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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Updates and Validation for the n+ 63,65 Cu Cross Sections [Abstract]

The neutron induced total, elastic, and capture cross sections of 63,65 Cu isotopes were selected for evaluation in the resolved and unresolved resonance energy ranges by the National Criticality Safety Program to resolve discrepancies related to benchmark performance. This is especially evident for the series of ZEUS benchmarks in which copper is used as a reflector. Because copper is also used as structural material in both fission and fusion reactors, the need to address benchmark discrepancies linked to nuclear data deficiencies is a task of primary importance. The aim of this work is to describe the steps of evaluation work towards a consistent improvement of the benchmark performance. The R-matrix analysis with the SAMMY code focused on the 63 Cu(n,γ) reaction channel between 100-300 keV coupled to unresolved resonance region parameters up to 650 keV to fit average cross section data from a recent experiment. Due to the high sensitivity of many benchmarks to elastic scattering angular distribution data, especially for the 65 Cu isotope, the impact of these data was tested by generating Legendre coefficients from both resonance parameters and the Hauser-Feshbach model. Guided by the findings of Shaw et al., the performance of the current evaluation for 65 Cu was compared to that of ENDF/B-VII.1 and ENDF/B-VIII.0 by testing the reactivity coefficients corresponding to the validation suite of experimental criticality benchmarks for thermal, intermediate, and fast systems taken from the International Criticality Safety Benchmark Experiments Project Handbook. The benchmark performance is especially sensitive to 63 Cu(n,γ) and 65 Cu elastic scattering for neutron energies in the 100–500 keV region, whereas 100 keV is the upper limit of the resolved resonance region in the ENDF/B-VIII.0 evaluations for 63,65 Cu. The results highlight the need to handle the transition from the resolved resonance region to the high energy region carefully.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Exploring switch II pocket conformation of KRAS(G12D) with mutant-selective monobody inhibitors

The G12D mutation is among the most common KRAS mutations associated with cancer, in particular, pancreatic cancer. Here, we have developed monobodies, small synthetic binding proteins, that are selective to KRAS(G12D) over KRAS(wild type) and other oncogenic KRAS mutations, as well as over the G12D mutation in HRAS and NRAS. Crystallographic studies revealed that, similar to other KRAS mutant-selective inhibitors, the initial monobody bound to the S-II pocket, the groove between switch II and α3 helix, and captured this pocket in the most widely open form reported to date. Unlike other G12D-selective polypeptides reported to date, the monobody used its backbone NH group to directly recognize the side chain of KRAS Asp12, a feature that closely resembles that of a small-molecule inhibitor, MTRX1133. The monobody also directly interacted with H95, a residue not conserved in RAS isoforms. These features rationalize the high selectivity toward the G12D mutant and the KRAS isoform. Structure-guided affinity maturation resulted in monobodies with low nM KD values. Deep mutational scanning of a monobody generated hundreds of functional and nonfunctional single-point mutants, which identified crucial residues for binding and those that contributed to the selectivity toward the GTP- and GDP-bound states. When expressed in cells as genetically encoded reagents, these monobodies engaged selectively with KRAS(G12D) and inhibited KRAS(G12D)-mediated signaling and tumorigenesis. These results further illustrate the plasticity of the S-II pocket, which may be exploited for the design of next-generation KRAS(G12D)-selective inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Measurements of Pion and Muon Nuclear Capture at Rest on Argon in the LArIAT Test Beam Experiment

LArIAT is a liquid argon time projection chamber (LArTPC) experiment in a test beam at Fermilab from 2015 to 2017 to understand and characterize interactions of particles in LAr which are commonly observed in neutrino-Ar final-states. Tracks for pions and muons in LArTPCs are difficult to differentiate since both particles exhibit very similar ionization profiles for muon and pion that stop in the TPC. We are exploring unique new particle discrimination capabilities by exploiting information from small, isolated ionization depositions, referred to as "blips", reconstructed near the endpoint of stopping tracks. These blips are formed by gammas emitted when an at-rest pion or muon captures on the argon nucleus. The relatively low beam energy provided by LArIAT makes it uniquely suited for performing this demonstration. In this poster, we present an overview of event candidate selection, blip reconstruction, and background subtraction corresponding to our signal of interest, nuclear captures of pions and muons at rest inside LArIAT's TPC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: A focus on cover crops

Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical and biotic properties of soil. However, cover crops, a group of plants that provide ecosystem services, can be utilised during fallow periods or used as an intercrop to restore soil health. The effectiveness of ecosystem services provided by cover crops varies widely as very little breeding has occurred in these species. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. In this study, we provide an overview of the range of belowground ecosystem services provided by cover crop roots: (1) soil structural remediation, (2) capture of soil resources and (3) maintenance of the rhizosphere and building of organic matter content. Based on the ecosystem services described, we outline current and promising phenotyping technologies and breeding strategies in cover crops that can enhance agricultural sustainability through improvement of root traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Discovering useful genetic variation in the seed parent gene pool for sorghum improvement

Multi-parent populations contain valuable genetic material for dissecting complex, quantitative traits and provide a unique opportunity to capture multi-allelic variation compared to the biparental populations. A multi-parent advanced generation inter-cross (MAGIC) B-line (MBL) population composed of 708 F 6 recombinant inbred lines (RILs), was recently developed from four diverse founders. These selected founders strategically represented the four most prevalent botanical races (kafir, guinea, durra, and caudatum) to capture a significant source of genetic variation to study the quantitative traits in grain sorghum [Sorghum bicolor (L.) Moench]. MBL was phenotyped at two field locations for seven yield-influencing traits: panicle type (PT), days to anthesis (DTA), plant height (PH), grain yield (GY), 1000-grain weight (TGW), tiller number per meter (TN) and yield per panicle (YPP). High phenotypic variation was observed for all the quantitative traits, with broad-sense heritabilities ranging from 0.34 (TN) to 0.84 (PH). The entire population was genotyped using Diversity Arrays Technology (DArTseq), and 8,800 single nucleotide polymorphisms (SNPs) were generated. A set of polymorphic, quality-filtered markers (3,751 SNPs) and phenotypic data were used for genome-wide association studies (GWAS). We identified 52 marker-trait associations (MTAs) for the seven traits using BLUPs generated from replicated plots in two locations. We also identified desirable allelic combinations based on the plant height loci (Dw1, Dw2, and Dw3), which influences yield related traits. Additionally, two novel MTAs were identified each on Chr1 and Chr7 for yield traits independent of dwarfing genes. We further performed a multi-variate adaptive shrinkage analysis and 15 MTAs with pleiotropic effect were identified. The five best performing MBL progenies were selected carrying desirable allelic combinations. Since the MBL population was designed to capture significant diversity for maintainer line (B-line) accessions, these progenies can serve as valuable resources to develop superior sorghum hybrids after validation of their general combining abilities via crossing with elite pollinators. Further, newly identified desirable allelic combinations can be used to enrich the maintainer germplasm lines through marker-assisted backcross breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Time-Evolving Chirality Loss in Molecular Photodissociation Monitored by X-ray Circular Dichroism Spectroscopy

In this work, the ultrafast photoinduced chirality loss of 2-iodobutane is studied theoretically by time- and frequency-resolved X-ray circular dichroism (TRXCD) spectroscopy. Following an optical excitation, the iodine atom dissociates from the chiral center, which we capture by quantum non-adiabatic molecular dynamics simulations. At variable time delays after the pump, the resonant X-ray pulse selectively probes the iodine and carbon atom involved in the chiral dissociation through a selected core-to-valence transition. The TRXCD signal at the iodine L 1 edge accurately captures the timing of C–I photodissociation and thereby chirality loss, c.a 70 fs. The strong electric dipole–electric quadrupole (ED–EQ) response makes this signal particularly sensitive to vibronic coherence at the high X-ray regime. At the carbon K-edges, the signals monitor the molecular chirality of the 2-butyl radical photoproduct and the spin state of the iodine atom. The ED–EQ response is masked under the strong electric dipole-magnetic dipole response, making this signal intuitive for the electronic population. The evolution of the core electronic states and its chiral sensitivity is discussed. Overall, the element-specific TRXCD signal provides a detailed picture of molecular dynamics and offers a unique sensitive window into the time-dependent chirality of molecules.

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

Microcanonical Kinetics of Water-Mediated Proton Transfer in 4ABAH + ·(H 2 O) n = 4–6 Clusters (ABA = Aminobenzoic Acid): A Model System for Size-Dependent Relaxation to Ergodic Behavior

Here, we leverage the unique properties of the 4ABAH + · (H 2 O) n clusters (ABA = 4-aminobenzoic acid, n = 4−6) to quantitatively address how a finite, isolated system evolves into an ergodic condition starting from localized arrangements in configuration space. This system adopts two distinct structural isomers in which water molecules cluster around the cationic centers of its two protomers with widely separated positive charge centers. These isomers arise from excess proton attachment to either the acid (O) or amino (N) group on opposite sides of the benzene ring. Both forms are captured and kinetically trapped using cryogenic ion methods and then selectively vibrationally excited through their mutually exclusive IR bands involving NH and OH stretching fundamentals. Because the IR excitation lies below the water binding energy, the system can evolve to explore slow, rare events that lead to the interconversion between the two isomers. The rates of these intracluster reactions are determined by using a pump−probe scheme involving ∼5 ns IR pump and UV probe lasers. The rates occur on the microsecond time scale, leading to steady state populations of the isomers, thus revealing the cluster size-dependent fractionation between the two species at microcanonical equilibrium. The steady state distributions are correlated with the expected trend in the cluster size-dependent reaction energetics, which are in turn consistent with changes in the relative densities of states of the two species. These results thus provide an unusually clear example in which complex, protic-solvent-mediated chemical transformations are captured within a finite system at a precisely determined internal energy.

Rana, Abhijit [Yale Univ., New Haven, CT (United S↗
DOE OSTI2024

Fine-tuning catalytic selectivity by modulating catalyst-environment interactions: CO 2 hydrogenation over Pd-based catalysts

Capturing catalytic behaviors under operational conditions is pivotal to gaining a mechanistic understanding and promoting the design of robust catalysts. The challenge lies in the difficulty of monitoring real-time surface dynamics driven by catalyst-environment interactions. Here, in this work, we introduce a framework based on density functional calculations and kinetic modeling. This framework significantly improves the accuracy of theoretical models’ descriptions of experimental observations by quantifying environmental impacts on surface phases and active sites. CO 2 hydrogenation over Pd-based catalysts is taken as a showcase. The observed selectivity variations of Pd and Pd-M bimetallic catalysts strongly correlate with hydrogen coverage maintained under typical CO 2 hydrogenation conditions. By reducing the amount of surface hydrogen, the selectivity tuned effectively from formic acid toward CO and methanol. This study not only deepens the comprehension of dynamics of active sites under active chemical conditions but also introduces an alternative opportunity for catalytic tuning by modulating catalyst-environment interactions.

03 NATURAL GAS↗
DOE OSTI2024

Enhanced CO 2 Adsorption Capacity in Highly Porous Carbon Materials Derived from Melamine-Formaldehyde Resin

Here, the present study explores the synthesis of N-doped carbon materials with large surface porosity using commercial melamine-formaldehyde resin as the precursor and KOH as the activator. The resin was carbonized first and then activated by KOH with varying KOH amount and activation temperature. Notably, the as-obtained sorbents display advanced porosity with the highest surface area and pore volume of 1591 m 2 /g and 0.74 cm 3 /g, respectively, along with high N content ranging from 6.43 to 18.34 wt %. Remarkably, maximum CO 2 capture amounts of 5.42 and 3.52 mmol/g were accomplished at 0 and 25 degrees C, 1 bar for as-synthesized carbons. Systematic studies point out that narrow microporosity is the major factor determining the CO 2 uptake of these carbons under ambient pressure. Furthermore, these sorbents display notable CO 2 selectivity, rapid adsorption kinetics, moderate heat of adsorption, substantial dynamic CO 2 capture capacity, and stable recyclability. These results underscore the potential of melamine-formaldehyde resin-derived N-doped porous carbon as an efficient and versatile adsorbent for CO 2 capture.

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

Bioinspired and biomolecular catalysts for energy conversion and storage

Metalloenzymes are remarkable for facilitating challenging redox transformations with high efficiency and selectivity. In the area of alternative energy, scientists aim to capture these properties in bioinspired and engineered biomolecular catalysts for the efficient and fast production of fuels from low-energy feedstocks such as water and carbon dioxide. In this short review, efforts to mimic biological catalysts for proton reduction and carbon dioxide reduction are highlighted. Two important recurring themes are the importance of the microenvironment of the catalyst active site and the key role of proton delivery to the active site in achieving desired reactivity. As a result, perspectives on ongoing and future challenges are also provided.

14 SOLAR ENERGY↗
DOE OSTI2024

Scale effects on core design, fuel costs, and spent fuel volume of pressurized water reactors

The desire to improve the economic competitiveness and deployment pace of nuclear energy through modularization, manufacturing, and series production had led to the development of smaller size reactors. As the standard 17x17 fuel technology is mainly maintained in the pressurized water reactors (PWRs) category, this translates into a lower number of fuel assemblies in the core and sometimes a reduced fuel height. To assess the impact of such scale change in core design on fuel cycle cost and spent fuel volume, a scoping analysis tool is developed based on infinite lattice calculations, leakage, fuel management reduced models, and levelized unit cost of electricity (LCOE) estimate. As such, cost dynamics driven by fuel specific power, burnup, core leakage, feed, cycle length, fuel assembly height as well as uranium market data are captured with consistent set of assumptions and analysis methods. A selection of 5 reactor designs representative of leading PWR developers is assessed and compared. Pursuing higher specific powers and optimal burnups are highlighted as the main fuel cost reduction drivers, nevertheless, practical limitations and opportunities must be evaluated to establish the feasibility of such enhanced fuel operation. In consequence, a detailed core design is performed using SIMULATE3 code for 5 PWR variations including natural and forced coolant circulation modes, two reactor scales, power densities of 73, 112, and 123 kW/l and higher discharge burnups. Design and optimization are performed at the lattice level, for the reflector, and at the core loading level. Satisfactory steady-state operation including power distribution, coolant operating limits, and reactivity requirements are analyzed and reported in this paper. The fuel economics of the detailed core designs confirm the scoping analysis findings. Despite the unlocked power uprates in small PWRs, the achievable burnup for a given fuel specific power requires more enrichment and shorter fuel height results in higher fabrication costs per mass of fuel, which makes scaling down core size a more expensive endeavor on the fuel cycle front. Spent fuel volumes are reported for the PWRs designed in this paper. Furthermore, these volumes are driven by the core average discharge burnup regardless of the scale in consideration. Additional cost and core performance aspects related to heavy reflector gains, fuel-reflector substitution, and disposal cost policy in the U.S. are examined.

42 ENGINEERING↗
DOE OSTI2022

Design considerations for high entropy alloys in advanced nuclear applications

The demanding operating environments of advanced nuclear reactors require the development of new nuclear materials that can withstand their increased physical, chemical, thermal, and radiation-related challenges. High-entropy alloys (HEAs) have shown often-impressive mechanical, thermomechanical, and corrosion-resistant properties, and offer a massive, unexplored compositional space which allows for the targeted development of application-specific materials. Furthermore, although still in a nascent stage, research has shown that HEAs may exhibit unique irradiation tolerance, including reduced defect production and resistance to irradiation-induced swelling and hardening. The mechanisms behind this increased tolerance are not yet well-understood, although the HEA-specific attributes of a complex energy landscape, reduced thermal conductivity, and shift in defect migration energies and pathways provide promising explanations. Here this work assesses the current and future challenges faced by structural nuclear materials, identifying the specific applications in which HEAs may provide a competitive advantage compared to industry-standard materials with the aid of Ashby material selection maps. Considerations are provided for the design of future nuclear HEAs, including calculations of nuclear-relevant properties to assist in the initial down-selection of elements depending on application requirements (e.g., low neutron capture for in-core applications), narrowing the existing compositional space of HEAs to a manageable scope.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of Multiplexed Immuno-N-Terminomics to Reveal the Landscape of Proteolytic Processing in Early Embryogenesis of Drosophila melanogaster

Protein expression levels are regulated through both translation and degradation mechanisms. Levels of degradation intermediates, i.e., partially degraded proteins, cannot be distinguished from those of intact proteins by global proteomics analysis, which quantify total protein abundance levels. This study aimed to develop a tool for assessing the aspects of degradation regulation via proteolytic processing through a new multiplexed N-terminomics method involving selective isobaric labeling of protein N-termini and immunoaffinity capture of the labeled N-terminal peptides. Our method allows for not only identification or proteolytic cleavage sites, but also highly multiplexed quantification of proteolytic processing. In this paper, we profiled a number of potential cleavage sites by signal peptidase and provided experimental confirmation of predicted cleavage sites of signal peptide. Furthermore, the present method uniquely represents the landscape of proteomic proteolytic processing rate during early embryogenesis in Drosophila melanogaster, revealing the underlying mechanism of stringent decay regulation of zygotically expressed proteins during early stages of embryogenesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Understanding How Ligand Functionalization Influences CO2 and N2 Adsorption in a Sodalite Metal–Organic Framework

In this work, a detailed study is conducted to understand how ligand substitution influences the CO2 and N2 adsorption properties of two highly crystalline sodalite metal–organic frameworks (MOFs) known as Cu–BTT (BTT–3 = 1,3,5-benzenetristetrazolate) and Cu–BTTri (BTTri–3 = 1,3,5-benzenetristriazolate). The enthalpy of adsorption and observed adsorption capacities at a given pressure are significantly lower for Cu–BTTri compared to its tetrazole counterpart, Cu–BTT. In situ X-ray and neutron diffraction, which allow visualization of the CO2 and N2 binding sites on the internal surface of Cu–BTTri, provide insights into understanding the subtle differences. As expected, slightly elongated distances between the open Cu2+ sites and surface-bound CO2 in Cu–BTTri can be explained by the fact that the triazolate ligand is a better electron donor than the tetrazolate. The more pronounced Jahn–Teller effect in Cu–BTTri leads to weaker guest binding. The results of the aforementioned structural analysis were complemented by the prediction of the binding energies at each CO2 and N2 adsorption site by density functional theory calculations. In addition, variable temperature in situ diffraction measurements shed light on the fine structural changes of the framework and CO2 occupancies at different adsorption sites as a function of temperature. Finally, simulated breakthrough curves obtained for both sodalite MOFs demonstrate the materials’ potential performance in dry postcombustion CO2 capture. The simulation, which considers both framework uptake capacity and selectivity, predicts better separation performance for Cu–BTT. The information obtained in this work highlights how ligand substitution can influence adsorption properties and hence provides further insights into the material optimization for important separations.

adsorption capacities↗
DOE OSTI2024

Enhancing carbon dioxide capture under humid conditions by optimizing the pore surface structure

Metal–organic frameworks (MOFs) exhibit significant potential for mitigating carbon emissions due to their high porosity and tunability. Despite numerous reports on CO 2 capture by MOF sorbents, a common challenge is their poor selectivity for CO 2 over water. Moreover, in-depth studies are much needed to elucidate the relationships among the pore surface structure, hydrophobicity, and CO 2 uptake capacity/selectivity. In this work, we investigate the factors influencing CO 2 adsorption capacity and selectivity under humidity in a series of isoreticular pillar-layer structures, Ni 2 (L) 2 (dabco) (L = bdc, ndc, adc). Our study shows that increasing ligand conjugation not only results in increased hydrophobicity, decreased pore size and BET surface area, but also leads to the change of primary binding sites of water molecules and higher binding energy of CO 2 , all of which contribute to largely increased CO 2 uptake capacity under humid conditions. Additionally, increasing ligand conjugation and consequently hydrophobicity slow down and reduce competitive water adsorption drastically. Notably, the MOF made of ligand with the highest conjugation, Ni 2 (adc) 2 (dabco), exhibits significantly enhanced CO 2 adsorption in N 2 /CO 2 binary mixtures under relatively high humidity (50% RH), with an increase of ~31% and ~36% for the composition of 15/85 and 50/50, respectively, compared to dry conditions. An experimental FTIR study and DFT theoretical calculations confirm that H 2 O occupies different primary binding site in Ni 2 (bdc) 2 (dabco) and Ni 2 (adc) 2 (dabco), and under humid conditions a higher binding energy of CO 2 is achieved with preferential H 2 O/CO 2 co-adsorption in Ni 2 (adc) 2 (dabco), potentially creating additional adsorption sites for CO 2 .

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

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗