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DOE OSTI2022

Qubit Assignment Using Time Reversal

As quantum computers with large numbers of qubits become increasingly available, experiments executed on a given device may not utilize all available qubits. In this case, the outcome of executing a quantum program will depend on the ability to efficiently select a subset of high-performing physical qubits. For any given quantum program and device there are many ways to assign physical qubits for execution of the program, and assignments will differ in performance due to the variability in quality across qubits and entangling operations on a single device. Evaluating the performance of each assignment using fidelity estimation introduces significant experimental overhead and will be infeasible for many applications, while relying on standard device benchmarks provides incomplete information about the performance of any specific program. Furthermore, the number of possible assignments grows combinatorially in the number of qubits on the device and in the program, motivating the use of heuristic optimization techniques. We demonstrate a practical solution to the problem of qubit assignment by using simulated annealing with a cost function based on the Loschmidt echo, a diagnostic that measures the reversibility of a quantum process. We provide theoretical justification for this choice of cost function by demonstrating that the optimal qubit assignment coincides with the optimal qubit assignment based on state fidelity in the weak error limit, and we provide experimental justification using diagnostics performed on Google’s superconducting qubit devices. We then establish the performance of simulated annealing for qubit assignment using classical simulations of noisy devices as well as optimization experiments performed on a quantum processor. Our results demonstrate that the use of Loschmidt echoes and simulated annealing provides a scalable and flexible approach to optimizing qubit assignment on near-term hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Versatile acid solvents for pristine carbon nanotube assembly

Chlorosulfonic acid and oleum are ideal solvents for enabling the transformation of disordered carbon nanotubes (CNTs) into precise and highly functional morphologies. Currently, processing these solvents using extrusion techniques presents complications due to chemical compatibility, which constrain equipment and substrate material options. Here, we present a novel acid solvent system based on methanesulfonic or p-toluenesulfonic acids with low corrosivity, which form true solutions of CNTs at concentrations as high as 10 g/liter (≈0.7 volume %). The versatility of this solvent system is demonstrated by drop-in application to conventional manufacturing processes such as slot die coating, solution spinning continuous fibers, and 3D printing aerogels. Through continuous slot coating, we achieve state-of-the-art optoelectronic performance (83.6 %T and 14 ohm/sq) at industrially relevant production speeds. This work establishes practical and efficient means for scalable processing of CNT into advanced materials with properties suitable for a wide range of applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Securing Federated Learning Against Active Reconstruction Attacks

Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. Here, this class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this paper, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗

DOE OSTI2025

Transforming Aeration Energy in Water Resource Recovery Facilities (WRRFs) through Suboxic Nitrogen Removal (Final Report)

The objective of this project was to advance two key technological components—aeration control strategies and process design methodologies—to support the development and broader adoption of suboxic biological nitrogen removal (SBNR). The project focused on achieving the following three goals: • Enhance Model Predictive Control (MPC) Technology: Advance the DO/Nmaster MPC platform from its initial 2018 pilot deployment at the Chico Water Resource Recovery Facility in California to full-scale integration. This included partnering with a blower technology commercialization partner and incorporating machine learning (ML) capabilities to enable nationwide deployment. • Bridge Knowledge Gaps in SBNR Process Design: Address fundamental gaps in SBNR process understanding through controlled pilot-scale testing at a dedicated pilot facility. These efforts supported the development of robust kinetic models to inform reliable SBNR control, operational strategies, and design frameworks. • Demonstrate Full-Scale Implementation of Low DO/SBNR with ML: Transition low dissolved oxygen (DO)/SBNR coupled with ML from pilot-scale trials to full-scale demonstration in flow-through biological nutrient removal (BNR) systems, with the goal of enabling scalable, nationwide adoption in activated sludge treatment processes. The project included demonstration of SBNR at the pilot scale as performed by Hampton Roads Sanitation District (HRSD) and at the full-scale as performed by the Los Angeles County Sanitation Districts' (LACSD) Pomona Water Reclamation Plant (POWRP).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Remote Americium Detection Using an Optical Sensor: A D-Optimal Strategy for Efficient PLS-Based Modeling

A fiber-optic visible–near-infrared absorption spectroscopy system in a glove box was demonstrated for remote quantification of Am(III) (0–500 µM) and HNO 3 (0.1–9 M) using partial least squares regression (PLSR) models. The sensor platform, featuring a simple plug-and-play spectrophotometer, can enable noninvasive, real-time monitoring of actinide process solutions. To establish a flexible PLSR model calibration strategy, a D-optimal design developed using Nd(III) in previous studies was successfully extended to an actinide system with Am(III) to effectively minimize sample set size while maintaining robust prediction performance. The results suggest strong spectral similarities between Nd(III) and Am(III) and validate Nd(III) as an effective optical surrogate for trivalent actinide species. This work also supports the generalizability of a D-optimal training set selection approach for two-factor systems. The PLS1 models for Am(III) and HNO 3 outperformed a PLS2 model and maintained reasonable performance in the presence of interfering U(VI). The resulting sensor system and multivariate approach provides a flexible and scalable solution for process monitoring, control, and safety in diverse nuclear applications.

actinide↗

NASA NTRS2000

SUPREM-DSMC: A New Scalable, Parallel, Reacting, Multidimensional Direct Simulation Monte Carlo Flow Code

An AFRL/NRL team has recently been selected to develop a scalable, parallel, reacting, multidimensional (SUPREM) Direct Simulation Monte Carlo (DSMC) code for the DoD user community under the High Performance Computing Modernization Office (HPCMO) Common High Performance Computing Software Support Initiative (CHSSI). This paper will introduce the JANNAF Exhaust Plume community to this three-year development effort and present the overall goals, schedule, and current status of this new code.

Campbell, David↗

NASA NTRS2013

Intravenous Fluid Generation System

The ability to stabilize and treat patients on exploration missions will depend on access to needed consumables. Intravenous (IV) fluids have been identified as required consumables. A review of the Space Medicine Exploration Medical Condition List (SMEMCL) lists over 400 medical conditions that could present and require treatment during ISS missions. The Intravenous Fluid Generation System (IVGEN) technology provides the scalable capability to generate IV fluids from indigenous water supplies. It meets USP (U.S. Pharmacopeia) standards. This capability was performed using potable water from the ISS; water from more extreme environments would need preconditioning. The key advantage is the ability to filter mass and volume, providing the equivalent amount of IV fluid: this is critical for remote operations or resource- poor environments. The IVGEN technology purifies drinking water, mixes it with salt, and transfers it to a suitable bag to deliver a sterile normal saline solution. Operational constraints such as mass limitations and lack of refrigeration may limit the type and volume of such fluids that can be carried onboard the spacecraft. In addition, most medical fluids have a shelf life that is shorter than some mission durations. Consequently, the objective of the IVGEN experiment was to develop, design, and validate the necessary methodology to purify spacecraft potable water into a normal saline solution, thus reducing the amount of IV fluids that are included in the launch manifest. As currently conceived, an IVGEN system for a space exploration mission would consist of an accumulator, a purifier, a mixing assembly, a salt bag, and a sterile bag. The accumulator is used to transfer a measured amount of drinking water from the spacecraft to the purifier. The purifier uses filters to separate any air bubbles that may have gotten trapped during the drinking water transfer from flowing through a high-quality deionizing cartridge that removes the impurities in the water before entering the salt bag and mixing with the salt to create a normal saline solution.

McQuillen, John↗

NASA NTRS2014

Statistical Symbolic Execution with Informed Sampling

Symbolic execution techniques have been proposed recently for the probabilistic analysis of programs. These techniques seek to quantify the likelihood of reaching program events of interest, e.g., assert violations. They have many promising applications but have scalability issues due to high computational demand. To address this challenge, we propose a statistical symbolic execution technique that performs Monte Carlo sampling of the symbolic program paths and uses the obtained information for Bayesian estimation and hypothesis testing with respect to the probability of reaching the target events. To speed up the convergence of the statistical analysis, we propose Informed Sampling, an iterative symbolic execution that first explores the paths that have high statistical significance, prunes them from the state space and guides the execution towards less likely paths. The technique combines Bayesian estimation with a partial exact analysis for the pruned paths leading to provably improved convergence of the statistical analysis. We have implemented statistical symbolic execution with in- formed sampling in the Symbolic PathFinder tool. We show experimentally that the informed sampling obtains more precise results and converges faster than a purely statistical analysis and may also be more efficient than an exact symbolic analysis. When the latter does not terminate symbolic execution with informed sampling can give meaningful results under the same time and memory limits.

Reliability↗

DOE OSTI2020

Distributed Computing for the Project 8 Experiment

The Project 8 collaboration aims to measure the absolute neutrino mass or improve on the current limit by measuring the tritium beta decay electron spectrum. We present the current distributed computing model for the Project 8 experiment. Project 8 is in its second phase of data taking with a near continuous data rate of 1Gbps. The current computing model uses DIRAC (Distributed Infrastructure with Remote Agent Control) for its workflow and data management. A detailed meta-data assignment using the DIRAC File Catalog is used to automate raw data transfers and subsequent stages of data processing. The DIRAC system is deployed on containers managed using a Kubernetes cluster to provide a scalable infrastructure. A modified DIRAC Site Director provides the ability to submit jobs using Singularity on opportunistic High-Performance Computing (HPC) sites.

Distributed Computing, Kubernetes, DIRAC, Project ↗

DOE OSTI2025

Atomically Fine-Tuning Organic–Inorganic Carbon Molecular Sieve Membranes for Hydrogen Production

Polymeric membranes with great processability are attractive for the H 2 /CO 2 separation required for hydrogen production from renewable biomass with carbon capture for utilization and sequestration. However, it remains elusive to engineer polymer architectures to obtain desired sub-3.3 Å ultramicropores to efficiently sieve H 2 from CO 2 . Herein, we demonstrate a scalable way of carbonizing polybenzimidazole (PBI) at low temperatures, followed by vapor phase infiltration (VPI) to atomically narrow ultramicropores throughout the films, forming hybrid organic–inorganic carbon molecular sieves (CMSs). One VPI cycle (100 s) for the PBI carbonized at 500 °C remarkably increases H 2 /CO 2 selectivity from 9.6 to 83 at 100 °C, surpassing Robeson’s upper bound. The CMS demonstrates a stable H 2 /CO 2 separation performance when challenged with simulated syngas streams and can be fabricated into thin-film composite membranes, outperforming state-of-the-art membranes. Finally, the scalable approach can be ubiquitous to molecularly fine-tune ultramicropores of leading polymeric membranes to further improve their size-sieving ability and thus separation efficiency.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

Abstract Generative models have the capacity to model and generate new examples from a dataset and have an increasingly diverse set of applications driven by commercial and academic interest. In this work, we present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum processor can be used to sample from the model to train the Boltzmann machine. This novel hybrid quantum-classical algorithm joins a growing family of algorithms that use a quantum processor sampling subroutine in deep learning, and provides a scalable framework to test the advantages of quantum-assisted learning. For the latent space model, fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies. Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset. Classical sampling is used to demonstrate the algorithm on the LSUN bedrooms dataset, indicating scalability to larger and color datasets. Though the quantum processor used here is a quantum annealer, the algorithm is general enough such that any quantum processor, such as gate model quantum computers, may be substituted as a sampler.

Wilson, Max (ORCID:0000000207983391)↗

DOE OSTI2024

Oxidation‐Driven Enhancement of Intrinsic Properties in MXene Electrodes for High‐Performance Flexible Energy Storage

Abstract MXenes, a novel class of 2D materials, exhibit great potential for energy storage due to their unique layered structure and excellent electrical conductivity. However, improving the intrinsic electrochemical storage capacity of MXenes remains a significant challenge, often requiring the incorporation of other Faradaic materials. Oxidation, in particular, poses a key issue that impacts the capacity of MXene devices. In this study, controlled oxidation is employed to create nanoscale holes within MXene, transforming them into holey MXene (H‐MXene) nanosheets. These porous structures shorten ion transport distances and increase ion transport pathways, thereby significantly enhancing the electrochemical storage capacity of MXenes. The resulting H‐MXene micro‐supercapacitors (MSCs) demonstrate exceptional performance, achieving an aerial capacitance 2.5 times that of unmodified MXene electrodes, along with excellent cycling stability, retaining 91.7% of their capacitance after 10 000 cycles. Additionally, a flexible integrated system combining energy storage and sensing functionalities is developed, showcasing its scalability in self‐powered sensing applications. The incorporation of self‐healing polyurethane (PU) enables the device to retain 90% of its storage capacity after undergoing self‐healing. This study presents a novel approach for developing high‐performance MXene‐based energy storage devices and provides valuable insights into efficient ion transport and storage in 2D materials.

Cheng, Yongfa [Department of Materials Science and↗

DOE OSTI2021

Scalable Plug-and-Play ADMM with Convergence Guarantees

Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the state-of-the-art performance of PnP algorithms using pre-trained deep neural nets as denoisers in a number of imaging applications. However, current PnP algorithms are impractical in large-scale settings due to their heavy computational and memory requirements. This work addresses this issue by proposing an incremental variant of the widely used PnP-ADMM algorithm, making it scalable to problems involving a large number measurements. Here, we theoretically analyze the convergence of the algorithm under a set of explicit assumptions, extending recent theoretical results in the area. Additionally, we show the effectiveness of our algorithm with nonsmooth data-fidelity terms and deep neural net priors, its fast convergence compared to existing PnP algorithms, and its scalability in terms of speed and memory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Industrial deposition of wavelength-shifting films for liquid argon photon detection systems

The Deep Underground Neutrino Experiment (DUNE) Phase-II Far Detector is considering an approximately 2000 m 2 photon detection system to achieve a target mean light yield of 180 PE/MeV. Meeting this requirement demands scalable, cost-effective, and high-quality wavelength-shifter (WLS) coatings capable of converting 127 nm liquid-argon scintillation light into visible photons with controlled and reproducible optical performance. We report on the successful realization of an industrial physical vapor deposition (PVD) process forp-terphenyl (pTP) coatings, adapted from vacuum deposition techniques developed for OLED display manufacturing, to produce uniform WLS layers on large-area inorganic substrates, a task traditionally challenged by adhesion and uniformity issues at organic-inorganic interfaces. Surface characterization by profilometry and spectroscopic measurements demonstrates edge-region thickness variation below 10% and emission spectra consistent with high-quality pTP reference samples. The industrial process demonstrates reproducibility, scalability, and significantly reduced production time compared to laboratory-based methods, while maintaining optical characteristics consistent with established pTP reference samples. These results establish a viable pathway for mass production of high-performance pTP coatings for DUNE FD3 and future neutrino experiments, from a coating manufacturing and process standpoint. Detector-level performance validation, including quantitative VUV conversion efficiency measurements at 127 nm, is identified as future work.

47 OTHER INSTRUMENTATION↗

NASA NTRS2013

Regression Verification Using Impact Summaries

Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program versions [19]. These techniques compare two programs with a large degree of syntactic similarity to prove that portions of one program version are equivalent to the other. Regression verification can be used for guaranteeing backward compatibility, and for showing behavioral equivalence in programs with syntactic differences, e.g., when a program is refactored to improve its performance, maintainability, or readability. Existing regression verification techniques leverage similarities between program versions by using abstraction and decomposition techniques to improve scalability of the analysis [10, 12, 19]. The abstractions and decomposition in the these techniques, e.g., summaries of unchanged code [12] or semantically equivalent methods [19], compute an over-approximation of the program behaviors. The equivalence checking results of these techniques are sound but not complete-they may characterize programs as not functionally equivalent when, in fact, they are equivalent. In this work we describe a novel approach that leverages the impact of the differences between two programs for scaling regression verification. We partition program behaviors of each version into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Only the impacted program behaviors are used during equivalence checking. We then prove that checking equivalence of the impacted program behaviors is equivalent to checking equivalence of all program behaviors for a given depth bound. In this work we use symbolic execution to generate the program behaviors and leverage control- and data-dependence information to facilitate the partitioning of program behaviors. The impacted program behaviors are termed as impact summaries. The dependence analyses that facilitate the generation of the impact summaries, we believe, could be used in conjunction with other abstraction and decomposition based approaches, [10, 12], as a complementary reduction technique. An evaluation of our regression verification technique shows that our approach is capable of leveraging similarities between program versions to reduce the size of the queries and the time required to check for logical equivalence. The main contributions of this work are: - A regression verification technique to generate impact summaries that can be checked for functional equivalence using an off-the-shelf decision procedure. - A proof that our approach is sound and complete with respect to the depth bound of symbolic execution. - An implementation of our technique using the LLVMcompiler infrastructure, the klee Symbolic Virtual Machine [4], and a variety of Satisfiability Modulo Theory (SMT) solvers, e.g., STP [7] and Z3 [6]. - An empirical evaluation on a set of C artifacts which shows that the use of impact summaries can reduce the cost of regression verification.

Backes, John↗

DOE OSTI2026

Single-crystal NMC622 from combustion-assisted solid-state synthesis: Tunable particle size, voltage-dependent stability, and Li-ion diffusion

Cost-effective and scalable synthesis of single-crystal lithium–metal-oxide cathodes with controlled size and morphology remains challenging for advancing high-performance lithium-ion batteries. Here, we demonstrate a solution combustion–assisted solid-state method for producing single-crystal NMC622 with tunable particle size at various lithium stoichiometries. Structural and electronic characteristics were probed using synchrotron-XANES, EXAFS, XRD and XPS, confirming well-defined coordination environments and phase purity up to Li ≈ 1.3. The electrochemical behavior of LixNMC (x = 1.0 and 1.3) single crystals was evaluated across 2.8–4.3 V and 2.8–4.7 V windows to elucidate the effects of lithium content and operating voltage. At 2.8–4.3V, Li1.3NMC exhibits higher initial capacity due to its larger lithium inventory, while Li1.0NMC shows superior long-term retention driven by its larger crystal size and reduced structural distortion. Increasing the cutoff voltage to 4.7 V enhances the initial capacity of both compositions by 17–25%, but long-term capacity retention ultimately converges to values comparable to those at 4.3 V due to voltage-induced degradation, where Li+ diffusion constant remains lying ranging from 10−12 –10−11 cm2s−1. Overall, this study establishes a scalable combustion-assisted route for synthesizing tunable single-crystal NMC622 and highlights the interplay between lithium stoichiometry, particle size, and voltage window in governing cathode stability.

Roy, Subrata C [Jackson State University]↗

DOE OSTI2023

Scalable and Cost-Effective Roll-To-Roll Additive Manufacturing of Highly Durable and Thermal Insulating Silica-Carbon Aerogel (Final Report)

The research objective of this project is to demonstrate low cost silica aerogel insulation materials with high R-value and low installed price of R7.5/inch, and $0.94/ft2-inch, respectively, which can meet the cost and performance targets of the Department of Energy’s 2030 Building Technology Office Emerging Technologies program. The main tasks of this project include the following three topics: a) Low-cost silica gel precursor: Repeat silica sol gel chemical approach which is reproducible and scalable. Also explore suitability of water-glass on silica aerogel system to further control cost efficiency. In addition, our aim is to synthesize hollow silica using low-cost precursors. Sodium silicate (water glass) was chosen as our silica source because of its availability and low price compared to the other precursors; b) In situ ambient pressure-drying (APD) and surface modification: confirm and improve silica aerogel under cost-effective continuous ambient pressure-drying and investigate silica aerogel performance under time and temperature dependence; c) Roll-to-Roll (R2R) manufacturing with the targeted structure, thermal and mechanical characteristics: evaluate 3D printing with silica aerogel precursor to modify aerogel formation.

36 MATERIALS SCIENCE↗