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DOE OSTI2019

A multi-layer SEU mitigation strategy to improve FPGA design robustness for the ATLAS muon spectrometer upgrade

We present a multi-layer single-event upset mitigation strategy implemented in a low-cost Xilinx Artix-7 FPGA. The implementation is targeted for a trigger data router for the ATLAS muon spectrometer upgrade. The mitigation strategy employs three layers of protection to improve overall FPGA design robustness: use of triple-modular redundancy for FPGA fabric logic and embedded soft-error mitigation in the first layer; further enhancement with multi-boot FPGA reconfiguration across multiple copies of configuration memory in the second layer; and FPGA power cycling and configuration memory re-initialization in the third layer. Here, the effectiveness of this scheme has been evaluated at two different neutron facilities, LANSCE and NCSR ‘‘Demokritos’’, with 800 MeV and 25 MeV beam energies, respectively. Testing was performed with a similar configuration to that planned for final operation. We discuss the testing strategy and summarize the test results to estimate the expected data loss over 10 years of operation in the ATLAS experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in high-rate online event selection, such as the ATLAS or CMS first-level trigger systems. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p T ) based on the geometrical shape of the charge deposition (“cluster”). To design viable detectors for deployment, the dependence of the NN as a function of the sensor geometry, external magnetic field, irradiation, and noise must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. For the CMS HL-LHC sensor geometry, we obtain a signal efficiency of (91.9 ± 0.7)% and a data reduction of (29.7 ± 1.0)%. A smaller sensor pitch in the bending direction improves the p T discrimination, but a larger pitch can be partially compensated with detector thickness. Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by approximately 30–60% in absolute terms, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data. •ASIC-compatible track-momentum classifier is robust in realistic detector conditions.•About 90% signal efficiency and 30% data reduction per layer for CMS HL-LHC geometry.•Single-layer signal efficiency increases for smaller pixel pitch or thicker sensors.•Performance with noise or after radiation damage mostly recovered by retraining.

Shekar, Danush [Illinois U., Chicago] (ORCID:00000↗

DOE OSTI2017

Testing the robustness of optimal access vessel fleet selection for operation and maintenance of offshore wind farms

Optimising the operation and maintenance (O&M) and logistics strategy of offshore wind farms implies the decision problem of selecting the vessel fleet for O&M. Different strategic decision support tools can be applied to this problem, but much uncertainty remains regarding both input data and modelling assumptions. Our paper aims to investigate and ultimately reduce this uncertainty by comparing four simulation tools, one mathematical optimisation tool and one analytic spreadsheet-based tool applied to select the O&M access vessel fleet that minimizes the total O&M cost of a reference wind farm. The comparison shows that the tools generally agree on the optimal vessel fleet, but only partially agree on the relative ranking of the different vessel fleets in terms of total O&M cost. The robustness of the vessel fleet selection to various input data assumptions was tested, and the ranking was found to be particularly sensitive to the vessels' limiting significant wave height for turbine access. Also the parameter with the greatest discrepancy between the tools, implies that accurate quantification and modelling of this parameter is crucial. The ranking is moderately sensitive to turbine failure rates and vessel day rates but less sensitive to electricity price and vessel transit speed.

17 WIND ENERGY↗

DOE OSTI2025

A novel peridynamics-based approach to predict pharmaceutical tablet robustness

The pharmaceutical drug product development process can be greatly accelerated through the use of modeling and simulation techniques to predict the manufacturability and performance of a given formulation. The anticipation and possible mitigation of tablet damage due to manufacturing stresses represents a specific area of interest in the pharmaceutical industry for predicting formulation and tableting performance. While the finite element method (FEM) has been extensively used for predicting the mechanical behavior of powder material in the compaction processes, a shortcoming of the approach is the inherent difficulty to predict discontinuities (e.g., damage or cracking) within a tablet as FEM is a continuum-based approach. In this work, we propose a novel method utilizing peridynamics (PD), a numerical method that can capture discontinuities such as tablet fracture, to predict the evolution of damage and breakage in pharmaceutical tablets. The approach links (1) the finite element method – to elucidate the behavior of powders during die compaction – with (2) the peridynamics modeling technique – to model the discontinuous nature of damage and predict tablet breakage during the critical stages of unloading and ejection from the compression die. This short communication presents a proof of concept including a workflow to calibrate the linked FEM-PD simulation models. Further, it demonstrates promising results from a preliminary experimental validation of the approach. Following further development, this approach could be used to guide the optimization of compression processes through targeted changes to formulation material properties, compression process conditions, and/or tooling geometries to deliver improved process efficiency and tablet robustness.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Protein-activated atomic layer deposition for robust crude-oil-repellent hierarchical nano-armored membranes

Atomic layer deposition (ALD) offers unique capabilities to fabricate atomically engineered porous materials with precise pore tuning and multi-functionalization for diverse applications like advanced membrane separations towards sustainable energy-water systems. However, current ALD technique is inhibited on most non-polar polymeric membranes due to lack of accessible nucleation sites. Here, in this study, we report a facile method to efficiently promote ALD coating on hydrophobic surface of polymeric membranes via novel protein activation/sensitization. As a proof of concept, TiO 2 ALD-coated membranes activated by bovine serum albumin exhibit remarkable superhydrophilicity, ultralow underwater crude oil adhesion, and robust tolerance to rigorous environments including acid, alkali, saline, and ethanol. Most importantly, excellent cyclable crude oil-in-water emulsion separation performance can be achieved. The mechanism for activation/sensitization is rooted in reactivity for a particular set of amino acids. Furthermore, the universality of protein-sensitized ALD is demonstrated using common egg white, promising numerous potential usages in biomedical engineering, environmental remediation, lowcarbon manufacturing, catalysis, and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Thin, robust, and chemically stable photo-cross-linked anion exchange membranes based on a polychlorostyrene-b-polycyclooctene-b-polychlorostyrene ABA triblock polymer

A polychloromethylstyrene-b-polypolycyclooctene-b-polychloromethylstyrene ABA triblock polymer was used as a backbone in a study to produce a chemically and mechanically robust anion exchange membrane (AEM). The material is easily scalable and the polypolycyclooctene segment (low T m ) makes the membrane flexible to handle. Comparison of the non-crosslinked triblock polymer functionalized with different cations namely; piperidinium, pyrrolidinium, tris(2,4,6-trimethoxyphenyl) phosphonium, and the benchmark trimethylammonium cation showed that the piperidinium functionalized membrane with an ionic exchange capacity of ca. 1.36 mmol.g -1 had the highest OH - conductivity ca. 95 mS·cm -1 at 80 °C, 95% RH, and the highest Cl - conductivity of ca. 31 mS·cm -1 at 70 °C, 95% RH. The membrane with the piperidinium cation was the most chemically stable when immersed in 1 M KOH at 80 °C (with only 16% degradation after 14 days) compared to all the other cation functionalized membranes studied here. Photo-crosslinking with 1,10-decanedithiol (DT) eliminated the melting behavior of the polycyclooctene black and improved the mechanical stability of the films allowing < 20 μm large area transparent membranes to be produced. The DT cross-linking reduced the membrane swelling on all length scales as shown by small angle X-ray scattering, water uptake, and dimensional swelling data, and allowed the membrane to be fully intact even at fully hydrated states based on the tensile strength measurements.

ABA triblock polymer↗

DOE OSTI2023

Leveraging visible and near-infrared spectroelectrochemistry to calibrate a robust model for Vanadium(IV/V) in varying nitric acid and temperature levels

Spectroelectrochemistry and optimal design of experiments can be used to rapidly build accurate models for species quantification and enable a greater level of process awareness. Optical spectroscopy can provide vital elemental and molecular information, but several hurdles must be overcome before it can become a widely adopted analytical method for remote analysis in the nuclear field. Analytes with varying oxidation state, acid concentration, and fluctuating temperature must be efficiently accounted for to minimize time and resources in restrictive hot cell environments. The classic one-factor-at-a-time approach is not suitable for frequent calibration/maintenance operations in this setting. Therefore, a novel alternative was developed to characterize a system containing vanadium(IV/V) (0.01–0.1 M), nitric acid (0.1–4 M), and varying temperatures (20–45 °C). Here, spectroelectrochemistry methods were used to acquire a sample set selected by optimal design of experiments. This new approach allows for the accurate analysis of vanadium and HNO 3 concentration by leveraging UV–Vis–NIR absorption spectroscopy with robust and accurate chemometric models. The top model's root mean squared error of prediction percent values were 3.47%, 4.06%, 3.40%, and 10.9% for V(IV), V(V), HNO 3 , and temperature, respectively. These models, efficiently developed using the designed approach, exhibited strong predictive accuracy for vanadium and acid with varying oxidation states and temperature using only spectrophotometry, which advances current technology for real-world hot cell applications. Additionally, Nernstian analysis of the V(IV/V) standard potential was performed using traditional absorbance methods and multivariate curve resolution (MCR). The successful tests demonstrated that MCR Nernst tests may be valuable in highly convoluted spectral systems to better understand the redox processes' behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Engineering Robust Production Microbes for Large-Scale Cultivation

Systems biology and synthetic biology are increasingly utilized to review and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these methods. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Design of robust and versatile hydrocarbon-based single-ion-conducting polymer electrolytes

Hydrocarbon-based polymers offer several advantages, including lower environmental impacts, cost effectiveness, and the ability to finely tune properties. Here, we have developed trifluoromethanesulfonimide (TFSI)-functionalized poly(norbornene) (PNB) polymers utilizing a specifically designed oxa-Michael addition of a vinyl TFSI anion to an alcohol. Our results reveal that PNB-TFSI derivatives exhibit superior thermal stability and mechanical robustness compared with Nafion. The optimized PNB-TFSI-H-48 polymer (IEC 1.86 mmol/g) exhibits equivalent performance to Nafion as an anode ionomer in a proton exchange membrane fuel cell. Exchanging the counter ion to Li + enables PNB-TFSI to be used for Li-ion battery applications. Propylene carbonate plasticized PNB-TFSI derivatives achieve an Li-ion conductivity of over 10 −5 S/cm at 30°C. This Li polymer electrolyte exhibits excellent electrochemical stability (5 V vs. Li + /Li) and good cycling in a Li symmetric cell. These results highlight the potential and rational design of PNB-TFSI polymers for next-generation energy storage and conversion technologies.

08 HYDROGEN↗

DOE OSTI2022

Data augmentation for disruption prediction via robust surrogate models

The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student t process regression. We apply Student t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Pressure-Induced Superconductivity in the Wide-Band-Gap Semiconductor Cu 2 Br 2 Se 6 with a Robust Framework

We report pressure-induced superconductivity in a ternary and nonmagnetic Cu-containing semiconductor, Cu 2 Br 2 Se 6 , with a wide band gap of 1.89 eV, in which the Cu and Br atoms generate infinite 2, helical chains along the c-axis and are linked by the cyclohexane-like Se 6 rings to form a three-dimensional framework. We find that this framework is remarkably robust under compression, and the ambient-pressure phase survives at least to our experimental limit of 32.1 GPa. Concurrent semiconductor-to-metal transition and superconductivity are observed above 21.0 GPa. The superconducting temperature monotonically increases from 4.0 to 6.7 K at 40.0 GPa. Furthermore, first-principles calculations show that the emergence of superconductivity is associated with the formation of weak multicentered bonds that involve the increase in coordination of the Cu atoms and a subset of the Se atoms. The observation of superconductivity in this type of nonmagnetic transition-metal-based material will inspire the exploration of related new superconductors under pressure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. Here, in this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Reformulated Red mud: a robust catalyst for in situ catalytic pyrolysis of biomass

Biomass feedstocks contain inorganic compounds generally classified as ash. The ash consists of compounds of potassium, calcium, magnesium, silicon, phosphorous and other elements. These elements have been reported to influence both the pyrolysis reactions as well as destabilizing the pyrolysis oils during storage. The inorganic elements have also been reported to deposit on catalyst surfaces during in situ catalytic pyrolysis leading to the eventual deactivation of acidic catalysts such as zeolites. The deposition of inorganic elements and their effect on formulated red mud (FRM) catalyst during in situ catalytic pyrolysis of pinyon juniper wood was investigated. The inorganic elements were measured for the fresh, coked, and regenerated catalysts. The BET specific surface area of the FRM catalyst decreased from 76 m2/g for the fresh catalyst to 53 m2/g for the stable regenerated catalyst. After three regenerations, the BET specific surface area stabilized at 53 m2/g and remained constant after all other regenerations. Potassium, calcium, magnesium, and phosphorous were deposited on the catalyst. Potassium deposition was linear with the number of regenerations while magnesium and calcium depositions were initially rapid but leveled off after three regenerations of the catalyst. Phosphorous deposition was almost linear, but the data were more scattered compared to potassium. The potassium deposition was attributed to physical phenomenon whereas calcium and magnesium depositions were more akin to chemical reactions related to the loss of BET surface area of the catalyst. The deposition of these elements on the surface of the catalyst did not deactivate it. After each catalyst regeneration, the oil yield was not significantly affected and the oil oxygen content and viscosity decreased slightly. This clearly showed that formulated red mud is a robust catalyst suitable for in situ catalytic fast pyrolysis of biomass.

Agblevor, Foster↗

DOE OSTI2016

Selective Carbon Dioxide Adsorption by Two Robust Microporous Coordination Polymers

In the present work we report the design, synthesis, crystal structure determination, and adsorption properties of two new cadmium-based porous coordination polymers, [Cd(pda) 0.5 (spiro-4-py) 0.5 (HCOO)]•2H 2 O•DMF (compound 1, pda = p-phenylenediacetate, spiro- 4-py = (2,2’,7,7’-tetra(pyridin-4-yl)-9,9’-spirobi[fluorene], DMF = N,N’-dimethylformamide) and [Cd 2 (pda)(spiro-4-py)(CH 3 COO) 2 ]•DMA (compound 2, DMA = N,N’-dimethylacetamide) with similar structures. The coordination between cadmium and two organic linkers, pda and spiro-4-py, has yielded two-dimensional frameworks with rhombic openings. Stacking of these two-dimensional networks does not block the openings but rather results in permanent porosity with one-dimensional channels in the final structures. The permanent porosity of these compounds is confirmed by gas adsorption measurements. Compounds 1 and 2 have BET (Brunauer-Emmett-Teller) surface areas of 687 and 584 m 2 /g, respectively. Both compounds show favorable adsorption towards carbon dioxide over other light gases such as nitrogen, oxygen, and carbon monoxide. IAST (Ideal adsorbed solution theory) is employed to predict the adsorption selectivity of binary gas mixtures. Though compounds 1 and 2 possess similar structures, differences are observed in their gas adsorption behaviors, which can be attributed to their different terminal ligands of formate and acetate, respectively. Strikingly, both compounds show exceptionally high stability in aqueous media with a wide pH range, a characteristic that is highly desirable for gas separation-related applications. The robustness of these structures suggests that the use of hydrophobic spiro based multipyridine ligands can lead to water stable frameworks built on late-transition metals that are otherwise sensitive to moisture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Correction to Selective Carbon Dioxide Adsorption by Two Robust Microporous Coordination Polymers

In the present work we report the design, synthesis, crystal structure determination, and adsorption properties of two new cadmium-based porous coordination polymers, [Cd(pda)0.5(spiro-4-py)0.5(HCOO)]•2H2O•DMF (compound 1, pda = p-phenylenediacetate, spiro- 4-py = (2,2’,7,7’-tetra(pyridin-4-yl)-9,9’-spirobi[fluorene], DMF = N,N’-dimethylformamide) and [Cd2(pda)(spiro-4-py)(CH3COO)2]•DMA (compound 2, DMA = N,N’-dimethylacetamide) with similar structures. The coordination between cadmium and two organic linkers, pda and spiro-4-py, has yielded two-dimensional frameworks with rhombic openings. Stacking of these two-dimensional networks does not block the openings but rather results in permanent porosity with one-dimensional channels in the final structures. The permanent porosity of these compounds is confirmed by gas adsorption measurements. Compounds 1 and 2 have BET (Brunauer-Emmett-Teller) surface areas of 687 and 584 m 2 /g, respectively. Both compounds show favorable adsorption towards carbon dioxide over other light gases such as nitrogen, oxygen, and carbon monoxide. IAST (Ideal adsorbed solution theory) is employed to predict the adsorption selectivity of binary gas mixtures. Though compounds 1 and 2 possess similar structures, differences are observed in their gas adsorption behaviors, which can be attributed to their different terminal ligands of formate and acetate, respectively. Strikingly, both compounds show exceptionally high stability in aqueous media with a wide pH range, a characteristic that is highly desirable for gas separation-related applications. The robustness of these structures suggests that the use of hydrophobic spiro based multipyridine ligands can lead to water stable frameworks built on late-transition metals that are otherwise sensitive to moisture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data

Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consuming to analyze and calibrate. Here, we design and train a regression neural network (NN) that accelerates and simplifies the extraction of local dynamics from SPM data directly in a cantilever-independent manner, allowing the network to process data taken with different cantilevers. We validate the NN’s ability to recover local dynamics with a fidelity equal to or surpassing conventional, more time-consuming, calibrations using both simulated and real microscopy data. We apply this method to extract accurate photoinduced carrier dynamics on n = 1 butylammonium lead iodide, a halide perovskite semiconductor film that is of interest for applications in both solar photovoltaics and quantum light sources. Lastly, we use SHapley Additive exPlanations to evaluate the robustness of the trained model, confirm its cantilever-independence, and explore which parts of the trEFM signal are important to the network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Large Scale Study of Ligand–Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

The accurate and reliable prediction of protein–ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the “enhanced sampling” method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Car–Parrinello Monitor for More Robust Born–Oppenheimer Molecular Dynamics

Born–Oppenheimer molecular dynamics (BOMD) is a promising simulation method for exploring the possible reaction pathways of a chemical system, but one significant challenge is the increased difficulty of converging the self-consistent field (SCF) calculation that often accompanies the breaking and forming of chemical bonds. To address this challenge, we developed an enhancement to the BOMD simulation method called the Car–Parrinello monitor (CPMonitor) that uses Car–Parrinello molecular dynamics (CPMD) to recover from SCF convergence failures. CPMonitor works by detecting SCF convergence failures in BOMD and switching to a CPMD Hamiltonian to propagate through the region of configuration space where the SCF calculation is unable to converge, then switching back to BOMD when good convergence behavior is re-established. We present a series of simulation studies that use CPMonitor, including detailed studies of the thermodynamic and dynamical properties of simple systems, as well as ab initio nanoreactor simulations containing transition metal atoms that were previously not possible to simulate using standard BOMD methods. Our studies show that CPMonitor can make BOMD simulations robust to SCF convergence difficulties and improve simulation performance and stability in reaction discovery applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗