SEARCH · Engineering Papers

Results for “Memory cell”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Entire expressed peripheral blood transcriptome in pediatric severe malarial anemia

Abstract This study on severe malarial anemia (SMA: Hb < 6.0 g/dL), a leading global cause of childhood morbidity and mortality, compares the entire expressed whole blood host transcriptome between Kenyan children (3-48 mos.) with non-SMA (Hb ≥ 6.0 g/dL, n = 39) and SMA ( n = 18). Differential expression analyses reveal 1403 up-regulated and 279 down-regulated transcripts in SMA, signifying impairments in host inflammasome activation, cell death, and innate immune and cellular stress responses. Immune cell profiling shows decreased memory responses, antigen presentation, and immediate pathogen clearance, suggesting an immature/improperly regulated immune response in SMA. Module repertoire analysis of blood-specific gene signatures identifies up-regulation of erythroid genes, enhanced neutrophil activation, and impaired inflammatory responses in SMA. Enrichment analyses converge on disruptions in cellular homeostasis and regulatory pathways for the ubiquitin-proteasome system, autophagy, and heme metabolism. Pathway analyses highlight activation in response to hypoxic conditions [Hypoxia Inducible Factor (HIF)−1 target and Reactive Oxygen Species (ROS) signaling] as a central theme in SMA. These signaling pathways are also top-ranking in protein abundance measures and a Ugandan SMA cohort with available transcriptomic data. Targeted RNA-Seq validation shows strong concordance with our entire expressed transcriptome data. These findings identify key molecular themes in SMA pathogenesis, offering potential targets for new malaria therapies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Biobased Semi-Interpenetrating Polymer Networks of Poly(ε-caprolactone) and Epoxidized Soybean Oil with Nanoscale Morphology, Shape-Memory Effect, and Biocompatibility

Creating biobased polymer blends with outstanding properties, nanoscale morphology, shape-memory capability, and biocompatibility is very crucial and requires a fundamental understanding of the phase behavior, macromolecular structure, and biological compatibility of the polymer blends with living cells. It is very critical to understand the complex relationships among the polymer structure, morphology, and performance of multifunctional smart materials under conditions that they are likely to encounter during use, particularly in biomedical applications. Biobased semi-interpenetrating polymer networks of poly(ε-caprolactone) and epoxidized soybean oil with nanoscale morphology have been successfully synthesized via in situ cationic polymerization and compatibilization in a homogeneous solution. Varies analytical and characterization techniques, such as Fourier transform infrared spectroscopy, differential scanning calorimetry, dynamic mechanical analysis, transmission electron microscopy, X-ray scattering, cell toxicity, and shape-memory effects (SMEs), have been employed to understand the structure–properties relationship of these smart, biobased nanostructured polymer blends. The synthesized nano blends were nontoxic or biocompatible and supported attachment of human vein endothelial cells, showing their potential use in biomedical applications. The current versatile, low-cost strategy for synthesizing the nanoscale morphology of semi-interpenetrating polymer networks with SMEs and biocompatibility should be widely applicable for polymer systems. This study is also considered as a continuation to our efforts in the area of biobased polymers to develop innovative technologies to transform natural resources into smart multifunctional materials for a wide range of applications, including coatings, adhesives, and medical devices.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Mechanical reticulation of polymeric-based closed cell foams

Polymeric based closed cell foams, such as shape memory polymer foams, contain bubbles. Making these bubbles continuous is called reticulation. Disclosed are embodiments of a device and method to controllably reticulate polymer-based closed cell foams by puncturing the membranes of these polymer-based closed cell foams.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Mechanical reticulation of polymeric-based closed cell foams

Rodriguez, Jennifer N.↗

DOE OSTI2024

Mechanical reticulation of polymeric-based closed cell foams

Rodriguez, Jennifer N.↗

DOE OSTI2022

An anti-PD-1–GITR-L bispecific agonist induces GITR clustering-mediated T cell activation for cancer immunotherapy

Costimulatory receptors such as glucocorticoid-induced tumor necrosis factor receptor–related protein (GITR) play key roles in regulating the effector functions of T cells. In human clinical trials, however, GITR agonist antibodies have shown limited therapeutic effect, which may be due to suboptimal receptor clustering-mediated signaling. To overcome this potential limitation, a rational protein engineering approach is needed to optimize GITR agonist-based immunotherapies. Here we show a bispecific molecule consisting of an anti-PD-1 antibody fused with a multimeric GITR ligand (GITR-L) that induces PD-1-dependent and FcγR-independent GITR clustering, resulting in enhanced activation, proliferation and memory differentiation of primed antigen-specific GITR + PD-1 + T cells. The anti-PD-1–GITR-L bispecific is a PD-1-directed GITR-L construct that demonstrated dose-dependent, immunologically driven tumor growth inhibition in syngeneic, genetically engineered and xenograft humanized mouse tumor models, with a dose-dependent correlation between target saturation and Ki67 and TIGIT upregulation on memory T cells. Anti-PD-1–GITR-L thus represents a bispecific approach to directing GITR agonism for cancer immunotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Molecular pathways for learning in the single-cell Stentor coeruleus

The single-cell Stentor coeruleus contracts in response to mechanical taps but habituates and learns to ignore the taps after repeated stimulation. Here, we explored the molecular changes that occur during the formation of this cellular memory in order to improve our understanding of non-synaptic learning. We impaired cellular protein synthesis with cycloheximide and puromycin and found that, contrary to the effects of such treatments on metazoa, these drugs accelerate habituation and prolong memory retention in Stentor . Exploratory proteomic and transcriptomic analyses identified candidate proteins and genes that changed over the course of habituation and response recovery, pointing toward the regulation of Stentor learning by calcium signaling and protein phosphorylation. Building on these results, we found that using RNA interference to knock down the calcium-binding, EF-hand domain-containing protein SteCoe_6763 accelerated habituation. Furthermore, increased extracellular calcium improved Stentor learning, while treatment with kinase and phosphatase inhibitors impaired learning. In particular, KN-93, a drug known to inhibit calcium/calmodulin-dependent kinase II and voltage-gated calcium channels, decreased both the rate and extent of habituation in Stentor , similar to its effects on learning in metazoa. We also discovered that habituation memory can be maintained in progeny following cell division. Taken together, these results suggest that response recovery in Stentor requires new protein synthesis and that memory formation involves the modification of delocalized mechanoreceptors by phosphorylation and calcium signaling. This is consistent with our previous model of Stentor learning, in which habituation occurs through the inactivation of cell-surface receptors.

CaMKII↗

DOE OSTI2023

Even Faster Exact Exchange for Solids via Tensor Hypercontraction

Hybrid density functional theory (DFT) remains intractable for large periodic systems due to the demanding computational cost of exact exchange. We apply the tensor hypercontraction (THC) (or interpolative separable density fitting) approximation to periodic hybrid DFT calculations with Gaussian-type orbitals using the Gaussian plane wave approach. This is done to lower the computational scaling with respect to the number of basis functions (N) and k-points (N k ) at a fixed system size. Additionally, we propose an algorithm to fit only occupied orbital products via THC (i.e., a set of points, N ISDF ) to further reduce computation time and memory usage. This algorithm has linear scaling cost with k-points, no explicit dependence of N ISDF on basis set size, and overall cubic scaling with unit cell size. Significant speedups and reduced memory usage may be obtained for moderately sized k-point meshes, with additional gains for large k-point meshes. Adequate accuracy can be obtained using THC-oo-K for self-consistent calculations. In conclusion, we perform illustrative hybrid density function theory calculations on the benzene crystal in the basis set and thermodynamic limits to highlight the utility of this algorithm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Superconducting neural networks with disordered Josephson junction array synaptic networks and leaky integrate-and-fire loop neurons

Fully coupled randomly disordered recurrent superconducting networks with additional open-ended channels for inputs and outputs are considered the basis to introduce a new architecture to neuromorphic computing in this work. Various building blocks of such a network are designed around disordered array synaptic networks using superconducting devices and circuits as an example, while emphasizing that a similar architectural approach may be compatible with several other materials and devices. A multiply coupled (interconnected) disordered array of superconducting loops containing Josephson junctions [equivalent to superconducting quantum interference devices (SQUIDs)] forms the aforementioned collective synaptic network that forms a fully recurrent network together with compatible neuron-like elements and feedback loops, enabling unsupervised learning. This approach aims to take advantage of superior power efficiency, propagation speed, and synchronizability of a small world or a random network over an ordered/regular network. Additionally, it offers a significant factor of increase in scalability. Here, a compatible leaky integrate-and-fire neuron made of superconducting loops with Josephson junctions is presented, along with circuit components for feedback loops as needed to complete the recurrent network. Several of these individual disordered array neural networks can further be coupled together in a similarly disordered way to form a hierarchical architecture of recurrent neural networks that is often suggested as similar to a biological brain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Spiers Memorial Lecture: Next generation chalcogenide-based absorbers for thin-film solar cells

Inorganic-based thin-film photovoltaics (TFPV) represents an important component of the growing low-carbon energy market and plays a vital role in the drive toward lower cost and increased penetration of solar energy. Yet, commercialized thin-film absorber technologies suffer from some non-ideal characteristics, such as toxic or non-abundant element use (e.g., CdTe and Cu(In,Ga)(S,Se) 2 , which bring into question their suitability for terawatt deployment. Numerous promising chalcogenide, halide, pnictide and oxide semiconductors are being pursued to bridge these concerns for TFPV and several promising paths have emerged, both as prospective replacements for the entrenched technologies, and to serve as partner (i.e., higher bandgap) absorbers for tandem junction devices—e.g., to be used with a lower bandgap Si bottom cell. The current perspective will primarily focus on emerging chalcogenide-based technologies and provide both an overview of absorber candidates that have been of recent interest and a deeper dive into an exemplary Cu 2 BaSnS 4 -related family. Altogether, considering the combined needs of high-performance, low-cost, and operational stability, as well as the experiences gained from existing commercialized thin-film absorber technologies, chalcogenide-based semiconductors represent a promising direction for future PV development and also serve to highlight common themes and needs among the broader TFPV materials family.

14 SOLAR ENERGY↗

DOE OSTI2024

Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells

Recently, the Generalized Cauchy (GC) process has been applied to capture a Long Memory (LM) phenomenon in product degradation modeling and life prediction. Compared with the traditional fractional Brownian motion that captures the LM using a single Hurst parameter, the GC process has two free parameters (Hurst and fractal dimension parameters) that flexibly capture both global LM and local irregularity. However, all existing GC-based degradation models are for a single Degradation Characteristic (DC). In this article, motivated by a real degradation problem of dye-sensitized solar cells that jointly exhibits multiple DCs, global LM, local irregularity and DC-wise cross-correlation, we propose a novel GC-based Multivariate Degradation Model (GC-MDM) to simultaneously capture the aforementioned effects. A maximum likelihood estimation approach is developed to estimate parameters of the GC-MDM. Subsequently, product life prediction based on the GC-MDM is developed. The proposed GC-MDM is validated through a simulation study and a physical experiment of dye-sensitized solar cells. Furthermore, results show that the proposed GC-MDM fundamentally improves the life prediction accuracy in comparison with conventional degradation models which significantly misestimate the uncertainty of product life.

14 SOLAR ENERGY↗

DOE OSTI2024

Formation and retrieval of cell assemblies in a biologically realistic spiking neural network model of area CA3 in the mouse hippocampus

The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.

hetero-association↗

DOE OSTI2025

Understanding Reliability Trade-Offs in 1T-nC and 2T-nC FeRAM Designs

Ferroelectric random access memory (FeRAM) is a promising candidate for energy-efficient nonvolatile memory, particularly for logic-in-memory and compute-in-memory (CIM) applications. Among the available cell architectures, One-Transistor–n-Capacitor (1T-nC) and two-transistor–n-capacitor (2T-nC) FeRAMs each offer distinct trade-offs in density, scalability, and reliability. In this work, we present a comparative study of these two architectures under both dimensional scaling ( XY/Z shrinkage) and vertical integration (increasing stacked capacitors per cell). Using technology computer-aided design (TCAD) and circuit-level simulations, we analyze how scaling impacts ferroelectric capacitance, parasitic coupling, and floating-node (FN) dynamics, which together dictate sense margin (SM) and read stability. A key mitigation strategy—floating unselected capacitors—is applied to both architectures, effectively decoupling the SM from the number of stacked capacitors and enabling tractable analysis across scaling regimes. Results show that 1T-nC suffers more from charge sharing with the bitline (BL), while 2T-nC benefits from transistor isolation and stronger low-voltage sensing at the cost of increased area. By systematically evaluating these behaviors across scaling directions, this work establishes the reliability trade-offs of 1T-nC and 2T-nC cells and provides design guidelines for high-density, vertically integrated FeRAM systems.

1T-nC↗

DOE OSTI2020

Layer-Dependent Bit Error Variation in 3-D NAND Flash Under Ionizing Radiation

In this paper, we studied the total ionization dose (TID) effects on the multilevel-cell (MLC) 3-D NAND flash memory using Co-60 gamma radiation. We found a significant page-to-page bit error variation within a physical memory block of the irradiated memory chip. Our analysis showed that the origin of the bit error variation is the unique vertical layer-dependent TID response of the 3-D NAND. We found that the memory pages located at the upper and lower layers of the 3-D stack show higher fails compared to the middle-layer pages of a given memory block. We confirmed our findings by comparing radiation response of four different chips of the same specification. In addition, we compared the TID response of the MLC 3-D NAND with that of the 2-D NAND chip, which showed less page-to-page variation in bit error within a given memory block. We discuss the possible application of our findings for the radiation-tolerant smart memory controller design.

3-D NAND flash↗

DOE OSTI2021

Surface Treatment Strategies for Mitigating Gas Hydrate & Asphaltene Formation, Growth, and Deposition in Flowloops

Abstract Numerous solids including gas hydrates, waxes, and asphaltenes have the potential to form in the production lines of gas and oil fields. This creates a highly non-ideal scenario as the accumulation of said species leads to flow assurance issues, especially with long-term processes like deposition. Since an ever-increasing amount of material is deposited in place at the pipe surface, production stoppage or active mitigation efforts become inevitable. The latter production issues result in increased safety risks and operational expenditures. Therefore, a cost-effective, passive deposition mitigation technology, such as a pipeline coating or surface treatment is especially appealing. The ability to address multiple pipeline flow assurance issues simultaneously without actively disrupting production would represent a dramatic step forward in this area. This study is part of a long-term ongoing effort that evaluates the performance and application of an omniphobic surface treatment for solids deposition prevention in industrially relevant systems. In particular, this specific work concentrates on the efficacy and robustness of the treatment under fully flowing conditions. The apparatuses utilized for this include two flowloops: a lab-scale, high-pressure flowloop for gas hydrate and surface treatment durability studies, and a bench-scale, atmospheric pressure loop for crude oil and asphaltene experiments. Film growth in high-pressure flowloop tests corroborated previous reports of delayed gas hydrate nucleation observed in rocking cells. Without the aid of the memory effect, treated oil-dominated experiments never experienced hydrate formation, spending upwards of a week in the hydrate stability zone (at the subcooled/fluid test conditions). Subsequent tests which utilized the memory effect then revealed that the hydrate formation rate reduced in the presence of the surface treatment compared to a bare stainless-steel surface. This testing was part of a larger set of trials conducted in the flowloop, which lasted about one year. The surface treatment durability under flowing conditions was evaluated during this time. Even after experiencing ∼4000 operating hours and 2 full pressure cycles, no evidence of delamination or damage was detected. Finally, as part of an extension to previous work, corroded surface asphaltene deposition experiments were performed in a bench-top flowloop. Treated experiments displayed an order of magnitude reduction in both total oil (all fractions of crude oil) and asphaltene fraction deposited.

03 NATURAL GAS↗

DOE OSTI2021

Hemorrhage management system

An embodiment includes a wound dressing comprising: a shape memory polymer (SMP) foam, including open cells, having first and second states; and a hydrogel (HG) included within the cells; wherein (a) in a first position a composite, including the SMP foam and the HG, is configured to be located proximate a hemorrhagic tissue with the SMP foam in the first state; (b) in a second position the composite is configured to be expanded to the second state against the hemorrhagic tissue when the SMP foam is plasticized at 37° C. depressing a glass transition temperature (Tg) of the SMP foam to below 25° C. Other embodiments are described herein.

Maitland, Duncan J.↗

DOE OSTI2024

Hemorrhage management system

Maitland, Duncan J.↗

DOE OSTI2022

Persistent memory as an effective alternative to random access memory in metagenome assembly

Abstract Background The assembly of metagenomes decomposes members of complex microbe communities and allows the characterization of these genomes without laborious cultivation or single-cell metagenomics. Metagenome assembly is a process that is memory intensive and time consuming. Multi-terabyte sequences can become too large to be assembled on a single computer node, and there is no reliable method to predict the memory requirement due to data-specific memory consumption pattern. Currently, out-of-memory (OOM) is one of the most prevalent factors that causes metagenome assembly failures. Results In this study, we explored the possibility of using Persistent Memory (PMem) as a less expensive substitute for dynamic random access memory (DRAM) to reduce OOM and increase the scalability of metagenome assemblers. We evaluated the execution time and memory usage of three popular metagenome assemblers (MetaSPAdes, MEGAHIT, and MetaHipMer2) in datasets up to one terabase. We found that PMem can enable metagenome assemblers on terabyte-sized datasets by partially or fully substituting DRAM. Depending on the configured DRAM/PMEM ratio, running metagenome assemblies with PMem can achieve a similar speed as DRAM, while in the worst case it showed a roughly two-fold slowdown. In addition, different assemblers displayed distinct memory/speed trade-offs in the same hardware/software environment. Conclusions We demonstrated that PMem is capable of expanding the capacity of DRAM to allow larger metagenome assembly with a potential tradeoff in speed. Because PMem can be used directly without any application-specific code modification, these findings are likely to be generalized to other memory-intensive bioinformatics applications.

59 BASIC BIOLOGICAL SCIENCES↗