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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Managing Power Systems-Induced Wildfire Risks Using Optimal Scheduled Shutoffs: Preprint

The growing demands for electricity and the increase in extreme weather conditions are putting unprecedented pressure on our electrical grids. Oftentimes, this pressure leads to electrical components failures which might ignite wildfires. This work develops a novel model to balance the reliability of power networks operations and risks of wildfires ignition by optimizing the operational schedule of power transmission networks considering time-varying risk measures that consider exogenous and operational factors. Energy storage systems are considered to deliver power during peak wildfire hours and enable temporal load shifting. The problem is formulated as a mixed-integer linear program that maximizes a weighted sum of the served power demand and the reduction in grid-induced wildfires risk. The results demonstrate the ability of the model to reduce wildfires risks significantly without considerable load shedding.

energy scheduling↗
DOE OSTI2024

Importance of Site Diversity and Connectivity in Electrochemical CO Reduction on Cu

Electrochemical CO 2 reduction on Cu is a promising approach to produce value-added chemicals using renewable feedstocks, yet various Cu preparations have led to differences in activity and selectivity toward single and multicarbon products. Here, we find, surprisingly, that the effective catalytic activity toward ethylene improves when there is a larger fraction of less active sites acting as reservoirs of *CO on the surface of Cu nanoparticle electrocatalysts. In an adaptation of chemical transient kinetics to electrocatalysis, we measure the dynamic response of a gas diffusion electrode (GDE) cell when the feed gas is abruptly switched between Ar (inert) and CO. When switching from Ar to CO, CO reduction (COR) begins promptly, but when switching from CO to Ar, COR can be maintained for several seconds (delay time) despite the absence of the CO reactant in the gas phase. A three-site microkinetic model captures the observed dynamic behavior and shows that Cu catalysts exhibiting delay times have a less active *CO reservoir that exhibits fast diffusion to active sites. The observed delay times and the estimated *CO reservoir sizes are affected by catalyst preparation, applied potential, and microenvironment (electrolyte cation identity, electrolyte pH, and CO partial pressure). Notably, we estimate that the *CO reservoir surface coverage can be as high as 88 ± 7% on oxide-derived Cu (OD-Cu) at high overpotentials (–1.52 V vs SHE) and this increases in reservoir coverage coincide with increased turnover frequencies to ethylene. We also estimate that *CO can travel substantial distances (up to 10s of nm) prior to desorption or reaction. It appears that active C–C coupling sites by themselves do not control selectivity to C 2+ products in electrochemical COR; the supply of CO to those sites is also a crucial factor. More generally, the overall activity of Cu electrocatalysts cannot be approximated from linear combinations of individual site activities. Future designs must consider the diversity of the catalyst network and account for intersite transportation pathways.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Improved Spatiotemporal Representativeness and Bias Reduction of Satellite-Based Evapotranspiration Retrievals via Use of In Situ Meteorology and Constrained Canopy Surface Resistance

Evapotranspiration (ET) is a key component of the atmospheric and terrestrial water and energy budgets. Satellite-based vegetation index approaches have used remotely-sensed vegetation and reanalysis meteorological properties with surface energy balance models to estimate global ET (MOD16 ET). We reconstructed satellite retrievals using in situ meteorology (Argonne-ET) and evaluated them using a dense network of surface turbulent heat flux measurements. Argonne-ET resolves spatial heterogeneity of ET across the U.S. Southern Great Plains that is not well characterized by MOD16; MOD16 ET exhibits spatial autocorrelation (Moran's I (MI) significantly > 0 in May – Aug.), whereas in situ and Argonne-ET exhibit characteristics of a random spatial process (MI not significantly different from 0). The skill in resolving ET temporal variability is not significantly different between MOD16 and Argonne-ET (correlation coefficient = 0.75 and 0.72, respectively). However, the root mean square errors were significantly lower for Argonne-ET (36 W m -2 ) than MOD16 (43 W m -2 ), and MOD16 exhibits substantial bias in annual ET relative to in situ measurements (-38%). This is attributed to overestimation in the dry canopy surface resistance (rs) parameterization. Using rs constrained to the range of typical measured values, Argonne-ET substantially reduces the bias in the annual ET (+1%). The improved ET estimates are critical for regional water budget analyses. The methodology presented herein also demonstrates the ability to retrieve high temporally resolved (30-min; cf. 8-day MOD16) ET that can be used for development of processed-based diagnostics of model biases, and to elucidate avenues to improve ET model parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications

Scientists conduct large-scale simulations to compute derived quantities from primary data. Thus, it is crucial that data compression techniques maintain bounded errors on these derived quantities or quantities of interest (QOI). For many spatiotemporal applications, these QOIs are binary in nature and represent presence or absence of a physical phenomenon. In this work, we propose to use a hybrid approah for differential compression for such applications. We use a neural network (NN) approach to determine regions-of-interest (ROIs) where the binary QOIs are going to be prevalent. This is then used with traditional approaches that compress at a lower level (and higher accuracy) for these ROIs as compared to other regions.

Li, Xiao↗
NASA NTRS2007

Solutions Network Formulation Report. Visible/Infrared Imager/Radiometer Suite and Advanced Microwave Scanning Radiometer Data Products for National Drought Monitor Decision Support

Drought effects are either direct or indirect depending on location, population, and regional economic vitality. Common direct effects of drought are reduced crop, rangeland, and forest productivity; increased fire hazard; reduced water levels; increased livestock and wildlife mortality rates; and damage to wildlife and fish habitat. Indirect impacts follow on the heels of direct impacts. For example, a reduction in crop, rangeland, and forest productivity may result in reduced income for farmers and agribusiness, increased prices for food and timber, unemployment, reduced tax revenues, increased crime, foreclosures on bank loans to farmers and businesses, migration, and disaster relief programs. In the United States alone, drought is estimated to result in annual losses of between $6 - 8 billion. Recent sustained drought in the United States has made decision-makers aware of the impacts of climate change on society and environment. The eight major droughts that occurred in the United States between 1980 and 1999 accounted for the largest percentage of weather-related monetary losses. Monitoring drought and its impact that occurs at a variety of scales is an important government activity -- not only nationally but internationally as well. The NDMC (National Drought Mitigation Center) and the USDA (U.S. Department of Agriculture) RMA (Risk Management Agency) have partnered together to develop a DM-DSS (Drought Monitoring Decision Support System). This monitoring system will be an interactive portal that will provide users the ability to visualize and assess drought at all levels. This candidate solution incorporates atmospherically corrected VIIRS data products, such as NDVI (Normalized Difference Vegetation Index) and Ocean SST (sea surface temperature), and AMSR-E soil moisture data products into two NDMC vegetation indices -- VegDRI (Vegetation Drought Response Index) and VegOUT (Vegetation Outlook) -- which are then input into the DM-DSS.

Estep, Leland↗
DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2019

Multi-stimuli responsive tetra-PPO 60 -PEO 20 ethylene diamine block copolymer enables pH, temperature, and solvent regulation of Au nanoparticle composite plasmonic response

In this work a gold nanoparticle (NP) hydrogel composite prepared by multi-step cascade synthesis undergoes temperature, pH, and solvent dependent nanoscale structural transformations promoting modulation of the plasmon resonance. The composite is spontaneously prepared from a non-covalent lamellar structured lyotropic mesophase (complex fluid) composed of amphiphiles (DMPC and Triton X-100) that support the reactive constituents, a mixture of hydroxyl and acrylate end-derivatized Tetronic (Poloxamine) polymer and [AuCl4] – . The reaction sequence begins with the reduction of [AuCl4] – by the ethylene diamine linkage and PEO blocks of the Tetronic, yielding Au NPs. The redox reaction generates free radicals initiating crosslinking of the acrylate end-derivatized polymer producing a chemical hydrogel. Optical spectroscopy and electron microscopy confirm Au NP formation. ATR/FT-IR spectroscopy and thermal analysis establish acrylate crosslinking and network formation. X-ray scattering reveals the composite reversibly converts between multilamellar and bicontinuous cubic structures as temperature is increased above 35 °C for composites containing protonated ethylene diamine linkages (un-buffered water or acidic buffer). The reversible changes in nanoarchitecture red shifts and broadens the collective plasmon (from 523 nm to 575 nm) of the composite, an observation attributed to re-organization of confined NPs from 2D sheets (lamellae) to 1D columns (bicontinuous cubic channels). Composites prepared in alkaline buffer do not undergo a structural conversion with temperature, retaining a multilamellar architecture. In addition to pH and thermo-responsivity, the composite swells reversibly in polar solvents, which acts to de-aggregate and aggregate the encapsulated NPs and varies the plasmon resonance by Δ52 nm. This work reports the first combined use of a polymer to achieve both the in situ synthesis of spatially confined plasmonic NPs and their reversible stimuli-triggered re-organization mediated by interconvertible changes in scaffold nanoarchitecture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Improving Primary Frequency Response to Support Networked Microgrid Operations

Individual microgrids have proven their ability to provide uninterrupted power to critical end-use load during severe events. Building on the performance of individual microgrids during extreme events, there has been an increasing interest in the operations of network microgrids. By networking microgrids during extreme events, it is possible to share resources, increase the duration for which they can operate islanded, and increase the resiliency of critical end-use loads. While there are benefits to networking the operations of resiliency-based microgrids, the switching operations that they require introduce transients which can result in a loss of dynamic stability. The issue of dynamic stability is especially acute in microgrids with high penetrations of inverter-connected generation, and a correspondingly low system inertia. While the low-inertia of these microgrids can be increased by over-sizing the rotating generators, the increased capital and operating costs can become a barrier to deployment. This work will present a method of augmenting primary frequency controls to support the switching transients necessary for the operation of networked microgrids, without the need to over-size rotating machines.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1980

Voyager-Jupiter radio science data papers

The reduction and interpretation of the radio science data from the Voyager 1 and 2 encounters of the planet Jupiter and its satellites resulted in the preparation of several papers for publication in the special Voyager-Jupiter issue of the Journal of Geophysical Research. The radio science and tracking systems of the Deep Space Network provide the data which makes this research possible. This article lists submitted papers by title, with their authors and with abstracts of their contents.

Levy, G. S.↗
NASA NTRS2000

Microwave Driven Actuators Power Allocation and Distribution

Design, fabrication and test of a power allocation and distribution (PAD) network for microwave driven actuators is presented in this paper. Development of a circuit that would collect power from a rectenna array amplify and distribute the power to actuators was designed and fabricated for space application in an actuator array driven by a microwave. A P-SPICE model was constructed initially for data reduction purposes, and was followed by a working real-world model. A voltage up - converter (VUC) is used to amplify the voltage from the individual rectenna. The testing yielded a 26:1 voltage amplification ratio with input voltage at 9 volts and a measured output voltage 230VDC. Future work includes the miniaturization of the circuitry, the use of microwave remote control, and voltage amplification technology for each voltage source. The objective of this work is to develop a model system that will collect DC voltage from an array of rectenna and propagate the voltage to an array of actuators.

Forbes, Timothy↗
DOE OSTI2026

Optimisation of the Kaplan hydropower system via PID 2 and digital twin

Here, this paper proposes a proportional–integral-double–derivative (PID 2 ) optimisation method for the Kaplan hydropower system by building a digital twin. The study first uses one multilayer perceptron (MLP) to model the hydroturbine dynamic and then adopts three connected MLPs to model the generator dynamic, both in an open-loop fashion. Inspired by stochastic distribution control (SDC) theory, we regard the training of the turbine's neural network model as a process control problem, and we propose minimising entropy loss to update the network parameters. The next step is to build the digital twin by connecting the neural network models with a PID 2 controller and a lead-lag exciter and run the whole model in a closed-loop fashion. After that, a binary search approach is applied to optimise the PID 2 parameters based on the obtained digital twin model. The simulation results show that the proposed method can reduce the mean square tracking error by more than 90%. Furthermore, the method is extended to jointly optimise the PID 2 controller and excitation system gains through multiobjective optimisation, leveraging Pareto frontier analysis to balance active power and voltage tracking performance. Simulation results confirm the effectiveness of the proposed method, achieving a 83.46% reduction in relative mean square error of active power, a 47.13% reduction in terminal voltage tracking error, and an 82.78% improvement in the overall scalarized objective.

Hydropower system↗
DOE OSTI2022

$\mathrm{IH}$-$\mathrm{GAN}$: A conditional generative model for implicit surface-based inverse design of cellular structures

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignoring the effects of unit cell geometry on properties, or considers the geometry–property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. Here we further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (R 2 -scores between target properties and properties of generated unit cells >98%) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a 79.7% reduction in concentrated stress and an extra 3.03% reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by 86.4% and 79.6% for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.

42 ENGINEERING↗
DOE OSTI2020

Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data

We collect vehicle trajectory data from major transportation network companies (TNCs) in New York City (NYC) in 2017 and 2019, and we use the trajectory data to understand how the growth of TNCs has impacted traffic congestion and emission in urban areas. By mining the large-scale trajectory data and conduct the case study in NYC, we confirm that the rise of TNC is the major contributing factor that makes urban traffic congestion worse. From 2017 to 2019, the number of for-hire vehicles (FHV) has increased by over 48% and served 90% more daily trips. These resulted in an average citywide speed reduction of 22.5% on weekdays, and the average speed in Manhattan decreased from 11.76 km/h in April 2017 to 9.56 km/h in March 2019. The heavier traffic congestion may have led to 136% more NOx, 152% more CO and 157% more HC emission per kilometer traveled by the FHV sector. Our results show that the traffic condition is consistently worse across different times of the day and at different locations in NYC. And we build the connection between the number of available FHVs and the reduction in travel speed between the two years of data and explain how the rise of TNC may impact traffic congestion in terms of moving speed and congestion time. Our findings provide valuable insights for different stakeholders and decision-makers in framing regulation and operation policies towards more effective and sustainable urban mobility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Chlorpyrifos- and chlorpyrifos oxon-induced neurite retraction in pre-differentiated N2a cells is associated with transient hyperphosphorylation of neurofilament heavy chain and ERK 1/2

Chlorpyrifos (CPF) and CPF-oxon (CPO) are known to inhibit neurite outgrowth but little is known about their ability to induce neurite retraction in differentiating neuronal cells. The aims of this study were to determine the ability of these compounds to destabilize neurites and to identify the key molecular events involved. N2a cells were induced to differentiate for 20 h before exposure to CPF or CPO for 2–8 h. Fixed cell monolayers labeled with carboxyfluorescein succinimidyl ester or immunofluorescently stained with antibodies to tubulin (B512) or phosphorylated neurofilament heavy chain (Ta51) showed time- and concentration-dependent reductions in numbers and length of axon-like processes compared to the control, respectively, retraction of neurites being observed within 2 h of exposure by live cell imaging. Neurofilament disruption was also observed in treated cells stained by indirect immunofluorescence with anti-phosphorylated neurofilament heavy chain (NFH) monoclonal antibody SMI34, while the microtubule network was unaffected. Western blotting analysis revealed transiently increased levels of reactivity of Ta51 after 2 h exposure and reduced levels of reactivity of the same antibody following 8 h treatment with both compounds, whereas reactivity with antibodies to anti-total NFH or anti-tubulin was not affected. The alteration in NFH phosphorylation at 2 h exposure was associated with increased activation of extracellular signal-regulated protein kinase ERK 1/2. However, increased levels of phosphatase activity were observed following 8 h exposure. These findings suggest for the first time that organophosphorothionate pesticide-induced neurite retraction in N2a cells is associated with transient increases in NFH phosphorylation and ERK1/2 activation. - Highlights: • Chlorpyrifos and chlorpyrifos oxon induced rapid neurite retraction in N2a cells. • This occurred following transient hyperphosphorylation of ERK 1/2. • It was concomitant with hyperphosphorylation of neurofilament heavy chain (NFH). • Subsequent NFH dephosphorylation was associated with increased phosphatase activity. • Thus, the above changes reflect key events in chlorpyrifos induced neurodegeneration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Synthesis of Interface-Driven Tunable Bandgap Metal Oxides

Mixed bandgap and bandgap tunability in semiconductors is critical in expanding their use. Composition altera-tions through single-crystal epitaxial growth and formation of multi-layer tandem structures are often employed to achieve mixed bandgaps albeit with limited tunability. In this study, self-assembled one-dimensional coordination polymers provide a fac-ile synthon and template for graphitic C-doped mesoporous oxides, gC-β-Ga 2 O 3 or gC-In 2 O 3 via controlled oxidative ligand ablation. These materials have mixed bandgaps, and colors, depending on amount of gC present. The carbon-oxide interface leads to induced gap states, hence, a stoichiometrically tunable band structure. Structurally, a multi-scale porous network percolating throughout the material is realized. The nature of heat-treatment and top-down process allows for facile tunabil-ity and formation of mixed bandgap metal oxides through controlled carbon deposition. As a proof of concept, gC-β-Ga 2 O 3 was utilized as a photocatalyst for CO 2 reduction, which demonstrated excellent selectivity and conversion rates into CH 4 and CO.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Shifts in Evapotranspiration Components During Heatwaves Alter Surface Cooling

Heatwave events significantly alter ecosystem water and energy balance and are often accompanied by extreme surface temperatures. Understanding how surface temperatures during such events are regulated by soil evaporation (E) and vegetation transpiration (T) remains limited due to challenges in partitioning total evapotranspiration (ET). Here, high-frequency turbulence methods are used to partition observed ET at 32 National Ecological Observatory Network sites across the contiguous United States. Heatwaves were defined as at least three consecutive days with daily maximum air temperature exceeding the site-specific 90th percentile of the 2019–2021 record. Across 268 identified events, the T/ET ratio decreased by 32% ± 16% relative to the non-heatwave baseline of 0.65, with greater reductions at lower biomass sites. The T/ET ratio was typically suppressed below non-heatwave conditions during the early and middle stages of the heatwave (first two-thirds of event duration), but was on average higher than non-heatwave baseline levels during late stages (final third) due to extremely low soil evaporation. Of the studied heatwaves, 71% of these had surface temperatures above 38°C in their late stage; however, heatwaves sustaining higher evaporation fluxes (upper tertile of observed fluxes) during the late stage were associated with relative surface temperature anomalies that were on average 45% lower than those of heatwaves with lower evaporation fluxes (lower tertile). The commensurate surface cooling induced by higher transpiration was only 2% during heatwaves, suggesting that transpiration has a limited ability to mitigate extreme surface temperatures. This study allows for improved prediction of ecosystem feedbacks under extreme thermal stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Abstract for CRADA among NETL, Mälardalen University, University College Cork (acting through its Tyndall National Institute), and Micro Electricity Generation Association

GeoCoHorT aims to accelerate the transition to 4 th generation district heating and cooling (4GDHC) in Europe and globally, by assessing, optimizing, and demonstrating the integration of shallow geothermal heat extraction with other renewable sources and smart buildings. The target geo-source is ground water from River Shannon (Limerick, Ireland), which will provide both an efficiency and noise-reduction benefit to the micro-district and a method of combatting climate-change-warming of the Shannon Estuary. The heat from this low-temperature source will be recycled and supplied to a smart district located in Limerick, Ireland, through heat pumps powered by renewable electricity and by means of a suitable DH network design. The mandate is to find solutions that work for entire communities in a fair and sustainable manner, with the involvement of the communities themselves to highlight their needs. The project brings together four partners from Ireland, Sweden, and the U.S.A. with strong multi-disciplinary competencies. The Tyndall Institute at University of Cork (UCC) and Mälardalen University (MDU) will design and optimize the 4GDH micro-district system, and the Micro Electricity Generation Association (MEGA) will work with the community to build a sense of ownership of the Climate Challenge and ensure close community involvement. The National Energy Technology Laboratory of the U.S. Department of Energy (NETL) will design the prospective geo-fluid loop to optimize heat-extraction effectiveness with ecological safety and assess the environmental benefits of the proposed solutions. Industrial advisors (Eskilstuna Strängnäs Energi och Miljö (ESEM) and Mimer in Sweden, and Smart MPOWER in Ireland) will steer the technology development to meet the needs of public utilities and consumers. Current infrastructure in the smart district in Limerick (demonstration site of an EU Lighthouse project) will be exploited and modified with support of MEGA, UCC, and MPOWER to integrate micro-DH from low-temperature heat sources, such as the river heat, and to allow increasing penetration of prosumers in the grid. Additional case studies will be developed with the help of industrial advisors to extend the results to other regions (e.g., Sweden and USA) and provide valuable insights for the development of 4GDH worldwide.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Mechanisms of Action of Dorsal Root Ganglion Stimulation

The dorsal root ganglion (DRG) serves as a pivotal site for managing chronic pain through dorsal root ganglion stimulation (DRG-S). In recent years, the DRG-S has emerged as an attractive modality in the armamentarium of neuromodulation therapy due to its accessibility and efficacy in alleviating chronic pain refractory to conventional treatments. Despite its therapeutic advantages, the precise mechanisms underlying DRG-S-induced analgesia remain elusive, attributed in part to the diverse sensory neuron population within the DRG and its modulation of both peripheral and central sensory processing pathways. Emerging evidence suggests that DRG-S may alleviate pain by several mechanisms, including the reduction of nociceptive signals at the T-junction of sensory neurons, modulation of pain gating pathways within the dorsal horn, and regulation of neuronal excitability within the DRG itself. However, elucidating the full extent of DRG-S mechanisms necessitates further exploration, particularly regarding its supraspinal effects and its interactions with cognitive and affective networks. Understanding these mechanisms is crucial for optimizing neurostimulation technologies and improving clinical outcomes of DRG-S for chronic pain management. This review provides a comprehensive overview of the DRG anatomy, mechanisms of action of the DRG-S, and its significance in neuromodulation therapy for chronic pain.

60 APPLIED LIFE SCIENCES↗