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DOE OSTI2021

MULTI-MODAL global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques

This paper presents the work completed towards the development of a multi-modal global surveillance methodology using cube satellite (CubeSat) platforms and novel data analysis techniques. A CubeSat system equipped with adequate sensors and data analytics capabilities can autonomously characterize various phenomena of interest on the Earth’s surface. CubeSats are advantageous over conventional satellites in certain remote monitoring applications because of their reduced construction costs (due to the availability of commercially-off-the-shelf components) and are easier to launch. The CubeSat surveillance system developed in this paper focused on phenomena of interest surrounding the nuclear fuel cycle in support of nuclear non-proliferation and emergency response. To observe the phenomena, a constellation of 3U and 6U CubeSats deployed from the ISS with adequate components was chosen. Four different sensor configurations were identified for remote sensing: panchromatic/multispectral in the visible and near-infrared spectrum, multispectral in infrared spectrum, hyperspectral in infrared spectrum, and multispectral in ultraviolet spectrum. While a panchromatic/multispectral sensor configuration has CubeSat flight heritage at the required spatial resolutions, the other three sensor types need future 3 development to meet signature and system requirements. Once each sensor onboard the CubeSat system collects data on a target of interest, the onboard computers would then apply the deep learning-based characterization methodology developed in this paper to identify phenomena. Four surrogate datasets containing representative simplified “images” were created for each sensor type to train the characterization methodology. A convolutional neural network was applied to each dataset and produced recall rates for the phenomena between 89.7% - 99.3% and precision rates between 92.3% - 99.9%. Each phenomenon’s presence probability from each network is then combined into a final characterization solution for a target area. This paper covers multiple interdisciplinary areas to develop the foundation for a CubeSat surveillance system focused on phenomena surrounding the nuclear fuel cycle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2017

Development of in-situ high-voltage and high-temperature stressing capability on atomic force microscopy platform

Reliability has become an increasingly important issue as photovoltaic technologies mature. However, researching reliability at the nanometer scale is in its infancy; in particular, in-situ studies have not been reported to date. Here, to investigate potential-induced degradation (PID) of solar cell modules, we have developed an in-situ stressing capability with applied high voltage (HV) and high temperature (HT) on an atomic force microscopy (AFM) platform. We designed a sample holder to simultaneously accommodate 1000-V HV and 200 degrees C HT stressing. Three technical challenges have been overcome along with the development: thermal drift at HT, HV interference with measurement, and arc discharge caused by HV. We demonstrated no observable measurement artifact under the stress conditions. Based on our in-situ stressing AFM, Kelvin probe force microscopy potential imaging revealed the evolution of electrical potential across the junction along with the PID stressing time, which provides vital information to further study the PID mechanism.

14 SOLAR ENERGY↗

DOE OSTI2012

VirtualToxLab — A platform for estimating the toxic potential of drugs, chemicals and natural products

The VirtualToxLab is an in silico technology for estimating the toxic potential (endocrine and metabolic disruption, some aspects of carcinogenicity and cardiotoxicity) of drugs, chemicals and natural products. The technology is based on an automated protocol that simulates and quantifies the binding of small molecules towards a series of proteins, known or suspected to trigger adverse effects. The toxic potential, a non-linear function ranging from 0.0 (none) to 1.0 (extreme), is derived from the individual binding affinities of a compound towards currently 16 target proteins: 10 nuclear receptors (androgen, estrogen α, estrogen β, glucocorticoid, liver X, mineralocorticoid, peroxisome proliferator-activated receptor γ, progesterone, thyroid α, and thyroid β), four members of the cytochrome P450 enzyme family (1A2, 2C9, 2D6, and 3A4), a cytosolic transcription factor (aryl hydrocarbon receptor) and a potassium ion channel (hERG). The interface to the technology allows building and uploading molecular structures, viewing and downloading results and, most importantly, rationalizing any prediction at the atomic level by interactively analyzing the binding mode of a compound with its target protein(s) in real-time 3D. The VirtualToxLab has been used to predict the toxic potential for over 2500 compounds: the results are posted on (http://www.virtualtoxlab.org). The free platform — the OpenVirtualToxLab — is accessible (in client–server mode) over the Internet. It is free of charge for universities, governmental agencies, regulatory bodies and non-profit organizations. -- Highlights: ► In silico technology for estimating the toxic potential of drugs and chemicals. ► Simulation of binding towards 16 proteins suspected to trigger adverse effects. ► Mechanistic interpretation and real-time 3D visualization. ► Accessible over the Internet. ► Free of charge for universities, governmental agencies, regulatory bodies and NPOs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Assessment of cisplatin-induced kidney injury using an integrated rodent platform

Current diagnosis of drug-induced kidney injury (DIKI) primarily relies on detection of elevated plasma creatinine (Cr) or blood urea nitrogen (BUN) levels; however, both are indices of overall kidney function and changes are delayed with respect to onset of nephron injury. Our aim was to investigate whether early changes in new urinary DIKI biomarkers predict plasma Cr, BUN, renal hemodynamic and kidney morphological changes associated with kidney injury following a single dose of cisplatin (CDDP) using an integrated platform in rodent. Conscious surgically prepared male Han Wistar rats were given a single intraperitoneal dose of CDDP (15 mg/kg). Glomerular filtration rate (GFR), effective renal plasma flow (ERPF), urinalysis, DIKI biomarkers, CDDP pharmacokinetics, blood pressures, heart rate, body temperature and electroencephalogram (EEG) were measured in the same vehicle- or CDDP-treated animals over 72 h. Plasma chemistry (including Cr and BUN) and renal tissues were examined at study termination. Cisplatin caused progressive reductions of GFR, ERPF, heart rate and body temperature from day 1 (0–24 h). DIKI biomarkers including alpha-glutathione S-transferase (α-GST) significantly increased as early as 6 h post-dose, which preceded significant declines of GFR and ERPF (24 h), increased plasma Cr and BUN (72 h), and associated with renal acute tubular necrosis at 72 h post-dose. The present study adds to the current understanding of CDDP action by demonstrating that early increases in urinary excretion of α-GST predict DIKI risk following acute exposure to CDDP in rats, before changes in traditional DIKI markers are evident. - Highlights: ► CDDP causes direct damage to kidneys without affecting EEG or CVS function. ► α-GST and albumin detect DIKI earlier when compared with traditional indices. ► Integrated “cardiovascular-EEG-renal” model to better understand DIKI mechanisms ► Promotes 3R's principles in drug discovery and development.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Optimization of greenhouse gas emissions in second-hand consumer product recovery through reuse platforms

Highlights: • Effects of waste diversion on flow of products to reuse processes. • Framework for solid waste management that focuses on reuse processes for waste diversion. • Application of framework on the material flow and GHG emissions from clothing reuse. - Abstract: Product reuse in the solid waste management sector is promoted as one of the key strategies for waste prevention. This practice is considered to have favorable impact on the environment, but its benefits have yet to be established. Existing research describes the perspective of “avoided production” only, but has failed to examine the interdependent nature of reuse practices within an entire solid waste management system. This study proposes a new framework that uses optimization to minimize the greenhouse gas emissions of an integrated solid waste management system that includes reuse strategies and practices such as reuse enterprises, online platforms, and materials exchanges along with traditional solid waste management practices such as recycling, landfilling, and incineration. The proposed framework uses material flow analysis in combination with an optimization model to provide the best outcome in terms of GHG emissions by redistributing product flows in the integrated solid waste management system to the least impacting routes and processes. The optimization results provide a basis for understanding the contributions of reuse to the environmental benefits of the integrated solid waste management system and the exploration of the effects of reuse activities on waste prevention. A case study involving second-hand clothing is presented to illustrate the implementation of the proposed framework as applied to the material flow. Results of the case study showed the considerable impact of reuse on GHG emissions even for small replacement rates, and helped illustrate the interdependency of the reuse sector with other waste management practices. One major contribution of this study is the development of a framework centered on product reuse that can be applied to identify the best management strategies to reduce the environmental impact of product disposal and to increase recovery of reusable products.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Organotypic platform for studying cancer cell metastasis

Highlights: • Liver and brain slices can be stable, realistic in vitro 3D models. • Cell stiffness and adhesion to the tissue play determinant roles in cellular invasion in 3D. • MEK inhibitors can lead to increased cell stiffness and adhesion forces, and consequently reduced cancer cell invasion. • MDA-MB-231 breast cancer cells migrate faster and deeper into brain tissue compared to liver tissue. Metastasis is the leading cause of mortality in cancer patients. To migrate to distant sites, cancer cells would need to adapt their behaviour in response to different tissue environments. Thus, it is essential to study this process in models that can closely replicate the tumour microenvironment. Here, we evaluate the use of organotypic liver and brain slices to study cancer metastasis. Morphological and viability parameters of the slices were monitored daily over 3 days in culture to assess their stability as a realistic 3D tissue platform for in vitro metastatic assays. Using these slices, we evaluated the invasion of MDA-MB-231 breast cancer cells and of a subpopulation that was selected for increased motility. We show that the more aggressive invasion of the selected cells likely resulted not only from their lower stiffness, but also from their lower adhesion to the surrounding tissue. Different invasion patterns in the brain and liver slices were observed for both subpopulations. Cells migrated faster in the brain slices (with an amoeboid-like mode) compared to in the liver slices (where they migrated with mesenchymal or collective migration-like modes). Inhibition of the Ras/MAPK/ERK pathway increased cell stiffness and adhesion forces, which resulted in reduced invasiveness. These results illustrate the potential for organotypic tissue slices to more closely mimic in vivo conditions during cancer cell metastasis than most in vitro models.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

A bimodular PKS platform that expands the biological design space

Traditionally engineered to produce novel bioactive molecules, Type I modular polyketide synthases (PKSs) could be engineered as a new biosynthetic platform for the production of de novo fuels, commodity chemicals, and specialty chemicals. Previously, our investigations manipulated the first module of the lipomycin PKS to produce short chain ketones, 3-hydroxy acids, and saturated, branched carboxylic acids. Building upon this work, we have expanded to multi-modular systems by engineering the first two modules of lipomycin to generate unnatural polyketides as potential biofuels and specialty chemicals in Streptomyces albus. First, we produce 20.6 mg/L of the ethyl ketone, 4,6 dimethylheptanone through a reductive loop exchange in LipPKS1 and a ketoreductase knockouts in LipPKS2. Furthermore, we then show that an AT swap in LipPKS1 and a reductive loop exchange in LipPKS2 can produce the potential fragrance 3-isopropyl-6-methyltetrahydropyranone. Highlighting the challenge of maintaining product fidelity, in both bimodular systems we observed side products from premature hydrolysis in the engineered first module and stalled dehydration in reductive loop exchanges. Collectively, our work expands the biological design space and moves the field closer to the production of "designer" biomolecules.

09 BIOMASS FUELS↗

DOE OSTI2021

Development of platforms for functional characterization and production of phenazines using a multi-chassis approach via CRAGE

Phenazines (Phzs), a family of chemicals with a phenazine backbone, are secondary metabolites with diverse properties such as antibacterial, anti-fungal, or anticancer activity. The core derivatives of phenazine, phenazine-1-carboxylic acid (PCA) and phenazine-1,6-dicarboxylic acid (PDC), are themselves precursors for various other derivatives. Recent advances in genome mining tools have enabled researchers to identify many biosynthetic gene clusters (BGCs) that might produce novel Phzs. Here, to characterize the function of these BGCs efficiently, we performed modular construct assembly and subsequent multi-chassis heterologous expression using chassis-independent recombinase-assisted genome engineering (CRAGE). CRAGE allowed rapid integration of a PCA BGC into 23 diverse γ-proteobacteria species and allowed us to identify top PCA producers. We then used the top five chassis hosts to express four partially refactored PDC BGCs. A few of these platforms produced high levels of PDC. Specifically, Xenorhabdus doucetiae and Pseudomonas simiae produced PDC at a titer of 293 mg/L and 373 mg/L, respectively, in minimal media. These titers are significantly higher than those previously reported. Furthermore, selectivity toward PDC production over PCA production was improved by up to 9-fold. The results show that these strains are promising chassis for production of PCA, PDC, and their derivatives, as well as for function characterization of Phz BGCs identified via bioinformatics mining.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Experimental platform for the investigation of magnetized-reverse-shock dynamics in the context of POLAR

The influence of a strong external magnetic field on the collimation of a high Mach number plasma flow and its collision with a solid obstacle is investigated experimentally and numerically. The laser irradiation ( $I\sim 2\times 10^{14}~\text{W}\cdot \text{cm}^{-2}$ ) of a multilayer target generates a shock wave that produces a rear side plasma expanding flow. Immersed in a homogeneous 10 T external magnetic field, this plasma flow propagates in vacuum and impacts an obstacle located a few mm from the main target. A reverse shock is then formed with typical velocities of the order of 15–20 $\pm$ 5 km/s. The experimental results are compared with 2D radiative magnetohydrodynamic simulations using the FLASH code. This platform allows investigating the dynamics of reverse shock, mimicking the processes occurring in a cataclysmic variable of polar type.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2011

Magnetic Electrochemical Sensing Platform for Biomonitoring of Exposure to Organophosphorus Pesticides and Nerve Agents Based on Simultaneous Measurement of Total Enzyme Amount and Enzyme Activity

We report a new approach for electrochemical quantification of enzymatic inhibition and phosphorylation for biomonitoring of exposure to organophosphorus (OP) pesticides and nerve agents based on a magnetic beads (MBs) immunosensing platform. The principle of this approach is based on the combination of MBs immuno-capture based enzyme activity assay and competitive immunoassay of total amount of enzyme for simultaneous detection of enzyme inhibition and phosphorylation in biological fluids. Butyrylcholinesterase (BChE) was chosen as a model enzyme. In competitive immunoassay, the target total BChE in a sample (mixture of OP-inhibited BChE and active BChE) competes with the BChE modified on the MBs to bind to the limited anti-BChE antibody labeled with quantum dots (QDs-anti-BChE), and followed by electrochemical stripping analysis of the bound QDs conjugate on the MBs. This assay shows a linear response over the total BChE concentration range of 0.1~20 nM. Simultaneously, real time BChE activity was measured on an electrochemical carbon nanotube-based sensor coupled with microflow injection system after immuno-capture by MBs-anti-BChE conjugate. Therefore, the formed phosphorylated adduct (OP-BChE) can be estimated by the difference values of the total amount BChE (including active and OP-inhibited) and active BChE from established calibration curves. This approach not only eliminates the difficulty in screening of low-dose OP exposure (less than 20% inhibition of BChE) because of individual variation of BChE values, but also avoids the drawback of the scarce availability of OP-BChE antibody. It is sensitive enough to detect 0.5 nM OP-BChE, which is less than 2% BChE inhibition. This method offers a new method for rapid, accurate, selective and inexpensive quantification of phosphorylated adducts and enzyme inhibition for biomonitoring of OP and nerve agent exposures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2013

OpenMSI: A High-Performance Web-Based Platform for Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) enables researchers to directly probe endogenous molecules directly within the architecture of the biological matrix. Unfortunately, efficient access, management, and analysis of the data generated by MSI approaches remain major challenges to this rapidly developing field. Despite the availability of numerous dedicated file formats and software packages, it is a widely held viewpoint that the biggest challenge is simply opening, sharing, and analyzing a file without loss of information. Here we present OpenMSI, a software framework and platform that addresses these challenges via an advanced, high-performance, extensible file format and Web API for remote data access (http://openmsi.nersc.gov). The OpenMSI file format supports storage of raw MSI data, metadata, and derived analyses in a single, self-describing format based on HDF5 and is supported by a large range of analysis software (e.g., Matlab and R) and programming languages (e.g., C++, Fortran, and Python). Careful optimization of the storage layout of MSI data sets using chunking, compression, and data replication accelerates common, selective data access operations while minimizing data storage requirements and are critical enablers of rapid data I/O. The OpenMSI file format has shown to provide >2000-fold improvement for image access operations, enabling spectrum and image retrieval in less than 0.3 s across the Internet even for 50 GB MSI data sets. To make remote high-performance compute resources accessible for analysis and to facilitate data sharing and collaboration, we describe an easy-to-use yet powerful Web API, enabling fast and convenient access to MSI data, metadata, and derived analysis results stored remotely to facilitate high-performance data analysis and enable implementation of Web based data sharing, visualization, and analysis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

N -Arylimide Molecular Balances: A Comprehensive Platform for Studying Aromatic Interactions in Solution

Noncovalent interactions of aromatic surfaces play a key role in many biological processes and in determining the properties and utility of synthetic materials, sensors, and catalysts. However, the study of aromatic interactions has been challenging because these interactions are usually very weak and their trends are modulated by many factors such as structural, electronic, steric, and solvent effects. Recently, N-arylimide molecular balances have emerged as highly versatile and effective platforms for studying aromatic interactions in solution. These molecular balances can accurately measure weak noncovalent interactions in solution via their influence on the folded–unfolded conformational equilibrium. The structure (i.e., size, shape, π-conjugation, and substitution) and nature (i.e., element, charge, and polarity) of the π-surfaces and interacting groups can be readily varied, enabling the study of a wide range of aromatic interactions. These include aromatic stacking, heterocyclic aromatic stacking, and alkyl–π, chalcogen–π, silver–π, halogen–π, substituent–π, and solvent–π interactions. The ability to measure a diverse array of aromatic interactions within a single model system provides a unique perspective and insights as the interaction energies, stability trends, and solvent effects for different types of interactions can be directly compared. Some broad conclusions that have emerged from this comprehensive analysis include: (1) The strongest aromatic interactions involve groups with positive charges such as pyridinium and metal ions which interact with the electrostatically negative π-face of the aromatic surface via cation–π or metal–π interactions. Attractive electrostatic interactions can also form between aromatic surfaces and groups with partial positive charges. (2) Electrostatic interactions involving aromatic surfaces can be switched from repulsive to attractive using electron-withdrawing substituents or heterocycles. These electrostatic trends appear to span many types of aromatic interactions involving a polar group interacting with a π-surface such as halogen–π, chalcogen–π, and carbonyl–π. (3) Nonpolar groups form weak but measurable stabilizing interactions with aromatic surfaces in organic solvents due to favorable dispersion and/or solvophobic effects. Furthermore, a good predictor of the interaction strength is provided by the change in solvent-accessible surface area. (4) Solvent effects modulate the aromatic interactions in the forms of solvophobic effects and competitive solvation, which can be modeled using solvent cohesion density and specific solvent–solute interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Topographical and Chemical Imaging of a Phase Separated Polymer Using a Combined Atomic Force Microscopy/Infrared Spectroscopy/Mass Spectrometry Platform

This article describes how the use of a hybrid atomic force microscopy/infrared spectroscopy/mass spectrometry imaging platform was demonstrated for the acquisition and correlation of nanoscale sample surface topography and chemical images based on infrared spectroscopy and mass spectrometry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

High Resolution Multimodal Chemical Imaging Platform for Organics and Inorganics

Chemical analysis at the nanoscale is critical to advance our understanding of materials and systems from medicine and biology to material science and computing. Macroscale-observed phenomena in these systems are in the large part driven by processes that take place at the nanoscale and are highly heterogeneous. Therefore, there is a clear need to develop a new technology that enables correlative imaging of material functionalities with nanoscale spatial and chemical resolutions that will enable us to untangle the structure–function relationship of functional materials. As such, here, we report on the analytical figures of merit of the newly developed correlative chemical imaging technique of helium ion microscopy coupled with secondary ion mass spectrometry (HIM-SIMS) that enables multimodal topographical/chemical imaging of organic and inorganic materials at the nanoscale. In HIM-SIMS, a focused ion beam acts as a sputtering and ionization source for chemical analysis along with simultaneous high-resolution surface imaging, providing an unprecedented level of spatial resolution for gathering chemical information on organic and inorganic materials. In this work, we demonstrate HIM-SIMS as a platform for a next-generation tool for an in situ material design and analysis capable of down to 8 nm spatial resolution chemical imaging, layered metal structure imaging in depth profiling, single graphene layer detection, and spectral analysis of metals, metal oxides, and polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Development of an Efficient Enzyme Production and Structure-Based Discovery Platform for BACE1 Inhibitors

BACE1 (Beta-site Amyloid Precursor Protein (APP) Cleaving Enzyme 1) is a promising therapeutic target for Alzheimer’s Disease (AD). However, efficient expression, purification, and crystallization systems are not well described or detailed in the literature nor are approaches for treatment of enzyme kinetic data for potent inhibitors well described. As such, we developed a platform for expression and purification of BACE1, including protein refolding from E.coli inclusion bodies, in addition to optimizing a reproducible crystallization procedure of BACE1 bound with inhibitors. We also report a detailed approach to the proper analysis of enzyme kinetic data for compounds that exhibit either rapid-equilibrium or tight-binding mechanisms. Our methods allow for the purification of ~15 mg of BACE1 enzyme from 1 L of culture which is higher than reported yields in the current literature. To evaluate the data analysis approach developed here, a well-known potent inhibitor and two of its derivatives were tested, analyzed, and compared. The inhibitory constants (K i ) obtained from the kinetic studies are in agreement with dissociation constants (K d ) that were also determined using isothermal titration calorimetry (ITC) experiments. The X-ray structures of these three compounds in complex with BACE1 were readily obtained and provide important insight into the structure and thermodynamics of the BACE1-inhibitor interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Direct Conversion of Biomass Carbohydrates to Platform Chemicals: 5-Hydroxymethylfurfural (HMF) and Furfural

5-hydroxymethylfurfural (HMF) and furfural are promising platform molecules which can be converted to fuel intermediates and hydrocarbon fuels. Herein, one-step, direct conversion of the carbohydrates in never treated biomass to HMF and furfural in a microwave reactor was investigated. The influence of reaction temperature, time, feedstock, addition of Lewis and Bronsted acids, solvent and solvent to aqueous ratio were studied to find the optimum process conditions to maximize the yields of furfurals. The reaction conditions of 200 degrees C for 5 min with 33 mM HCl and 8 mM AlCl 3 using a dioxane/water (4:1) miscible solvent system were found to be the most conducive for direct dehydration of the glucan and hemicelluloses present in untreated poplar wood to furfural and HMF in yields of 91 and 69%, respectively. Furfural could be easily produced in yields greater than 80%, and once formed, it was found to be stable under the various process conditions explored. In contrast, HMF yields varied with both reaction time and temperature, as it readily underwent rehydration to levulinic acid in yields approaching 25-30% at increased reaction severity. The additions of both Bronsted (HCl) and Lewis (AlCl 3 ) acids in catalytic amounts, i.e., 33 and 8 mM, respectively, were required to maximize the yield of furfurals. Reactions were also performed on other feedstocks (corn stover, switchgrass, loblolly pine, and pure cellulose) to investigate the effect of feedstock type on furfurals yields. Results showed that HMF and furfural yields were primarily dependent on hexosan and pentosan loading regardless of the type of feedstock. In addition, biomass physical features, such as crystallinity or the presence of other biomass constituents, such as lignin, had little influence on furfurals yields.

09 BIOMASS FUELS↗

DOE OSTI2020

Multiobjective Optimal Controlled Variable Selection for a Gas Turbine–Solid Oxide Fuel Cell System Using a Multiagent Optimization Platform

Hybrid gas turbine–fuel cell systems have immense potential for high efficiency in electrical power generation with cleaner emissions compared with fossil-fueled power generation. We report a systematic controlled variable (CV) selection method is deployed for a hybrid gas turbine–fuel cell system in the HyPer (hybrid performance) facility at the U.S. Department of Energy’s National Energy Technology Laboratory (NETL) for maximizing its economic and control performance. A three-stage approach is used for the CV selection comprising a priori analysis, multiobjective optimization, and a posteriori analysis. The a priori analysis helps to screen off several candidate CVs, thus reducing the size of the combinatorial optimization problem for multiobjective CV selection. For optimal CV selection, a transfer function model of the HyPer facility is identified. By considering several candidate models, the final transfer function model is selected using Akaike’s Final Prediction Error criterion. Experimental data from the HyPer facility are used to estimate the noise in the measurement data. For solving the combinatorial multiobjective optimization problem for CV selection, a multiagent optimization platform comprising simulated annealing, genetic algorithm, and efficient ant colony optimization algorithms is used. Pareto-optimal CV sets exhibit a high trade-off between the economic and control objective. The a posteriori analysis is undertaken for several top Pareto-optimal CV sets. An optimal CV set is selected that shows the best compromise between process economics and controllability under both nominal and off-design conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Engineered Recognition of Tetravalent Zirconium and Thorium by Chelator–Protein Systems: Toward Flexible Radiotherapy and Imaging Platforms

Targeted α therapy holds tremendous potential as a cancer treatment: it offers the possibility of delivering a highly cytotoxic dose to targeted cells while minimizing damage to surrounding healthy tissue. The metallic α-generating radioisotopes 225 Ac and 227 Th are promising radionuclides for therapeutic use, provided adequate chelation and targeting. In this work, we demonstrate a new chelating platform composed of a multidentate high-affinity oxygen-donating ligand 3,4,3-LI(CAM) bound to the mammalian protein siderocalin. Respective stability constants log β 110 = 29.65 ± 0.65, 57.26 ± 0.20, and 47.71 ± 0.08, determined for the Eu III (a lanthanide surrogate for Ac III ), Zr IV , and Th IV complexes of 3,4,3-LI(CAM) through spectrophotometric titrations, reveal this ligand to be one of the most powerful chelators for both trivalent and tetravalent metal ions at physiological pH. The resulting metal-ligand complexes are also recognized with extremely high affinity by the siderophore-binding protein siderocalin, with dissociation constants below 40 nM and tight electrostatic interactions, as evidenced by X-ray structures of the protein:ligand:metal adducts with Zr IV and Th IV . Finally, differences in biodistribution profiles between free and siderocalin-bound 238 Pu IV -3,4,3-LI(CAM) complexes confirm in vivo stability of the protein construct. The siderocalin:3,4,3-LI(CAM) assembly can therefore serve as a "lock" to consolidate binding to the therapeutic 225 Ac and 227 Th isotopes or to the positron emission tomography emitter 89 Zr, independent of metal valence state.

62 RADIOLOGY AND NUCLEAR MEDICINE↗