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DOE OSTI2012

Final Progress Report: Internship at Los Alamos National Laboratory

Originally I was tasked fluidized bed modeling, however, I changed projects. While still working with ANSYS Fluent, I performed a study of particle tracks in glove boxes. This is useful from a Health-Physics perspective, dealing respirable particles that can be hazardous to the human body. I iteratively tested different amounts of turbulent particles in a steady-state flow. The goal of this testing was to discover how Fluent handles built-in Rosin-Rammler distributions for particle injections. I worked on the health physics flow problems and distribution analysis under the direction of two mentors, Bruce Letellier and Dave Decroix. I set up and ran particle injection calculations using Fluent. I tried different combinations of input parameters to produce sets of 500,000, 1 million, and 1.5 million particles to determine what a good test case would be for future experiments. I performed a variety of tasks in my work as an Undergraduate Student Intern at LANL this summer, and learned how to use a powerful CFD application in addition to expanding my skills in MATLAB. I enjoyed my work at LANL and hope to be able to use the experience here to further my career in the future working in a security-conscious environment. My mentors provided guidance and help with all of my projects and I am grateful for the opportunity to work at Los Alamos National Laboratory.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation

Abstract. We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden–Julian oscillation. The Madden–Julian oscillation is a multi-scale pattern within the tropical atmosphere that has been extensively studied over the past decades, which makes it an ideal test case to ensure the interpretability methods can recover the current state of knowledge regarding its spatial structure. The neural networks can, indeed, reproduce the current state of knowledge and can also provide new insights into the seasonality of the Madden–Julian oscillation and its relationships with atmospheric state variables. The neural network identifies the phase of the Madden–Julian oscillation twice as accurately as a linear regression approach, which means that nonlinearities used by the neural network are important to the structure of the Madden–Julian oscillation. Interpretations of the neural network show that it accurately captures the spatial structures of the Madden–Julian oscillation, suggest that the nonlinearities of the Madden–Julian oscillation are manifested through the uniqueness of each event, and offer physically meaningful insights into its relationship with atmospheric state variables. We also use the interpretations to identify the seasonality of the Madden–Julian oscillation and find that the conventionally defined extended seasons should be shifted later by 1 month. More generally, this study suggests that neural networks can be reliably interpreted for geoscientific applications and may thereby serve as a dependable method for testing geoscientific hypotheses.

58 GEOSCIENCES↗

NASA NTRS2008

Assessing Requirements Quality through Requirements Coverage

In model-based development, the development effort is centered around a formal description of the proposed software system the model. This model is derived from some high-level requirements describing the expected behavior of the software. For validation and verification purposes, this model can then be subjected to various types of analysis, for example, completeness and consistency analysis [6], model checking [3], theorem proving [1], and test-case generation [4, 7]. This development paradigm is making rapid inroads in certain industries, e.g., automotive, avionics, space applications, and medical technology. This shift towards model-based development naturally leads to changes in the verification and validation (V&V) process. The model validation problem determining that the model accurately captures the customer's high-level requirements has received little attention and the sufficiency of the validation activities has been largely determined through ad-hoc methods. Since the model serves as the central artifact, its correctness with respect to the users needs is absolutely crucial. In our investigation, we attempt to answer the following two questions with respect to validation (1) Are the requirements sufficiently defined for the system? and (2) How well does the model implement the behaviors specified by the requirements? The second question can be addressed using formal verification. Nevertheless, the size and complexity of many industrial systems make formal verification infeasible even if we have a formal model and formalized requirements. Thus, presently, there is no objective way of answering these two questions. To this end, we propose an approach based on testing that, when given a set of formal requirements, explores the relationship between requirements-based structural test-adequacy coverage and model-based structural test-adequacy coverage. The proposed technique uses requirements coverage metrics defined in [9] on formal high-level software requirements and existing model coverage metrics such as the Modified Condition and Decision Coverage (MC/DC) used when testing highly critical software in the avionics industry [8]. Our work is related to Chockler et al. [2], but we base our work on traditional testing techniques as opposed to verification techniques.

Rajan, Ajitha↗

DOE OSTI2022

Preliminary Design of Reactor Cavity Cooling System for a Horizontal Compact HTGR

The Horizontal Compact High Temperature Gas Reactor (HC-HTGR) is being designed by a multi-disciplinary team of nuclear, mechanical, and structural engineers under the support of a DOE-NE Advanced Reactor Demonstration Program’s Advanced Reactor Concepts-20 (ARC-20) award. The objective of this ARC-20 project is to deliver a conceptual design for the proposed MIGHTR in 3 years and support its commercialization as a safe and low-cost HTGR. Argonne National Laboratory (Argonne) is responsible for the design and analysis of the reactor cavity cooling system (RCCS) as a safety system for passive decay heat removal of the reactor concept. This report documents the preliminary design study of the RCCS for the HC-HTGR. It includes the establishment of the design requirements, a high-level design study by initial scoping calculations, and preliminary performance calculations of the HC-HTGR RCCS design. Design requirements for the HC-HTGR RCCS have been established to guide preliminary design activities and scoping performance calculations. Initial scoping calculations including estimation of the water inventory, estimation of HVAC thermal capability, and a parametric study on loop dimensions by standalone RCCS analysis. Based on scoping calculation results, a set of baseline dimensions of the HC-HTGR RCCS was derived. A water panel modeling approach was investigated to explore various potential design options for the water panel under consideration for the HC-HTGR RCCS using RELAP5-3D. A test case study was performed to assess the prediction capability of two modeling approaches. The results were compared with CFD simulations conducted in constant RPV temperature and heat flux boundary conditions. It confirms the capability of the RELAP5-3D modeling approach to include all important heat transfer mechanisms expected in the HC-HTGR RCCS operation conditions. Then, a reference RELAP5-3D model for the 1/8 th of a compartment of the preliminary design of the HC-HTGR RCCS was developed. A preliminary performance analysis was conducted to evaluate single-phase natural circulation performance with different top tank temperature values and panel conduction performance in various operation conditions. From single-phase natural circulation performance analysis, the system operation mode was investigated in normal operating and limiting design conditions. It showed operation mode in a subcooled state with a proper top tank water cooling system. Parasitic heat loss by both internal air flow and RCCS was estimated, showing it satisfies maintaining below target maximum heat loss of the HC-HTGR RCCS. From the panel conduction performance analysis, two candidate materials for the riser tube such as carbon steel and stainless steel were compared in the thermal performance of HC-HTGR RCCS. From a single water panel test compared with CFD simulation results, it was confirmed that the current capability of the RELAP5-3D modeling approach for the water panel predicts the thermal conduction of two different materials of the water panel. Then, system-level thermal performance analysis was performed for 1/8 th of the compartment of the preliminary HC-HTGR RCCS design. It was first observed that the current preliminary HC-HTGR RCCS design had minimal impact on the overall thermal performance of the water panel by changing pipe material from carbon steel to stainless steel. From Argonne’s effort on the ongoing water-based NSTF testing program, several considerations other than the thermal performance point of view were addressed to be considered in selecting pipe materials.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

FIDES-II/P2M Simulation Exercise on AN3 and AN10 bump tests: main results

This paper presents simulations of bump tests conducted as part of the P2M (Power to Melt and Maneuverability) project within the OECD/NEA FIDES-II framework (Framework for Irradiation Experiments). To prepare fuel performance codes for analyzing the P2M experiments, an international Simulation Exercise (SE) was initiated under the P2M project. The objective of this exercise was to model the AN3 and AN10 bump tests carried out in the 1980s at the DR3 reactor (3rd RISØ Fission Gas Project), during which instrumented fuel rodlets (equipped with thermocouples and pressure sensors) were subjected to prolonged power transients and power dips. The ongoing simulation exercise involves seven organizations from industry, research, and regulatory bodies, each using different fuel performance codes. This paper provides detailed descriptions of the test cases and simulation results from six fuel performance codes (ALCYONE, BISON, FAST, FEMAXI, FRAPCON, and TRANSURANUS), with a particular focus on fission gas release kinetics.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Solving gyrokinetic systems with higher-order time dependence

We discuss theoretical and numerical aspects of gyrokinetics as a Lagrangian field theory when the field perturbation is introduced into the symplectic part. A consequence is that the field equations and particle equations of motion in general depend on the time derivatives of the field. The most well-known example is when the parallel vector potential is introduced as a perturbation, where a time derivative of the field arises only in the equations of motion, so an explicit equation for the fields may still be written. We will consider the conceptually more problematic case where the time-dependent fields appear in both the field equations and equations of motion, but where the additional term in the field equations is formally small. The conceptual issues were described by Burby (J. Plasma Phys., vol. 82 (3), 2016, 905820304): these terms lead to apparent additional degrees of freedom to the problem, so that the electric field now requires an initial condition, which is not required in low-frequency (Darwin) Vlasov–Maxwell equations. Also, the small terms in the Euler–Lagrange equations are a singular perturbation, and these two issues are interlinked. For well-behaved problems the apparent additional degrees of freedom are spurious, and the physically relevant solution may be directly identified. Because we needed to assume that the system is well behaved for small perturbations when deriving gyrokinetic theory, we must continue to assume that when solving it, and the physical solutions are thus the regular ones. The spurious nature of the singular degrees of freedom may also be seen by changing coordinate systems so the varying field appears only in the Hamiltonian. We then describe how methods appropriate for singular perturbation theory may be used to solve these asymptotic equations numerically. We then describe a proof-of-principle implementation of these methods for an electrostatic strong-flow gyrokinetic system; two basic test cases are presented to illustrate code functionality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1998

Prediction of Transitional Flows in the Low Pressure Turbine

Current turbulence models tend to give too early and too short a length of flow transition to turbulence, and hence fail to predict flow separation induced by the adverse pressure gradients and streamline flow curvatures. Our discussion will focus on the development and validation of transition models. The baseline data for model comparisons are the T3 series, which include a range of free-stream turbulence intensity and cover zero-pressure gradient to aft-loaded turbine pressure gradient flows. The method will be based on the conditioned N-S equations and a transport equation for the intermittency factor. First, several of the most popular 2-equation models in predicting flow transition are examined: k-e [Launder-Sharina], k-w [Wilcox], Lien-Leschiziner and SST [Menter] models. All models fail to predict the onset and the length of transition, even for the simplest flat plate with zero-pressure gradient(T3A). Although the predicted onset position of transition can be varied by providing different inlet turbulent energy dissipation rates, the appropriate inlet conditions for turbulence quantities should be adjusted to match the decay of the free-stream turbulence. Arguably, one may adjust the low-Reynolds-number part of the model to predict transition. This approach has so far not been very successful. However, we have found that the low-Reynolds-number model of Launder and Sharma [1974], which is an improved version of Jones and Launder [1972] gave the best overall performance. The Launder and Sharma model was designed to capture flow re-laminarization (a reverse of flow transition), but tends to give rise to a too early and too fast transition in comparison with the physical transition. The three test cases were for flows with zero pressure gradient but with different free-stream turbulent intensities. The same can be said about the model when considering flows subject to pressure gradient(T3C1). To capture the effects of transition using existing turbulence models, one approach is to make use of the concept of the intermittency to predict the flow transition. It was originally based on the intermittency distribution of Narasimha [1957], and then gradually evolved into a transport equation for the intermittency factor. Gostelow and associates [1994,1995] have made some improvements to Narasimha's method in an attempt to account for both favorable and adverse pressure gradients. Their approach is based on a linear, explicit combination of laminar and turbulent solutions. This approach fails to predict the overshoot of the skin friction on a flat plate near the end of transition zone, even though the length of transition is well predicted. The major flaw of Gostelow's approach is that it assumes the non-turbulent part being the laminar solution and the turbulent part being the turbulent solution and they do not interact across the transitional region. The technique in condition averaging the flow equations in intermittent flows was first introduced by Libby [1975] and Dopazo [1977] and further refined by Dick and associates [1988, 1996]. This approach employs two sets of transport equations for the non-turbulent part and the other for the turbulent part. The advantage of this approach is that it allows the interaction of non-turbulent and turbulent velocities through the introduction of additional source terms in the continuity and momentum equations for the non-turbulent and turbulent velocities. However, the strong coupling of the two sets of equations has caused some numerical difficulties, which requires special attention. The prediction of the skin friction can be improved by this approach via the implicit coupling of non-turbulent and turbulent velocity flelds. Another improvement of the interrmittency model can be further made by allowing the intermittency to vary in the cross-stream direction. This is one step prior to testing any proposal for the transport equation for the intermittency factor. Instead of solving the transport equation for the intermittency factor, the distribution for the intermittency factor is prescribed by Klebanoff's empirical formula [1955]. The skin friction is very well predicted by this new modification, including the overshoot of the profile near the end of the transition zone. The outcome of this study is very encouraging since it indicates that the proper description of the intermittency distribution is the key to the success of the model prediction. This study will be used to guide us on the modelling of the intermittency transport equation.

Huang, George↗

DOE OSTI2022

Modernization efforts for the R -Matrix code SAMMY [Abstract]

The R-Matrix code SAMMY is a widely used nuclear data evaluation code focused on the resolved range, which includes corrections for experimental effects. The code is still mostly written in Fortran 77, and uses a memory management system suitable for the time of its initial writing (1984). A modernization effort is under way to bring the code in-line with modern software development practices. A continuous-integration testing framework was added, automating the large existing set of test cases. It is run on every commit. The memory management was updated to current standard practices suitable for modern software analysis tools. The code can be obtained from https://code.ornl.gov/RNSD/SAMMY. The resonance parameters and covariance information are now stored in C++ objects shared by SAMMY and AMPX, the processing code that generates nuclear data libraries for SCALE. This allows for easier maintenance and access to the resonance parameters inside and outside of SAMMY. This feature is already used by accessing and changing parameters in memory in the Bayesian Monte Carlo Evaluation Framework for Cross Sections Nuclear Data and Integral Benchmark Experiments project, Further plans include the switch to the ENDF reading and writing routines in AMPX, as these routines are more robust, easier to maintain, and support more features. Of note here is support for the new GNDS format. Previously it wasn’t easy to share the full covariance matrix for evaluations containing more than one isotope due to limitations on the ENDF format; this is now supported in GNDS. The data are currently available in a binary SAMMY format and can be exported to GNDS to make them more widely available and sharable. The next step will be to use the same resonance processing code at 0K in AMPX and SAMMY as one of the available Reich-Moore R-Matrix formalism. The first step toward this goal is to isolate the reconstruction into a module that takes resonance parameters as its input and does not depend on SAMMY global parameters. This goal has been achieved and it should now be possible to more easily change the resonance formalism and add enhancements as the Phenomenological R-Matrix parameterization of direct, doorway, and compound nuclear reactions discussed elsewhere on this conference. This concerted modernization and enhancement effort provides multiple advantages to the nuclear data community. It will allow parameter optimization using enhanced formalisms, including experimental effects, that better match complex experimental data. Then those evaluated parameters can immediately be passed off to AMPX to be reconstructed with the exact same cross section model and be put into a data library for subsequent testing using SCALE and the Valid Benchmark suite or other suitable benchmark suites.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Roofline Analysis in the Intel® Advisor to Deliver Optimized Performance for applications on Intel® Xeon Phi™ Processor

In this session we show, in two case studies, how the roofline feature of Intel Advisor has been utilized to optimize the performance of kernels of the XGC1 and PICSAR codes in preparation for Intel Knights Landing architecture. The impact of the implemented optimizations and the benefits of using the automatic roofline feature of Intel Advisor to study performance of large applications will be presented. This demonstrates an effective optimization strategy that has enabled these science applications to achieve up to 4.6 times speed-up and prepare for future exascale architectures. # Goal/Relevance of Session The roofline model [1,2] is a powerful tool for analyzing the performance of applications with respect to the theoretical peak achievable on a given computer architecture. It allows one to graphically represent the performance of an application in terms of operational intensity, i.e. the ratio of flops performed and bytes moved from memory in order to guide optimization efforts. Given the scale and complexity of modern science applications, it can often be a tedious task for the user to perform the analysis on the level of functions or loops to identify where performance gains can be made. With new Intel tools, it is now possible to automate this task, as well as base the estimates of peak performance on measurements rather than vendor specifications. The goal of this session is to demonstrate how the roofline feature of Intel Advisor can be used to balance memory vs. computation related optimization efforts and effectively identify performance bottlenecks. A series of typical optimization techniques: cache blocking, structure refactoring, data alignment, and vectorization illustrated by the kernel cases will be addressed. # Description of the codes ## XGC1 The XGC1 code [3] is a magnetic fusion Particle-In-Cell code that uses an unstructured mesh for its Poisson solver that allows it to accurately resolve the edge plasma of a magnetic fusion device. After recent optimizations to its collision kernel [4], most of the computing time is spent in the electron push (pushe) kernel, where these optimization efforts have been focused. The kernel code scaled well with MPI+OpenMP but had almost no automatic compiler vectorization, in part due to indirect memory addresses and in part due to low trip counts of low-level loops that would be candidates for vectorization. Particle blocking and sorting have been implemented to increase trip counts of low-level loops and improve memory locality, and OpenMP directives have been added to vectorize compute-intensive loops that were identified by Advisor. The optimizations have improved the performance of the pushe kernel 2x on Haswell processors and 1.7x on KNL. The KNL node-for-node performance has been brought to within 30% of a NERSC Cori phase I Haswell node and we expect to bridge this gap by reducing the memory footprint of compute intensive routines to improve cache reuse. ## PICSAR is a Fortran/Python high-performance Particle-In-Cell library targeting at MIC architectures first designed to be coupled with the PIC code WARP for the simulation of laser-matter interaction and particle accelerators. PICSAR also contains a FORTRAN stand-alone kernel for performance studies and benchmarks. A MPI domain decomposition is used between NUMA domains and a tile decomposition (cache-blocking) handled by OpenMP has been added for shared-memory parallelism and better cache management. The so-called current deposition and field gathering steps that compose the PIC time loop constitute major hotspots that have been rewritten to enable more efficient vectorization. Particle communications between tiles and MPI domain has been merged and parallelized. All considered, these improvements provide speedups of 3.1 for order 1 and 4.6 for order 3 interpolation shape factors on KNL configured in SNC4 quadrant flat mode. Performance is similar between a node of cori phase 1 and KNL at order 1 and better on KNL by a factor 1.6 at order 3 with the considered test case (homogeneous thermal plasma).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Investigation of Aerosol Indirect Effects using a Cumulus Microphysics Parameterization in a Regional Climate Model

A new Zhang and McFarlane (ZM) cumulus scheme includes a two-moment cloud microphysics parameterization for convective clouds. This allows aerosol effects to be investigated more comprehensively by linking aerosols with microphysical processes in both stratiform clouds that are explicitly resolved and convective clouds that are parameterized in climate models. This new scheme is implemented in the Weather Research and Forecasting (WRF) model, which is coupled with the physics and aerosol packages from the Community Atmospheric Model version 5 (CAM5). A test case of July 2008 during the East Asian summer monsoon is selected to evaluate the performance of the new ZM scheme and to investigate aerosol effects on monsoon precipitation. The precipitation and radiative fluxes simulated by the new ZM scheme show a better agreement with observations compared to simulations with the original ZM scheme that does not include convective cloud microphysics and aerosol convective cloud interactions. Detailed analysis suggests that an increase in detrained cloud water and ice mass by the new ZM scheme is responsible for this improvement. To investigate precipitation response to increased anthropogenic aerosols, a sensitivity experiment is performed that mimics a clean environment by reducing the primary aerosols and anthropogenic emissions to 30% of that used in the control simulation of a polluted environment. The simulated surface precipitation is reduced by 9.8% from clean to polluted environment and the reduction is less significant when microphysics processes are excluded from the cumulus clouds. Ensemble experiments with ten members under each condition (i.e., clean and polluted) indicate similar response of the monsoon precipitation to increasing aerosols.

aerosol indirect effects↗

DOE OSTI2015

A hybrid pore‐scale and continuum‐scale model for solute diffusion, reaction, and biofilm development in porous media

Abstract It is a challenge to upscale solute transport in porous media for multispecies bio‐kinetic reactions because of incomplete mixing within the elementary volume and because biofilm growth can change porosity and affect pore‐scale flow and diffusion. To address this challenge, we present a hybrid model that couples pore‐scale subdomains to continuum‐scale subdomains. While the pore‐scale subdomains involving significant biofilm growth and reaction are simulated using pore‐scale equations, the other subdomains are simulated using continuum‐scale equations to save computational time. The pore‐scale and continuum‐scale subdomains are coupled using a mortar method to ensure continuity of solute concentration and flux at the interfaces. We present results for a simplified two‐dimensional system, neglect advection, and use dual Monod kinetics for solute utilization and biofilm growth. The results based on the hybrid model are consistent with the results based on a pore‐scale model for three test cases that cover a wide range of Damköhler (Da = reaction rate/diffusion rate) numbers for both homogeneous (spatially periodic) and heterogeneous pore structures. We compare results from the hybrid method with an upscaled continuum model and show that the latter is valid only for cases of small Damköhler numbers, consistent with other results reported in the literature.

Tang, Youneng↗

DOE OSTI2016

Integrated surface/subsurface permafrost thermal hydrology: Model formulation and proof-of-concept simulations

The need to understand potential climate impacts and feedbacks in Arctic regions has prompted recent interest in modeling of permafrost dynamics in a warming climate. A new fine-scale integrated surface/subsurface thermal hydrology modeling capability is described and demonstrated in proof-of-concept simulations. The new modeling capability combines a surface energy balance model with recently developed three-dimensional subsurface thermal hydrology models and new models for nonisothermal surface water flows and snow distribution in the microtopography. Surface water flows are modeled using the diffusion wave equation extended to include energy transport and phase change of ponded water. Variation of snow depth in the microtopography, physically the result of wind scour, is also modeled heuristically with a diffusion wave equation. The multiple surface and subsurface processes are implemented by leveraging highly parallel community software. Fully integrated thermal hydrology simulations on the tilted open book catchment, an important test case for integrated surface/subsurface flow modeling, are presented. Fine-scale 100-year projections of the integrated permafrost thermal hydrological system on an ice wedge polygon at Barrow Alaska in a warming climate are also presented. Finally, these simulations demonstrate the feasibility of microtopography-resolving, process-rich simulations as a tool to help understand possible future evolution of the carbon-rich Arctic tundra in a warming climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Redistribution of ice nuclei between cloud and rain droplets: Parameterization and application to deep convective clouds: ICE NUCLEI IN RAIN DROPLETS

In model studies of aerosol-dependent immersion freezing in clouds, a common assumption is that each ice nucleating aerosol particle corresponds to exactly one cloud droplet. Conversely, the immersion freezing of larger drops—“rain”—is usually represented by a liquid volume-dependent approach, making the parameterizations of rain freezing independent of specific aerosol types and concentrations. This may lead to inconsistencies when aerosol effects on clouds and precipitation shall be investigated, since raindrops consist of the cloud droplets—and corresponding aerosol particles—that have been involved in drop-drop-collisions. We introduce an extension to a two-moment microphysical scheme in order to account explicitly for particle accumulation in raindrops by tracking the rates of selfcollection, autoconversion, and accretion. This also provides a direct link between ice nuclei and the primary formation of large precipitating ice particles. A new parameterization scheme of drop freezing is presented to consider multiple ice nuclei within one drop and effective drop cooling rates. In our test cases of deep convective clouds, we find that at altitudes which are most relevant for immersion freezing, the majority of potential ice nuclei have been converted from cloud droplets into raindrops. Compared to the standard treatment of freezing in our model, the less efficient mineral dust-based freezing results in higher rainwater contents in the convective core, affecting both rain and hail precipitation. The aerosol-dependent treatment of rain freezing can reverse the signs of simulated precipitation sensitivities to ice nuclei perturbations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Toward Intelligent Multimodal Holography for Real-Time Chemical Imaging of Dynamic Ion Separation

Molecular-level visualization of ion transport and separation dynamics in complex environments is crucial for advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms that combine structural sensitivity, chemical specificity, and real-time operation. Digital off-axis holography (DOAH) provides high-throughput, label-free quantitative phase imaging but inherently lacks chemical selectivity. Integrating DOAH with complementary spectroscopic channels such as fluorescence or hyperspectral imaging introduces the needed molecular specificity, while also creating challenges in multimodal data fusion, synchronization, and computational throughput. Artificial intelligence offers a powerful route to address these limitations by uniting physics-based reconstruction with data-driven interpretation. In this Perspective, we outline a framework for intelligent multimodal holography and demonstrate its potential using a preliminary AI-driven test case. Raw DOAH holograms of lanthanide solutions subjected to magnetic field gradients were analyzed using multi-agent AI workflows that autonomously selected reconstruction tools, extracted NMF components, and generated scientific claims consistent with true paramagnetic and diamagnetic behavior. This demonstration shows how AI-enabled reasoning can deliver real-time chemical–structural interpretation directly from raw holograms. Together, these advances define a path toward adaptive, intelligent holography platforms capable of supporting in situ chemical separations, dynamic ion transport analysis, and next-generation interfacial science.

Ricchiuti, Giovanna↗

DOE OSTI2020

Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K

Abstract Porous carbons are an important class of porous materials with many applications, including gas separation. An N 2 adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N 2 adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N 2 isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO 2 /N 2 as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO 2 /N 2 selectivity. Porous carbons with a bimodal pore‐size distribution of well‐separated mesopores (3–7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.

Wang, Song↗

DOE OSTI2020

Prediction by Convolutional Neural Networks of CO 2 /N 2 Selectivity in Porous Carbons from N 2 Adsorption Isotherm at 77 K

Wang, Song↗

DOE OSTI2024

17 O NMR Spectroscopy Reveals CO 2 Speciation and Dynamics in Hydroxide-Based Carbon Capture Materials

Carbon dioxide capture technologies are set to play a vital role in mitigating the current climate crisis. Solid-state 17 O NMR spectroscopy can provide key mechanistic insights that are crucial to effective sorbent development. In this work, we present the fundamental aspects and complexities for the study of hydroxide-based CO 2 capture systems by 17 O NMR. We perform static density functional theory (DFT) NMR calculations to assign peaks for general hydroxide CO 2 capture products, finding that 17 O NMR can readily distinguish bicarbonate, carbonate and water species. However, in application to CO 2 binding in two test case hydroxide-functionalised metal-organic frameworks (MOFs) – MFU-4l and KHCO 3 -cyclodextrin-MOF, we find that a dynamic treatment is necessary to obtain agreement between computational and experimental spectra. We therefore introduce a workflow that leverages machine-learning force fields to capture dynamics across multiple chemical exchange regimes, providing a significant improvement on static DFT predictions. In MFU-4l, we parameterise a two-component dynamic motion of the bicarbonate motif involving a rapid carbonyl seesaw motion and intermediate hydroxyl proton hopping. For KHCO 3 -CD-MOF, we combined experimental and modelling approaches to propose a new mixed carbonate-bicarbonate binding mechanism and thus, we open new avenues for the study and modelling of hydroxide-based CO 2 capture materials by 17 O NMR.

NMR spectroscopy↗

DOE OSTI2022

Examining polymer‐protein biophysical interactions with small‐angle x‐ray scattering and quartz crystal microbalance with dissipation

Abstract Polymer‐protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure–function behavior. Here, we show the value of tandem small‐angle x‐ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer‐protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer‐protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration ( R g ) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer‐protein hybrids.

60 APPLIED LIFE SCIENCES↗