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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Data‐Driven Engineering of Thermostable Collagen‐Mimetic Peptoid Triple Helices

Collagen-mimetic peptides (CMPs) are engineered molecules designed to replicate the triple-helical structure of natural collagen. A repeating x–y-Gly sequence is the defining motif of CMPs and is critical to their triple-helical structure and stability. Substitutions to the residues occupying the x and y positions present a means to modulate the CMP structure and properties. Peptoid residues—N-substituted glycine derivatives—present an attractive potential substitution due to their thermal stability, proteolytic resistance, biocompatibility, and diverse palette of non-natural side chains, but also tend to introduce a high degree of backbone flexibility that can diminish the stability of the triple helix. In this work, we report a computational active learning cycle comprising molecular dynamics simulation, Gaussian process regression, and Bayesian optimization to computationally identify a number of promising peptoid substitutions predicted to stabilize the desired quaternary structure through side chain interactions and produce stable peptoid-based collagen-like triple helices. To experimentally test the computational predictions, a top candidate identified by the screen was synthesized and imaged using scanning electron microscopy to resolve fibril-like bundles consistent with collagen-like triple helices. This work predicts a number of CMP peptoid substitutions capable of forming stable triple-helical structures, presents a generalizable design strategy for engineering desired peptoid structures, and opens new avenues for the design of peptoid-based biomimetic materials.

active learning↗
DOE OSTI2021

Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗
DOE OSTI2025

Scaled-up fabrication of durable and porous adsorbent-coated minichannels on aluminum for CO 2 separation

A method was developed to fabricate zeolite 13X adsorbent-coated minichannels on aluminum for CO₂ adsorption applications in this study. It emphasizes the innovative use of aluminum as a substrate, which offers airtight assembly, paving the way for highly efficient adsorption systems. An optimized coating process was developed using a slurry of Zeolite 13X, yeast, sugar, and xanthan gum, resulting in durable and highly porous layers that enhance CO₂ capture performance. A PETG peeler was designed to remove the top layer of the yeast-engineered adsorbent coatings, revealing a super porous and foamy structure. The teeth of the peeler were designed and fabricated for high repeatability and rapid prototyping. Breakthrough experiments were conducted on the scaled-up adsorbent bed using gas mixtures of 80% CO₂, 20% N₂, and 20% CO₂, 80% N₂ to represent different industrial scenarios. The performance of the bed was evaluated at flow rates of 160 and 190 cm³ min -1 using a Raman Laser Gas Analyzer (RLGA), demonstrating stable adsorption without degradation across multiple cycles. Computational modeling of integral transport phenomena under the chosen experimental conditions was pursued using gPROMS ProcessBuilder™, and the modeling results were compared with those from the tests for adsorption time, which resulted in an error margin of 2% to 9% for the breakthrough time, confirming the easy reproducibility of the design through modeling. This research advances CO₂ capture technologies by providing an effective and scalable solution for producing aluminum-based adsorbent coated beds, supporting industrial carbon capture efforts.

CO2 capture↗
DOE OSTI2024

Tailoring Local Chemical Ordering via Elemental Tuning in High-Entropy Alloys

Due to the large multi-elemental space desired for property screening and optimization, high-entropy alloys (HEAs) hold greater potential over conventional alloys for a range of applications, such as structural materials, energy conversion, and catalysis. However, the relationship between the HEA composition and its local structural/elemental configuration is not well understood, particularly in noble-metal-based HEA nanomaterials, hindering the design and development of nano-HEAs in energy conversion and catalysis applications. In this work, we determined precise atomic-level structural and elemental arrangements in model HEAs composed of RhPtPdFeCo and RuPtPdFeCo to unveil their local characteristics. Notably, by changing just one constituent element in the HEA (Rh to Ru), we found dramatic changes in the elemental arrangement from complete random mixing to a local single elemental ordering feature. Additionally, we demonstrate that the local ordering in RuPtPdFeCo can be further controlled by varying the Ru concentration, allowing us to toggle between local Ru clustering and distinct heterostructures in multicomponent systems. Overall, our study presents a practical approach for manipulating local atomic structures and elemental arrangements in noble-metal-based HEA systems, which could provide in-depth knowledge to mechanistically understand the functionality of noble-metal-based HEA nanomaterials in practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Data-driven based coordinated smart inverter control for distributed energy resources

Smart inverters (SI) for distributed energy resources (DER) are becoming popular since they have the ability to stabilize as well as restore the voltage and frequency of power systems. Aiming at establishing the mathematical models combined with SI control methods, multiple optimization methods are developed. However, the computational complexity of solving such a mathematical model with various uncertainties limits the real-time application of the SI control. To conquer this challenge, a data-driven-based SI control approach is developed to achieve coordinated control in the high penetration DER system. First, an optimization problem for maximizing the active power generation and minimizing the power loss is designed using the Volt/VAR control. To reduce the time consumption, the recurrent neural network (RNN) is proposed to model the relationship between the uncertainties and control actions during the offline site. The RNN with different sub-structures such as the long short-term memory cell and gated recurrent unit cell are included to enrich the diversity of features. In the last stage, different experiment comparisons, including multiple uncertainties maps and stateof- art machine learning methods, are conducted to verify the effectiveness of the proposed method based on the IEEE 123 bus power system. The results demonstrate that the proposed method can effectively achieve a rapid and coordinated control with a lower error rate.

Qiu, Wei↗
DOE OSTI2021

Solid Phase Gradient Alloying Method via ShAPE

Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Design and simulation of n -type solar cells based on an iodine-doped CdTe absorber using SCAPS-1D

The performance of conventional p-type CdTe solar cells has plateaued in recent years, motivating exploration of n-type absorbers. Here, we evaluate a homojunction solar cell employing iodine-doped CdTe (CdTe:I) as the absorber in a Ti 3 C 2 T x MXene/p-CdTe:As/n-CdTe:I/indium structure through SCAPS-1D simulations and prototype devices. Optimized simulations predict efficiencies above 25% for thin CdTe:I absorbers (~0.7 μm). In contrast, the first prototype achieved only ~1.36% efficiency with V OC = 0.48 V, J SC = 6.45 mA/cm 2 and FF = 43.8%. When the simulation is adjusted to match the actual device structure, and the effective illumination is reduced to account for front-side light loss, the predicted V OC (0.48 V) and JSC (6.74 mA/cm 2 ) closely reproduce the experimental values. This suggests that the device performance is primarily limited by reduced front-side photon transmission and other material non-idealities. These results highlight the promise of iodine-doped n-type CdTe and identify clear pathways for further efficiency improvement.

14 SOLAR ENERGY↗
DOE OSTI2021

Melt Pool and Heat Treatment Optimization for the Fabrication of High-Strength and High-Toughness Additively Manufactured 4340 Steel

Additively manufactured (AM) components offer superior design flexibility compared to their conventionally manufactured counterparts, and optimizing processing parameters is key to achieving high-quality depositions with desirable and predictable mechanical properties. This study was focused on 4340 steel fabricated using laser powder bed fusion (LPBF), and 42 laser power and scan speed combinations have been systematically investigated to determine an optimized melt pool geometry that would ensure fully-dense parts. The AM material was compared with a wrought 4340 equivalent and studied in two customized heat treated conditions, optimized for strength and toughness, respectively. The microstructures of the as-fabricated and heat treated AM and wrought materials were characterized to assess differences introduced by the layer-by-layer fabrication process and subsequent heat treatment. Tensile properties of both materials were also evaluated and demonstrate that the AM materials offer equal or superior properties compared to the wrought equivalents. Differences in fracture surface morphologies indicate the distinct failure mechanisms associated with the materials’ characteristic microstructures, and the role of inclusions in the failures was studied to elucidate these differences. Complementary to the experimental investigations, the dataset was leveraged to make recommendations for future design of experiments to optimize AM build parameters in other material systems. A statistical Monte Carlo analysis was used to predict the interpolation error produced using reduced datasets and to enable informed processing parameters selection. These findings are discussed to make recommendations for the use of AM materials for high-integrity structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantum-Based Molecular Dynamics Simulations Using Tensor Cores

Tensor cores, along with tensor processing units, represent a new form of hardware acceleration specifically designed for deep neural network calculations in artificial intelligence applications. Tensor cores provide extraordinary computational speed and energy efficiency but with the caveat that they were designed for tensor contractions (matrix–matrix multiplications) using only low-precision floating-point operations. Despite this perceived limitation, we demonstrate how tensor cores can be applied with high efficiency to the challenging and numerically sensitive problem of quantum-based Born–Oppenheimer molecular dynamics, which requires highly accurate electronic structure optimizations and conservative force evaluations. The interatomic forces are calculated on-the-fly from an electronic structure that is obtained from a generalized deep neural network, where the computational structure naturally takes advantage of the exceptional processing power of the tensor cores and allows for high performance in excess of 100 Tflops on a single Nvidia A100 GPU. Stable molecular dynamics trajectories are generated using the framework of extended Lagrangian Born–Oppenheimer molecular dynamics, which combines computational efficiency with long-term stability, even when using approximate charge relaxations and force evaluations that are limited in accuracy by the numerically noisy conditions caused by the low-precision tensor core floating-point operations. A canonical ensemble simulation scheme is also presented, where the additional numerical noise in the calculated forces is absorbed into a Langevin-like dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Analytic Nuclear Gradients Including Oriented External Electric Fields in a Molecule-Fixed Frame

Electric-field-assisted chemistry has attracted much attention in recent years, particularly in the context of oriented external electric fields for controlling molecular structure and reactivity. Such fields have been explored in a wide range of applications, including switching materials, nanoparticles, controllable catalysts, medicines, and clinical therapies. However, the determination of fixed fields in the laboratory frame becomes ineffective for flexible molecules, as conformational changes can significantly alter the relative orientation between the applied field and molecular structure. In this work, we propose two molecular reference frames─the principal axis frame and the local reference frame─to define oriented electric fields within the molecular framework. These coordinate systems powerfully eliminate ambiguities in the relative orientation between the applied field and the molecule. Analytic nuclear gradients in the presence of external electric fields are derived and implemented, with an initial application to field-dependent geometry optimizations of cis - and trans -formanilide. Analysis of the resulting field-induced equilibrium structures reveals distinct structural responses, validating the accuracy and robustness of the proposed formalism. The analytic gradient framework enables systematic investigations of molecular properties and reactivity under arbitrarily oriented electric fields, opening new opportunities for computational modeling and rational design in electric-field-controlled chemistry.

electric fields↗
DOE OSTI2026

Optimized Photoemission from Organic Molecules in 2D Layered Halide Perovskites

In recent years, hybrid organic−inorganic metal halides have been at the forefront of materials research. Typically, the functional (e.g., optoelectronic) properties of hybrid halides are derived from the inorganic structural part, whereas the organic structural units can add extra advantages in terms of stability, rigidity, and processability. Here, we report the design, synthesis, and characterization of two new hybrid materials in which the outstanding photophysical properties originate from the organic structural part. The new compounds, (C 15 H 16 N) 2 CdCl 4 and ((Br)C 15 H 15 N) 2 CdCl 4 , have 2D layered Ruddlesden−Poppertype perovskite structures. These hybrids are blue-white light emitters just like their corresponding pure organic salts, but with much improved emission efficiencies. Optical spectroscopy and density functional theory (DFT) studies confirm that photoemission comes from the trans-stilbene organic cations. The photoluminescence quantum yield (PLQY) values of these new materials are among the highest known, 50.83% and 26.60% for (C 15 H 16 N) 2 CdCl 4 and ((Br)C 15 H 15 N) 2 CdCl 4 , respectively. This is up to a 5-fold increase as compared to the light emission efficiency of the precursor salt C 15 H 16 NCl (PLQY of 10.33%). Alongside their outstanding optical properties, their environmental and thermal stability allow their consideration for potential practical applications such as radiation detection. This work shows that hybrid metal halides can be compositionally and structurally engineered to have highly efficient photoemission originating from the organic components for fast scintillation applications.

Halogens↗
DOE OSTI2024

Using interfaces to: create strongly-coupled magnetic-ferroelectrics

Starting at the level of electrons and atoms our long-term goal is to rationally design complex oxide heterostructures and interface-materials with targeted emergent behaviors. This is not a matter of simply optimizing material parameters, but rather begins with understanding a mechanism to control the interplay between the diverse microscopic degrees of freedom prevalent in complex oxides in order to create targeted macroscopic phenomena and ends with the design of new material realizations. These realizations are in turn created with atomic-layer precision, structurally assessed to see that they are the intended realization, and finally their relevant properties are measured. During this program we have developed the scientific ideas necessary to apply this materials-by-design paradigm to the creation of materials offering electrical control of magnetism. At the heart of our approach lies a partnership between theory, synthesis, and characterization. These are the areas of expertise of the three co-PIs, who have worked closely together since their arrival at Cornell. During this project bold ideas on where to put the atoms to provide electrical control of magnetism were met with the latest methods of synthesis science and electron microscopy in an attempt create targeted new multiferroics. Our work applying this materials-by-design methodology to multiferroics has enjoyed multiple successes. The Co-PIs have invented new mechanisms for multiferroics, which provide strong coupling between polarization and magnetism, and reduced them to practice. Specifically, we pioneered multiferroics based on spin-phonon coupling, rotation-driven multiferroicity, and novel geometric multiferroics. Importantly, the latter two of these mechanisms enable the deterministic switching of magnetism by an applied electric field, which is key to the application of multiferroics to future device technologies. Most recently, we performed detailed theoretical analysis on a system involving the combination of magnetoelectric and colossal magnetoresistance behaviors and predict that electrical control of a metal-insulator transition is possible in appropriately strained SmBaMn2O6. The research enabled by this grant led to 63 publications in leading refereed journals including Nature, Nature Materials, Physical Review Letters, and Advanced Materials demonstrating a materials-by-design paradigm to the creation of materials offering electrical control of magnetism.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Design of AlGaN-Zn(Si,Ge)N2 quantum wells for high-efficiency ultraviolet light emitters

The effect of inserting a nm-scale layer of Zn(Si,Ge)N2 into an AlGaN quantum well structure designed for light emission in the wavelength range from 255 to 305 nm is investigated here. The enhanced confinement of the hole within the quantum well results in an enhancement of the overlap of the hole and electron wave functions, resulting in an enhancement of the radiative recombination rate. In this theoretical calculation, for emission at a 270 nm wavelength, the enhancement in the wavefunction overlap can reach a factor of 7 when compared to an AlGaN quantum well device specifically engineered for optimal emission at the identical wavelength. Increases of almost an order of magnitude in both the peak spontaneous emission intensity and the radiative recombination rate are predicted. The peak emission wavelength can be tuned from 255 to 305 nm by adjusting the width and/or the composition of the inserted layer. The proposed structures provide a route to higher efficiency ultraviolet practical light emitting diodes and lasers.

Physics↗
DOE OSTI2021

Data Science Meets Physical Organic Chemistry

At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.

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

UniKP: a unified framework for the prediction of enzyme kinetic parameters

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (k cat ), Michaelis constant (K m ), and catalytic efficiency (k cat / K m ), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust k cat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗
DOE OSTI2023

Metal‐Organic Frameworks for C6 Alkane Separation

Abstract The separation of alkane isomers is an important yet challenging process in the petrochemical industry. Being a crucial step to produce premium gasoline components as well as optimum ethylene feed, the current industrial separation by distillation is extremely energy intensive. Adsorptive separation based on zeolite is limited by insufficient adsorption capacity. Metal‐organic frameworks (MOFs) hold enormous promise as alternative adsorbents due to their diverse structural tunability and exceptional porosity. Precise control of their pore geometry/dimensions has led to superior performance. In this minireview, we highlight the recent progresses in developing MOFs for the separation of C6 alkane isomers. Representative MOFs are reviewed based on their separation mechanisms. Emphasis is put on the material design rationale for achieving optimal separation capability. Finally, we briefly discuss the existing challenges, possible solutions, and future directions of this important field.

Xie, Feng↗