SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Halogen impurities are poisonous and/or corrosive • Poison for catalysts and equipment • Causes pitting in most austenitic stainless steels • Minimized to prevent damage over time • Prone to appear in certain processes • Appear if certain materials degrade (i.e., elastomers, etc.) • Present as trace impurities in a large number of metals and zeolites (<200 ppm)
Non-destructive measurement techniques, with high spatial resolution, capable of correlating composition and structure with device properties, are few and far between. For the case of polycrystalline and inhomogeneous materials, the added challenge is that large sampling areas are necessary in order to have a statistical representation of the specimen under study. For the study of grain cores and grain boundaries in polycrystalline solar absorbers this is of particular importance since their dissimilar behavior and variability throughout the samples makes it difficult to draw conclusions and ultimately optimize the materials and devices. Our approach to use state-of-the-art x-ray microscopy to study full module stacks under operating conditions is unique. Understanding the effects of elemental and charge migration under full encapsulation and operating conditions will allow CIGS and CdTe manufactures and in particular our partners in this proposal to modify materials, stacks, architectures, and processing steps to minimize the performance losses through time.
A fast and accurate beam-based alignment (BBA) method for harmonic sextupoles has been devel oped at NSLS-II using Linear Optics from Closed Orbit Modulation (LOCOM). The approach excites the beam with simultaneous sine-wave signals at two fast correctors, chosen with an appropriate phase advance to span the full betatron phase space. This strategy suppresses systematic errors from hysteresis, while additional errors from orbit drift and power-supply calibration are minimized by the short measurement time (a few minutes) and reliance solely on beam-based current-to-field conver sion of the sextupoles, with hysteresis explicitly included. Simulations indicate that Linear Optics from Closed Orbits (LOCO) combined with 0.5 mm local orbit bumps can resolve relative sextupole field offsets (∆k₂) with precision better than 10% of k₂, reflecting to beam-based alignment (BBA) accuracy finer than 50 µm. Moreover, employing machine-learning-optimized local orbit bumps en hances sextupole-induced quadrupole signals in a deterministic manner and maintains orbit stability under large sextupole strength variations (±40%). The proposed method is experimentally validated through proof-of-principle measurements at NSLS-II, demonstrating its potential as a fast, precise, and robust tool for harmonic sextupole alignment.
Prior research has shown that radio frequency identification (RFID) technology offers potential benefits for streamlining inventory management amidst rising production levels across the NNSA/DOE complex, yet it also presents substantial risks. RFID presents challenges never faced by LANL but also significant opportunities for accelerated inventory identification, real-time locating of assets, and minimized radiation exposure. This risk assessment evaluates the implementation of RFID technology at different Technical Area 55 (TA-55) locations, primarily withing the RLUOB basement and PF-4.
Scaling up bacterial fermentation from bench to industrial scale often results in unpredictable performance losses, possibly in part due to changes in microenvironmental conditions such as pH. To investigate this, we developed a suite of pH-sensitive biosensors from bacterial two-component systems (TCSs) that provide a dynamic, fluorescent readout in response to extracellular pH changes. TCSs consist of a periplasmic sensor histidine kinase (HK) that, in response to an extracellular stimulus, autophosphorylates intracellularly and subsequently transfers the phosphate to a cognate response regulator (RR) that modulates transcription of target genes. We utilized three pH-responsive TCSs (referred to here as CVJ1, CVJ30, and CVJ79) and linked their output to GFP. This was achieved by placing the RR promoter upstream of GFP or by constructing a chimeric RR composed of the native receiver domain and the DNA-binding domain of another well-characterized RR with a defined promoter. All components - HK, RR (native or chimeric), and GFP under its corresponding promoter - were cloned into a broad-host-range plasmid. Sensors were validated in Escherichia coli and Pseudomonas putida, including the muconic acid-producing strain P. putida TL207. All three biosensors successfully reported pH, with fluorescence (normalized to optical density) correlating strongly with media pH. Among the native sensors, CVJ79 showed the most robust performance while CVJ1 also performed best in its native form; CVJ30 exhibited improved functionality as a chimera, suggesting that modular RR design can enhance compatibility in some heterologous hosts. Further, CVJ79 was activated by alkaline conditions, while CVJ30 responded to acidic environments. Notably, CVJ1 was induced by high pH in wild-type E. coli and P. putida, but low pH in TL207. The observed differences in sensor activation between strains - particularly the divergent response of CVJ1 - suggest that host-specific regulatory pathways may influence how cells perceive and adapt to pH stress. Moving forward, these biosensors can be used to guide the rational design of more robust strains, optimize process conditions in real time, and inform strategies to minimize physiological heterogeneity during scale-up. Integrating these tools into high-throughput screening and bioreactors will be a key step toward improving predictability and performance in industrial bioprocesses.
Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO 2 ) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO 2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO 2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO 2 fluxes and to better estimate the terrestrial ABZ CO 2 budget.
During operation, it was observed that a specific mechanical system experienced undesirable vibration and it became necessary to understand and mitigate this phenomenon. This document investigates the tools, methodology, and results of the dynamic characterization of the system. The characterization makes use of the experimental modal analysis (EMA) methods of single input multiple output (SIMO) and single input single output (SISO). The validity of the theory of reciprocity is confirmed to minimize measurement error, cost, and time of repeat testing. Finite element analysis (FEA) is used in choosing transducer and modal impact locations to adequately characterize the system. Single degree of freedom (SDOF) and multiple degree of freedom (MDOF) curve fitting is used to fully characterize the system’s mode shapes and natural frequencies. The EMA characterization results are used to modify and validate the FEA model so that FEA can be used to model potential structural modifications to the system to mitigate the undesirable vibration. Structural modifications are chosen, implemented, and their effectiveness is quantified using EMA. Finally, a qualitative evaluation of the methodology of FEA validation by EMA and tuning of the model to match the experimental results is discussed.
Hybrid additive featured plates used to form an overall microchannel heat exchanger and corresponding method of manufacture are disclosed. Various additive manufacturing (AM) techniques may be used to form walls defining microchannel features on a plate substrate. The manufacturing method is a hybrid process in that leverages both additive and conventional manufacturing techniques to minimize both cost and fabrication time.
As robotic arms are becoming increasingly common alongside humans as collaborative robots, their high precision in motion enables tasks to be performed at significantly higher speeds with reduced disruption in the environment. The Fermi National Accelerator Laboratory is exploring this application by incorporating a UR16e from Universal Robots in a cleanroom setting during assembly of couplers to superconducting radio frequency cavities as part of the PIP-II project. The goal of the robotic assembly process is to precisely position the UR16e robot so that the coupler flange, mounted on the robot’s end-effector, is accurately aligned with and pressed against the cavity flange, requiring only final fastening by a lab technician. This thesis builds upon an initial system in which the robotic process was limited to the alignment phase using position-based visual servoing with an eye-in-hand camera to only align the coupler to the cavity with an offset distance. The objective of this thesis is to further advance autonomous robotic assembly by extending the process. To this end, the entire software framework was reconstructed, as the previous development environment posed significant challenges in modifying the software and adapting to hardware changes. The main contributions of this thesis are as follows: (i) a modular and scalable software framework based on ROS~2 was developed to facilitate performance expansion and interchangeability of software and hardware components; (ii) the desired alignment position for position-based visual servoing was parameterized to enable flexible configuration; and (iii) a methodology was developed to close the offset distance between the coupler and cavity utilizing the internal force-torque sensing capability of the UR16e, as visual feedback is not available during the offset-closing phase. The proposed autonomous robotic assembly reduces assembly time and technician involvement, thereby minimizing the risk of airborne particulate contamination, which is essential in the cleanroom setting. Moreover, the ROS~2-based framework provides a foundation for further expansion and continued advancement of robotic automation in Fermilab.
Multi-reflection time-of-flight mass separators and spectrometers (MR-ToF MSs) are indispensable tools at radioactive ion beam (RIB) facilities. These electrostatic ion beam traps act as highly selective mass separators and high-precision mass spectrometers for rare and exotic nuclei. When well-tuned and designed to minimize higher-order flight-time aberrations, state-of-the-art MR-ToF MSs approach, and slightly exceed, mass resolving powers of m/Δm = 10 6 . Achieving m/Δm ≥ 3 ⋅ 10 6 would provide the ability to resolve > 90 % of all known isomeric states with half-lives above 10 ms. However, the ability to mass separate in all practical setups is limited by non-ideal conditions which place such resolving powers out of reach. Here, to this end, we present a simulated analysis of these conditions in the newly proposed high-voltage MR-ToF MS for the Facility for Rare Isotope Beams (FRIB). It is expected to store ions at 30 keV beam energy and increase ion throughput by two orders of magnitude compared to current devices. Existing efforts to mitigate the effects of non-ideal conditions employed for current MR-ToF devices storing ions at < 3 keV beam energy will already enable mass resolving powers approaching 10 6 for FRIB’s high-voltage MR-ToF device. Simulations of newly proposed mitigation strategies show that even mass resolving powers approaching 10 7 might become feasible.
β-Mn 2 V 2 O 7 (β-MVO) was recently reported to be a promising candidate for photoelectrochemical (PEC) water splitting, with a suitable band gap and band edge positions and reasonable stability and photoactivity in preliminary tests in alkaline solution. Here, we present an in-depth evaluation of the PEC performance and stability of phase-pure nanostructured β-Mn 2 V 2 O 7 films made by calcination of a spin-cast molecular ink. We show that β-Mn 2 V 2 O 7 dissolves in pure water, corrodes in aqueous electrolytes at pH 7 and 9, and converts to amorphous manganese (hydr)oxides within minutes at pH 13. Our β-Mn 2 V 2 O 7 films yielded only miniscule photocurrents (~uA cm -2 ) for the oxidation of iodide, sulfite, or water and the reduction of iodate or water in borate- and phosphate-buffered electrolytes at pH 7 and 9, the oxidation of [Fe(CN) 6 ] 4- or water at pH 13, and the oxidation of bromide in acetonitrile, regardless of film calcination temperature and time, film thickness, and illumination geometry. Minimal photoactivity was observed even in electrolytes in which film degradation was insignificant over the duration of the PEC tests. Ultrafast transient absorption spectroscopy shows that the poor photoactivity is likely the result of fast hole trapping and recombination at the MVO surface that leaves few free charge carriers beyond the picosecond time scale. Given its poor charge transport/extraction and chemical stability, native nanostructured β-Mn 2 V 2 O 7 is ineffective for solar water splitting and future research on this material should focus on the development of superior syntheses and film morphologies, MVO alloys, heterostructures, and surface coatings to alleviate recombination and boost operational stability. This work underscores the importance of careful follow-up studies of materials identified as "hits" in combinatorial materials discovery campaigns.
Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.
Future quantum networks are expected to integrate a heterogeneous combination of quantum systems, owing to the diverse advantages offered by different physical platforms in terms of scalability, coherence times, and interfacing capabilities. Within the context of this emerging quantum communication paradigm, this paper presents an analytical comparison of two photonic encoding schemes—time-bin and Fock state (single rail)—in hybrid quantum systems where flying qubits are entangled with stationary matter qubits. We evaluate their resilience against fiber channel and quantum transduction effects by calculating the fidelity of the final states relative to their ideal forms. Employing the characteristic function approach, we derive analytical fidelity expressions and investigate their dependence on parameters such as transmissivity, noise levels, fiber length, and source generation success probability. Additionally, we simulate the scenario with a dedicated QuTiP software implementation to verify the validity of the theoretical models. Our findings reveal that due to its inherent single-mode structure, the Fock state encoding consistently outperforms time-bin encoding in fidelity, as this structure significantly minimizes susceptibility to losses compared to the two-mode nature of the time-bin scheme. This analysis offers valuable insights for future hybrid quantum communication and information processing applications.
We extend the finite-temperature Keldysh non-equilibrium coupled cluster theory (Keldysh-CC) [A. F. White and G. K.-L. Chan, J. Chem. Theory Comput. 15, 6137–6253 (2019)] to include a time-dependent orbital basis. When chosen to minimize the action, such a basis restores local and global conservation laws (Ehrenfest’s theorem) for all one-particle properties while remaining energy conserving for time independent Hamiltonians. We present the time-dependent Keldysh orbital-optimized coupled cluster doubles method in analogy with the formalism for zero-temperature dynamics, extended to finite temperatures through the time-dependent action on the Keldysh contour. To demonstrate the conservation property and understand the numerical performance of the method, we apply it to several problems of nonequilibrium finite-temperature dynamics: a 1D Hubbard model with a time-dependent Peierls phase, laser driving of molecular H2, driven dynamics in warm-dense silicon, and transport in the single impurity Anderson model.
During the summer and fall 2018, the Cornell High Energy Synchrotron Source (CHESS), underwent an upgrade that recast the facility as primarily a low emittance X-ray source. The upgrade replaced one sixth of the Cornell Electron Storage Ring (CESR) with six double-bend achromats, featuring four novel CHESS Compact Undulator (CCU) insertion devices, and switched CESR to single particle beam operation. At the time of this writing, the upgraded storage ring has achieved the CHESS-U design goal of operating with 200 mA of positron current at 6.0 GeV beam energy. Additionally, seven out of eleven CHESS-U beamlines are currently in X-ray user operation. The construction and the commissioning of the new section of the CESR accelerator required novel solutions to design, fabrication, and vacuum system operations. Significant design considerations and efforts were made to minimize X-ray ‘dark-time’, returning to operation less than 1 year. This paper describes the important milestones of the process, the solutions implemented, and the present status of CHESS-U.
Abstract Polyelectrolyte brushes are widely used as model systems for investigating electrostatic interactions at soft interfaces and to achieve exceptional lubrication properties. Previous studies have primarily focused on their behavior in the presence of multivalent counterions, which induce brush collapse, ionic crosslinking, and pronounced changes in interfacial structures. However, interactions between polyelectrolyte brushes and oppositely charged macromolecular counterions, such as polycations, remain poorly understood. Here, we prepare well-defined polystyrene sulfonate (PSS) brushes fabricated via surface-initiated grafting and employ surface forces apparatus measurements to investigate their interactions with oppositely charged polycations. In contrast to multivalent ions, polycations do not induce noticeable brush collapse, but instead generate significant adhesion between symmetric PSS brush layers. This adhesion increases with both contact time and applied load, eventually reaching a steady plateau, indicating that the interaction is governed not simply by electrostatic screening but by the gradual formation of polycation-mediated bridging under confinement. Furthermore, the introduction of monovalent Na+ ions disrupts the adhesive interaction even at very low concentrations, suggesting that the bridging function of the adsorbed polycation is highly sensitive to competitive ionic screening. In comparison, measurements with trivalent counterions reveal the expected brush collapse behavior but minimal dependence of adhesion on contact time, highlighting a clear mechanistic distinction from the polymeric counterion case. Collectively, these results demonstrate that molecular size, configurational restriction, and confinement-induced rearrangement, rather than charge valency alone, govern the interaction behavior of macromolecular counterions at brush interfaces. This work provides new insight into the molecular origins of adhesion and the regulation of interfacial interactions in charged polymer brush systems.
Dependence assessment refers to an approach used in human reliability analysis (HRA) to adjust a human error probability (HEP) for the following action by considering the impact of the preceding action. It has been known to significantly affect the overall results of probabilistic safety assessment (PSA). If the dependence assessment is not adequate, the result could be unconvincing for explaining the operator failures in the context of PSA. To date, several methods and some recent research have identified suggestions for treating dependence issues in HRA; however, these are still exclusively based on the intrinsic approach of the Technique for Human Error Rate Prediction (THERP), an HRA method. THERP inevitably has a challenge with the subjectivity of expert evaluation as well as the requirement for PSA and HRA expertise with resource-intensive and time-consuming processes. This paper suggests an approach to dependence assessment that could not only minimize the influence of expert judgment, but also saves time to perform the analysis with reasonable manpower. It modifies existing HRA methods with considering lag and linger effects to apply dependence effects for them. Based on a representative HRA method, i.e., Standardized Plant Analysis Risk - HRA (SPAR-H), guidance for how to apply lag and linger effects for the HRA method is suggested. Then, an investigation is carried out to compare quantification results of the revised HRA method with that of the original approach based on experimental data.
In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.