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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Socioeconomic Effects of Pension Spending: Evidence from Spain

The purpose of this study is to provide broader understanding of the significant role that the pension system has in the Spanish economy by estimating the sectoral production, employment and income sustained by pensioners' consumption. Based on input-output tables by the World Input-Output Database and consumption data from the Household Budget Survey by the Spanish Statistical Office, a demoeconomic model is applied to quantify the direct impacts, indirect impacts from interindustry links and induced impacts from income-consumption connections over a nine-year period (2006–2014). Then, the factors driving the evolution of total output, employment and value added during such period have been examined by using structural decomposition analysis. The growing participation of consumption by pensioner households in final demand had proven crucial during the 2008 crisis to alleviate the negative trend in production and employment derived from the collapse in consumption suffered by the rest of households. Determining the underlying factors driving changes in both employment and income during the 2008 crisis can be of interest in political decision-making on the sustainability of the Spanish pension system. The results of estimating both the employment and income supported by pensioners' consumption reveal the significant stabilizing effect of the public spending on pensions, particularly during the 2008 crisis. The current Spanish approach of attaining the pension system sustainability by merely reducing social protection costs ignores the adverse consequences of a lower pensioners' demand. This paper addresses an alternative view in which pension spending is not considered a burden on economic growth but rather a means of improving the level of production and employment.

employment↗
DOE OSTI2020

Design and Testing of the Address in Real-Time Data Driver Card for the Micromegas Detector of the ATLAS New Small Wheel Upgrade

The Address in Real Time Data Driver Card (ADDC) is designed to transmit the trigger data in the Micro-Mesh Gaseous Structure (Micromegas) detector of the ATLAS New Small Wheel (NSW) upgrade. The Address in Real Time (ART) signals are generated by the front-end ASIC, named VMM chip, to indicate the address of the first above-threshold event. A custom ASIC (ART ASIC) is designed to receive the ART signals from the VMM chip and implement the hit-selection. The processed data from the ART ASIC will be transmitted out of the NSW through the GBTx serializer, the unidirectional Versatile Twin Transmitter (VTTx) and fiber optical links. The ART signal is critical for the ATLAS experiment trigger selection thus the performance and stability of the ADDC is very important. To ensure extensive testing of the ADDC, an FPGA Mezzanine Card (FMC) based testing platform and a special designed firmware/software are developed. We find that this test platform works with the commercial Xilinx VC707 FPGA development kit, independent of the other electronics of the NSW it can test all the functions of the ADDC and its long-term stability. This paper will introduce the design, testing procedure and results of the ADDC and the FMC testing platform.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Alabama Carbon Storage: Data Sharing and Engagement (Final Report)

This report is the final technical report on Alabama Carbon Storage: Data Sharing Engagement (ACS:DSE) project activities. The goals of the ACS:DSE project are to compile geologic, geophysical, infrastructure, and other relevant CCUS datasets for the study area and develop a geologic model of the study area; develop an online platform to serve data to stakeholders; engage with the public, students, and industry to educate them about CCUS and the data platform; and ensure energy and environmental justice is central to all aspects of the project. Datasets compiled and expanded include formation depths and elevations, digital geophysical well logs, reservoir properties, geologic structures, and geologic models. The geologic data were used to create a three-dimensional geologic model, structure grids, structure contour maps, and fault trace maps. In addition to downloadable datasets, links to CCUS relevant regulatory agencies (e.g., OGB, U.S. Environmental Protection Agency) and sources for infrastructure and educational information were included on the website Educational materials on CCUS for use by K-12 teachers were produced as part of the ACS:DSE project.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Permafrost Active Layer Microbes From Ny Ålesund, Svalbard (79°N) Show Autotrophic and Heterotrophic Metabolisms With Diverse Carbon-Degrading Enzymes

The active layer of permafrost in Ny Ålesund, Svalbard (79°N) around the Bayelva River in the Leirhaugen glacier moraine is measured as a small net carbon sink at the brink of becoming a carbon source. In many permafrost-dominating ecosystems, microbes in the active layers have been shown to drive organic matter degradation and greenhouse gas production, creating positive feedback on climate change. However, the microbial metabolisms linking the environmental geochemical processes and the populations that perform them have not been fully characterized. In this paper, we present geochemical, enzymatic, and isotopic data paired with 10 Pseudomonas sp. cultures and metagenomic libraries of two active layer soil cores (BPF1 and BPF2) from Ny Ålesund, Svalbard, (79°N). Relative to BPF1, BPF2 had statistically higher C/N ratios (15 ± 1 for BPF1 vs. 29 ± 10 for BPF2; n = 30, p < 10 –5 ), statistically lower organic carbon (2% ± 0.6% for BPF1 vs. 1.6% ± 0.4% for BPF2, p < 0.02), statistically lower nitrogen (0.1% ± 0.03% for BPF1 vs. 0.07% ± 0.02% for BPF2, p < 10 –6 ). The d 13 C values for inorganic carbon did not correlate with those of organic carbon in BPF2, suggesting lower heterotrophic respiration. An increase in the δ 13 C of inorganic carbon with depth either reflects an autotrophic signal or mixing between a heterotrophic source at the surface and a lithotrophic source at depth. Potential enzyme activity of xylosidase and N-acetyl-β-D-glucosaminidase increases twofold at 15°C, relative to 25°C, indicating cold adaptation in the cultures and bulk soil. Potential enzyme activity of leucine aminopeptidase across soils and cultures was two orders of magnitude higher than other tested enzymes, implying that organisms use leucine as a nitrogen and carbon source in this nutrient-limited environment. Besides demonstrating large variability in carbon compositions of permafrost active layer soils only ∼84 m apart, results suggest that the Svalbard active layer microbes are often limited by organic carbon or nitrogen availability and have adaptations to the current environment, and metabolic flexibility to adapt to the warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)—also known as multiple reaction monitoring (MRM)—and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

mathematical modeling↗
DOE OSTI2023

Availability Estimates of Low-Cost, Dispatch-Constrained Electricity (LDE)

This dataset accompanies the report "Potential Availability of and Supply Curves for Low-Cost, Dispatch-Constrained Electricity", which provides initial estimates of the quantity and availability of the potential low-cost, dispatch-constrained electricity (LDE) resource in the United States under scenarios with high variable renewable energy (VRE) penetrations. The analysis also estimates supply curves that can be used in subsequent analysis of the opportunity to use the LDE. The estimated LDE resource could be used for various applications that value low-cost electricity and can operate at reduced capacity factors, including electrolytic hydrogen production and carbon capture. The analysis accompanying this dataset was performed in 2018-2019 to support an analysis of "The Technical and Economic Potential of the H2@Scale Concept within the United States" ( https://www.nlr.gov/docs/fy21osti/77610.pdf ). This dataset includes the quantity of estimated LDE resource for each hour of the year in 2050, aggregated to both the national level and the ReEDS balancing area level. As described in the accompanying report, the ReEDS model was used to project the evolution of the power sector over time. Links to the mapping of ReEDS balancing areas to states and ReEDS balancing area shape files are provided. This dataset also includes the report figure data.

08 HYDROGEN↗
DOE OSTI2020

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.

benchmark↗
DOE OSTI2023

Closed Loop Geothermal Working Group: GeoCLUSTER App, Subsurface Simulation Results, and Publications

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided. This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes. GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions. **To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Low-Income Energy Affordability Data - LEAD Tool - 2022 Update

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.

AMI↗
DOE OSTI2025

Arctic Impact Identification with Less Data Using Variable Relationships: An Exploratory Express LDRD project.

Regional impacts from sea ice loss can be challenging to separate from internal climate variability, potentially requiring thousands of ensemble members. East Asian wintertime cooling has been linked to sea ice loss from present day conditions in the Polar Amplification Model Intercomparison Project with these large ensemble counts. This cooling is theorized to arise from a strengthened Siberian High and East Asian Jet response. The strengthened Siberian High can be detected with one fifth the ensemble members needed for the East Asian wintertime cooling in a single model. We thus hypothesize that leveraging relationships between multiple variables in a conditional pathways-based approach would reduce the number of required ensemble members to conclusively attribute East Asian wintertime cooling to future sea ice concentrations. In all analyzed cases, confidence was increased when evaluating sea ice loss’s responsibility for the joint effects of East Asian cooling, East Asian Jet strengthening, and Siberian High strengthening over just East Asian cooling. However, we were not able to confidently attribute future East Asian wintertime cooling to sea ice loss in a single model. We found that significant intra-ensemble variability within single Earth System Models (ESMs) produced highly uncertain forcing response models upon which attribution results were undermined. We were able to show that ensemble mean seasonally averaged metrics from multiple ESMs greatly improved the accuracy of the forcing response linear models and exposed the necessity of all three steps in the pathway (sea ice area, Siberian High pressure, and East Asian Jet speed) for accurate prediction of East Asian wintertime cooling. Although all three steps were necessary, East Asian wintertime cooling possesses a large dependence on the Siberian High pressure, which weakens the confidence associated with overall strong joint-attribution comparing present day and future scenarios. We believe transitioning the pathway nodes to relative changes between the Siberian High and Aleutian Low as well as between the midlatitude westerlies and subtropical jet in the East Asianj Jet region may be able to produce significant attribution more fully dependent upon all three steps. Ultimately, this research demonstrates the simple extensibility of conditional pathways-based attribution to sea ice loss forcing on the Earth system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Situ Process Monitoring Evaluation and Demonstration using Advanced Characterization with Laser Powder Bed Systems

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with EOS Group to evaluate the current in-situ sensor capabilities of an EOS M290 Laser Powder Bed Fusion machine. The M290 was fitted with a 1 Mega-Pixel (MP) grayscale visible-light camera and a 5 MP temporally integrated (TI) near-infrared (NIR) camera. One print from stainless steel (SS) 316 and two from Inconel 625 (IN625) were performed where data including in-situ imaging and a machine log file were captured. These data were subsequently analyzed using a Dynamic Multi-Scale Segmentation Convolutional Neural Network (DMSCNN) trained on user defined classes and correlated to as-printed flaws, in the form of porosity, discovered in X-Ray Computed Tomography (XCT). In Phase I, two indications were detected in-situ and spatially correlated to stochastic lack-of-fusion flaws discovered using XCT. In Phase II, using these links from in-situ signatures to XCT flaw populations, a second neural network (NN) was trained to create a Voxelized Property Prediction Model (VPPM) to predict porosity percentages within the part using only features garnered from the in-situ data from two IN625 complex geometries. The VPPM was able to accurately predict porosity values for IN625 parts with an R 2 value of 0.764.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An Intelligent Distributed Ledger Construction Algorithm for IoT

Blockchain is the next generation of secure data management that creates near-immutable decentralized storage. Secure cryptography created a niche for blockchain to provide alternatives to well-known security compromises. However, design bottlenecks with traditional blockchain data structures scale poorly with increased network usage and are extremely computation-intensive. This made the technology difficult to combine with limited devices, like those in Internet of Things networks. In protocols like IOTA, replacement of blockchain's linked-list queue processing with a lightweight dynamic ledger showed remarkable throughput performance increase. However, current stochastic algorithms for ledger construction suffer distinct trade-offs between efficiency and security. This work proposed a machine-learning approach with a multi-arm bandit that resolved these issues and was designed for auditing on limited devices. This algorithm was tested in a reinforcement-learning environment simulating the IOTA ledger's construction with a decision tree. This study showed through regret analysis and experimentation that this approach was secure against impulse manipulation attacks while remaining energy-efficient. Although the IOTA protocol was a pioneer for lightweight distributed ledgers, it is expected that future blockchain protocols will adopt techniques similar to those presented in this work.

multi-arm bandit↗
DOE OSTI2021

Disrupted NOS2 metabolism drives myoblast response to wasting-associated cytokines

Highlights: • Amino acid metabolism is impacted in myoblasts treated with cancer cell conditioned media. • Inflammatory cytokines induce nitric oxide synthase 2 in myoblasts. • Elevated nitric oxide synthase 2 activity impairs myoblast proliferation and differentiation. Skeletal muscle wasting drives negative clinical outcomes and is associated with a spectrum of pathologies including cancer. Cancer cachexia is a multi-factorial syndrome that encompasses skeletal muscle wasting and remains understudied, despite being a frequent and serious co-morbidity. Deviation from the homeostatic balance between breakdown and regeneration leads to muscle wasting disorders, such as cancer cachexia. Muscle stem cells (MuSCs) are the cellular compartment responsible for muscle regeneration, which makes MuSCs an intriguing target in the context of wasting muscle. Molecular studies investigating MuSCs and skeletal muscle wasting largely focus on transcriptional changes, but our group and others propose that metabolic changes are another layer of cellular regulation underlying MuSC dysfunction in cancer cachexia. In the present study, we combined gene expression and non-targeted metabolomic profiling of myoblasts exposed to wasting conditions (cancer cell conditioned media, CC-CM) to derive a more complete picture of the myoblast response to wasting factors. After mapping these features to annotated pathways, we found that more than half of the mapped pathways were amino acid-related, linking global amino acid metabolic disruption to conditioned media-induced myoblast defects. Notably, arginine metabolism was a highly enriched pathway in combined metabolomic and transcriptomic data. Arginine catabolism generates nitric oxide (NO), an important signaling molecule known to have negative effects on mature muscle. We hypothesize that tumor-derived disruptions in Nitric Oxide Synthase (NOS)2-regulated arginine catabolism impair differentiation of MuSCs. The work presented here further investigates the effect of NOS2 overactivity on myoblast proliferation and differentiation. We show that NOS2 inhibition is sufficient to rescue wasting phenotypes associated with inflammatory cytokines. Ultimately, this work provides new insights into MuSC biology and opens up potential therapeutic avenues for addressing disrupted MuSC dynamics in cancer cachexia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Fire and Drought Affect Multiple Aspects of Diversity in a Migratory Bird Stopover Community

Drought and high-severity, stand-replacing wildfires can have substantial impacts on the composition of avian communities, including stop-over communities during migration. An inextricable link exists between drought and wildfire, each operating and impacting across different timescales. Many studies have found nonlinear avian abundance trends in breeding community time series data that include pre- and post-fire observations, describing an initial decrease in abundance followed by rapid increases that can attenuate over time. Here, we use a fall bird-banding dataset to evaluate shifts in a drought-impacted avian community following wildfire from taxonomic, functional, and phylogenetic perspectives. We looked at the community as a whole and also categorized birds as residents, migrants, and breeders to assess potential varying responses at the study site. We observed post-fire shifts in functional and phylogenetic diversity that corresponded to changes in vegetation. An influx of migratory insectivores post-fire drove much of the variation between pre- and post-fire avian communities and toward a more related, less phylogenetically dispersed community. A concurrent monsoon season drought was also associated with functional and phylogenetic diversity, highlighting the intertwined pulse press effects on avian communities. Overall, our results suggest that, although bird communities are immediately impacted by fire-driven resource changes, they can rebound over time, it is unclear how long-term drought may continue to shape the composition of these avian communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Interlinked tuples in coordination namespace

A system and method for supporting tuple record interlinking in one or more tuple space/coordinated namespace (CNS) extended memory storage systems. A system-wide CNS provides for efficient storing and communicating of data generated by local processes running at the nodes, and coordinated to generate a union/intersection of multiple CNS where tuple records are interlinked in multiple CNS hashtables, and/or share tuple data between two sets of processes that are part of different CNSs. Local node processes further generate multi-key tuples where two or more tuple records are interlinked within the same CNS hash table, thereby permitting a look up of the tuple data by either tuple name/keys. A CNS controller further provides a tuple iterator for a key-value storage in a CNS system that adds more links between tuples enables creation of iterator structures such as linked list or trees etc. of “different” tuples in a tuple database.

Jacob, Philip↗
DOE OSTI2026

Signatures of Thermoreversible Associations in X-ray and Neutron Scattering from Dilute Polyzwitterion Solutions

In aqueous solutions of polyzwitterions (PZs), an interplay between dipole–dipole interactions and hydration of zwitterionic groups can lead to thermoreversible associations, which have been difficult to detect in experiments. Here, in this study, we investigated dilute aqueous solutions of poly(1-(3-sulphonatopropyl)-2-vinylpyridinium) (P2VPPS) using small-angle X-ray and neutron scattering (SAXS and SANS) to probe the structure and neutron spin-echo (NSE) spectroscopy to probe dynamics. The SAXS and SANS data show that the correlation length increases with an increase in the concentration of P2VPPS for three different molecular weights. The addition of 0.1 M NaCl to one of the solutions led to almost no dependence of the correlation length on the concentration. Such a concentration dependence of the correlation length suggests the formation of clusters driven by thermoreversible associations in the solutions. The NSE measurements show that the solutions with a larger correlation length display slower relaxation, reflecting the reduced mobility of larger clusters. These results should be considered as signatures of thermoreversible associations in the solutions of P2VPPS. To establish a quantitative link between local structure (clusters) and dynamics in dilute solutions of P2VPPS, we combined a thermoreversible gelation theory for the structure of PZ solutions (Li, S.-F.; Muthukumar, M. Theory of Thermoreversible Gelation and Anomalous Concentration Fluctuations in Polyzwitterion Solutions. J. Chem. Phys. 2024, 161, 024903) with a model for the dynamics of the clusters (generalized Zimm model), developed in this work. Using such a theoretical framework, we have predicted the distribution of clusters in the solutions probed with SANS and SAXS. With the distributions, the generalized Zimm model has been used to extract the diffusion constant of the clusters and their characteristic size from the NSE data, where the latter agrees with the values estimated from the SAXS/SANS data. These findings confirm the presence of thermoreversible associations and establish a quantitative link between local structure (clusters) and dynamics in dilute solutions of P2VPPS. Furthermore, with growing interest in technological applications, this work can provide useful insights into the structural and dynamical properties of other PZ solutions.

field theory↗