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DOE OSTI2024

Unraveling the multi-step crystallization mechanism of polytetrafluoroethylene, modified polytetrafluoroethylene, and their nanocomposites with boron nitride nanobarbs: Experimental insights and theoretical analysis

The non-isothermal crystallization behavior and kinetics of polytetrafluoroethylene (PTFE) composites with boron nitride nanobarb (BNNB), a new generation nanostructure with unique surface morphology and mechanical “barbs” have been analyzed, understanding these properties is essential for their high-end applications as thermal interface materials (TIM) for microwave, 5G and microelectronic devices. The analysis of the crystallization parameters includes crystallization onset, peak and end temperatures, crystallization half-life and overall crystallinity of PTFE, modified PTFE and their BNNB composites. The results were further analyzed using theoretical models such as the combined Avrami-Ozawa model. It was found that BNNB supports crystallization in the modified PTFE but shows minimal effect on the crystallization of PTFE. Due to the limitation of the classical theoretical models used above in fully characterizing the multi-step crystallization process of PTFE, an in-depth analysis using the model-free advanced isoconversional computation was used to characterize the PTFE crystallization based on the evolution of activation energy with fractional crystallinity and for the first time with temperature. Three kinetic regions were identified in the crystallization mechanism. Here, this study investigated the molecular organization and microstructural evolution of PTFE, modified PTFE and their composites during non-isothermal crystallization using advanced X-ray scattering measurements. An insight into the changes undergone by the material's microstructural units including crystallite size and morphology, lamellar thickness and lamellar interfacial layer thickness, and crystallographic phase dynamics during non-isothermal cooling from the melt, was provided here in this work. The effect of copolymer modification of PTFE and the inclusion of pristine and functionalized BNNB (a thermally conductive and electrically insulating ceramic) are both new investigations that provide valuable knowledge for the development of materials with strong matrix-nanofiller interaction and guidance for optimizing sintering and cooling cycles, two key steps in PTFE processing that largely affect the material microstructural features. Overall, the result of the three-part investigation demonstrates that BNNB supports crystallization in the modified PTFE up to 20 wt% concentration and at low and high cooling rates typically used in the industrial processing of PTFE.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Direct measurements for the kinetics of C–C bond fission in the high temperature decomposition of isopropanol

The thermal decomposition of isopropanol was studied experimentally and theoretically with a view to isolate and directly measure rate coefficients for the dominant radical channel in this multi-channel process. Two complementary shock tube methods, laser schlieren densitomtery and H-atom atomic resonance absorption spectroscopy, were used to obtain rate coefficents for the C–C bond fission channel. The experimental ranges span temperatures from 1200–2100 K and pressures between 30–690 torr. These are the most direct measurements of this rate coefficent in the fall-off regime of relevance to high-temperature reacting systems. Theoretical studies also performed in this work confirm that the title reaction and a molecular elimination involving dehydration were the sole unimolecular processes at high temperatures. A master equation analysis yielded k ( T, P ) for the C–C fission and these are shown to be in good agreement with the present experimental measurements. Comparisons with prior experimental and theoretical studies in the literature indicate branching ratios as well as absolute k ( T, P ) for C–C bond-fission have been under-predicted for this decomposition reaction. Consequently, results from the present studies place an increased emphasis on radical-driven secondary processes in high-temperature pyrolysis of this simplest secondary alcohol.

Kim, John H. [Univ. of Michigan, Ann Arbor, MI (Un↗

DOE OSTI2024

Mapping Support for Targeted Critical Minerals Exploration and Extraction

The United States’ dependency on imported minerals poses significant risks to economic stability and national security due to potential supply disruptions. Recognizing the strategic importance of critical minerals, the Department of Energy (DOE) emphasizes the need for a secure and resilient supply chain to support emissions reduction, technology development, and capitalization on clean energy opportunities. The DOE’s Office of Manufacturing and Energy Supply Chains (MESC), in collaboration with the Office of Policy (OP), addresses these vulnerabilities by focusing on upstream domestic critical minerals production, balancing extraction with social and environmental goals, including conservation, environmental justice, and respect for Tribal sovereignty. This report showcases a collaborative effort involving Idaho National Laboratory (INL), Argonne National Laboratory (Argonne), National Renewable Energy Laboratory (NREL), and the U.S. Geological Survey (USGS) to map mineral development potential along with key social and environmental datasets. A geographical information system (GIS)-based web map application was developed as a preliminary tool for environmental analysis, integrating 158 geospatial data layers such as critical habitat, land ownership, economic indicators, and environmental concerns. Data were sourced from agencies like the Bureau of Land Management (BLM) and USGS and processed using GIS technology to enhance visualization and analysis. The proposed analysis framework categorizes areas into high, mid, and low concern based on withdrawn lands, special status species, the Economic Development Capacity Index (EDCI) Mining Composite Index, and the Climate and Economic Justice Screening Tool (CEJST). While the application provides broad visualizations, it is not a substitute for detailed environmental reviews required under the National Environmental Policy Act (NEPA). Users must conduct further analyses and engage with tribal entities and other stakeholders for comprehensive planning. A case study of the Idaho Cobalt Belt (ICB) in Lemhi County, Idaho, has been provided in the report to illustrate the tool's practical use. This report introduces a GIS application and framework to support stakeholders in identifying and prioritizing areas for critical mineral exploration, promoting secure supply chains, and advancing the nation's energy independence through responsible resource stewardship.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Cradle-to-Gate greenhouse gas emissions of the production of ethylene from U.S. Corn ethanol and comparison to fossil-derived ethylene production

Conventional ethylene production heavily depends on fossil-derived feedstocks via steam cracking, a very energy- and emission-intensive process. Researchers have been exploring alternatives to reduce CO 2 emissions including producing ethylene from biobased feedstocks. This paper evaluates the cradle-to-gate greenhouse gas (GHG) emissions of bioethylene produced from U.S. corn ethanol. The analysis includes different pathways for the dehydration of corn ethanol to ethylene and co-processing routes via fluid catalytic cracking (FCC) processes. For the FCC co-processing route carbon-14 analysis is used to determine bioethanol yields. A 127% reduction in life cycle GHG emissions of bioethylene is estimated compared to fossil-derived ethylene for the base case. Additional case studies are also discussed to understand the reduction of GHG emissions due to sustainable corn farming and renewable power use, biogenic carbon capture, and fuel switch with biofuels at the ethanol plant, and its impact on bioethylene GHG emissions.

carbon footprint↗

DOE OSTI2025

Ring Pull Strain Analysis Version 1.1

This report details an analysis package, Ring Pull Strain Analysis (RPSA), that can be used to present and quantify digital image correlation (DIC) data as it relates to a gaugeless ring pull test. Gaugeless ring pull is a testing technique for mechanical testing of small annular samples, usually cut from a thin-walled tube. DIC data is often necessary for this kind of test because bending moments present on the ring cause a non-uniform strain distribution and localized measurements are necessary. In addition, the annular geometry of a ring lends itself to a polar representation, which is not present with typical DIC analysis methods. RPSA was made to calculate and plot the polar representation of strain from standard pre-processed DIC data of a gaugeless ring pull test. Further analysis can be done on ring pull including a quasi-uniaxial tensile analysis and coating analysis, which are also performed by RPSA. In addition, due to the universality of DIC plotting and ring pull test analysis, RPSA can accommodate a wide variety of tests, though it is tailored for ring pull testing. This report details how RPSA works, including the theory, assumptions, and logic behind the calculations and the structure of the program.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Microbial inoculum effects on the rumen epithelial transcriptome and rumen epimural metatranscriptome in calves

Manipulation of the rumen microbial ecosystem in early life may affect ruminal fermentation and enhance the productive performance of dairy cows. The objective of this experiment was to evaluate the effects of dosing three different types of microbial inoculum on the rumen epithelium tissue (RE) transcriptome and the rumen epimural metatranscriptome (REM) in dairy calves. For this objective, 15 Holstein bull calves were enrolled in the study at birth and assigned to three different intraruminal inoculum treatments dosed orally once weekly from three to six weeks of age. The inoculum treatments were prepared from rumen contents collected from rumen fistulated lactating cows and were either autoclaved (control; ARF), processed by differential centrifugation to create the bacterial-enriched inoculum (BE), or through gravimetric separation to create the protozoal-enriched inoculum (PE). Calves were fed 2.5 L/d pasteurized waste milk 3x/d from 0 to 7 weeks of age and texturized starter until euthanasia at 9 weeks of age, when the RE tissues were collected for transcriptome and microbial metatranscriptome analyses, from four randomly selected calves from each treatment. The different types of inoculum altered the RE transcriptome and REM. Compared to ARF, 9 genes were upregulated in the RE of BE and 92 in PE, whereas between BE and PE there were 13 genes upregulated in BE and 114 in PE. Gene ontology analysis identified enriched GO terms in biological process category between PE and ARF, with no enrichment between BE and ARF. The RE functional signature showed different KEGG pathways related to BE and ARF, and no specific KEGG pathway for PE. We observed a lower alpha diversity index for RE microbiome in ARF (observed genera and Chao1 (p < 0.05)). Five microbial genera showed a significant correlation with the changes in host gene expression: Roseburia (25 genes), Entamoeba (two genes); Anaerosinus, Lachnospira, and Succiniclasticum were each related to one gene. sPLS-DA analysis showed that RE microbial communities differ among the treatments, although the taxonomic and functional microbial profiles show different distributions. Co-expression Differential Network Analysis indicated that both BE and PE had an impact on the abundance of KEGG modules related to acyl-CoA synthesis, type VI secretion, and methanogenesis, while PE had a significant impact on KEGGs related to ectoine biosynthesis and D-xylose transport. Our study indicated that artificial dosing with different microbial inocula in early life alters not only the RE transcriptome, but also affects the REM and its functions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

ARCADE Analysis Methods & Validation Pathway

The Advanced Reactor Cyber Analysis and Development Environment (ARCADE) provides an automated analysis system which supports risk-informed performance based (RIPB) evaluations of nuclear control systems. Every possible cyber threat which could lead to consequence is identified by simulating the unsafe control action sequences which transform digital harm into physical harm. Eliminating the simulation of complex digital cyber attack chains cuts out unnecessary computational overhead and focuses directly on the physics of cyber-physical attacks. This focus enables designers to make informed decisions which can entirely eliminate categories of cyber threats against advanced reactors through the physical nature of the plant design. This narrowing of cyber threat against nuclear power plants through the physics of the system is intended to make any remaining threat management and cost efficient. This is the goal of the Tiered Cyber Analysis (TCA) outlined in NRC Draft Regulation Guide (RG) 5.96, which provides a RIPB cybersecurity approach for new reactors. ARCADE has been custom developed to meet the demands of the rigorous analysis required in Tier 1 of the TCA, which forms the foundation of the TCA process. Currently, ARCADE is still under development, but has made significant leaps in capability. A pilot analysis on the opensource Asherah simulator was performed which demonstrated key functionality goals. The next stage of ARCADE development involves improvements to the applications which support the analysis system, and enabling the analysis system to utilize the full suite of unsafe control action simulations. Since the analysis method’s core functions are complete, validation of the analysis method will be started concurrent to the next development stages. The automated analysis ARCADE will provide can radically change the cybersecurity design process for advanced reactors, reducing the cost of security implementation while enhancing cyber resilience. The pathway for ARCADE’s development to this goal has become much clearer. The majority of technical hurdles have been cleared, and the remaining development needs have been solidified. ARCADE is now capable of assisting the advanced reactor design process and directly support advanced reactor industry RIPB practices.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Towards a Circular Economy for PET Bottles in the U.S. - 4P Model

The United States generates the most plastic waste of any country. Along with that GHG emissions from the global plastic economy are expected to increase to 15% of the global carbon budget by 2050. It is imperative that plastic recycling is made a reality to reduce both plastic pollution in the environment and GHG emissions. A portfolio of end-of-life strategies must be implemented to minimize environmental impacts and retain valuable plastic material, but it is challenging to compare options that generate products with different utility and lifetime. Plastic use reduction, reuse and recycling are thus increasingly important, but making informed policy and research decisions within this space can be challenging given the diverse range of available solutions. The novel analysis framework, Plastic Parallel Pathways Platform (4P) has been equipped with consequential life cycle assessment, techno-economic analysis, and a plastic circularity indicator to estimate the greenhouse gas (GHG) emissions, circularity, and cost of polyethylene terephthalate (PET) down-cycling to lower-quality resin, closed-loop recycling to food-grade PET bottles, up-cycling to fiber-reinforced plastic (FRP), and conversion to non-plastic products (electricity, oil) on a United States economy-wide basis. Integrating system dynamics into this robust plastics model that already incorporates techno-economics, circularity, and environmental impacts will enable identification of key bottlenecks between manufacturers, waste sorters, and reclaimers that currently prevent rapid decarbonization of the plastics economy. System dynamics (SD) explore the evolution of activities and technologies based on changed macro parameters such as plastic demand and supply, market shifts, and cross-sectoral interactions. This project particularly aims to explore the interplay between, waste collection, plastic waste sorting, recycling, and manufacturing, as well as the effect of plastic bale quality and plastic reuse initiatives on the surrounding process stages. This functionality will facilitate combinatory analysis in which a portfolio of end-of-life pathways are assessed simultaneously, with the exact makeup of that portfolio affected by parameters such as technology scales, resource constraints, and waste mitigation efforts. Integrating SD with the 4P framework enables analyzing the effect of increased revenue and reinvestment into improving process efficiencies, sorting and collection quantities. Through that, market effects of increased recycled resin availability can be studied for the plastics systems model for the US. The results will help identify technical or economic bottlenecks that currently limit efforts to decarbonize the U.S. plastics economy.

carbon↗

DOE OSTI2024

Towards a Circular Economy for PET Bottles in the U.S. - 4P Framework

circular economy↗

DOE OSTI2024

Mapping of flumioxazin tolerance in a snap bean diversity panel leads to the discovery of a master genomic region controlling multiple stress resistance genes

Effective weed management tools are crucial for maintaining the profitable production of snap bean (Phaseolus vulgaris L.). Preemergence herbicides help the crop to gain a size advantage over the weeds, but the few preemergence herbicides registered in snap bean have poor waterhemp (Amaranthus tuberculatus) control, a major pest in snap bean production. Waterhemp and other difficult-to-control weeds can be managed by flumioxazin, an herbicide that inhibits protoporphyrinogen oxidase (PPO). However, there is limited knowledge about crop tolerance to this herbicide. We aimed to quantify the degree of snap bean tolerance to flumioxazin and explore the underlying mechanisms. We investigated the genetic basis of herbicide tolerance using genome-wide association mapping approach utilizing field-collected data from a snap bean diversity panel, combined with gene expression data of cultivars with contrasting response. The response to a preemergence application of flumioxazin was measured by assessing plant population density and shoot biomass variables. Snap bean tolerance to flumioxazin is associated with a single genomic location in chromosome 02. Tolerance is influenced by several factors, including those that are indirectly affected by seed size/weight and those that directly impact the herbicide's metabolism and protect the cell from reactive oxygen species-induced damage. Transcriptional profiling and co-expression network analysis identified biological pathways likely involved in flumioxazin tolerance, including oxidoreductase processes and programmed cell death. Transcriptional regulation of genes involved in those processes is possibly orchestrated by a transcription factor located in the region identified in the GWAS analysis. Several entries belonging to the Romano class, including Bush Romano 350, Roma II, and Romano Purpiat presented high levels of tolerance in this study. The alleles identified in the diversity panel that condition snap bean tolerance to flumioxazin shed light on a novel mechanism of herbicide tolerance and can be used in crop improvement.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging [Slides]

Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.

09 BIOMASS FUELS↗

DOE OSTI2025

Hyperspectral segmentation of plants in fabricated ecosystems

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach provides a scalable solution for spectral segmentation, and facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

Zwart, Petrus H.↗

DOE OSTI2025

Joint Factorization of QCD and QED Radiation in Lepton-Hadron Scattering

The factorization theorem plays an important role in the analysis of high energy quantum chromodynamic (QCD) processes, separating the nonperturbative hadronic interaction into the universal parton distribution functions (PDFs) and fragmentation functions (FFs) and the process-dependent interactions into short distance perturbative calculations, with any interference power suppressed. With a virtual photon exchange, lepton-hadron deep inelastic scattering (DIS) provides an electromagnetic hard probe for the partonic structure of colliding hadrons and has played an important role in the development of QCD factorization. However, the collision induced QED radiation can change the momentum of the exchanged but unobserved virtual photon, making the photon-hadron frame, where the factorization formalism for DIS and semi-inclusive DIS (SIDIS) was derived, ill defined. A new analogous factorization approach has been introduced to separate the leading power process-independent QED radiative contributions to the single photon exchange by introducing lepton distribution functions (LDFs) and lepton fragmentation functions (LFFs), while process-dependent effects are perturbatively calculated with large logarithms removed [J. High Energ. Phys. 2021, 157 (2021)]. These LDFs and LFFs are considered global, as they appear in many different interactions, such as e+e-, DIS and SIDIS, so data from experiments can be used to fit and describe these functions across a wide range of lepton scattering. In this work, I will apply this new hybrid factorization approach to lepton-hadron DIS and SIDIS. For DIS, I derive the NLO short distance perturbative contribution to the cross section and demonstrate the effects the QED radiation has on the cross section using this approach using the CTEQ parameterization for the QCD functions. As part of the SIDIS analysis, I study the cross-section in two different kinematic regions: (1) the scattered lepton and observed hadron are not near back-to-back, and (2) they are close to back-to-back, where collinear QCD factorization works for (1) and TMD QCD factorization for (2) while collinear QED factorization works for both. As part of this work, I show the effects on the SIDIS cross section using fixed order calculations for the unpolarized structure function by first showing the effect of the radiative corrections on the main kinematic variables, especially how the internal transverse momentum is significantly correlated to the external angular dependence, and then the unpolarized structure function (or cross section) with matching between the descriptions for low and high transverse momentum. This work will impact the calculations for predictions for data from COMPASS and various Jefferson Lab experiments.

Cammarota, Justin [Univ. of Kentucky, Lexington, K↗

DOE OSTI2025

Critical statistical assessment of data in metal additive manufacturing

Obtaining high quality data reflecting the relationships between the additive manufacturing (AM) process parameters, material microstructure and mechanical properties is crucial for the use of machine learning in AM. A database of over 4,000 data entries of metal AM was created thanks to a large number of literature studies on key process parameters and indicators of build quality. Meta-analysis reveals critical biases in the literature. Firstly, majority of studies report only high quality builds, these imbalances in reporting result in weak correlation between process parameters, properties and consolidation, limiting the ability of machine learning models to generalize beyond optimized conditions. Nevertheless, the trained models accurately predict yield strength ($R^2 = 0.85$), suggesting that certain process–property relationships are effectively captured within these models. Secondly, quantitative microstructural data are largely absent, limiting the learning of the microstructure-mechanical properties relationships. Finally, current process window identification is based largely on the consolidation, despite significant uncertainty in its measurement. It is important to identify the process map on the basis of not only the consolidation, but also mechanical behaviour under loading. Such a identification shows that 316 L and Inconel have much larger process map (i.e. highly printable) in comparison to the AlSi10Mg and Ti6Al4V.

Additive manufacturing↗

DOE OSTI2025

Microfluidic cells for the 1–10 2 MPa pressure range

Thin membrane-delimited fluid cells supporting up to 1 at (0.1 MPa) of pressure are well known and commercially available for use in vacuum chambers of electron, photon, or various particle beam microscopies or spectroscopies. Hereby, we report on the development of fluid cells capable of working at 1–10 MPa, extending the analysis domain for investigating chemical, biochemical, or physical processes at pressures of interest in chemical synthesis, underwater biochemistry studies or underground geology. We explored ways to optimize cell membranes to better resist pressure beyond simply increasing the thickness or decreasing the size of the membranes, using finite element analysis and experimental validation via membrane bulging experiments and failure statistics. Fluid cell prototypes were fabricated using ∼75 nm-thick SiN x membranes, engineered to withstand 4.7 MPa (average value), compared to regular (un-engineered) membranes withstanding only 3.4 MPa (average value). The fluid cell prototypes include eight microchannels for feeding/evacuating the fluids and applying pressure into micro-reaction chambers, two electrodes for electrochemical or conduction measurements in the sample, and a possible pressure or temperature sensor, customizable for specific experiments.

hi-pressure↗

DOE OSTI2025

Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking

Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationships among these models remain largely unexplored, a gap that hinders the development of a unified theory of AI learning. In this work, we address two central challenges: clarifying the connections between different deep generative models and deepening our understanding of their learning mechanisms. We focus on Restricted Boltzmann Machines (RBMs), a class of generative models known for their universal approximation capabilities for discrete distributions. By introducing a reciprocal space formulation for RBMs, we reveal a connection between these models, diffusion processes, and systems of coupled bosons. Our analysis shows that at initialization, the RBM operates at a saddle point, where the local curvature is determined by the singular values of the weight matrix, whose distribution follows the Marc̆enko-Pastur law and exhibits rotational symmetry. During training, this rotational symmetry is broken due to hierarchical learning, where different degrees of freedom progressively capture features at multiple levels of abstraction. This leads to a symmetry breaking in the energy landscape, reminiscent of Landau’s theory. This symmetry breaking in the energy landscape is characterized by the singular values and the weight matrix eigenvector matrix. We derive the corresponding free energy in a mean-field approximation. We show that in the limit of infinite size RBM, the reciprocal variables are Gaussian distributed. Our findings indicate that in this regime, there will be some modes for which the diffusion process will not converge to the Boltzmann distribution. To illustrate our results, we trained replicas of RBMs with different hidden layer sizes using the MNIST dataset. Our findings not only bridge the gap between disparate generative frameworks but also shed light on the fundamental processes underpinning learning in deep generative models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Transformational Nano-confined Ionic Liquid Membrane for Greater than or Equal to 97 Percent Carbon Dioxide Capture from Natural Gas Combined Cycle Flue Gas

A transformational process based on nano-confined ionic liquid (NCIL) membranes was developed for capturing ≥97% CO 2 from natural gas combined cycle (NCCC) flue gas. The NCIL membranes were prepared by loading amino acid ionic liquid into a framework composed of single-walled carbon nanotube mesh filled with graphene oxide quantum dots. The membranes exhibited CO 2 permeance as high as 2,000 GPU with a CO 2 /N2 selectivity of 2,300 for a typical NGCC flue gas composition. When H 2 O vapor sweep was applied in the permeate side, 96.6% CO2 dry-basis purity and 97.6% CO 2 capture rate were achieved for a simulated NGCC flue gas with single stage. In the process design, a highly H 2 O-selective membrane would be needed to recover majority of the H 2 O vapor, and the recovered H 2 O vapor could be recycled to the permeate side of the NCIL membrane. Sulfonated poly(ether ether ketone) membranes were successfully developed for this purpose. These membranes exhibited H 2 O permeance great than 11,000 GPU and H 2 O/CO 2 selectivity greater than 1,000 at 70ºC for a feed mixture consisting of 14.5 vol% H2O and balanced CO 2 . A standalone membrane model using MATLAB platform was developed for process simulation. The model was validated with experimental data. Techno-economic analysis based on the testing data collected during the current program suggests this transformational membrane process can achieve 97% CO 2 capture efficiency with a cost of $47.8/tonne of CO 2 , which is a 21% reduction versus DOE’s reference case B31B.97.

03 NATURAL GAS↗

DOE OSTI2024

Energy conversion and transport in molecular-scale junctions

Molecular-scale junctions (MSJs) have been considered the ideal testbed for probing physical and chemical processes at the molecular scale. Due to nanometric confinement, charge and energy transport in MSJs are governed by quantum mechanically dictated energy profiles, which can be tuned chemically or physically with atomic precision, offering rich possibilities beyond conventional semiconductor devices. While charge transport in MSJs has been extensively studied over the past two decades, understanding energy conversion and transport in MSJs has only become experimentally attainable in recent years. As demonstrated recently, by tuning the quantum interplay between the electrodes, the molecular core, and the contact interfaces, energy processes can be manipulated to achieve desired functionalities, opening new avenues for molecular electronics, energy harvesting, and sensing applications. This Review provides a comprehensive overview and critical analysis of various forms of energy conversion and transport processes in MSJs and their associated applications. We elaborate on energy-related processes mediated by the interaction between the core molecular structure in MSJs and different external stimuli, such as light, heat, electric field, magnetic field, force, and other environmental cues. Key topics covered include photovoltaics, electroluminescence, thermoelectricity, heat conduction, catalysis, spin-mediated phenomena, and vibrational effects. Furthermore, the review concludes with a discussion of existing challenges and future opportunities, aiming to facilitate in-depth future investigation of promising experimental platforms, molecular design principles, control strategies, and new application scenarios.

Charge transport↗