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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Extending Calendar Life of Si-Based Lithium-Ion Batteries by a Localized High Concentration Electrolyte

Silicon (Si) is one of the most promising anode materials for the next generation lithium-ion batteries (LIBs). Although significant progresses have been made on the cycle life of Si-based LIBs, their calendar-life is still far less than those required for electrical vehicle applications. Here, in this work, the fundamental mechanisms behind the limited calendar life of Si-LIBs have been investigated. It is found that the unstable interphase layers formed on electrodes during the formation/cycling of batteries using conventional electrolyte with fluoroethylene carbonate (FEC) additive are responsible for the rapid impedance-increase of Si-LIBs during storage at elevated temperature (55°C). By using an FEC-free localized high concentration electrolyte (lithium bis(fluorosulfonyl)imide:ethyl propionate:ethylene carbonate:1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropyl ether (1:2.8:0.2:1 by mol.) with 1 wt.% lithium difluorophosphate), stable interphase layers formed on electrodes can effectively block the crosstalk between cathode and anode, minimize the impedance increase of Si||LiNi 0.6 Mn 0.2 Co 0.2 (NMC622) batteries during storage at elevated temperature (55°C), therefore largely improve their calendar life. Si||NMC622 batteries using this electrolyte also demonstrated a high-capacity retention of ~92.4% after 500 cycles at 45°C with well-preserved electrode structure. Hence, this novel electrolyte is a good candidate to extend the cycling life and calendar life of Si-LIBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Chaotrope-Based Approach for Rapid In Vitro Assembly and Loading of Bacterial Microcompartment Shells

Bacterial microcompartments (BMCs) are proteinaceous organelles that self-assemble into selectively permeable shells that encapsulate enzymatic cargo. BMCs enhance catalytic pathways by reducing crosstalk among metabolites, preventing harmful intermediates from leaking into the cytosol and increasing reaction efficiency via enzyme colocalization. The intrinsic properties of BMCs make them attractive for biotechnological engineering. However, in vivo expression methods for shell synthesis have significant drawbacks that limit the potential design space for these nanocompartments. Here, we describe the development of an efficient and rapid method for the in vitro assembly of BMC shells from their protein building blocks. Our method enables large-scale construction of BMC shells by utilizing urea as a chaotropic agent to control self-assembly and provides an approach for encapsulation of both biotic and abiotic cargo under a broad range of reaction conditions. We demonstrate an enhanced level of control over the assembly of BMC shells in vitro and expand the design parameter space for engineering BMC systems with specialized and enhanced catalytic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Excitation spectral microscopy for highly multiplexed fluorescence imaging and quantitative biosensing

The multiplexing capability of fluorescence microscopy is severely limited by the broad fluorescence spectral width. Spectral imaging offers potential solutions, yet typical approaches to disperse the local emission spectra notably impede the attainable throughput. Here we show that using a single, fixed fluorescence emission detection band, through frame-synchronized fast scanning of the excitation wavelength from a white lamp via an acousto-optic tunable filter, up to six subcellular targets, labeled by common fluorophores of substantial spectral overlap, can be simultaneously imaged in live cells with low (~1%) crosstalks and high temporal resolutions (down to ~10 ms). The demonstrated capability to quantify the abundances of different fluorophores in the same sample through unmixing the excitation spectra next enables us to devise novel, quantitative imaging schemes for both bi-state and Förster resonance energy transfer fluorescent biosensors in live cells. We thus achieve high sensitivities and spatiotemporal resolutions in quantifying the mitochondrial matrix pH and intracellular macromolecular crowding, and further demonstrate, for the first time, the multiplexing of absolute pH imaging with three additional target organelles/proteins to elucidate the complex, Parkin-mediated mitophagy pathway. Together, excitation spectral microscopy provides exceptional opportunities for highly multiplexed fluorescence imaging. The prospect of acquiring fast spectral images without the need for fluorescence dispersion or care for the spectral response of the detector offers tremendous potential.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

RIViT-seq enables systematic identification of regulons of transcriptional machineries

Abstract Transcriptional regulation is a critical process to ensure expression of genes necessary for growth and survival in diverse environments. Transcription is mediated by multiple transcription factors including activators, repressors and sigma factors. Accurate computational prediction of the regulon of target genes for transcription factors is difficult and experimental identification is laborious and not scalable. Here, we demonstrate regulon identification by in vitro transcription-sequencing (RIViT-seq) that enables systematic identification of regulons of transcription factors by combining an in vitro transcription assay and RNA-sequencing. Using this technology, target genes of 11 sigma factors were identified in Streptomyces coelicolor A3(2). The RIViT-seq data expands the transcriptional regulatory network in this bacterium, discovering regulatory cascades and crosstalk between sigma factors. Implementation of RIViT-seq with other transcription factors and in other organisms will improve our understanding of transcriptional regulatory networks across biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

TRPV4-Rho GTPase complex structures reveal mechanisms of gating and disease

Crosstalk between ion channels and small GTPases is critical during homeostasis and disease, but little is known about the structural underpinnings of these interactions. TRPV4 is a polymodal, calcium-permeable cation channel that has emerged as a potential therapeutic target in multiple conditions. Gain-of-function mutations also cause hereditary neuromuscular disease. Here, we present cryo-EM structures of human TRPV4 in complex with RhoA in the ligand-free, antagonist-bound closed, and agonist-bound open states. These structures reveal the mechanism of ligand-dependent TRPV4 gating. Channel activation is associated with rigid-body rotation of the intracellular ankyrin repeat domain, but state-dependent interaction with membrane-anchored RhoA constrains this movement. Notably, many residues at the TRPV4-RhoA interface are mutated in disease and perturbing this interface by introducing mutations into either TRPV4 or RhoA increases TRPV4 channel activity. Together, these results suggest that RhoA serves as an auxiliary subunit for TRPV4, regulating TRPV4-mediated calcium homeostasis and disruption of TRPV4-RhoA interactions can lead to TRPV4-related neuromuscular disease. These insights will help facilitate TRPV4 therapeutics development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Disordered-to-ordered transitions in assembly factors allow the complex II catalytic subunit to switch binding partners

Complex II (CII) activity controls phenomena that require crosstalk between metabolism and signaling, including neurodegeneration, cancer metabolism, immune activation, and ischemia-reperfusion injury. CII activity can be regulated at the level of assembly, a process that leverages metastable assembly intermediates. The nature of these intermediates and how CII subunits transfer between metastable complexes remains unclear. In this work, we identify metastable species containing the SDHA subunit and its assembly factors, and we assign a preferred temporal sequence of appearance of these species during CII assembly. Structures of two species show that the assembly factors undergo disordered-to-ordered transitions without the appearance of significant secondary structure. The findings identify that intrinsically disordered regions are critical in regulating CII assembly, an observation that has implications for the control of assembly in other biomolecular complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Johnson-noise-limited cancellation-free microwave impedance microscopy with monolithic silicon cantilever probes

Abstract Microwave impedance microscopy (MIM) is an emerging scanning probe technique for nanoscale complex permittivity mapping and has made significant impacts in diverse fields. To date, the most significant hurdles that limit its widespread use are the requirements of specialized microwave probes and high-precision cancellation circuits. Here, we show that forgoing both elements not only is feasible but also enhances performance. Using monolithic silicon cantilever probes and a cancellation-free architecture, we demonstrate Johnson-noise-limited, drift-free MIM operation with 15 nm spatial resolution, minimal topography crosstalk, and an unprecedented sensitivity of 0.26 zF/ √ Hz. We accomplish this by taking advantage of the high mechanical resonant frequency and spatial resolution of silicon probes, the inherent common-mode phase noise rejection of self-referenced homodyne detection, and the exceptional stability of the streamlined architecture. Our approach makes MIM drastically more accessible and paves the way for advanced operation modes as well as integration with complementary techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Multi-layered heterochromatin interaction as a switch for DIM2-mediated DNA methylation

Functional crosstalk between DNA methylation, histone H3 lysine-9 trimethylation (H3K9me3) and heterochromatin protein 1 (HP1) is essential for proper heterochromatin assembly and genome stability. However, how repressive chromatin cues guide DNA methyltransferases for region-specific DNA methylation remains largely unknown. Here, we report structure-function characterizations of DNA methyltransferase Defective-In-Methylation-2 (DIM2) in Neurospora . The DNA methylation activity of DIM2 requires the presence of both H3K9me3 and HP1. Our structural study reveals a bipartite DIM2-HP1 interaction, leading to a disorder-to-order transition of the DIM2 target-recognition domain that is essential for substrate binding. Furthermore, the structure of DIM2-HP1-H3K9me3-DNA complex reveals a substrate-binding mechanism distinct from that for its mammalian orthologue DNMT1. In addition, the dual recognition of H3K9me3 peptide by the DIM2 RFTS and BAH1 domains allosterically impacts the DIM2-substrate binding, thereby controlling DIM2-mediated DNA methylation. Together, this study uncovers how multiple heterochromatin factors coordinately orchestrate an activity-switching mechanism for region-specific DNA methylation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

High-performance 2D electronic devices enabled by strong and tough two-dimensional polymer with ultra-low dielectric constant

As the feature size of microelectronic circuits is scaling down to nanometer order, the increasing interconnect crosstalk, resistance-capacitance (RC) delay and power consumption can limit the chip performance and reliability. To address these challenges, new low-k dielectric (k < 2) materials need to be developed to replace current silicon dioxide (k = 3.9) or SiCOH, etc. However, existing low-k dielectric materials, such as organosilicate glass or polymeric dielectrics, suffer from poor thermal and mechanical properties. Two-dimensional polymers (2DPs) are considered promising low-k dielectric materials because of their good thermal and mechanical properties, high porosity and designability. Here, we report a chemical-vapor-deposition (CVD) method for growing fluoride rich 2DP-F films on arbitrary substrates. We show that the grown 2DP-F thin films exhibit ultra-low dielectric constant (in plane k = 1.85 and out-of-plane k = 1.82) and remarkable mechanical properties (Young’s modulus > 15 GPa). We also demonstrated the improved performance of monolayer MoS 2 field-effect-transistors when utilizing 2DP-F thin films as dielectric substrates.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network’s various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. Furthermore, these elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

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

Glycan shield of the ebolavirus envelope glycoprotein GP

The envelope glycoprotein GP of the ebolaviruses is essential for host cell entry and the primary target of the host antibody response. GP is heavily glycosylated with up to 17 N-linked sites, numerous O-linked glycans in its disordered mucin-like domain (MLD), and three predicted C-linked mannosylation sites. Glycosylation is important for host cell attachment, GP stability and fusion activity, and shielding from neutralization by serum antibodies. Here, we use glycoproteomics to profile the site-specific glycosylation patterns of ebolavirus GP. We detect up to 16 unique O-linked glycosylation sites in the MLD, and two O-linked sites in the receptor-binding GP1 subunit. Multiple O-linked glycans are observed within N-linked glycosylation sequons, suggesting crosstalk between the two types of modifications. We confirmed C-mannosylation of W288 in full-length trimeric GP. We find complex glycosylation at the majority of N-linked sites, while the conserved sites N257 and especially N563 are enriched in unprocessed glycans, suggesting a role in host-cell attachment via DC-SIGN/L-SIGN. Our findings illustrate how N-, O-, and C-linked glycans together build the heterogeneous glycan shield of GP, guiding future immunological studies and functional interpretation of ebolavirus GP-antibody interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Systematic characterization of human gut microbiome-secreted molecules by integrated multi-omics

The human gut microbiome produces a complex mixture of biomolecules that interact with human physiology and play essential roles in health and disease. Crosstalk between micro-organisms and host cells is enabled by different direct contacts, but also by the export of molecules through secretion systems and extracellular vesicles. The resulting molecular network, comprised of various biomolecular moieties, has so far eluded systematic study. Here we present a methodological framework, optimized for the extraction of the microbiome-derived, extracellular biomolecular complement, including nucleic acids, (poly)peptides, and metabolites, from flash-frozen stool samples of healthy human individuals. Our method allows simultaneous isolation of individual biomolecular fractions from the same original stool sample, followed by specialized omic analyses. The resulting multi-omics data enable coherent data integration for the systematic characterization of this molecular complex. Our results demonstrate the distinctiveness of the different extracellular biomolecular fractions, both in terms of their taxonomic and functional composition. This highlights the challenge of inferring the extracellular biomolecular complement of the gut microbiome based on single-omic data. The developed methodological framework provides the foundation for systematically investigating mechanistic links between microbiome-secreted molecules, including those that are typically vesicle-associated, and their impact on host physiology in health and disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Selective photo-excitation of molecules enabled by stimulated Raman pre-excitation

Double resonance excitation, where the energies of vibrational and electronic molecular transitions are combined in a single, sequential excitation process, was introduced in the 1970s but has only been recently applied to microscopy due to the immense progress in Raman spectroscopy. The value of the technique is in combining the chemical selectivity of IR or Raman excitation with the much larger cross-sections of electronic transitions. Recently, it has been shown to be particularly suited for the detection and identification of chromophores at low concentrations and in the presence of spectral crosstalk. However, despite its low quantum yield per pulse sequence, we believe the technique has potential for selective photochemical transformations. There are some cases (e.g., the selective excitation of optogenetic switches) where the low yield may be overcome by repeated excitations to build up biochemically relevant concentrations. Here we show that double resonance excitation using general, non-resonant Raman pre-excitation is a viable candidate for selectively promoting molecules to chemically active energy levels. Further, the use of non-resonant Raman pre-excitation is less constraining than resonant Raman (used in previous double resonance microscopy works) since the choice of Raman pump-Stokes frequencies may be rather freely chosen.

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

Multimodal quantification of degradation pathways during extreme fast charging of lithium-ion batteries

Enabling fast charging of Li-ion batteries will be a key step towards realizing the technology's full potential in electric vehicles. Currently, fast charging is limited by a variety of processes that reduce cell capacity upon extended cycling. Using a multimodal approach combining incremental capacity analysis (dQ/dV), high energy X-ray diffraction (HEXRD), and mass spectrometry titration (MST), we identify specific degradation mechanisms—including Li plating, dead Li x C 6 formation, Li 2 C 2 formation, solid carbonate solid-electrolyte interphase (SEI) deposition, and loss of positive electrode active material (LAM PE )—that occur during extended fast charge cycling. We find that Li plating is the major source of capacity loss in cells cycled at 6C, while non-carbonate SEI species deposition on the graphite anode is the main source of capacity loss when cycled at 4C. We also study local degradative phenomena by examining specific ~1–5 cm 2 regions of the cells using HEXRD and MST. Here, we find that plated Li is often collocated with dead Li x C 6 , Li 2 C 2 , and solid carbonate SEI species, and these additional species cumulatively account for ~20% of the capacity lost during 6C cycling. Finally, in a cell with an anomalously high amount of LAM PE (quantified via dQ/dV), we find that regions of cathode degradation were accompanied by non-carbonate SEI products on the adjacent region of the anode. We postulate that this phenomenon arises due to crosstalk between the electrodes, wherein soluble electrolyte oxidation products formed at the delithiated cathode migrate to the graphite anode and are ultimately deposited on the graphite surface. This work demonstrates the utility of combining multiple characterization techniques to reveal a more holistic understanding of degradative phenomena that occur across multiple length scales during fast charge.

25 ENERGY STORAGE↗
DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Parallel high-frequency magnetic sensing with an array of flux transformers and multi-channel optically pumped magnetometer for hand MRI application

Here, we investigate an approach for parallel high-frequency magnetic sensing based on a multi-channel radio frequency (RF) optically pumped magnetometer (OPM) coupled to multiple flux transformers (FTs) with a focus on hand magnetic resonance imaging (MRI) application at ultra-low field (ULF). Multiple RF OPM sensing channels are realized by using a single large-area alkali-metal vapor cell and two laser beams for pumping and probing, shared for all the channels. This design leads to significant cost reduction when multi-channel sensing is desirable, as in the case of ULF MRI. The FT, composed of two connected coils, serves as a transmitter of a target magnetic field to the OPM, while decoupling the OPM from untargeted magnetic fields in the sensing area that can limit the OPM performance. For hand MRI application, theoretical and numerical analysis is performed to determine an optimal geometry for the FT array that could improve signal-to-noise ratio (SNR) and sufficiently reduce crosstalk between FTs. We estimate that the optimized multi-channel FT-OPM sensor can achieve a magnetic field sensitivity of the order of 1 fT / Hz 1 / 2 above 100 kHz, which would be sufficient for 1 mm resolution MRI. In general, the multi-channel capability enables simultaneous magnetic measurements, thus reducing the sensing time and improving the SNR, and we anticipate many applications of the multi-channel FT-OPM sensor beyond the targeted here hand MRI: anatomical parallel ULF MRI of the human brain and other parts of the body, airport security screening, magnetic material imaging, and many others.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Robust identification of multiple-input single-output system response for efficient pickup noise removal from tokamak diagnostics

We report electromagnetic pickup noise in the tokamak environment imposes an imminent challenge for measuring weak diagnostic photocurrents in the nA range. The diagnostic signal can be contaminated by an unknown mixture of crosstalk signals from coils powered by currents in the kA range. To address this issue, an algorithm for robust identification of linear multi-input single-output (MISO) systems has been developed. The MISO model describes the dynamic relationship between measured signals from power sources and observed signals in the diagnostic and allows for a precise subtraction of the noise component. The proposed method was tested on experimental diagnostic data from the DIII-D tokamak, and it has reduced noise by up to 20 dB in the 1–20 kHz range.

47 OTHER INSTRUMENTATION↗