributions from radiating modes, indicating that characteristic modes are a predictably sparse basis in which to study reflection phenomena.
Here, we study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in that it does not employ any form of “belief-averaging”. Instead, agents update their beliefs based on a min-rule. Under standard assumptions on the observation model and the network structure, we establish that each agent learns the truth asymptotically almost surely. As our main contribution, we prove that with probability 1, each false hypothesis is ruled out by every agent exponentially fast, at a network-independent rate that is strictly larger than existing rates. We then develop a computationally-efficient variant of our learning rule that is provably resilient to agents who do not behave as expected (as represented by a Byzantine adversary model) and deliberately try to spread misinformation.
The ability to effectively store energy produced by intermittent renewable sources is a critical challenge for chemists and materials scientists. Redox flow batteries (RFB), which generate current by the flow of electrons between dissolved redox active compounds in separate solutions, are envisioned as a method to store renewable energy at the electrical grid scale. The best known examples of RFBs are driven by redox active metal or main group complexes. Within the past decade, redox active organic molecules have begun to be used in the construction of RFBs with high cell potential and cycle stability, at economical price points. Of particular interest are substituted dihydroxy phenazines, a class of heterocycles, that have recently been used as an anolyte for aqueous organic RFBs. Recent work indicates that different regioisomers of dihydroxyphenazine show dramatically different solubility and stability under electrochemical cycling conditions. Of particular interest was 1,3-dihydroxyphenazine (1,3-DHP), which showed greater than 1.5 M solubility in 2M KOH and excellent electrochemical stability. Current methods of producing 1,3-DHP, however, are low yielding and cumbersome. In the article proposal that follows, we offer an improved method of producing 1,3-DHP at high purity and moderate yield. The method detailed below was used by the Materials Engineering Research Facility (MERF) at Argonne National Lab to deliver more than 1.5 kg of this compound for use in the construction of aqueous organic RFBs.
Jacketed Superconducting Radio Frequency (SRF) cavities structurally comprise of an inner niobium vessel surrounded by a liquid helium containment vessels. The pressure of the helium bath and/or its volume might be such that a jacketed SRF cavity shall be considered a system of pressure vessels. Thus, methods described in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC) should be used to analyze the structural soundness of jacketed SRF cavities. This paper will report the use of the set of rules developed at Fermilab for the design of SRF cavities, such as jacketed 1.3 GHz cavities for LCLS-II HE and jacketed Single Spoke Resonator type~2 (SSR2) for PIP-II, to ensure a similar level of safety as prescribed by the ASME BPVC.
Objective: Improve the resiliency of power systems with optimization-based methods that leverage advanced microgrid technologies to reduce system recovery times after extreme event induced outages. Outcome: First-of-kind, high-fidelity physics-based optimization method for modeling networked microgrids which includes key engineering constraints associated with system recovery after extreme events.
Instead of genetic engineering, we sought to determine if a rapid method of membrane protein delivery could generate functional CAR-T cells in a rapid, safer, cost-effective manner. Using cell free protein synthesis, we generated CAR proteins embedded in a nanodisc, and thus effectively solubilized the protein. We demonstrated the first functional CAR proteins outside of a cellular context. We next tested uptake into immune cells and found extremely high uptake into T cells and multiple other populations of immune cells. We found some evidence of cytotoxicity in vitro conferred by the nanodisc delivered protein.
This report provides an introduction to selected graph theoretic topics with pertinence to the structural analysis of electric power grid and communication systems. We focus on methodologies for defining, scoring, and identifying connectivity, spectral, and bottleneckeness properties in graphs, as well as vertex and edge importance measures such as centrality. We apply these measures to power systems and communications graph data, discuss and visualize the results, and comment on computational aspects of these methods. We show that graph theoretic methods can provide useful insights into grid and communication network structure, leading to tools and methods that could be used by electric utility engineers to improve key grid and communication network characteristics, such as resilience and scalability.
The Quantum Zeno Effect (QZE) dictates how the dynamics of a quantum system can be modified through continuous or discrete measurements [1]. It has gained increasing attention in quantum computing community in recent years, both due to its inevitable implications for qubit readout as well as for its promise for enabling new methods of quantum state control such as reservoir engineering and Zeno-dragging. Here, we investigate Zeno effects implemented via weak measurements for controlling superconducting-qubit coherence during gate and readout operations in a multi-qubit setup. Building on prior observations [2] that measurement backaction can both suppress and enhance qubit relaxation, we predict and measure QZE-altered qubit coherence times by manipulating the spectral overlap of qubit spectrum with background noise sources. We also discuss modifications and opportunities for controllable QZE due to anharmonic effects in multi-level superconducting atoms. [1] S. Greenfield, A. Kamal, J. Dressel, E. Levenson-Falk, arXiv:2506.12679 (2025) [2] Thorbeck, Z. Xiao, L. Govia, A. Kamal, Phys. Rev. Lett. 132, 090602 (2024)
Quantum control aims to manipulate quantum systems toward specific quantum states or desired operations. Designing highly accurate and effective control steps is vitally important to various quantum applications, including energy minimization and circuit compilation. In this paper we focus on discrete binary quantum control problems and apply different optimization algorithms and techniques to improve computational efficiency and solution quality. Specifically, we develop a generic model and extend it in several ways. We introduce a squared L 2 -penalty function to handle additional side constraints, to model requirements such as allowing at most one control to be active. We introduce a total variation (TV) regularizer to reduce the number of switches in the control. We modify the popular gradient ascent pulse engineering (GRAPE) algorithm, develop a new alternating direction method of multipliers (ADMM) algorithm to solve the continuous relaxation of the penalized model, and then apply rounding techniques to obtain binary control solutions. We propose a modified trust-region method to further improve the solutions. Our algorithms can obtain high-quality control results, as demonstrated by numerical studies on diverse quantum control examples.
The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.