Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “METRICS”

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.

At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The bioprocess TEA calculator: An online technoeconomic analysis tool to evaluate the commercial competitiveness of potential bioprocesses

Techno-economic analysis connects R&D, engineering, and business. By linking process parameters to financial metrics, it allows researchers to understand the factors controlling the potential success of their technologies. In particular, metabolic and bioprocess engineering, as disciplines, are aimed at engineering cells to synthesize products with an ultimate goal of commercial deployment. As a result it is critical to be able to understand the potential impact of strain engineering strategies and lab scale results on commercial potential. To date, while numerous techno-economic models have been developed for a wide variety of bioprocesses, they have either required process engineering expertise to adapt and/or use or do not directly connect financial outcomes to potential strain engineering results. Despite the clear value of techno-economic analysis, these challenges have made it inaccessible to many researchers. Furthermore, the tool, currently focused on aerobic fermentation processes, can be used to understand the impact of fermentation level metrics on the commercial potential of a bioprocess for the production of a wide variety of organic molecules. Using the calculator, I review the commercially relevant targets for an aerobic bioprocess for the production of diethyl malonate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Simple Criterion for Feasibility of Heat Integration between Distillation Streams Based on Relative Volatilities

In a multicomponent distillation configuration, there are numerous sources and sinks of heat, and a potential way to reduce the heat duty requirement is to perform heat integration. Unfortunately, an algorithmic search of the optimal heat integration opportunities is intractable when the required temperatures of intermediate streams are computed via complex models. Instead, in this work, we introduce pressure-scaled pseudo relative volatility, a new metric to compare stream temperatures. Here, we justify the use of pseudo relative volatility by proving that this variable is a monotonically increasing function of the liquid fraction in a saturated mixture stream. Using this metric, we derive a shortcut criterion to check the feasibility of various heat integration opportunities, such as thermal coupling via heat transfer (TCH). The advantage of this approach is that it circumvents the need for explicit temperatures and instead relies on composition, component relative volatilities, and pressure—quantities that are readily available in shortcut models for optimization of distillation configurations. Leveraging this fact, we propose a new optimization framework that identifies feasible TCHs that we consider within the formulation while minimizing the total heat duty of a distillation configuration. We demonstrate, on a few examples, that our formulation can identify heat duty efficient configurations, some of which are multieffect configurations. Using this methodology, we discover configurations that are not only simpler than the fully thermally coupled (FTC) configuration but also have a much lower heat duty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Ion-Exchange Membrane-Centric Durability Testing and Degradation Characterization for Industry-Relevant CO 2 Reduction

Electrochemical CO 2 reduction is a promising conversion process for producing value-added fuels and chemicals from electricity and CO 2 as a sustainable carbon feedstock to domestically produce fuels and chemicals from industrial waste. Having reached industrially viable performance metrics with small-scale CO 2 electrolysis cells, the field must now increasingly focus on extending the device durability of large stacks to achieve equivalent metrics for 35,000+ hours to decrease maintenance and capital costs. Reported device lifetimes have increased in recent years, with the longest stability studies for CO, ethylene, and formic acid production being published in 2024–2025 with operation times of 4500, 1000, and 5200 h, respectively. Unfortunately, significant extension of the device durability is still required. Here, we provide an overview of ion-exchange membranes (IEMs) and provide insight into the variety of degradation mechanisms that must be overcome to enable the community to meet durability targets. In an effort to accelerate the extension of device lifetimes, we propose a general approach for characterizing CO 2 electrolysis cell degradation before and after durability testing to better elucidate the mechanisms and failure modes of IEMs in zero-gap cells. Furthermore, we encourage the adoption of operando characterizations in tandem with accelerated stress and durability tests, postulating that their combined applications will be increasingly valuable. We hope that this perspective motivates future durability studies to evaluate degradation across the entire electrolysis cell.

09 BIOMASS FUELS↗
DOE OSTI2025

Comparative Analysis of TCR and TCR-pMHC Complex Structure Prediction Tools

The rapid development of computational approaches for predicting the structures of T cell receptors (TCRs) and TCR-peptide-major histocompatibility (TCR-pMHC) complexes, accelerated by AI breakthroughs such as AlphaFold, has made it feasible to calculate these structures with increasing accuracy. Although these tools show great potential, their relative accuracy and limitations remain unclear due to the lack of standardized benchmarks. Here, we systematically evaluate seven tools for predicting isolated TCR structures together with six tools for predicting TCR-pMHC complex structures. The methods include homology-based approaches, general prediction tools using AlphaFold, TCR-specific tools derived from AlphaFold2, and the newly developed tFold-TCR model. The evaluation uses a post-training data set comprising 40 αβ TCRs and 27 TCR-pMHC complexes (21 Class I and 6 Class II). Model accuracy is assessed at global, local, and interface levels using a variety of metrics. We find that each tool offers distinct advantages in various aspects of its predictions. AlphaFold2, AlphaFold3, and tFold-TCR excel in overall accuracy of TCR structure prediction, and TCRmodel2 and AlphaFold2 perform well in overall accuracy of TCR-pMHC structure prediction. However, TCR-specific tools derived from AlphaFold2 show lower accuracy in the framework region than both homology-based methods and general-purpose tools such as AlphaFold, and challenges remain for all in modeling CDR3 loops, docking orientations, TCR-peptide interfaces, and Class II MHC-peptide interfaces. Furthermore, these findings will guide researchers in selecting appropriate tools, emphasize the importance of using multiple evaluation metrics to assess model performance, and offer suggestions for improving TCR and TCR-pMHC structure prediction tools.

Chemical structure↗
DOE OSTI2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Crystallization↗
DOE OSTI2020

Method for accurately predicting solvation structure

Accurately predicting the molecular structure of solutions is a fundamental scientific challenge. Using quantum mechanical density functional theory (DFT) to make these predictions is hindered by significant variation depending on which DFT functional is used. Here, we present a simple metric that can determine the reliability of a DFT functional for predicting solvation structure. We then show that including a simple interaction term to correct this metric leads to quantitative agreement with experimental measurements of liquid structure. We demonstrate the utility of this method by using it to accurately describe the hydration structure around the Na+ a

basis sets, molecular structure, cluster chemistry↗
DOE OSTI2026

Multiresolution Quantum Chemistry: Nonlinear Response Properties at the Basis Set Limit

We benchmark the accuracy of Dunning correlation-consistent Gaussian basis sets for computing frequencydependent second-order hyperpolarizabilities relevant to second-harmonic generation (SHG), using multiresolution analysis (MRA) as a reference. Basis set errors are analyzed using a unit-sphere representation of the effective hyperpolarizability vector, enabling direct assessment of directional error structure. We introduce a relative RMS total error metric that integrates directional deviations over the unit sphere and complement it with signed projection errors that distinguish over- and underestimation. Unsupervised clustering based on these signed directional metrics reveals four distinct convergence behaviors across a set of 68 molecules. Unitsphere visualizations of representative systems show that basis set errors are often highly anisotropic and localized along specific bond directions, even when global error measures appear small. Doubly augmented basis sets consistently outperform singly augmented ones, and core-polarization functions are required for uniform convergence in second-row systems. Overall, this work demonstrates that directional analysis combined with clustering provides a robust framework for understanding basis set convergence in nonlinear optical response properties.

Basis sets↗
DOE OSTI2020

Field Programmable Gate Arrays for Enhancing the Speed and Energy Efficiency of Quantum Dynamics Simulations

We present the first application of field programmable gate arrays (FPGAs) as new, customizable hardware architectures for carrying out fast and energy-efficient quantum dynamics simulations of large chemical/material systems. Instead of tailoring the software to fixed hardware, which is the typical case for writing quantum chemistry code for central processing units (CPUs) and graphics processing units (GPUs), FPGAs allow us to directly customize the underlying hardware (even at the level of specific electrical signals in the circuit) to give a truly optimized computational performance for quantum dynamics calculations. By offloading the most intensive and repetitive calculations onto an FPGA, we show that the computational performance of our real-time electron dynamics calculations can even exceed that of optimized commercial mathematical libraries running on high-performance GPUs. In addition to this impressive computational speedup, we show that FPGAs are immensely energy-efficient and consume 4 times less energy than modern GPU or CPU architectures. These energy savings are a practical and important metric for supercomputing centers (many of which exceed over $1 million in power costs alone), as exascale computing capabilities become more widespread and commonplace. Taken together, the implementation techniques and performance metrics of our study demonstrate that FPGAs could play a promising role in upcoming quantum chemistry and materials science applications, particularly for the acceleration and energy-efficient execution of quantum dynamics calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Evaluating the Importance of Conformers for Understanding the Vacuum-Ultraviolet Spectra of Oxiranes: Experiment and Theory

Vacuum-ultraviolet (VUV) absorption spectroscopy enables electronic transitions that offer the unambiguous identification of molecules. As target molecules become more complex, multifunctional species present a great challenge to both experimental and computational spectroscopy. This research reports both experimental and theoretical studies of oxiranes. Computationally, the nuclear ensemble approach has been used to accurately predict experimental spectra for a variety of molecules. However, this approach incurs great computational cost, as ensembles generally consist of thousands of geometries. The present study aims to drastically reduce the ensemble by evaluating the significance of the conformers to the predicted spectra. This approach was applied to 11 substituted oxiranes using the Conformer Rotamer Ensemble Sampling Tool (CREST) of Grimme to generate an ensemble of unique conformers determined by their Boltzmann populations. Five TD-DFT functionals (BMK, CAM-B3LYP, M06-2X, MN15, ωB97X-D) and EOM-CCSD were used to simulate the spectrum of each substituted oxirane ensemble. Computed spectra were then compared to the experiment using both qualitative and quantitative metrics. Based on these metrics, it was observed that certain conformers may not be necessary to characterize this set of oxiranes despite the temperature (323 K) of the experiment. A single conformer can then be used with TD-DFT and EOM-CCSD to replicate the experimental spectra of these medium-sized combustion species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Beyond Thermodynamics: Assessing the Dynamical Softness of Hydrated Ions from First Principles

Ion (de)hydration is a key rate-determining step in interfacial processes from corrosion to electrochemical energy storage. However, predicting the kinetics of ion (de)hydration remains challenging, prompting the use of static proxies such as hydration energy and valence. While useful for assessing thermodynamic preferences, such descriptors cannot fully capture the dynamical softness of the hydration shell that dictates kinetics. Accordingly, we use first-principles molecular dynamics to analyze hydration shell softness for a diverse set of metal cations. Additionally, three dynamic metrics are introduced to intuitively describe the bond rigidity, shape deformability, and exchange fluidity of the solvation shell. Together, these metrics capture the relevant physics in the static descriptors, while offering a far more complete and efficient representation for the overall propensity for (de)hydration. Application to the hydrated ion set demonstrates a weak connection between dynamical softness and hydration energy, confirming that dynamical descriptors of hydration are key for correctly describing ion transfer processes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Glassy Carbon Substrate Oxidation Effects on Electrode Stability for Oxygen Evolution Reaction Catalysis Stability Benchmarking

Employing benchmarking metrics to capture the activity and stability of electrocatalysts for the oxygen evolution reaction (OER) in acid is a critical practice that enables meaningful comparison of catalyst material candidates reported throughout the literature. In this work, we find that ubiquitously used glassy carbon electrode substrates oxidize under typical OER operating conditions, forming a pacified, electrically insulating, and oxygen-rich surface layer that causes drastic loss of current density over the course of extended chronoamperometric stability tests at an anodic potential of 1.7 VRHE. We show that the experimentally observed stability of glassy carbon-based electrodes is approximately two orders of magnitude lower than that expected solely from dissolution-based catalyst intrinsic stability of Ir-based catalysts. We additionally find that glassy carbon-based electrode stability measured by chronoamperometric holds is greatly impacted by catalyst loading, with high catalyst loadings improving the stability of the overall electrode via a protective effect on the glassy carbon substrate. Altogether, our investigation highlights that glassy carbon is not electrochemically inert under OER conditions on the timescale of common stability tests, which can cause electrodes to exhibit performance losses that do not reflect the intrinsic stability of the actual catalyst material being investigated. In light of our findings, we underscore the usefulness of metrics, such as the S-number, to reflect intrinsic catalyst material stability.

25 ENERGY STORAGE↗
DOE OSTI2021

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques

Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. Additionally, a diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Perspective on the Electrochemical Oxidation of Methane to Methanol in Membrane Electrode Assemblies

Direct conversion of methane to methanol has been a long-sought objective. Partial oxidation by thermal catalysis is possible but suffers from a rapid loss in methanol selectivity with increasing methane conversion. More recently, the electrochemical oxidation of methane, using water, rather than oxygen, as the oxidizing agent has been considered in both aqueous electrolyte systems and membrane-electrode assemblies (MEAs). While promising results have been demonstrated using MEAs, the absence of key metrics of system performance make it hard to compare and contrast the results of different investigators. Overall, this Perspective examines why MEAs are well-suited for the electrochemical oxidation of methane, defines the metrics for assessing MEA performance, and reviews the progress in the field. An analysis of the challenges to finding suitable electrocatalysts is included with the aim of guiding the search for electrocatalysts that would be both active and selective for the conversion of methane to methanol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Technoeconomic Model and Pathway to <$2/kg Green Hydrogen Using Integrated Halide Perovskite Photoelectrochemical Cells

The cost of gray hydrogen produced via fossil fuel-based steam-methane reforming has led the U.S. Department of Energy to specify <$\$$2/kg H 2 as a target for commercially competitive green hydrogen generation methods. Integrated photoelectrochemical cells have been proposed as a solar-to-hydrogen conversion technology. In this paper we describe a technoeconomically feasible pathway to reaching <$\$$2/kg green H 2 using integrated photoelectrochemical cells with halide perovskite photoabsorbers, low-cost conductive barriers, and low precious metal-content catalysts in an aqueous, membrane-separated cell. A base-case solar-to-hydrogen conversion efficiency of 20%, stable lifetime of 10 years, and a combined electrocatalyst-plus-panel cost of $\$$50/m 2 enabled a levelized cost of hydrogen of $\$$2.43/kg, which dropped below $\$$2/kg with improved performance metrics including material cost, improvements in process design, or subsidies. We relate these metrics to lab-scale reports to recommend best research practices for scientists and funding agencies working at this intersection of photovoltaics, electrocatalysis, and surface science.

$1/kg↗
DOE OSTI2026

Membranes for Lithium Recovery From Conventional and Unconventional Sources

Lithium has been deemed a critical mineral of national importance that finds uses in a wide range of applications, and its demand has been rising significantly in recent years. The urgency of meeting this demand requires lithium extraction from various aqueous sources such as continental brines, geothermal brines, seawater, produced water, and battery waste. While direct lithium extraction (DLE) technologies such as adsorption, ion exchange, and solvent extraction have emerged as possible solutions, membrane technologies are also being investigated for various sources and at different stages of the recovery process. Here, we analyze the application of membranes for pretreatment of lithium source waters, bring management, lithium/magnesium separation, lithium/sodium separation, and lithium hydroxide conversion, and evaluate performance metrics for critical lithium separations from the literature. We explore the potential of membranes at every stage of the recovery process and describe their current status and future prospects. We describe hypothetical process trains with integrated membrane technologies for each source type and address their feasibility and challenges. The potential energy and water impacts of membrane-integrated and conventional DLE processes are also critically considered alongside performance and selectivity metrics, and this is illustrated using examples and calculated from published technical reports. This paper thus provides a comprehensive overview of the application of membranes along every stage of the lithium recovery process, emphasizing the versatility and potential of membrane technologies for critical mineral recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Approaching Angstrom-Scale Resolution in Lithography Using Low-Molecular-Mass Resists (<500 Da)

Resists that enable high-throughput and high-resolution patterning are essential in driving the semiconductor technology forward. The ultimate patterning performance of a resist in lithography is limited because of the trade-off between resolution, line-width roughness, and sensitivity; improving one or two of these parameters typically leads to a loss in the third. As the patterned feature sizes approach angstrom scale, the trade-off between these three metrics becomes increasingly hard to resolve and calls for a fundamental rethinking of the resist chemistry. Low-molecular-mass monodispersed metal-containing resists of high atom economy can provide not only very high resolution but also very low line-width roughness without sacrificing sensitivity. Here we describe a modular metal-containing resist platform (molecular mass <500 Da) where a molecular resist consists of just two components: a metal and a radical initiator bonded to it. This simple system not only is amenable to high-resolution electron beam lithography (EBL) and extreme ultraviolet lithography (EUVL) but also unites them mechanistically, giving a consolidated perspective of molecular and chemical processes happening during exposure. Irradiation of the resist leads to the production of secondary electrons that generate radicals in the initiator bonded to metal. This brings about an intramolecular rearrangement and causes solubility switch in the exposed resist. We demonstrate record 1.9–2.0 nm isolated patterns and 7 nm half-pitch dense line-space features over a large area using EBL. With EUVL, 12 nm half-pitch line-space features are shown at a dose of 68 mJ/cm 2 . In both of these patterning techniques, the line-width roughness was found to be ≤2 nm, a record low value for any resist platform, also leading to a low-performance trade-off metric, Z factor, of 0.6 × 10 –8 mJ·nm 3 . With the ultimate resolution limited by instrumental factors, potential patterning at the level of a unit cell can be envisaged, making low-molecular-mass resists best poised for angstrom-scale lithography.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Liquid Fed Pyrolysis of Polyethylene Films: Environmental and Economic Assessments of Co-located and Remotely-Located U.S. Facilities

Polyethylene (PE) films are one of the highest production volume plastic products, but they have very low recycling rates. A novel liquid fed pyrolysis process (LFP) is an advanced recycling technology that can be applied to waste PE films. In this work, two environmental and six economic metrics were evaluated for the LFP process under different scenarios with a baseline production capacity of 8,400 Metric Tons (MT) of pyrolysis products/year. The studied scenarios considered process improvement such as heat integration, changing the final product yields, and location of the LFP process facility (i.e., co-located at a petrochemical facility or located remotely). Results show that producing refined pyrolysis wax in remote areas is the most environmentally favorable and profitable scenario. The LFP process co-located at a petrochemical facility and selling only liquid and gaseous pyrolysis products to the facility would require a capacity of >18,000 MT/year to be economically feasible. Heat integration led to greenhouse gas emission savings of at least 14%, 19%, and 32% for the pyrolysis oil, gas, and wax products, respectively. Fianlly, the LFP process in remote and less populated locations producing a high yield of refined wax may be a feasible solution for increasing recycling rates of PE films.

Advanced recycling↗
DOE OSTI2020

Design and Evaluation of a Tethered, Open Port Sampling Interface for Liquid Extraction-Mass Spectrometry Chemical Analysis

Presented in this work is a tethered, liquid-extraction-sampling interface designed for the mass spectrometric surface sampling/analysis of 3D objects. The tethered, open port sampling interface (TOPSI) incorporates a vacuum line between the sampling probe and ionization source, which enables the ability for an extended, tethered sample transfer line. Herein, several designs of the hand-held TOPSI are presented and evaluated on the basis of the analytical metrics of analyte transport time, peak width, and analyte sensitivity. The best analytical metrics were obtained with capillary flow resistances arranged in a particular order and the vacuum region set at 6.2 kPa. This TOPSI design incorporated a transfer capillary 1 m in length, while retaining a fast analyte transport time (12 s), short signal peak width (5 s baseline-to-baseline), and high analyte signal at 90% of that obtained with a regular open port sampling interface (OPSI). The hand-held TOPSI was demonstrated for the characterization of extracted small molecules and metabolites from the surface of mint and rosemary leaves.

47 OTHER INSTRUMENTATION↗