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

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Treatment of cholinergic‐induced status epilepticus with polytherapy targeting GABA and glutamate receptors

Abstract Despite new antiseizure medications, the development of cholinergic‐induced refractory status epilepticus (RSE) continues to be a therapeutic challenge as pharmacoresistance to benzodiazepines and other antiseizure medications quickly develops. Studies conducted by Epilepsia . 2005;46:142 demonstrated that the initiation and maintenance of cholinergic‐induced RSE are associated with trafficking and inactivation of gamma‐aminobutyric acid A receptors (GABA A R) thought to contribute to the development of benzodiazepine pharmacoresistance. In addition, Dr. Wasterlain's laboratory reported that increased N‐methyl‐ d ‐aspartate receptors (NMDAR) and alpha‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid receptors (AMPAR) contribute to enhanced glutamatergic excitation ( Neurobiol Dis . 2013;54:225; Epilepsia . 2013;54:78). Thus, Dr. Wasterlain postulated that targeting both maladaptive responses of reduced inhibition and increased excitation that is associated with cholinergic‐induced RSE should improve therapeutic outcome. We currently review studies in several animal models of cholinergic‐induced RSE that demonstrate that benzodiazepine monotherapy has reduced efficacy when treatment is delayed and that polytherapy with drugs that include a benzodiazepine (eg midazolam and diazepam) to counter loss of inhibition, concurrent with an NMDA antagonist (eg ketamine) to reduce excitation provide improved efficacy. Improved efficacy with polytherapy against cholinergic‐induced seizure is demonstrated by reduction in (1) seizure severity, (2) epileptogenesis, and (3) neurodegeneration compared with monotherapy. Animal models reviewed include pilocarpine‐induced seizure in rats, organophosphorus nerve agent (OPNA)‐induced seizure in rats, and OPNA‐induced seizure in two mouse models: (1) carboxylesterase knockout (Es1 −/− ) mice which, similarly to humans, lack plasma carboxylesterase and (2) human acetylcholinesterase knock‐in carboxylesterase knockout (KIKO) mice. We also review studies showing that supplementing midazolam and ketamine with a third antiseizure medication (valproate or phenobarbital) that targets a nonbenzodiazepine site rapidly terminates RSE and provides further protection against cholinergic‐induced SE. Finally, we review studies on the benefits of simultaneous compared with sequential drug treatments and the clinical implications that lead us to predict improved efficacy of early combination drug therapies. The data generated from seminal rodent studies of efficacious treatment of cholinergic‐induced RSE conducted under Dr. Wasterlain's guidance suggest that future clinical trials should treat the inadequate inhibition and temper the excess excitation that characterize RSE and that early combination therapies may provide improved outcome over benzodiazepine monotherapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Phenanthroline-strapped calix[4]pyrroles: anion receptors displaying affinity reversal as a function of solvent polarity

Calix[4]pyrroles 1 and 2, diametrically strapped with a phenanthroline via ester and amide linkages, respectively, have been synthesized as anion receptors. It was revealed by 1 H NMR spectroscopic analysis that receptors 1 and 2 possessing both hydrogen bonding donors and acceptors were able to bind the bicarbonate anion (as its tetraethylammonium (TEA + ) or sodium salt) in CDCl 3 , as well as in 15% aqueous DMSO with high affinity and selectivity over other anions. The amide-based receptor 2 contains additional potential hydrogen bonding donors relative to its ester-based congener 1. Nevertheless, in CDCl 3 receptor 1 was found to display a higher affinity for all test anions than receptor 2. In contrast, in 15% aqueous DMSO solution the affinities of receptor 2 for anions, in particular chloride, bicarbonate, and dihydrogen phosphate, were enhanced, whereas those of receptor 1 were reduced dramatically with no appreciable interaction being seen in the case of most test anions considered in this study. These reversals in selectivity and affinities underscore the importance of solvent in regulating the recognition features of seemingly similiar anion binding agents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

ELASTOMERIC MICROVASCULAR SELF-HEALING MATERIALS

Damaged elastomeric diaphragms within pneumatic controllers used in the oil and gas industry lead to an unintended release of methane. Self-healing microvascular materials capable of healing various types of damage have been fabricated. These microvascular materials are designed to replace currently available commercial diaphragms found in pneumatic controllers and provide a solution to reduce unintended methane leaks. Poly(dimethylsiloxane) (PDMS) was used as the main matrix material, with additional testing conducted on polyurethane and flexibilised epoxy materials. Microvascular networks were implemented into the elastomeric membranes to act as ves- sels to deliver healing agents to the damaged areas. The complex, interdigitated channel networks were created using a 3D printed custom compounded filament composed of polylactic acid (PLA) and tin(II) oxalate. Two-part liquid casting polymers were poured around the channels into a mould to create samples with the scaffolding of the microvascular channels intact. Hollow microvascular networks were created by placing the samples within a vacuum oven at 250◦C which causes the thermal depolymerisation of PLA into its gaseous monomers. The manufactured materials were placed in test stands and pressurized using nitrogen gas to determine their healing and mechanical behaviour. Self-healing behaviour was demonstrated using PDMS matrix materials and healing chemistries within a pressure test cell. This test cell was designed to detect any damage to a sample by record- ing an outlet pressure. Damages including puncture holes, diagonal cuts and star-shaped central iv cuts were applied to the samples and a reduction in the outlet pressure was recorded for all sam- ples. A variation in channel spacing and diameter was studied to determine the optimal design of the microvascular network for self-healing performance. The mechanical performance of elastomeric membrane materials within a diaphragmatic pressurised stand was observed. Deformations and strains around the channels and channel inter- sections at the midplane of the materials were recorded using digital image correlation. Normal stresses were calculated using Hooke’s law and the material properties of the PDMS matrix. A reduction in leak rate, implying the success of self-healing, was recorded within manu- factured diaphragm samples tested within a commercial valve. PDMS is the favourable material when compared to polyurethane and flexibilised epoxy for creating

03 NATURAL GAS↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a two-agent soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak demand reduction. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operations to enhance control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment and reduce peak load demand from feeder's head.

deep reinforcement learning↗
DOE OSTI2024

Wholesale Electricity Market Design to Support Resource Adequacy

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

agent based modeling↗
DOE OSTI2024

Hydrogel-Salt Hydrate Composite for Highly Stable Heat Energy Storage with Reduced Supercooling

Phase change materials (PCM) have potential for use in thermal energy storage in buildings, medical devices, and water heat pumps. Sodium sulfate hydrate (SSD) is appealing due to its high energy storage capability and affordability. However, SSD has issues including high supercooling (> 15°C) and low thermal cyclic stability. In this study, we introduced an ionic molecular nucleating agent that decreases the supercooling temperature to under 2°C. When this SSD was combined with a hydrogel, it maintained its thermal energy storage capacity for over 100 cycles without any decline. The success is attributed to the polymer confining the SSD crystals, preventing large-scale phase separation and the nucleating agent which resulted in nucleation of many small SSD crystals at small undercoolings rather than a small number of larger crystals. As a proof-of-application, we synthesized this composite at a kg scale and demonstrated its properties in a close to real-world demonstration.

chemical composition, thermodynamics↗
DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
DOE OSTI2023

Avirulent Isolates of Penicillium chrysogenum to Control the Blue Mold of Apple Caused by P. expansum

Blue mold is an economically significant postharvest disease of pome fruit that is primarily caused by Penicillium expansum. To manage this disease and sustain product quality, novel decay intervention strategies are needed that also maintain long-term efficacy. Biocontrol organisms and natural products are promising tools for managing postharvest diseases. Here, two Penicillium chrysogenum isolates, 404 and 413, were investigated as potential biocontrol agents against P. expansum in apple. Notably, 404 and 413 were non-pathogenic in apple, yet they grew vigorously in vitro when compared to the highly aggressive P. expansum R19 and Pe21 isolates. Whole-genome sequencing and species-specific barcoding identified both strains as P. chrysogenum. Each P. chrysogenum strain was inoculated in apple with the subsequent co-inoculation of R19 or Pe21 simultaneously, 3, or 7 days after prior inoculation with 404 or 413. The co-inoculation of these isolates showed reduced decay incidence and severity, with the most significant reduction from the longer establishment of P. chrysogenum. In vitro growth showed no antagonism between species, further suggesting competitive niche colonization as the mode of action for decay reduction. Both P. chrysogenum isolates had incomplete patulin gene clusters but tolerated patulin treatment. Finally, hygromycin resistance was observed for both P. chrysogenum isolates, yet they are not multiresistant to apple postharvest fungicides. Overall, we demonstrate the translative potential of P. chrysogenum to serve as an effective biocontrol agent against blue mold decay in apples, pending practical optimization and formulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Lasofoxifene as a potential treatment for therapy-resistant ER-positive metastatic breast cancer

Background: Endocrine therapy remains the mainstay of treatment for estrogen receptor-positive (ER+) breast cancer. Constitutively active mutations in the ligand binding domain of ERα render tumors resistant to endocrine agents. Breast cancers with the two most common ERα mutations, Y537S and D538G, have low sensitivity to fulvestrant inhibition, a typical second-line endocrine therapy. Lasofoxifene is a selective estrogen receptor modulator with benefits on bone health and breast cancer prevention potential. This study investigated the anti-tumor activity of lasofoxifene in breast cancer xenografts expressing Y537S and D538G ERα mutants. The combination of lasofoxifene with palbociclib, a CDK4/6 inhibitor, was also evaluated. Methods: Luciferase-GFP tagged MCF7 cells bearing wild-type, Y537S, or D538G ERα were injected into the mammary ducts of NSG mice (MIND model), which were subsequently treated with lasofoxifene or fulvestrant as single agents or in combination with palbociclib. Tumor growth and metastasis were monitored with in vivo and ex vivo luminescence imaging, terminal tumor weight measurements, and histological analysis. Results: As a monotherapy, lasofoxifene was more effective than fulvestrant at inhibiting primary tumor growth and reducing metastases. Adding palbociclib improved the effectiveness of both lasofoxifene and fulvestrant for tumor suppression and metastasis prevention at four distal sites (lung, liver, bone, and brain), with the combination of lasofoxifene/palbociclib being generally more potent than that of fulvestrant/palbociclib. X-ray crystallography of the ERα ligand binding domain (LBD) shows that lasofoxifene stabilizes an antagonist conformation of both wild-type and Y537S LBD. The ability of lasofoxifene to promote an antagonist conformation of Y537S, combined with its long half-life and bioavailability, likely contributes to the observed potent inhibition of primary tumor growth and metastasis of MCF7 Y537S cells. Conclusions: We report for the first time the anti-tumor activity of lasofoxifene in mouse models of endocrine therapy-resistant breast cancer. The results demonstrate the potential of using lasofoxifene as an effective therapy for women with advanced or metastatic ER+ breast cancers expressing the most common constitutively active ERα mutations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

LDRD FY25 Program Overview

As Lawrence Livermore National Laboratory’s (LLNL’s) Laboratory Directed Research and Development (LDRD) program enters its fifth decade of leading-edge research and development, its impact and importance have never been stronger. The program continues to advance strategic investments in pioneering science, technology, and engineering, ensuring LLNL will be ready to deliver on our mission as it evolves over the coming decades. Investing in LDRD research, and the people who perform this critical work, gives LLNL the ability to sustain our role as a leader in the Department of Energy and National Nuclear Security Administration enterprise. The LDRD program enables high-risk, high-payoff research that anticipates emerging threats and future mission needs. By nurturing the ingenuity of the Lab’s greatest asset, its people, LDRD funding advances not only our research but also grows and nurtures our workforce: engaging future innovators with student mentoring, challenging postdoctoral researchers to apply their skills to support national security, and strengthening the leadership skills of early career staff. This annual report documents how LDRD investments advance LLNL’s science, technology, and engineering across our mission space. To assess LDRD’s impact we track both short and long-term metrics such as peer-reviewed publications, number of students, or professional fellows. In addition to reviewing these metrics, I encourage you to delve deeper into the breadth of science and technology that illustrate the strategic value of this research portfolio. For instance, a recent exploratory research project used advanced manufacturing to construct miniaturized three-dimensional ion traps for a quantum computer with reduced quantum error rates to enable applications that address national security missions and support basic science. Another project has delved into studying detonation by examining deflagration to enhance the safety and security of the nuclear weapons stockpile. LDRD researchers are also deploying AI agents on two of the world’s most powerful supercomputers to automate and accelerate inertial confinement fusion experiments. Other teams are delivering more accurate optical constants to enable improved validation for aluminum to advance atomic and molecular physics models. LDRD-driven discoveries of how metals deform under extreme conditions strengthen our ability to model and design materials for demanding national security environments. National security challenges are increasingly complex and continuously evolving. LDRD focuses our most innovative science and technology on these challenges, ensuring the Laboratory is developing creative, forward-leaning solutions for our nation and the world. The following pages feature highlights of published scientific advances, patents, and honors that stem from LDRD investments. As you read this report, I hope you will understand how these investments position the Laboratory, and our partners, to meet the demands of the decades ahead.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Role of Lignin Molecular Weight on Activated Carbon Pore Structure

Over the past decade, the production of biofuels from lignocellulosic biomass has steadily increased to offset the use of fuels from petroleum. To make biofuels cost-competitive, however, it is necessary to add value to the “ligno-” components (up to 30% by mass) of the biomass. The properties of lignin, in terms of molecular weight (MW), chemical functionality, and mineral impurities often vary from biomass source and biorefinery process, resulting in a challenging precursor for product development. Activated carbon (AC) is a feasible target for the lignin-rich byproduct streams because it can be made from nearly any biomass, and it has a market capacity large enough to use much of the lignin generated from the biorefineries. However, it is not known how the variability in the lignin affects the key properties of AC, because, until now, they could not be well controlled. In this work, various fractions of ultraclean (<0.6% ash) lignin are created with refined MW distributions using Aqueous Lignin Purification using Hot Agents (ALPHA) and used as precursors for AC. AC is synthesized via zinc chloride activation and characterized for pore structure and adsorption capacity. We show that AC surface area and the adsorption capacity increase when using lignin with increasing MW, and, furthermore, that reducing the mineral content of lignin can significantly enhance the AC properties. The surface area of the AC from the highest MW lignin can reach ~1830 m2/g (absorption capacity). Furthermore, single step activation carbonization using zinc chloride allows for minimal carbon burn off (<30%), capturing most of the lignin carbon compared to traditional burn off methods in biorefineries for heat generation.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Analogous wheat root rhizosphere microbial successions in field and greenhouse trials in the presence of biocontrol agents Paenibacillus peoriae SP9 and Streptomyces fulvissimus FU14

Abstract Two Pythium ‐infested soils were used to compare the wheat root and rhizosphere soil microbial communities from plants grown in the field or in greenhouse trials and their stability in the presence of biocontrol agents. Bacteria showed the highest diversity at early stages of wheat growth in both field and greenhouse trials, while fungal diversity increased later on, at 12 weeks of the crop cycle. The microbial communities were stable in roots and rhizosphere samples across both soil types used in this study. Such stability was also observed irrespective of the cultivation system (field or greenhouse) or addition of biocontrol coatings to wheat seeds to control Pythium disease (in this study soil infected with Pythium sp. clade F was tested). In greenhouse plant roots, Archaeorhizomyces , Debaryomyces , Delftia , and unclassified Pseudeurotiaceae were significantly reduced when compared to plant roots obtained from the field trials. Some operational taxonomic units (OTUs) represented genetic determinants clearly transmitted vertically by seed endophytes (specific OTUs were found in plant roots) and the plant microbiota was enriched over time by OTUs from the rhizosphere soil. This study provided key information regarding the microbial communities associated with wheat roots and rhizosphere soils at different stages of plant growth and the role that Paenibacillus and Streptomyces strains play as biocontrol agents in supporting plant growth in infested soils.

Araujo, Ricardo↗
DOE OSTI2021

Investigation of interlayer adhesion of 3D printable cementitious material from the aspect of printing process

The safety of structure printed by extrusion-based 3D concrete printing (3DCP) is significantly influenced by interlayer bonding, which is governed by fresh material properties and processes, including mixing, printing, and post-processing phases. Much literature focused on improving interlayer bond strength via material tailoring/bonding agent addition but gave insufficient attention to other phases' impacts. This study investigates the effects of parameters on interlayer bond strength from different phases in 3DCP, including superplasticizer dosage, printing speed, and curing condition. In the mixing phase, the superplasticizer dosage was increased to reduce I{sub thix} and increase surface moisture content (SMC), consequently improving interface microstructure and interlayer bond strength. In the printing phase, similar results were observed by increasing pumping speed to increase material shear rate. In the post-processing phase, proper curing also improved interlayer bond strength. While SMC has a critical impact on interlayer bond strength, material thixotropic index (I{sub thix}) probably has a stronger influence.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗
DOE OSTI2025

Upcycling of polyethylene terephthalate to high-value chemicals by carbonate-interchange deconstruction

Condensation thermoplastics have become ubiquitous. The emergence of chemical upcycling could transform them into valuable feedstocks for chemical manufacturing at their end-of-life. However, current solvolysis processes suffer equilibrium limitations due to the liberation of reactive byproducts. We report a carbonate interchange deconstruction (CID) methodology for poly(ethylene terephthalate) (PET), where carbonates act as both latent nucleophiles and byproduct sequestering agents. High product selectivity (>95%) is achieved regardless of the targeted terephthalate product, originating from removal of ethylene glycol from the reaction equilibrium via its conversion into various oligoethers. CID is robust to the impurities present in post-consumer waste plastics and significantly reduces solvent demand, with just 10 mL of dimethyl carbonate successfully converting ca. 5 g of mixed PET waste into highly pure dimethyl terephthalate in excellent isolated yield (92%). CID opens a new upcycling paradigm wherein CO2, embedded in carbonates, is leveraged to choreograph the selectivity of an otherwise equilibrium-controlled polymer upcycling process.

Galan, Nick [ORNL]↗
DOE OSTI2022

Network Reconfiguration for Enhanced Operational Resilience Using Reinforcement Learning

This paper proposes a reinforcement learning-based approach for distribution network reconfiguration(DNR) to enhance the resilience of the electric power supply. Resilience enhancements usually require solving large-scale stochastic optimization problems that are computationally expensive and sometimes infeasible. The exceptional performance of reinforcement learning techniques has encouraged their adoption in various power system control studies, specifically resilience-based real-time applications. In this paper, a single agent framework is developed using an Actor-Critic algorithm (ACA) to determine statuses of tie-switches in a distribution feeder impacted by an extreme weather event. The proposed approach provides a fast-acting control algorithm that reconfigures the feeder topology to reduce or even avoid load shedding. The problem is formulated as a discrete Markov decision process in such a way that a system state captures the system topology and its operational characteristics. An action is made to open or close a specific set of tie-switches after which a reward is calculated to evaluate the practicality and advantage of that action. The iterative Markov process is used to train the proposed ACA under diverse failure scenarios and is demonstrated on the 33-node distribution feeder system. Results show the capability of the proposed ACA to determine proper switching action of tie-switches with accuracy exceeding 93%.

actor critic↗
DOE OSTI2025

Force-Triggered, Biobased Sealants for Prefabricated Building Components: Toward Improved Efficiency and Performance

The prefabricated building construction industry has made extensive progress in expediting the manufacture of prefabricated components at off-site plants. However, the sealing of joints between these components, which is crucial to ensuring the weatherproofing of the assembly, still represents a labor-intensive, on-site effort that relies on the manual installation of tapes and caulks. Here, to reduce work at the jobsite and improve the airtightness and waterproofness of building envelopes, we developed a sealant that can be installed at the plant on prefab components and have the curing reaction triggered at the jobsite by using microencapsulation technology to separate the reactive agents. A series of force-triggered, high-strength, and fast-curing sealants derived from biobased feedstocks were developed, which consist of a biobased epoxy agent encapsulated in a polymer shell, embedded in a biobased amine curing agent. The shell of the microcapsules allows an effective separation of the reactive species in the one-part sealant, allowing shelf stability to an otherwise fast-curing system as well as improving the hydrophobicity of the whole system. When force activates and breaks the microcapsules, the highly reactive epoxy and amine mix and cure, exhibiting peel strength values of up to 143 ppi (pounds per inch). The hydrophobicity of the sealants allows them to retain up to 94% of the original peel strength after complete submersion in water for 24 h, showcasing the water resistivity of the sealant system. The open-air shelf stability of the sealant complex is demonstrated by the obtention of peel strength values of ∼16 ppi when triggering the curing reaction even after being exposed 8 months to open air and humidity. The successful on-demand triggering of curing reactions and the shelf stability provide efficacy of these force-triggered sealants for installation on prefabricated components, storage for months prior to delivery, and assembly at a jobsite. These force-triggered biobased sealants for prefabricated buildings can result in lower installation time and cost and better performance than tapes and caulks at the jobsite.

biobased↗
DOE OSTI2026

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture Codesign Package (SNAC-Pack), an open-source AutoML framework for hardware-aware neural architecture codesign and end-to-end FPGA deployment. SNAC-Pack runs a multi-objective global search with Optuna and NSGA-II, loading trials to a shared SQLite store that enables parallel workers across compute nodes. A hardware surrogate model outputs per-trial resource and latency estimates, avoiding the synthesis cost that would otherwise dominate the search loop. A local search stage then applies quantization-aware training (QAT) together with iterative magnitude pruning in a combined compression loop, after which the final model is synthesized to FPGA firmware via the hls4ml Python library. A YAML configuration and an optional agentic frontend let users run the pipeline on new datasets without modifying the framework. We demonstrate SNAC-Pack on jet classification at the Large Hadron Collider and superconducting qubit readout, discovering compact architectures that match or exceed strong baselines on the task metric while reducing FPGA resource utilization and, in the qubit readout case, reducing the design space exploration process from months of manual fine-tuning to hours of automated search.

Weitz, Jason [UC, San Diego]↗