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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Wearable Microfluidic Diaphragm Pressure Sensor for Health and Tactile Touch Monitoring

Flexible pressure sensors have many potential applications in wearable electronics, robotics, health monitoring, and more. In particular, liquid-metal-based sensors are especially promising as they can undergo strains of over 200% without failure. However, current liquid-metal-based strain sensors are incapable of resolving small pressure changes in the few kPa range, making them unsuitable for applications such as heart-rate monitoring, which require a much lower pressure detection resolution. In this paper, a microfluidic tactile diaphragm pressure sensor based on embedded Galinstan microchannels (70 µm width × 70 µm height) capable of resolving sub-50 Pa changes in pressure with sub-100 Pa detection limits and a response time of 90 ms is demonstrated. An embedded equivalent Wheatstone bridge circuit makes the most of tangential and radial strain fields, leading to high sensitivities of a 0.0835 kPa -1 change in output voltage. The Wheatstone bridge also provides temperature self-compensation, allowing for operation in the range of 20–50 °C. Finally, as examples of potential applications, a polydimethylsiloxane (PDMS) wristband with an embedded microfluidic diaphragm pressure sensor capable of real-time pulse monitoring and a PDMS glove with multiple embedded sensors to provide comprehensive tactile feedback of a human hand when touching or holding objects are demonstrated.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Organic Microbial Electrochemical Transistor Monitoring Extracellular Electron Transfer

Abstract Extracellular electron transfer (EET) denotes the process of microbial respiration with electron transfer to extracellular acceptors and has been exploited in a range of microbial electrochemical systems (MESs). To further understand EET and to optimize the performance of MESs, a better understanding of the dynamics at the microscale is needed. However, the real‐time monitoring of EET at high spatiotemporal resolution would require sophisticated signal amplification. To amplify local EET signals, a miniaturized bioelectronic device, the so‐called organic microbial electrochemical transistor (OMECT), is developed, which includes Shewanella oneidensis MR‐1 integrated onto organic electrochemical transistors comprising poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) combined with poly(vinyl alcohol) (PVA). Bacteria are attached to the gate of the transistor by a chronoamperometric method and the successful attachment is confirmed by fluorescence microscopy. Monitoring EET with the OMECT configuration is achieved due to the inherent amplification of the transistor, revealing fast time‐responses to lactate. The limits of detection when using microfabricated gates as charge collectors are also investigated. The work is a first step toward understanding and monitoring EET in highly confined spaces via microfabricated organic electronic devices, and it can be of importance to study exoelectrogens in microenvironments, such as those of the human microbiome.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Real-time monitoring of high-gravity corn mash fermentation using in situ raman spectroscopy

In situ Raman spectroscopy was employed for real-time monitoring of simultaneous saccharification and fermentation (SSF) of corn mash by an industrial strain of Saccharomyces cerevisiae. An accurate univariate calibration model for ethanol was developed based on the very strong 883 cm -1 C-C stretching band. Multivariate partial least squares (PLS) calibration models for total starch, dextrins, maltotriose, maltose, glucose, and ethanol were developed using data from eight batch fermentations and validated using predictions for a separate batch. The starch, ethanol, and dextrins models showed significant prediction improvement when the calibration data were divided into separate high- and low-concentration sets. Collinearity between the ethanol and starch models was avoided by excluding regions containing strong ethanol peaks from the starch model and, conversely, excluding regions containing strong saccharide peaks from the ethanol model. The two-set calibration models for starch (R 2 = 0.998, percent error = 2.5%) and ethanol (R 2 = 0.999, percent error = 2.1%) provide more accurate predictions than any previously published spectroscopic models. Glucose, maltose, and maltotriose are modeled to accuracy comparable to previous work on less complex fermentation processes. Our results demonstrate that Raman spectroscopy is capable of real time in situ monitoring of a complex industrial biomass fermentation. To our knowledge, this is the first PLS-based chemometric modeling of corn mash fermentation under typical industrial conditions, and the first Raman-based monitoring of a fermentation process with glucose, oligosaccharides and polysaccharides present.

biofuels↗
DOE OSTI2016

Monitoring the Strain Evolution of Lithium‐Ion Battery Electrodes using an Optical Fiber Bragg Grating Sensor

Abstract More advanced characterization and developmental tools are essential to improve the performance and safety of Li‐ion batteries. Conventional tools have been limited to customized test cell configurations that require special facilities and expensive equipment. As a practical solution for the in situ monitoring of realistic battery cells, we have embedded fiber optic sensors within Li‐ion battery pouch cells to monitor the internal electrode strain and temperature during cycling. Here we report the direct monitoring of strain evolution using implanted fiber‐optic sensors within the individual electrodes in a Li‐ion battery. Reproducible peak shifting and splitting in the implanted fiber optic sensor originate from the accumulated longitudinal and transverse strains associated with the expansion or contraction of the anode electrode. These discoveries demonstrate the feasibility and utility of fiber Bragg grating (FBG) sensors to be used as diagnostic tools in the development of new battery materials and structures.

Bae, Chang‐Jun↗
DOE OSTI2019

Introducing “ The Integrator ”: A novel technique to monitor environmental flow systems

Abstract We introduce “ The Integrator ,” a novel technique to quantify transport and reaction metrics commonly used to characterize flow systems. This development consists of two products: (1) The Integrator sampling device and (2) its supporting mathematical framework, which is compatible with semi‐continuous sensor data. The use of The Integrator device simplifies the logistics of sample collection and greatly reduces the number of samples needed, making it ideal to characterize systems that are: (1) difficult to access, (2) large and thus intractable or highly heterogeneous, and (3) highly instrumented otherwise but where a more holistic, mechanistic understanding may be gained by monitoring one or more currently untracked elements. We tested and validated The Integrator technique using experimental data collected from a heart rate monitor (high‐quality, high‐frequency data in response to known excitation events) and solute tracer experiments conducted in two contrasting (fourth and seventh order) rivers. In the Supporting Information , we provide details concerning the design of The Integrator device used in our field case studies and provide insight into potential improvements. Despite our case studies focus on the analysis of conservative and reactive transport of solutes in rivers, the principles behind The Integrator technique can be used to monitor water quality in hyporheic zones, aquifers, wetlands, swamps, karsts, oceans, wastewater treatment plants, pipe networks, and air quality. Furthermore, special arrangements of Integrator devices can be used to gather data at spatial and temporal resolutions that are currently unattainable due to high transportation and/or personnel costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Coupling of NMR to ACOMP for Terpolymerization Monitoring and Control

Abstract Monitoring and control of free radical copolymerization using automatic continuous online monitoring of polymerization reactions (ACOMP) with UV detection has been recently achieved. It is difficult, however, to achieve spectral separation of similar monomers with UV alone. Here, nuclear magnetic resonance (NMR) is coupled to ACOMP for the first time and terpolymerization reactions involving acrylamide (Am), sodium acrylate (Ac), and styrene sulfonate (SS) are monitored, and a first attempt at active composition control is made. The NMR resolves the concentrations of Am and Ac, while the UV resolves SS and the sum of Am and Ac. NMR analysis is performed in water, using signal suppression, to eliminate the need for deuterated solvents. From this, instantaneous fractions of each comonomer in the terpolymer are continuously known, along with weight average molecular weight and intrinsic viscosity, IV. Am and Ac have similar reactivities, whereas the reactivity ratio of SS is much larger. Hence, there is high composition drift in batch polymerization and SS is rapidly consumed, leaving a final copolymer of Am and SS to form. Maintaining constant terpolymer composition is taken as a first step toward active control.

McAfee, Terry↗
DOE OSTI2023

Effects of Livestock Exclusion on Stream Habitat and Aquatic Biota: A Review and Recommendations for Implementation and Monitoring

Abstract To inform riparian restoration, research, and monitoring and to provide management recommendations, we reviewed published studies evaluating the physical and biological effectiveness of livestock exclusion and grazing reduction on various metrics in riparian and aquatic areas. We identified 95 North American studies that reported the effects of livestock grazing reduction on physical habitat (channel morphology, mesohabitats, substrate, and bank stability), biological assemblages (riparian vegetation, macroinvertebrates, fish, and birds), and water quality metrics (temperature, nitrates, phosphorus, and turbidity). Most studies reported that methods to reduce or exclude livestock decreased channel width, width-to-depth ratio, bank erosion, soil bulk density, bare ground, water temperature, nitrogen, and phosphorus and increased riparian vegetation (cover, height, productivity, biomass, and abundance), riparian bird abundance, and young-of-the-year fishes. Results for channel depth, instream substrate, mesohabitats, water depth, juvenile and adult fishes, and macroinvertebrates showed no consistent response to exclusion. Project success was influenced by the time since exclusion; whether there was complete exclusion or continued grazing; and local climate, geology, and soils. Apart from bank erosion and stability, most of the physical and biological metrics took more than a decade to respond to livestock exclusion. However, coupling exclusion with planting and other restoration measures decreased the recovery time. Complete exclusion of livestock produced more consistent improvements in riparian condition and other metrics than rest–rotation or other grazing management strategies. Understanding how physical and biological metrics respond to livestock exclusion will require (1) focused, long-term studies using before–after or before–after, control–impact designs; and (2) monitoring of metrics that most consistently respond to exclusion. Ultimately, the design of exclusions should be driven by local climate, geology, biophysical conditions, and management history. Our results highlight the need for watershed-scale approaches to excluding livestock from broad areas and the need for implementation monitoring to ensure that fencing and other exclusion measures continue to exclude livestock and produce the desired responses.

Krall, Michelle↗
DOE OSTI2021

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2016

Feasibility of Intraoperative Nerve Monitoring in Preventing Thermal Damage to the “Nerve at Risk” During Image-Guided Ablation of Tumors

PurposeTo assess feasibility of intraoperative neurophysiologic monitoring (IONM) during image-guided, percutaneous thermal ablation of tumors.Materials and MethodsFrom February 2009 to October 2013, a retrospective review of all image-guided percutaneous thermal ablation interventions using IONM was performed and data was compiled using electronic medical records and imaging studies.ResultsTwelve patients were treated in 13 ablation interventions. In 4 patients, real-time feedback from the monitoring neurologist was used to adjust applicator placement and ablation settings. IONM was technically feasible in all procedures and there were no complications related to monitoring or ablation. All nerves at risk remained intact and of the 11 patients who could be followed, none developed new nerve deficit up to a minimum of 2 months after ablation.ConclusionIONM is safe and feasible for use during image-guided thermal ablation of tumors in the vicinity of nerves. Outcomes in this study demonstrate its potential utility in image-guided ablation interventions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

Groundwater characterization and monitoring at a complex industrial waste site using electrical resistivity imaging

A contaminated industrial waste site in Washington State (USA) containing buried, metallic-waste storage tanks, pipes, and wells, was evaluated to determine the feasibility of monitoring groundwater remediation activities associated with an underlying perched aquifer system using electrical resistivity tomography. The perched aquifer, located ~65 m below ground surface and ~10 m above the regional water table, contains high concentrations of nitrate, uranium, and other contaminants of concern from past tank leaks and intentional releases of wastes to surface disposal sites. The extent of the perched water aquifer is not well known, and the effectiveness of groundwater extraction for contaminant removal is uncertain, so supplemental characterization and monitoring technologies are being evaluated. Numerical simulations of subsurface flow and contaminant transport were performed with a highly resolved model of the hydrogeologic system and waste site infrastructure, and these simulations were used as the physical basis for electrical resistivity tomography modeling. The modeling explicitly accounted for metallic infrastructure at the site. The effectiveness of using surface electrodes versus surface and horizontal subsurface electrodes, for imaging groundwater extraction from the perched water aquifer, was investigated. Although directional drilling is a mature technology, its use for electrode emplacement in the deep subsurface under a complex industrial waste site via horizontal wells has not yet been demonstrated. Results from this study indicate that using horizontal subsurface electrode arrays could significantly improve the ability of electrical resistivity tomography to image deep subsurface features and monitor remediation activities under complex industrial waste sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗
DOE OSTI2013

Geant4 Model Validation of Compton Suppressed System for Process monitoring of Spent Fuel

Nuclear material accountancy is of continuous concern for the regulatory, safeguards, and verification communities. In particular, spent nuclear fuel reprocessing facilities pose one of the most difficult accountancy challenges: monitoring highly radioactive, fluid sample streams in near real-time. The Multi-Isotope Process monitor will allow for near-real-time indication of process alterations using passive gamma-ray detection coupled with multivariate analysis techniques to guard against potential material diversion or to enhance domestic process monitoring. The Compton continuum from the dominant 661.7 keV 137Cs fission product peak obscures lower energy lines which could be used for spectral and multivariate analysis. Compton suppression may be able to mitigate the challenges posed by the high continuum caused by scattering. A Monte Carlo simulation using the Geant4 toolkit is being developed to predict the expected suppressed spectrum from spent fuel samples to estimate the reduction in the Compton continuum. Despite the lack of timing information between decay events in the particle management of Geant4, encouraging results were recorded utilizing only the information within individual decays without accounting for accidental coincidences. The model has been validated with single and cascade decay emitters in two steps: as an unsuppressed system and with suppression activated. Results of the Geant4 model validation will be presented.

Bender, Sarah↗
DOE OSTI2023

A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring

Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. Here, a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Quantitative monitoring of two simultaneously binding species using Label-Enhanced surface plasmon resonance

Highlights: • SPR discriminates and monitors two molecules simultaneously binding to a surface. • Accurate concentration determination of two different molecules simultaneously. • Deconstruction of a sum sensorgram into two molecular component sensorgrams. • Simultaneous measurement of binding constants of two binding molecules in a mixture. • Applicability to allosteric effects, bi-specific binders, and conformation changes. Surface plasmon resonance (SPR) is a well-established method for biomolecular interaction studies. SPR monitors the binding of molecules to a solid surface, embodied as refractive index changes close to the surface. One limitation of conventional SPR is the universal nature of the detection that results in an inability to qualitatively discriminate between different binding species. Furthermore, it is impossible to directly discriminate two species simultaneously binding to different sites on a protein, which limits the utility of SPR, for example, in the study of allosteric binders or bi-specific molecules. It is also impossible in principle to discriminate protein conformation changes from actual binding events. Here we demonstrate how Label-Enhanced SPR can be utilized to discriminate and quantitatively monitor the simultaneous binding of two different species – one dye-labeled and one unlabeled - on a standard, single-wavelength SPR instrument. This new technique increases the versatility of SPR technology by opening up application areas where the usefulness of the approach has previously been limited.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Designing an effective drug combination for ER stress loading in cancer therapy using a real-time monitoring system

Excess stress caused by accumulation of misfolded proteins inside the endoplasmic reticulum (ER) lumen can cause cells to undergo apoptosis. Misfolded proteins exported from ER to cytoplasm are ubiquitinated and mostly degraded by the proteasome, but can also be destroyed by autophagy mediated by the docking proteins p62 and NBR1. When misfolded proteins accumulate beyond the capacity of these clearance systems, they are transported to the microtubule organization center to form aggresomes, which are also degraded by autophagy. Together, these phenomena suggest the existence of a coordinated intracellular network for coping with the accumulation of misfolded proteins. Thus, rational inhibition of this network system might enhance killing of cancer cells subjected to pronounced ER stress loading. Based on this rationale, we sought to establish a quantitative assay for monitoring ER stress loading. MDA-MB231 cells stably transfected with the ERAI-Venus vector exhibited a strong XBP1 splicing signal in response to ER stress. Using the IncuCyte cell imaging system, we monitored the fluorescence intensity of XBP1-Venus, normalized against cell density, as an ER stress indicator. This parameter correlated closely with other reporters of unfolded protein responses. Assessment of the XBP1-Venus signal during exposure to various drug combinations revealed that simultaneous inhibition of the proteasome, autophagy, and aggresome formation led to more effective ER stress loading and higher cytotoxicity than inhibition of only two components. Our data suggest that this monitoring system is a useful tool for designing effective drug combinations for ER stress loading in cancer therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Real-time monitoring of IL-6 and IL-10 reporter expression for anti-inflammation activity in live RAW 264.7 cells

Highlights: • This assay could monitor expression of IL-6 and IL-10 reporters in real-time. • The assay continuously determines expression of reporters in living single cells. • Lipopolysaccharide strongly induces expression of IL-6 and IL-10 reporters. In previous study, we suggested that the interleukin (IL)-6 and IL-10 could serve as a good biomarker for anti-inflammation that related to chronic inflammatory disease. Recently, we are finding new anti-inflammation compounds from natural products by screening of IL-6 and IL-10 levels. Although, we could measure IL-6 and IL-10 levels by several methods. However, all methods could not measure continuous kinetic of IL-6 and IL-10 levels. Most methods have multiple steps and take a long time. Therefore, there is no a suitable method for screening. To this end, we established IL-6 and IL-10 promoter assay which can monitor with reference gene as Glyceraldehyde 3-phosphate dehydrogenase (gapdh) promoter in living single cell. It could determine IL-6 and IL-10 levels continuously in real-time within two steps. We evaluated IL-6 and IL-10 reporter expression in LPS-induced RAW 264.7 cells with well-known anti-inflammatory compounds such as quercetin, xanthones, β-D-glucan and dexamethasone. As the results, the expression of IL-6 and IL-10 reporters were strongly induced by LPS. The expression of IL-6 reporter was inhibited by all anti-inflammation compounds in LPS-induced RAW 264.7 cells. The expression of IL-10 reporter was inhibited by quercetin, xanthones and dexamethasone in LPS-induced RAW 264.7 cells. While, expression of IL-10 reporter was induced by β-D-glucan. These results indicated that this assay could use for determination of IL-6 and IL-10 reporter expression in LPS-induced RAW 264.7 cells for anti-inflammation activity. Moreover, the results showed that natural compounds have an effect on the time course of IL-6 and IL-10 expressions. Therefore, real-time monitoring has a merit for natural compounds screening. We suggested that this assay could serve as a compound screening assay for anti-inflammation activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Enhancing predictive understanding and accuracy in geological carbon dioxide storage monitoring: Simulation and history matching of tracer transport dynamics

Co-injection of conservative tracers with carbon dioxide (CO 2 ) is a viable tool for monitoring subsurface processes during geological CO 2 storage (GCS). This research investigates the simulation and history-matching of a gas tracer (sulfur hexafluoride, SF 6 ) during CO 2 flooding, employing a core flooding result in Berea sandstone. Four extensively used saturation functions are assessed for their efficacy in history matching of CO 2 /SF 6 injection at the core scale. The history-matching process incorporates particle swarm optimization (PSO) to fine-tune constitutive relationships parameters. Next, employing transport models at the aquifer scale, we interrogate the impact on tracer transport and mixing of saturation function uncertainties, arising from the non-uniqueness of constitutive relationships parameters and saturation function types. To assess the effects of geological heterogeneity on behavior of tracer breakthrough curves (BTCs), we employ two normalized parameters assessing the degree of mixing and SF 6 breakthrough time. The aquifer-scale investigation encompasses both homogeneous and heterogeneous systems with and without capillary heterogeneity effects. Our findings underscore the critical importance of addressing saturation function uncertainties, emphasizing the significance of auxiliary experiments and innovative methodologies to enhance predictive accuracy. The findings highlight significant disparities in arrival times, BTC peaks, tails, and mixing levels, even under optimal conditions. Heterogeneity, with or without capillary heterogeneity, plays a crucial role in shaping BTC variations, resulting in accelerated SF 6 breakthrough times and reduced BTC peaks. Evaluation of monitoring points distant from the injector reveals a dampening effect on the SF6 BTC peak, particularly in heterogeneous systems with capillary heterogeneity, where the peak is halved. These insights underscore the challenges associated with tracer monitoring and the necessity for enhanced methodologies to improve predictive accuracy in subsurface processes during GCS.

58 GEOSCIENCES↗