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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

The commercial U.S. light-water reactor fleet has been operating at historical efficiency, reliability, and safety over the last decade. Nuclear power has the highest capacity factor of any other power generation technology while also serving as the largest baseload source for carbon-free energy. Despite this remarkable achievement, continued operations for many plants are threatened due to fierce electricity market competition and rising operations and maintenance costs of which continued maintenance of obsolete analog equipment is a contributor. The digital age and associated technologies are where the future lies in process control, and nuclear has yet to take full advantage of the capabilities offered therein. The Light Water Reactor Sustainability Program (LWRS) at Idaho National Laboratory (INL), sponsored by the Department of Energy, has a mission to help the light-water reactor fleet manage its foundational capabilities to continue providing safe and reliable carbon-free power. LWRS helps support that mission by providing scientific, technology-based solutions for advanced concepts of operations with a more viable business model that will allow the fleet to continue to operate at peak levels through extended plant operation. The LWRS Digitalization Project at INL seeks to leverage digital technologies to synthesize and transform work processes. We provide a state-of-the-art analysis of digitalized work processes in nuclear power and investigate ways in which researchers at INL and the nuclear industry can work together to identify what data to access, how to access it, what to do with the data, and most importantly, how to use the insights for decision-making across all levels within the business. Borne from these considerations, we present four guiding principles for digitalization: develop a coherent digitalization plan, apply human factors engineering, establish data governance, and anticipate unintended consequences. Together, these principles form a method that plants can use to effectively to digitalize nuclear industry work processes. Our guiding principles are informed by multiple knowledge sources. First, we document activities from the Work Digitalization Initiative, which was conceived as a means for nuclear organizations to help define and standardize the industry’s approach to digitalizing work. Second, we detail primary research conducted with industry professionals regarding drivers and barriers to digitalization adoption. We present survey results that demonstrate what the industry hopes to get out of digitalization and the ways that INL can continue to support the industry’s digital transformation. Third, we present a digitalization use case with industry partners NextAxiom Technology and Xcel Energy. The project objective was to transform the current condition report work process from paper to digital, incorporating digitalized principles. We report the development of the application and lessons learned. The accomplishments achieved by this research and development serve to identify critical needs for plant guidance in support of digitalization implementation and contribute to the knowledge and strategies available for utilities considering or undertaking digitalization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Accelerating Structure–Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy

Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting data set diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral data sets. Dual-VAE embeds local structures and spectroscopic responses into a shared latent manifold that serves as a structure–property relationship map. We applied this framework for the investigation of halide perovskite films by using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress the charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials.

atomic force microscopy↗
DOE OSTI2020

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices, including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Finally, future research directions are discussed, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, improved metrics of measuring building performance, and improved industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The aging proteostasis decline: From nematode to human

The aging proteostasis decline manifests in a failure of aging cells and organisms to properly respond to proteotoxic challenges. This proteostasis collapse has long been considered a hallmark of aging in nematodes, and has recently been shown to occur also in human cells upon entry to senescence, opening the way to exploring the phenomenon in the broader context of human aging. Cellular senescence is part of the normal human physiology of aging, with senescent cell accumulation as a prominent feature of aged tissues. Being highly resistant to cell death, senescent cells, as they accumulate, become pro-inflammatory and promote disease. Here we discuss the causes of human senescence proteostasis decline, in view of the current literature on nematodes, on the one hand, and senescence, on the other hand. We review two major aspects of the phenomenon: (1) the decline in transcriptional activation of stress-response pathways, and (2) impairments in proteasome function. We further outline potential underlying mechanisms of transcriptional proteostasis decline, focusing on reduced chromatin dynamics and compromised nuclear integrity. Finally, we discuss potential strategies for reinforcing proteostasis as a means to improve organismal health and address the relationship to senolytics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Model metamers reveal divergent invariances between biological and artificial neural networks

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated ‘model metamers’, stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances. Targeted model changes improved human recognizability of model metamers but did not eliminate the overall human–model discrepancy. The human recognizability of a model’s metamers was well predicted by their recognizability by other models, suggesting that models contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A perspective on iron (Fe) in the atmosphere: air quality, climate, and the ocean

As scientists engage in research motivated by climate change and the impacts of pollution on air, water, and human health, we increasingly recognize the need for the scientific community to improve communication and knowledge exchange across disciplines to address pressing and outstanding research questions holistically. Our professional paths have crossed because our research activities focus on the chemical reactivity of Fe-containing minerals in air and water, and at the air-sea interface. (Photo)chemical reactions driven by Fe can take place at the surface of the particles/droplets or within the condensed phase. The extent and rates of these reactions are influenced by water content and biogeochemical activity ubiquitous in these systems. One of these reactions is the production of reactive oxygen species (ROS) that cause damage to respiratory organs. Another is that the reactivity of Fe and organics in aerosol particles alter surficial physicochemical properties that impact aerosol-radiation and aerosol–cloud interactions. Also, upon deposition, aerosol particles influence ocean biogeochemical processes because micronutrients such as Fe or toxic elements such as copper become bioavailable. We provide a perspective on these topics and future research directions on the reactivity of Fe in atmospheric aerosol systems, from sources to short- and long-term impacts at the sinks with emphasis on needs to enhance the predictive power of atmospheric and ocean models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Structural basis for severe pain caused by mutations in the voltage sensors of sodium channel NaV1.7

Voltage-gated sodium channels in peripheral nerves conduct nociceptive signals from nerve endings to the spinal cord. Mutations in voltage-gated sodium channel NaV1.7 are responsible for a number of severe inherited pain syndromes, including inherited erythromelalgia (IEM). Here, we describe the negative shifts in the voltage dependence of activation in the bacterial sodium channel NaVAb as a result of the incorporation of four different IEM mutations in the voltage sensor, which recapitulate the gain-of-function effects observed with these mutations in human NaV1.7. Crystal structures of NaVAb with these IEM mutations revealed that a mutation in the S1 segment of the voltage sensor facilitated the outward movement of S4 gating charges by widening the pathway for gating charge translocation. In contrast, mutations in the S4 segments modified hydrophobic interactions with surrounding amino acid side chains or membrane phospholipids that would enhance the outward movement of the gating charges. These results provide key structural insights into the mechanisms by which these IEM mutations in the voltage sensors can facilitate outward movements of the gating charges in the S4 segment and cause hyperexcitability and severe pain in IEM. Our work gives new insights into IEM pathogenesis at the near-atomic level and provides a molecular model for mutation-specific therapy of this debilitating disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

JAK inhibition reduces SARS-CoV-2 liver infectivity and modulates inflammatory responses to reduce morbidity and mortality

Using AI, we identified baricitinib as having antiviral and anticytokine efficacy. We now show a 71% (95% CI 0.15 to 0.58) mortality benefit in 83 patients with moderate-severe SARS-CoV-2 pneumonia with few drug-induced adverse events, including a large elderly cohort (median age, 81 years). An additional 48 cases with mild-moderate pneumonia recovered uneventfully. Using organotypic 3D cultures of primary human liver cells, we demonstrate that interferon-α2 increases ACE2 expression and SARS-CoV-2 infectivity in parenchymal cells by greater than fivefold. RNA-seq reveals gene response signatures associated with platelet activation, fully inhibited by baricitinib. Using viral load quantifications and superresolution microscopy, we found that baricitinib exerts activity rapidly through the inhibition of host proteins (numb-associated kinases), uniquely among antivirals. This reveals mechanistic actions of a Janus kinase-1/2 inhibitor targeting viral entry, replication, and the cytokine storm and is associated with beneficial outcomes including in severely ill elderly patients, data that incentivize further randomized controlled trials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

BONCAT-Live for isolation and cultivation of active environmental bacteria

In diverse environments, microbes drive a myriad of processes, from geochemical and nutrient cycling to interspecies interactions, including associations with plants and animals. Their physiological state is dynamic and impacted by abiotic and biotic conditions, responding to environmental fluctuations by changes in cellular metabolism, according to their genetic potential. Molecular, cellular, and genomic approaches can identify and measure microbial responses and adaptation to environmental changes in native communities. However, isolating individual microbial cells that respond to specific changes for cultivation has been difficult. To address this, we implemented a novel bacterial isolation approach (BONCAT-Live) by integrating bio-orthogonal non-canonical amino acid tagging (BONCAT) in diverse native communities, with isolation and cultivation of cells responding to specific stimuli, at different time scales. In frozen Arctic permafrost samples, we identified and isolated dormant bacteria that become active after thawing under native or nutrient-enriched conditions. From the Populus tree rhizosphere, we isolated strains that thrive under high concentrations of root exudates that act as defense compounds and nutrients. In the human microbiome, we identified and isolated bacteria that rapidly proliferated when exposed to metabolites provided by the host or other co-occurring microbes. Further characterization of isolated bacterial strains will provide opportunities for in-depth determination of how these microbes adapt to changes in their environments, individually and as part of model communities.

Analytical Methods↗
DOE OSTI2022

A Novel EGFRvIII T-Cell Bispecific Antibody for the Treatment of Glioblastoma

Abstract T-cell bispecific antibodies (TCB) are engineered molecules that bind both the T-cell receptor and tumor-specific antigens. Epidermal growth factor receptor variant III (EGFRvIII) mutation is a common event in glioblastoma (GBM) and is characterized by the deletion of exons 2–7, resulting in a constitutively active receptor that promotes cell proliferation, angiogenesis, and invasion. EGFRvIII is expressed on the surface of tumor cells and is not expressed in normal tissues, making EGFRvIII an ideal neoantigen target for TCBs. We designed and developed a novel 2+1 EGFRvIII-TCB with optimal pharmacologic characteristics and potent antitumor activity. EGFRvIII-TCB showed specificity for EGFRvIII and promoted tumor cell killing as well as T-cell activation and cytokine secretion only in patient-derived models expressing EGFRvIII. Moreover, EGFRvIII-TCB promoted T-cell recruitment into intracranial tumors. EGFRvIII-TCB induced tumor regression in GBM animal models, including humanized orthotopic GBM patient-derived xenograft models. Our results warrant the clinical testing of EGFRvIII-TCB for the treatment of EGFRvIII-expressing GBMs.

Oncology↗
DOE OSTI2022

Δ133p53 coordinates ECM-driven morphogenesis and gene expression in 3-dimensional mammary epithelial acini

Growing evidence indicates that p53 has a crucial role in normal tissue development. The role of the canonical p53 (p53α) and its 12± isoforms in development and homeostasis of healthy tissue remains poorly understood. Here we demonstrate that Δ133p53, three short isoforms of p53 isoform, responds specifically to laminin-111 and plays an important regulatory role in formation of mammary organoids in concert with p53α. We demonstrate that down-modulation of Δ133p53 isoforms leads to changes in gene expression of extracellular matrix molecules: fibronectin (FN), EDA+-FN, laminin α5, and laminin α3 in human breast epithelial cells. These changes resulted in increased actin stress fibers and enhanced migratory behavior of cells in 2-dimensional culture. We found that α5β1-integrin coupled with the extracellularly deposited EDA+-FN activates Akt signaling pathway in 3-dimensional (3D) culture when Δ133p53 is dysregulated. Cells that do not express detectable Δ133p53 isoforms or express low levels of these isoforms failed to form polarized structures in 3D. These results uncover that Δ133p53 isoforms coordinate expression and deposition of organ-specific ECM molecules that are critical for maintenance of tissue architecture and function.

Lee, Sun-Young (ORCID:000000034970241X)↗
DOE OSTI2022

A positive feedback loop involving the Spa2 SHD domain contributes to focal polarization

Focal polarization is necessary for finely arranged cell-cell interactions. The yeast mating projection, with its punctate polarisome, is a good model system for this process. We explored the critical role of the polarisome scaffold protein Spa2 during yeast mating with a hypothesis motivated by mathematical modeling and tested by in vivo experiments. Our simulations predicted that two positive feedback loops generate focal polarization, including a novel feedback pathway involving the N-terminal domain of Spa2. We characterized the latter using loss-of-function and gain-of-function mutants. The N-terminal region contains a Spa2 Homology Domain (SHD) which is conserved from yeast to humans, and when mutated largely reproduced the spa2Δ phenotype. Our work together with published data show that the SHD domain recruits Msb3/4 that stimulates Sec4-mediated transport of Bud6 to the polarisome. There, Bud6 activates Bni1-catalyzed actin cable formation, recruiting more Spa2 and completing the positive feedback loop. We demonstrate that disrupting this loop at any point results in morphological defects. Gain-of-function perturbations partially restored focal polarization in a spa2 loss-of-function mutant without restoring localization of upstream components, thus supporting the pathway order. Thus, we have collected data consistent with a novel positive feedback loop that contributes to focal polarization during pheromone-induced polarization in yeast.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Procedure Parsing: A Method for Parsing Handwritten Documents into Computer-Based Procedures

The nuclear industry is heavily procedure driven, where almost everything has a step-by-step instruction that is expected to be followed in detail. Historically, these procedures were printed on paper copies. Recently, the industry transitioned towards electronic copies (i.e., PDFs on tablets). One major drive for this transition is the introduction of human error and loss of situation awareness when using paper copies. However, electronic copies of documents inherently have the same error traps as their paper cousins. Therefore, there is an increased interest in a way to utilize the information in the step-by-step guidance, but to present it in a dynamic manner that guides the user and adapts to any encountered conditions. Researchers at Idaho National Laboratory propose a flexible, automated method based on document parsing and augmented by natural language processing (NLP) techniques, to address these shortcomings and capitalize on these recent advancements in machine learning. The proposed method provides a cost-effective solution for computer-assisted procedure parsing of hand-written control room procedures, originally authored in Word or PDF formats, into instructions that can be displayed as computer-based procedures (CBP) in a modern graphical user interface. The researchers devised, implemented and demonstrated the Operating Procedure Extender for Novel Systems (OPENS) method in 2020. The key to OPENS is to map the original procedure text into a context-free grammar, tying content to equipment, locations, and other steps, actions, etc. This formal grammar is then used to isolate and define keywords and actions verbs, such as “measure” or “evaluate” and tie them to specific equipment referenced within that step or located in other steps, substeps, actions, subactions and tables throughout the procedure. OPENS generates an abstract syntax tree from the document which it uses to store a copy of this information in the open-standard, machine-readable and human-readable file formats XML and JSON. The XML is useful to preserve the relational aspects of the procedure for referencing tables and branching information so the user can be directed to the next appropriate active step based on the values entered for that step and previous steps. The JSON is useful for storing and exchanging data objects used to track responses to previous steps and state changes in simulated environments. In future iterations, these formats can also be used for storing more detailed information about input during plant operation or simulation. The techniques the researcher developed could further be improved by integration of recent advancements in machine learning. NLP methods could standardize documents, correct for grammatical error, and provide automated semantic validation. The researcher expects that self-supervised techniques applied to collections of natural language instructions could strengthen the model with broader context. All these methods together give us a practical way to automatically extract protocols from documents and user interactions, empowering researchers, procedure writers and nuclear operators while moving the industry forward.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Structural basis for aminoacylation of cellular modified tRNALys3 by human lysyl-tRNA synthetase

Abstract The average eukaryotic transfer ribonucleic acid (tRNA) contains 13 post-transcriptional modifications; however, their functional impact is largely unknown. Our understanding of the complex tRNA aminoacylation machinery in metazoans also remains limited. Herein, using a series of high-resolution cryo-electron microscopy (cryo-EM) structures, we provide the mechanistic basis for recognition and aminoacylation of fully modified cellular tRNALys3 by human lysyl-tRNA synthetase (h-LysRS). The tRNALys3 anticodon loop modifications S34 (mcm5s2U) and R37 (ms2t6A) play an integral role in recognition by h-LysRS. Modifications in the T-, variable-, and D-loops of tRNALys3 are critical for ordering the metazoan-specific N-terminal domain of LysRS. The two catalytic steps of tRNALys3 aminoacylation are structurally ordered; docking of the 3′-CCA end in the active site cannot proceed until the lysyl–adenylate intermediate is formed and the pyrophosphate byproduct is released. Association of the h-LysRS–tRNALys3 complex with a multi-tRNA synthetase complex-derived peptide shifts the equilibrium toward the 3′-CCA end “docked” conformation and allosterically increases h-LysRS catalytic efficiency. The insights presented here have broad implications for understanding the role of tRNA modifications in protein synthesis, the human aminoacylation machinery, and the growing catalog of metabolic and neurological diseases linked to it.

Devarkar, Swapnil C. (ORCID:000000029271243X)↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Arginine Kinase from the Tick Rhipicephalus sanguineus Is an Efficient Biocatalyst

Arginine kinase (AK) is a reversible enzyme that regulates invertebrates’ phosphagen arginine phosphate levels. AK also elicits an immune response in humans, and it is a major food allergen in crustacea and may be a target for novel antiparasitic drugs. Although AK has been primarily described in the shrimp, it is also present in other invertebrates, such as the brown tick Rhipicephalus sanguineus (Rs), the vector for Rocky Mountain Spotted Fever. Here we report the enzymatic activity and the crystal structure of AK from Rhipicephalus sanguineus (RsAK) in an open conformation without substrate or ligands and a theoretical structure of RsAK modeled bound with the substrate/product (Arg-ADP) in a closed conformation. The Michaelis-Menten kinetics confirmed that RsAK is an efficient biocatalyst due to its high kcat/Km parameter. The recombinant enzyme was expressed in bacteria and purified to a 20 mg/L culture yield. AK is an essential enzyme in invertebrates. Future work will be focused on the RsAK enzymatic inhibition that may lead to novel strategies to control this pest, a burden to animal and human health.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Epitope topography of agonist antibodies to the checkpoint inhibitory receptor BTLA

B and T lymphocyte attenuator (BTLA) is an attractive target for a new class of therapeutics that attempt to rebalance the immune system by agonizing checkpoint inhibitory receptors (CIRs). Herpesvirus entry mediator (HVEM) binds BTLA in both trans- and cis-orientations. We report here the development and structural characterization of three humanized BTLA agonist antibodies, 22B3, 25F7, and 23C8. We determined the crystal structures of the antibody-BTLA complexes, showing that these antibodies bind distinct and non-overlapping epitopes of BTLA. While all three antibodies activate BTLA, 22B3 mimics HVEM binding to BTLA and shows the strongest agonistic activity in functional cell assays and in an imiquimod-induced mouse model of psoriasis. 22B3 is also capable of modulating HVEM signaling through the BTLA-HVEM cis-interaction. The data obtained from crystal structures, biochemical assays, and functional studies provide a mechanistic model of HVEM and BTLA organization on the cell surface and informed the discovery of a highly active BTLA agonist.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Machine learning for automated experimentation in scanning transmission electron microscopy

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

36 MATERIALS SCIENCE↗