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At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Towards 3D printed multifunctional immobilization for proton therapy: Initial materials characterization

Purpose: 3D printing technology is investigated for the purpose of patient immobilization during proton therapy. It potentially enables a merge of patient immobilization, bolus range shifting, and other functions into one single patient-specific structure. In this first step, a set of 3D printed materials is characterized in detail, in terms of structural and radiological properties, elemental composition, directional dependence, and structural changes induced by radiation damage. These data will serve as inputs for the design of 3D printed immobilization structure prototypes. Methods: Using four different 3D printing techniques, in total eight materials were subjected to testing. Samples with a nominal dimension of 20 × 20 × 80 mm{sup 3} were 3D printed. The geometrical printing accuracy of each test sample was measured with a dial gage. To assess the mechanical response of the samples, standardized compression tests were performed to determine the Young’s modulus. To investigate the effect of radiation on the mechanical response, the mechanical tests were performed both prior and after the administration of clinically relevant dose levels (70 Gy), multiplied with a safety factor of 1.4. Dual energy computed tomography (DECT) methods were used to calculate the relative electron density to water ρ{sub e}, the effective atomic number Z{sub eff}, and the proton stopping power ratio (SPR) to water SPR. In order to validate the DECT based calculation of radiological properties, beam measurements were performed on the 3D printed samples as well. Photon irradiations were performed to measure the photon linear attenuation coefficients, while proton irradiations were performed to measure the proton range shift of the samples. The directional dependence of these properties was investigated by performing the irradiations for different orientations of the samples. Results: The printed test objects showed reduced geometric printing accuracy for 2 materials (deviation > 0.25 mm). Compression tests yielded Young’s moduli ranging from 0.6 to 2940 MPa. No deterioration in the mechanical response was observed after exposure of the samples to 100 Gy in a therapeutic MV photon beam. The DECT-based characterization yielded Z{sub eff} ranging from 5.91 to 10.43. The SPR and ρ{sub e} both ranged from 0.6 to 1.22. The measured photon attenuation coefficients at clinical energies scaled linearly with ρ{sub e}. Good agreement was seen between the DECT estimated SPR and the measured range shift, except for the higher Z{sub eff}. As opposed to the photon attenuation, the proton range shifting appeared to be printing orientation dependent for certain materials. Conclusions: In this study, the first step toward 3D printed, multifunctional immobilization was performed, by going through a candidate clinical workflow for the first time: from the material printing to DECT characterization with a verification through beam measurements. Besides a proof of concept for beam modification, the mechanical response of printed materials was also investigated to assess their capabilities for positioning functionality. For the studied set of printing techniques and materials, a wide variety of mechanical and radiological properties can be selected from for the intended purpose. Moreover the elaborated hybrid DECT methods aid in performing in-house quality assurance of 3D printed components, as these methods enable the estimation of the radiological properties relevant for use in radiation therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Oxidative dehydrogenation of isobutane over vanadia catalysts supported by titania nanoshapes

Support plays a complex role in catalysis by supported metal oxides and the exact support effect still remains elusive. One of the approaches to gain fundamental insights into the support effect is to utilize model support systems. In this study, we employed for the first time titania nanoshapes as the model supports and investigated how the variation of surface structure of the support (titania, TiO 2 ) impacts the catalysis of supported oxide (vanadia, VO x ). TiO 2 truncated rhombi, spheres and rods were synthesized via hydrothermal method and characterized with XRD and TEM. These TiO 2 nanoshapes represent different mixtures of surface facets including [1 0 1], [0 1 0] and [0 0 1] and were used to support vanadia. The structure of supported VO x species was characterized in detail with in situ Raman spectroscopy as a function of loading on the three TiO 2 nanoshapes. Oxidative dehydrogenation (ODH) of isobutane to isobutene was used as a model reaction to test how the support shape influences the activity, selectivity and activation energy of the surface VO x species. It was shown that the shape of TiO 2 support does not pose evident effect on either the structure of surface VO x species or the catalytic performance of surface VO x species in isobutane ODH reaction. Finally, this insignificant support shape effect was ascribed to the small difference in the surface oxygen vacancy formation energy among the different TiO 2 surfaces and the multi-faceting nature of the TiO 2 nanoshapes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fabrication of yeast engineered porous 13X adsorbent layers for CO 2 capture

This study investigates a novel environmentally friendly coating technique using yeast fermentation to obtain super porous Zeolite 13X adsorbent layers through freeze-drying technique for CO 2 capture applications. Several mass ratios of sugar to yeast were used to control the porosity and pore sizes in the adsorbent layer. A unique peeling process was integrated to obtain foamy, hollow structures to have deeper layer accessibility of adsorbent for adsorption applications. These coated adsorbent layers were experimentally characterized as a function of the mass ratio of sugar to yeast and adsorbent solid fraction. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared spectroscopy (FT-IR) are used to analyze the coated layers. Here, the void fraction for coated samples is found to vary between 0.48 and 0.56, making the samples applicable for adsorption applications. Adsorption performance experiments are performed to test the coated samples' performance compared to 13X powder. Due to their simplistic fabrication, cost-effectiveness, rapid pore formation, and high adsorption capacity, these porous 13X layers hold potential for large-scale fabrication and CO 2 capture applications.

Adsorbent coating↗
DOE OSTI2018

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Machine learning the electronic structure of open shell transition metal complexes presents unique challenges, including robust and automated data set generation. In this report we introduce tools that simplify data acquisition from density functional theory (DFT) and validation of trained machine learning models using the molSimplify automatic design (mAD) workflow. We demonstrate this workflow by training and comparing the performance of LASSO, kernel ridge regression (KRR), and artificial neural network (ANN) models using heuristic, topological revised autocorrelation (RAC) descriptors we have recently introduced for machine learning inorganic chemistry. On a series of open shell transition metal complexes, we evaluate set aside test errors of these models for predicting the HOMO level and HOMO–LUMO gap. The best performing models are ANNs, which show 0.15 and 0.25 eV test set mean absolute errors on the HOMO level and HOMO–LUMO gap, respectively. Poor performing KRR models using the full 153-feature RAC set are improved to nearly the same performance as the ANNs when trained on down-selected subsets of 20–30 features. Analysis of the essential descriptors for HOMO level and HOMO–LUMO gap prediction as well as comparison to subsets previously obtained for other properties reveal the paramount importance of nonlocal, steric properties in determining frontier molecular orbital energetics. We demonstrate our model performance on diverse complexes and in the discovery of molecules with target HOMO–LUMO gaps from a large 15,000 molecule design space in minutes rather than days that full DFT evaluation would require.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Electrocatalytic Hydrogenation of Oxygenated Compounds in Aqueous Phase

Electrocatalytic hydrogenation is a fascinating strategy to hydrogenate biogenic compounds at ambient conditions by replacing the thermal and H2 inputs by cathodic potential. This work compares the performance of this approach (in aqueous phase at room temperature) for a variety of model oxygenated compounds reacted over a series of metals. The target functionalities were carbonyl groups, aromatic rings, and ether bonds. All metals explored (Pt, Rh, Pd, Cu) are active for the reduction of carbonyl compounds to alcohols. For instance, conversion rates of benzaldehyde increased as a function of metal as: Pt < Rh < Pd (Cu was tested at different conditions). In contrast, only Rh and Pt were found active for hydrogenation of aromatic rings (Rh was more active than Pt). Comparing among the target functionalities, carbonyl groups are more reactive than aromatic rings and ether bonds in phenolic compounds and di-aryl ethers on all explore metals. This carbonyl reactivity, however, is enabled by the aromaticity of the molecule. Hence, the reactivity trend of the examined molecules is: butyraldehyde < furfural < acetophenone < benzaldehyde. For phenolic compounds, phenol is more reactive than cresol and methoxy phenol. Thus, the presence of substituting groups at the functionality being converted (either carbonyl or aromatic ring) decreases conversion rates. Ether bonds are cleaved under electrocatalytic conditions, which opens two main pathways for the conversion of aryl ethers: hydrogenation of the aromatic ring and hydrogenolysis of the ether bonds, whereas hydrolysis occurs as a minor pathway. Electrocatalytic hydrogenation competes with the H2 evolution reaction at the conditions of the tests, therefore Faradaic efficiency (fraction of current utilized in hydrogenation) and hydrogenation rates are correlated. That is, within the potential range explored, increasing hydrogenation rates lead to higher Faradaic efficiencies. The slope of this correlation, however, depends on the potential and on the functionality being hydrogenated. Keywords: Hydrogenation, electrocatalysis, metal catalysis, aqueous phase hydrogenation, biomass conversion. Acknowledgements The authors would like to thank Yang Song, Prof. Johannes Lercher, and the group of Prof. Hubert A. Gasteiger at the Technische Universität München for advice and support. The authors are grateful to Donald M. Camaioni, Nirala Singh, and Juan Herranz for fruitful discussions. The research described in this paper is part of the Chemical Transformation Initiative at Pacific Northwest National Laboratory (PNNL), conducted under the Laboratory Directed Research and Development Program at PNNL, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy.

Hydrogenation, electrocatalysis, metal catalysis, ↗
DOE OSTI2020

Extreme Antiscaling Performance of Slippery Omniphobic Covalently Attached Liquids

Scale formation presents an enormous cost to the global economy. Classical nucleation theory dictates that to reduce the heterogeneous nucleation of scale, the surface should have low surface energy and be as smooth as possible. Past approaches have focused on lowering surface energy via the use of hydrophobic coatings and have created atomically smooth interfaces to eliminate nucleation sites, or both, via the infusion of low-surface-energy lubricants into rough superhydrophobic substrates. Although lubricant-based surfaces are promising candidates for antiscaling, lubricant drainage inhibits their utilization. Here, we develop methodologies to deposit slippery omniphobic covalently attached liquids (SOCAL) on arbitrary substrates. Similar to lubricant-based surfaces, SOCAL has ultralow roughness and surface energy, enabling low nucleation rates and eliminating the need to replenish the lubricant. To enable SOCAL coating on metals, we investigated the surface chemistry required to ensure high-quality functionalization as measured by ultralow contact angle hysteresis (<3°). Using a multilayer deposition approach, we first electrophoretically deposit (EPD) silicon dioxide (SiO 2 ) as an intermediate layer between the metallic substrate and SOCAL. The necessity of EPD SiO 2 is to smooth (<10 nm roughness) as well as to enable the proper surface chemistry for SOCAL bonding. To characterize antiscaling performance, we utilized calcium sulfate (CaSO 4 ) scale tests, showing a 20× reduction in scale deposition rate than untreated metallic substrates. Descaling tests revealed that SOCAL dramatically decreases scale adhesion, resulting in rapid removal of scale buildup. Furthermore, our work not only demonstrates a robust methodology for depositing antiscaling SOCAL coatings on metals but also develops design guidelines for the creation of antifouling coatings for alternate applications such as biofouling and high-temperature coking.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Stereo-particle image velocimetry uncertainty quantification

Particle image velocimetry (PIV) measurements are subject to multiple elemental error sources and thus estimating overall measurement uncertainty is challenging. Recent advances have led to a posteriori uncertainty estimation methods for planar two-component PIV. However, no complete methodology exists for uncertainty quantification in stereo PIV. In the current paper, a comprehensive framework is presented to quantify the uncertainty stemming from stereo registration error and combine it with the underlying planar velocity uncertainties. The disparity in particle locations of the dewarped images is used to estimate the positional uncertainty of the world coordinate system, which is then propagated to the uncertainty in the calibration mapping function coefficients. Next, the calibration uncertainty is combined with the planar uncertainty fields of the individual cameras through an uncertainty propagation equation and uncertainty estimates are obtained for all three velocity components. The methodology was tested with synthetic stereo PIV data for different light sheet thicknesses, with and without registration error, and also validated with an experimental vortex ring case from 2014 PIV challenge. Thorough sensitivity analysis was performed to assess the relative impact of the various parameters to the overall uncertainty. The results suggest that in absence of any disparity, the stereo PIV uncertainty prediction method is more sensitive to the planar uncertainty estimates than to the angle uncertainty, although the latter is not negligible for non-zero disparity. Overall the presented uncertainty quantification framework showed excellent agreement between the error and uncertainty RMS values for both the synthetic and the experimental data and demonstrated reliable uncertainty prediction coverage. Finally, this stereo PIV uncertainty quantification framework provides the first comprehensive treatment on the subject and potentially lays foundations applicable to volumetric PIV measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Simulation-Based Parameter Optimization Framework for Large-Scale Hybrid Smart Grid Communications Systems Design

The design of reliable, dynamic, fault-tolerant hybrid smart grid communication networks is a challenge to achieve for autonomous power grids. Hybrid networks use different communications technologies for different area networks. A simulation-based parameter optimization framework is proposed to tune parameters of hybrid communication technologies to achieve the optimal network performance. It consists of three main components: a parallel executor used to speedup a list of simulations; a sampler running simulations using the parallel executor at each generation; and a hybrid stochastic optimization algorithm for tuning configurable parameters of hybrid designs and applications. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient method to quickly achieve an approximately global optimum solution. Three optimization test functions are employed to train the adjustable parameters of the hybrid algorithm. Results show the proposed parameter optimization framework can help the designer choose the right hybrid architecture with an optimal parameter set for a large-scale broadband PLC-WiMAX hybrid smart grid communication network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Verification of RESRAD-BUILD Code Version 4

This report documents the verification of the RESRAD-BUILD code, Version 4.0, which was released on December 22, 2022. Two earlier reports verifying Versions 3.0 and 3.1, respectively, were published in 2001 (Kamboj, et al. 2001) and 2003 (Tetra Tech NUS 2003). Version 4.0 of the RESRAD-BUILD code has many new features and modeling enhancements over the earlier versions, including the previously released Version 3.5. Chapter 2 of this report focuses on verifying the external dose and risk modeling for point, line, area, and volume sources, as well as for floor deposition. Besides verification, the external radiation doses calculated by RESRAD-BUILD were also benchmarked with those calculated by the MCNP code (Briemeister 1993). Section J.3 of the RESRAD-BUILD User’s Manual Vol. 1 (Yu et al. 2022) documents the results of that benchmarking effort. Chapter 3 of this report focuses on verifying the ventilation modeling, from checking the remaining source inventory, releases of radionuclides to the air, air concentrations and deposited floor concentrations over time, to the radiation dose and risk associated with inhalation, ingestion, and air submersion, with and without vacuuming. The verification efforts involve designing spreadsheets to perform calculations the same as or like those performed by the RESRAD-BUILD code and then comparing the spreadsheet results with those produced by the code. When the results agree or the differences are within acceptable range, the accuracy of model implementation in the code is verified. In addition to model implementation, the implementation of key functions and features that facilitate the modeling or the use of the code were also verified during the release testing of the code. Appendix A presents the test cases developed for these verification testing, and Appendix B presents the testing results that verify proper implementation of key functions and features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Long-term performance of engineered barriers for high-level waste repositories - 15358

The evolution of the engineered barrier system (EBS) of geological repositories for high-level radioactive waste (HLW) and spent fuel (SF) has been the subject of many national and international research programs during recent years. The emphasis of the research activities was on the elaboration of a detailed understanding of the complex coupled thermo-hydro-mechanical and -chemical (THM-C) processes, which are expected to evolve in the early post-closure period in the near field. From the perspective of radiological long-term safety, an in-depth understanding of these coupled processes is of great significance, because the evolution of the EBS during the early post-closure phase may have a non-negligible impact on the safety functions at later stages of the repository's lifetime. Process interactions during the re-saturation phase (heat pulse, gas generation, non-uniform water uptake from the host rock) could impair the homogeneity of the safety-relevant properties in the EBS (e.g. swelling pressure, hydraulic conductivity, diffusivity). The 7. Framework EURATOM PEBS project (long-term Performance of Engineered Barrier Systems) was initiated in 2010 and completed in March 2014. The project aimed at evaluating the sealing and barrier performance of clay-based EBS over time. The project approach included experiments, model development and consideration of the potential impact on long-term safety functions. The specific project aims were to: -deepen the understanding of the THM and THM-C evolution of the EBS system with time, - provide an enhanced quantitative basis for relating the evolutionary behavior to the safety functions, - clarify further the significance of residual uncertainties for long-term performance assessment. It can be concluded that uncertainties in process understanding occurring in the re-saturation period have been better constrained through PEBS studies, thus the uncertainties in the long-term performance of bentonite barriers have been reduced in some areas. Improvements are in the areas of evolution of materials properties and model development and testing at various scales. On-going in-situ experiments in underground research laboratories may play an important role in further confirming bentonite performance over periods of 10 - 20 years. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Development of a ZnS (Ag)-Silicon Photomultiplier Fast Neutron Detector for the Fast Neutron Hodoscope at TREAT

The Transient Reactor Test (TREAT) Facility at Idaho National Laboratory is being restored to support the mission of advanced reactor and fuel development in the United States. A key part of this restoration is the fast neutron hodoscope which images the fuel under test during extreme transient conditions. Traditionally, this has been comprised of ZnS (Ag)-based proton recoil scintillators (PRS) coupled to photomultiplier tubes (PMTs), but silicon photomultipliers (SiPMs) have emerged as a promising candidate to replace PMTs in this application. This work develops, tests, and evaluates a SiPM-based PRS detector for incorporation into the updated Fuel Motion Monitoring System (FMMS) at TREAT. The primary characteristic to be optimized is the gamma rejection efficiency of the detector which will be done using pulse shape discrimination (PSD). To do so multiple types of SiPMs will be tested with multiple PRS buttons and compared using standard PSD figures of merit. Consideration to count rates will also be given and consequently to the detector’s electrical time constants. Once the ideal SiPM has been selected for the PRS detectors, its gamma rejection efficiency will be characterized, and benchmarked to the traditional PMT-PRS detectors. PSD performance for this detector will also be characterized as a function of temperature.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

Predictive modeling typically relies on Bayesian model calibration to provide uncertainty quantification. Variational inference utilizing fully independent (“mean-field”) Gaussian distributions are often used as approximate probability density functions. This simplification is attractive since the number of variational parameters grows only linearly with the number of unknown model parameters. However, the resulting diagonal covariance structure and unimodal behavior can be too restrictive to provide useful approximations of intractable Bayesian posteriors that exhibit highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors for these problems can be obtained by considering the family of Gaussian mixtures. Gaussian mixtures are capable of capturing multiple modes and approximating any distribution to an arbitrary degree of accuracy, while maintaining some analytical tractability. Unfortunately, variational inference using Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. The existence of multiple local minima due to strong nonconvex trends in the loss functions often associated with variational inference present additional complications, These challenges motivate the need for robust initialization procedures to improve the performance and computational scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with a global optimization stage in model parameter space. In this step, local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace approximation. Finally, the mixture weights are determined through constrained least squares regression. The robustness and scalability of the proposed methodology is demonstrated through application to an ensemble of synthetic tests using high-dimensional, multimodal probability density functions. Here, the practical aspects of the approach are demonstrated with inversion problems in structural dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Models and Algorithms for Equilibrium Analysis of Mixed-Material Nucleic Acid Systems

Dynamic programming algorithms within the NUPACK software suite enable analysis of equilibrium base-pairing properties for complex and test tube ensembles containing arbitrary numbers of interacting nucleic acid strands. Currently, calculations are limited to single-material systems that are either all-RNA or all-DNA. Here, to enable analysis of mixed-material systems that are critical for modern applications in vitro, in situ, and in vivo, we develop physical models and dynamic programming algorithms that allow the material of the system to be specified at nucleotide resolution. Free energy parameter sets are constructed for both RNA/DNA and RNA/2'OMe-RNA mixed-material systems by combining available empirical mixed-material parameters with single-material parameter sets to enable treatment of the full complex and test tube ensembles. New dynamic programming recursions account for the material of each nucleotide throughout the recursive process. For a complex with N nucleotides, the mixed-material dynamic programming algorithms maintain the O(N 3 ) time complexity of the single-material algorithms, enabling efficient calculation of diverse physical quantities over complex and test tube ensembles (e.g., complex partition function, equilibrium complex concentrations, equilibrium base-pairing probabilities, minimum free energy secondary structure(s), and Boltzmann-sampled secondary structures) at a cost increase of roughly 2.0-3.5×. The results of existing single-material algorithms are exactly reproduced when applying the new mixed-material algorithms to single-material systems. Accuracy is significantly enhanced using mixed-material models and algorithms to predict RNA/DNA and RNA/2'OMe-RNA duplex melting temperatures from the experimental literature as well as RNA/DNA melt profiles from new experiments. In conclusion, mixed-material analyses can be performed online using the NUPACK web app (www.nupack.org) or locally using the NUPACK Python module.

2′OMe-RNA↗
DOE OSTI2023

Synergistic effect of polyaniline on stabilizing Pt nanoparticles in PEMFCs†

The stability of Pt nanoparticles on a carbon support is crucial for the lifespan of polymer electrolyte membrane fuel cells as well as other electrocatalysis systems such as electrolyzers. Here we took the approach of utilizing the covalently grafted polyaniline on carbon to stabilize Pt nanoparticles by simultaneously alleviating the particle migration and mitigating the Ostwald ripening, which are two major mechanisms for loss of the catalyst stability. A comprehensive investigation of the stability and performance of Pt catalyst nanoparticles on these supports reveals that the polyaniline can shift the Pt L edge binding energy and the Pt 4f peak energy, leading to the increased redox potential of the Pt nanoparticles and making them more resistant to oxidation. Interestingly, the highest polyaniline density (50 wt%) does not show the best catalyst stability, rather, the second (33 wt%) demonstrates the best catalyst stability among these catalysts. Combining the XPS, XAS and fuel cell stability studies, we concluded that the locations of the Pt nanoparticles over the polyaniline layer on the carbon surface depend on the surface density of the polyaniline over the carbon surface. The Pt nanoparticles in the 50 wt% polyaniline catalyst are wrapped by the polyaniline polymer but do not sit directly on the carbon surface, while Pt nanoparticles in the 33 wt% polyaniline catalyst sit on the carbon surface and are densely surrounded by the polyaniline polymer. The results show that the mass activity loss is 12.5% while the ECSA (electrochemically active surface area) loss is 37.1% after 30k cycles of accelerating stress test for the 33 wt% polyaniline catalyst, far exceeding the DOE 2020 target for MEA (i.e., mass activity loss < 50%, and ECSA loss < 40%). Overall, the polyaniline has a synergistic effect on mitigating the surface migration and slowing down the Ostwald ripening to stabilize the Pt nanoparticles in fuel cells. The novel strategy to stabilize Pt nanoparticles using polyaniline opens a new pathway in the development of highly stable catalysts: relying on the functionalization group on the carbon support, instead of just focusing on the catalyst itself.

Li, Chenzhao↗
DOE OSTI2022

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.

42 ENGINEERING↗
DOE OSTI2021

Open-Loop Control of Adjustable Tuned Mass Dampers for Floating Wind Turbine Platforms: Preprint

Floating offshore wind turbines have several advantages over their land-based counterparts, including access to stronger, more consistent winds and proximity to population centers. However, increased costs from the platform have been a challenge inhibiting their widespread adoption. New damping technologies with tunable target frequencies and damping ratios promise a greater degree of control over platform motions, allowing hulls to be designed smaller and reducing the overall cost of the turbines. In this work, a control strategy is proposed for these adjustable tuned mass dampers (TMDs). A frequency-domain model of the system is developed, from which response amplitude operators (RAOs) for the platform's rigid-body modes can be obtained for different natural frequencies and damping ratios of the dampers. Using these RAOs, and JONSWAP spectrums of operational and extreme sea states, the performance of the various damper settings are compared by evaluating the standard deviation of a rigid-body motion of interest (platform pitch or heave). By finding the optimal damper setting for a range of representative design load cases (DLCs), a lookup function is made to return the damper target frequency given the peak period of the sea state. To determine the current sea state, we propose a simple estimator that uses wave measurements (e.g., from a measurement buoy) to determine the peak period and significant wave height. The significant wave height is determined using the statistics of the past 100 seconds of wave elevation information and the peak period is computed using a frequency locked loop. Initial results show that we can use these estimated values to control the TMD natural frequency with an open loop controller. The controlled system is tested in a range of environmental conditions determined by the International Electrotechnical Commission design load cases (DLCs), which include normal and extreme wind and wave models. In these tests, we compare the performance of 4 cases: (1) no TMD, (2) a constant TMD based on the worst case DLC, (3) a controlled TMD based on known wind and wave environments, and (4) a real-time controlled TMD using estimated wind and wave environments. The effect of hull-based TMD control is also compared to changes in traditional wind turbine control via blade pitch.

control↗