Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Selective capture”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integrating Data From In Vitro New Approach Methodologies for Developmental Neurotoxicity

Abstract In vivo developmental neurotoxicity (DNT) testing is resource intensive and lacks information on cellular processes affected by chemicals. To address this, DNT new approach methodologies (NAMs) are being evaluated, including: the microelectrode array neuronal network formation assay; and high-content imaging to evaluate proliferation, apoptosis, neurite outgrowth, and synaptogenesis. This work addresses 3 hypotheses: (1) a broad screening battery provides a sensitive marker of DNT bioactivity; (2) selective bioactivity (occurring at noncytotoxic concentrations) may indicate functional processes disrupted; and, (3) a subset of endpoints may optimally classify chemicals with in vivo evidence for DNT. The dataset was comprised of 92 chemicals screened in all 57 assay endpoints sourced from publicly available data, including a set of DNT NAM evaluation chemicals with putative positives (53) and negatives (13). The DNT NAM battery provides a sensitive marker of DNT bioactivity, particularly in cytotoxicity and network connectivity parameters. Hierarchical clustering suggested potency (including cytotoxicity) was important for classifying positive chemicals with high sensitivity (93%) but failed to distinguish patterns of disrupted functional processes. In contrast, clustering of selective values revealed informative patterns of differential activity but demonstrated lower sensitivity (74%). The false negatives were associated with several limitations, such as the maximal concentration tested or gaps in the biology captured by the current battery. This work demonstrates that this multi-dimensional assay suite provides a sensitive biomarker for DNT bioactivity, with selective activity providing possible insight into specific functional processes affected by chemical exposure and a basis for further research.

Carstens, Kelly E.↗
DOE OSTI2021

Solving the sample size problem for resource selection functions

Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.

Street, Garrett M.↗
DOE OSTI2024

Metal Organic Frameworks for Noble Gas Isotope Harvesting at FRIB (Final Technical Report)

This project was a collaborative effort between Lawrence Livermore National Laboratory (LLNL) and Michigan State University (MSU) to investigate the use of promising metal organic frameworks (MOFs) for radioactive noble gas capture, with a focus on harvesting exotic radiokryptons from FRIB. After screening several candidate materials, two MOFs were selected for testing: SIFSIX-3Cu and SBMOF-1. Further evaluation showed that although SIFSIX-3Cu has a high selectivity for Kr, SBMOF-1 is less sensitive to the humidity that is present in the FRIB harvesting system and is more readily integrated into the harvesting infrastructure. SBMOF-1 was then evaluated for temperature-dependent Kr and Xe uptake in order to determine the sorption enthalpy. The SBMOF-1 data led to the design of a noble gas capture system that will be fabricated and put into service for isotope harvesting at FRIB as part of a separate project.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate

Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.

Accelerated aging↗
DOE OSTI2022

Advances in systems metabolic engineering of autotrophic carbon oxide-fixing biocatalysts towards a circular economy

High levels of anthropogenic CO 2 emissions are driving the warming of global climate. If this pattern of increasing emissions does not change, it will cause further climate change with severe consequences for the human population. On top of this, the increasing accumulation of solid waste within the linear economy model is threatening global biosustainability. The magnitude of these challenges requires several approaches to capture and utilize waste carbon and establish a circular economy. Microbial gas fermentation presents an exciting opportunity to capture carbon oxides from gaseous and solid waste streams with high feedstock flexibility and selectivity. Here we discuss available microbial systems and review in detail the metabolism of both anaerobic acetogens and aerobic hydrogenotrophs and their ability to utilize C1 waste feedstocks. More specifically, we provide an overview of the systems-level understanding of metabolism, key metabolic pathways, scale-up opportunities and commercial successes, and the most recent technological advances in strain and process engineering. Finally, we also discuss in detail the gaps and opportunities to advance the understanding of these autotrophic biocatalysts for the efficient and economically viable production of bioproducts from recycled carbon.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Quantifying nitrogen loss hotspots and mitigation potential for individual fields in the US Corn Belt with a metamodeling approach

The high productivity in the US Corn Belt is largely enabled by the consumption of millions of tons of manufactured fertilizer. Excessive application of nitrogen (N) fertilizer has been pervasive in this region, and the unrecovered N eventually escaped from croplands in forms of nitrous oxide (N 2 O) emission and N leaching. Mitigating these negative impacts is hindered by a lack of practical information on where to focus and how much mitigation potential to expect. At a large scale, process-based crop models are the primary tools for predicting variables required by decision making, but their applications are prohibited by expensive computational and data storage costs. To overcome these challenges, we built a series of metamodels to learn the key mechanisms regarding the carbon (C) and N cycle from a well-validated process-based biogeochemical model, ecosys. The trained metamodel captures over 98% of the variability of the ecosys simulated outputs for 99 randomly selected counties in Iowa, Illinois, and Indiana. To identify hotspots with high mitigation potential, we introduce net societal benefit (NSB) as an indicator for synthesizing the loss in yield and social benefits through emissions and pollutants avoided. Our results show that reducing N fertilizer by 10% leads to 9.8% less N 2 O emissions and 9.6% less N leaching at the cost of 4.9% more SOC depletion and 0.6% yield reduction over the study region. The estimated total annual NSB is $\$395$ M (uncertainty ranges from $\$114$ M to $\$1271$ M), including $\$334$ from social benefits (uncertainty ranges from $\$46$ M to $\$1076$ M), $\$100$ M from saving fertilizer (uncertainty ranges from $\$13$ M to $\$455$ M), and –$\$40$ M due to yield changes (uncertainty ranges from –$\$261$ M to $\$69$ M). For the median scenario, we noted that 20% of the study area accounts for nearly 50% of the NSB, and thus represent hotspot locations for targeted mitigation. Although the uncertainty range suggests that developing such a high-resolution framework is not yet settled and the scenario based estimations are not appropriate to inform the management practices for individual farmers, our efforts shed light on the new generation of analytical tools for life cycle assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Universal Spreading of Conditional Mutual Information in Noisy Random Circuits

For this work, we study the evolution of conditional mutual information (CMI) in generic open quantum systems, focusing on one-dimensional random circuits with interspersed local noise. Unlike in noiseless circuits, where CMI spreads linearly while being bounded by the light cone, we find that noisy random circuits with an error rate 𝑝 exhibit superlinear propagation of CMI, which diverges far beyond the light cone at a critical circuit depth 𝑡 𝑐 ∝ 𝑝 −1 . We demonstrate that the underlying mechanism for such rapid spreading is the combined effect of local noise and a scrambling unitary, which selectively removes short-range correlations while preserving long-range correlations. To analytically capture the dynamics of CMI in noisy random circuits, we introduce a coarse-graining method, and we validate our theoretical results through numerical simulations. Furthermore, we identify a universal scaling law governing the spreading of CMI.

decoherence↗
DOE OSTI2025

Performance of wave function and Green's function methods for non-equilibrium many-body dynamics

Theoretical descriptions of the non-equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on the truncation of the expansion of the many-body wave function, (ii) compressed representations of the many-body wave function, or (iii) evolution of an effective (downfolded) representation through Green's functions. In this work, we select representative cases of each of the methods and address how these complementary approaches capture the dynamics driven by intense field perturbations to non-equilibrium states. Under strong driving, the systems are characterized by strong entanglement of the single-particle density matrix and natural populations approaching those of a strongly interacting equilibrium system. We generate a representative set of results that are numerically exact and form a basis for a critical comparison of the distinct families of methods. We demonstrate that the compressed formulation based on similarity-transformed Hamiltonians (coupled-cluster approach) is practically exact in weak fields and, hence, weakly or moderately correlated systems. Coupled cluster, however, struggles for strong driving fields, under which the system exhibits strongly correlated behavior, as measured by the von Neumann entropy of the single-particle density matrix. The dynamics predicted by Green's functions in the (widely popular) G W approximation are less accurate, but improve significantly upon the mean-field results in the strongly driven regime. Published by the American Physical Society 2025

Reeves, Cian C. (ORCID:0009000642581845)↗
DOE OSTI2026

DIVA/DeviceEditor v6.1.2

DIVA is an end-to-end DNA design and construction management platform that streamlines how researchers design, build, and receive sequence-verified DNA constructs. Through a web-based BioCAD interface (DeviceEditor), researchers independently design DNA constructs and submit them to a centralized queue with a single action. Designs progress transparently through standardized states which allow researchers to track status and access finished constructs via a central DNA repository. Submitted designs are reviewed by dedicated staff for feasibility and optimization, reducing costly failures and improving downstream execution. Automated DNA assembly software optimizes construction strategies by reusing existing parts where possible and sourcing synthetic DNA only when needed. Standardized, sequence-agnostic assembly methods enable many independent constructs to be built in parallel using lab automation, dramatically increasing throughput. High-throughput next-generation sequencing is used to verify construct accuracy, with flexible platforms selected based on task requirements. Throughout the process, detailed success and failure data are captured and analyzed, enabling continuous improvement of assembly protocols. Compared to traditional, manual DNA construction workflows, DIVA offers higher scalability, transparency, reproducibility, and data-driven optimization.

Plahar, Hector [Lawrence Berkeley National Laborat↗
DOE OSTI2026

Machine Tool Data Analytics for Digital Twin and Machine Predictive Maintenance

The primary objective of this project is to improve machining process performance using in-process machining data from the machine tool controller and external sensors. Advances in the Industrial Internet of Things (IIoT) enable monitoring of machines using controller data. Examples of the data provided by a controller include execution status of the controller, part count, block of code being executed, door status, tool position, the spindle and axis load, etc. MTConnect and OPC-UA are the two common protocols for capturing machine information. In this collaboration, methods for retrieving the machine controller data from selected machine tool controls and making these data accessible in different subsystems (such as digital twins and machine maintenance portals, etc.) will be developed and tested. In addition, analytics to improve machining process performance (by increasing productivity and reducing downtime) will be developed.

42 ENGINEERING↗
DOE OSTI2022

Biocontainment of Genetically Engineered Algae

Algae (including eukaryotic microalgae and cyanobacteria) have been genetically engineered to convert light and carbon dioxide to many industrially and commercially relevant chemicals including biofuels, materials, and nutritional products. At industrial scale, genetically engineered algae may be cultivated outdoors in open ponds or in closed photobioreactors. In either case, industry would need to address a potential risk of the release of the engineered algae into the natural environment, resulting in potential negative impacts to the environment. Genetic biocontainment strategies are therefore under development to reduce the probability that these engineered bacteria can survive outside of the laboratory or industrial setting. These include active strategies that aim to kill the escaped cells by expression of toxic proteins, and passive strategies that use knockouts of native genes to reduce fitness outside of the controlled environment of labs and industrial cultivation systems. Several biocontainment strategies have demonstrated escape frequencies below detection limits. However, they have typically done so in carefully controlled experiments which may fail to capture mechanisms of escape that may arise in the more complex natural environment. The selection of biocontainment strategies that can effectively kill cells outside the lab, while maintaining maximum productivity inside the lab and without the need for relatively expensive chemicals will benefit from further attention.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Role of Intermolecular Interactions in Deep Eutectic Solvents for CO 2 Capture: Vibrational Spectroscopy and Quantum Chemical Studies

Recent research and reviews on CO 2 capture methods, along with advancements in industry, have highlighted high costs and energy-intensive nature as the primary limitations of conventional direct air capture and storage (DACS) methods. In response to these challenges, deep eutectic solvents (DESs) have emerged as promising absorbents due to their scalability, selectivity, and lower environmental impact compared to other absorbents. However, the molecular origins of their enhanced thermal stability and selectivity for DAC applications have not been explored before. Therefore, the current study focuses on a comprehensive investigation into the molecular interactions within an alkaline DES composed of potassium hydroxide (KOH) and ethylene glycol (EG). Combining Fourier transform infrared (FT-IR) and quantum chemical calculations, the study reports structural changes and intermolecular interactions induced in EG upon addition of KOH and its implications on CO 2 capture. Experimental and computational spectroscopic studies confirm the presence of noncovalent interactions (hydrogen bonds) within both EG and the KOH-EG system and point to the aggregation of ions at higher KOH concentrations. Additionally, molecular electrostatic potential (MESP) surface analysis, natural bond orbital (NBO) analysis, quantum theory of atoms-in-molecules (QTAIM) analysis, and reduced density gradient-noncovalent interaction (RDG-NCI) plot analysis elucidate changes in polarizability, charge distribution, hydrogen bond types, noncovalent interactions, and interaction strengths, respectively. Evaluation of explicit and hybrid models assesses their effectiveness in representing intermolecular interactions. This research enhances our understanding of molecular interactions in the KOH-EG system, which are essential for both the absorption and desorption of CO 2 . The study also aids in predicting and selecting DES components, optimizing their ratios with salts, and fine-tuning the properties of similar solvents and salts for enhanced CO 2 capture efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Reactive Capture and Conversion of Carbon Dioxide to Methanol with ZnZrO 2 and Alkali-Promoted Mg 3 AlO x Mixed Oxide Catalytic Sorbents

Reactive capture and conversion (RCC) explores the use of a single-unit process to capture CO 2 and produce a product, in this case, methanol (MeOH). In this study, different configurations of a catalytic sorbent (CS) composed of ZnZrO 2 catalyst and Mg 3 AlO x sorbent with and without alkali modification are evaluated for CO 2 adsorption, steady-state catalysis with cofed CO 2 and H 2 , and transient RCC performance. A catalyst composed of a physical mixture of Mg 3 AlO x with ZnZrO 2 resulted in a slight increase in CO 2 uptake, with a low impact on the catalytic activity and RCC of the materials compared to ZnZrO 2 alone. In contrast, Na impregnation significantly increased the level of CO 2 uptake from 0.28 mmol/g (ZnZrO 2 alone) to 0.6 and 1.1 mmol/g for the CS with Na on the catalyst or Mg 3 AlO x , respectively. However, Na impregnation reduced the CO 2 conversion rate and MeOH selectivity during steady-state cofeed experiments at 300 °C and 6 bar. In contrast to steady-state catalysis conditions, RCC, which is a cyclic capture and conversion process, creates dynamic CO 2 and H 2 surface coverages, favoring CH 4 in the early stages of the conversion step and then CO and MeOH as the catalyst CO 2 coverage reduces. The highest MeOH productivity during RCC was achieved with CS that balanced the CO 2 uptake with only moderate catalyst rate reductions caused by Na addition. The optimal material, ZnZrO 2 +10%Na/Mg 3 AlO x , achieved a CO 2 uptake of 0.8 mmol/g and a MeOH productivity of 0.5 mmol/g with 100% selectivity at 260 °C and 6 bar during RCC. This marks the highest RCC MeOH productivity reported to date, although the process needs further optimization and even with optimization, may remain impractical. The results further demonstrate that optimization of catalytic sorbents under steady-state flow conditions does not easily correlate to transient capture and conversion cycles for methanol synthesis from CO 2 .

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

Metal–Ligand Cooperativity via Exchange Coupling Promotes Iron- Catalyzed Electrochemical CO 2 Reduction at Low Overpotentials

Biological and heterogeneous catalysts for the electrochemical CO 2 reduction reaction (CO 2 RR) often exhibit a high degree of electronic delocalization that serves to minimize overpotential and maximize selectivity over the hydrogen evolution reaction (HER). Here, we report a molecular iron(II) system that captures this design concept in a homogeneous setting through the use of a redox non-innocent terpyridine-based pentapyridine ligand (tpyPY2Me). As a result of strong metal-ligand exchange coupling between the Fe(II) center and ligand, [Fe(tpyPY2Me)] 2+ exhibits redox behavior at potentials 640 mV more positive than the isostructural [Zn(tpyPY2Me)] 2+ analog containing the redox-inactive Zn(II) ion. This shift in redox potential is attributed to the requirement for both an open-shell metal ion and a redox non-innocent ligand. The metal-ligand cooperativity in [Fe(tpyPY2Me)] 2+ drives the electrochemical reduction of CO 2 to CO at low overpotentials with high selectivity for CO 2 RR (>90%) and turnover frequencies of 100,000 s -1 with no degradation over 20 h. The decrease in the thermodynamic barrier engendered by this coupling also enables homogeneous CO 2 reduction catalysis in water without compromising selectivity or rates. Synthesis of the two-electron reduction product, [Fe(tpyPY2Me)] o , and characterization by X-ray crystallography, Mössbauer spectroscopy, X-ray absorption spectroscopy (XAS), variable temperature NMR, and density functional theory (DFT) calculations, support assignment of an open-shell singlet electronic structure that maintains a formal Fe(II) oxidation state with a doubly reduced ligand system. Furthermore, this work provides a starting point for the design of systems that exploit metal-ligand cooperativity for electrocatalysis where the electrochemical potential of redox non-innocent ligands can be tuned through secondary metal-dependent interactions.

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

In Vivo Molecular Insights into Syntrophic Geobacter Aggregates

Direct interspecies electron transfer (DIET) has been considered as a novel and highly efficient strategy in both natural anaerobic environments and artificial microbial fuel cells. A syntrophic model consisting of Geobacter metallireducens and Geobacter sulfurreducens was studied in this work. We conducted in vivo molecular mapping of the outer surface of the syntrophic community as the interface of nutrients and energy exchange. System for Analysis at the Liquid Vacuum Interface combined with time-of-flight secondary ion mass spectrometry was employed to capture the molecular distribution of syntrophic Geobacter communities in the living and hydrated state. Principal component analysis with selected peaks revealed that syntrophic Geobacter aggregates were well differentiated from other control samples, including syntrophic planktonic cells, pure cultured planktonic cells, and single population biofilms. Our in vivo imaging indicated that a unique molecular surface was formed. Specifically, aromatic amino acids, phosphatidylethanolamine components, and large water clusters were identified as key components that favored the DIET of syntrophic Geobacter aggregates. Moreover, the molecular changes in depths of the Geobacter aggregates were captured using dynamic depth profiling. Our findings shed new light on the interface components supporting electron transfer in syntrophic communities based on in vivo molecular imaging.

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

Mechanistic Insights into Dinitrogen Reduction to Ammonia in Light-Controlled Nanocrystal:Nitrogenase Complexes

Developing systems that can efficiently capture photon energy and convert this energy into fuels and chemicals requires understanding how to assemble molecular components with diverse functions into complete systems possessing selectivity and efficiency in directing charge carriers to catalytic reactions. There are many challenges to achieving this goal. One promising approach is the development of hybrid systems that combine semiconductor nanocrystals (NCs) for light capture and enzymes as efficient catalysts. Such biohybrid systems capitalize on the tunable electronic and optical properties of NCs while leveraging the unmatched specificity and efficiency of enzymes in catalyzing chemical reactions, thereby offering opportunities to surpass the limitations of each component alone. Here, we focus on recent progress in developing a biohybrid system that combines CdS NCs for photon capture with the enzyme nitrogenase to accomplish light-driven dinitrogen (N 2 ) reduction to ammonia (NH 3 ). Integrating light-harvesting materials with biological catalysts requires a deep understanding of NC properties, protein stability, and electron transfer (ET), making it an inherently multidisciplinary problem. The reduction of N 2 to NH 3 is a challenging reaction, with a high demand in both agriculture and industrial chemical production. This reaction is intrinsically energy intensive, due to the need to activate the N≡N triple bond. The current standard industrial approach to N 2 reduction, the Haber−Bosch reaction, obtains the necessary energy input from fossil fuels, whereas biological systems capable of N 2 reduction utilize the hydrolysis of ATP as their energy source. Replacing these costly, energy-intensive inputs with renewable light energy represents a critical step toward sustainable NH 3 production. Recent progress has demonstrated that semiconductor CdS NCs can be coupled to the catalytic component of nitrogenase, the MoFe protein, to form a biohybrid CdS NC:MoFe protein complex, enabling light-driven N 2 reduction rather than energy input from fossil fuels or ATP. This illustrates how inorganic NCs can functionally replace the natural Fe protein partner, yielding a biohybrid catalyst that enables controlled electron delivery and provides not only light-driven NH 3 production but also new approaches for probing enzyme catalytic function. The CdS NC:MoFe protein biohybrid system enables light-initiated electron delivery at ambient temperature, as well as temperatures below freezing, allowing for stabilization and spectroscopic characterization of key reaction intermediates. These findings highlight how photochemical biohybrids can serve as both functional catalysts and mechanistic probes. Beyond studies of the nitrogenase mechanism, studies of the CdS NC:MoFe system reveal how variables such as NC size, electrostatic binding interactions, and sacrificial electron donors (SEDs) govern complex stability, charge transfer efficiency, and catalytic performance. In addition, studies of nitrogenase and the high activation barrier for N 2 reduction are enabling investigations of new and interesting questions regarding the properties and limitations of NC biocatalysis. In this Account, we describe the key features of CdS NC:MoFe protein biohybrids and the parameters for optimal light-driven N 2 reduction, and how controlling ET with light illuminates the path to new insights into the nitrogenase mechanism.

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

In Silico Screening of CO 2 –Dipeptide Interactions for Bioinspired Carbon Capture

Carbon capture, sequestration and utilization offers a viable solution for reducing the total amount of atmospheric CO 2 concentrations. On an industrial scale, amine-based solvents are extensively employed for CO 2 capture through chemisorption. Nevertheless, this method is marked by the high cost associated with solvent regeneration, high vapor pressure, and the corrosive and toxic attributes of by-products, such as nitrosamines. An alternative approach is the biomimicry of sustainable materials that have strong affinity and selectivity for CO 2 . Bioinspired approaches, such as those based on naturally occurring amino acids, have been proposed for direct air capture methodologies. In this study, we present a database consisting of 960 dipeptide molecular structures, composed of the 20 naturally occurring amino acids. Furthermore, those structures were analyzed with a novel computational workflow presented in this work that considers certain interaction sites that determine CO 2 affinity. Density functional theory (DFT) and symmetry-adapted perturbation theory (SAPT) computations were performed for the calculation of CO 2 interaction energies, which allowed to limit our search space to 400 unique dipeptide structures. Using this computational workflow, we provide statistical insights into dipeptides and their affinity for CO 2 binding, as well as design principles that can further enhance CO 2 capture through cooperative binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗