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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Degradation Diagnostics from the Subsurface of Lithium-Ion Battery Electrodes

Despite the long-established rocking-chair theory of lithium-ion batteries (LIBs), developing novel characterization methodology with higher spatiotemporal resolution facilitates a better understanding of the solid electrolyte interphase studies to shape the reaction mechanisms. In this work, we develop a Xenon ion plasma focused ion beam (Xe + PFIB)-based characterization technique to probe the cross-sectional interface of both ternary cathode and graphite anode electrodes, with the focus on revealing the chemical composition and distribution underneath the electrode surface by in-depth analysis of secondary ions. Particularly, the lithium fluoride is detected in the pristine cathode prior to contact with the electrolyte, reflecting that the electrode degradation is in the form of the loss of lithium inventory during electrode preparation. This degradation is related to the hydrolysis of the cathode material and the decomposition of the PVDF binder. Here, through the quantitative analysis of the transition-metal degradation products, manganese is found to be the dominant element in the newly formed inactive fluoride deposition on the cathode, while no transition metal signal can be found inside the anode electrode. These insights at high resolution implemented via a PFIB-based characterization technique not only enrich the understanding of the degradation mechanism in the LIBs but also identify and enable a high-sensitivity methodology to obtain the chemical survey at the subsurface, which will help remove the capacity-fade observed in most LIBs.

25 ENERGY STORAGE↗
DOE OSTI2021

The Colorado East River Community Observatory Data Collection

Abstract The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow‐dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long‐term and spatially‐extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross‐disciplinary process‐based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value‐added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tidal frequencies and quasiperiodic subsurface water level variations dominate redox dynamics in a salt marsh system

Salt marshes are hotspots of nutrient processing en route to sensitive coastal environments. Whilst our understanding of these systems has improved over the years, we still have limited knowledge of the spatiotemporal variability of critical biogeochemical drivers within salt marshes. Sea-level rise will continue to force change on salt marsh functioning, highlighting the urgency of filling this knowledge gap. Our study was conducted in a central California estuary experiencing extensive marsh drowning and relative sea-level rise, making it a model system for such an investigation. Here we instrumented three marsh positions subjected to different degrees of tidal inundation (6.7%, 8.9%, and 11.2% of the time for the upper, middle, and lower marsh positions, respectively), providing locations with varied biogeochemical characteristics and hydrological interactions at the site. We continuously monitored redox potential (Eh) at depths of 0.1, 0.3, and 0.5 m, subsurface water levels (WL), and temperature at 0.7 m depth at each marsh position. To understand how drivers of subsurface biogeochemical processes fluctuate across tidal cycles, we used wavelet analyses to explain the interactions between Eh and WL. We found that tidal forcing significantly affects key drivers of biogeochemical processes by imparting controls on Eh variability, likely driving subsurface hydro-biogeochemistry of the salt marsh. Wavelet coherence showed that the Eh-WL relationship is nonlinear, and their lead–lag relationship is variable. We found that precipitation events perturb Eh at depth over timescales of hours, even though WL shows relatively minimal change during events. Furthermore, this work highlights the importance of high frequency in situ measurements, such as Eh, to help explain factors that govern subsurface biogeochemistry and hydrological processes in salt marshes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Seasonal and tidal variations in hydrologic inputs drive salt marsh porewater nitrate dynamics

Salt marshes remove terrestrially derived nutrients en route to coasts. While these systems play a critical role in improving water quality, we still have a limited understanding of the spatiotemporal variability of biogeochemically reactive solutes and processes within salt marshes. We implemented a high-frequency sampling system to monitor sub-hourly nitrate (NO$^{–}_{3}$) concentrations in salt marsh porewater at Elkhorn Slough in central California, USA. We instrumented three marsh positions along an elevation gradient subjected to different amounts of tidal inundation, which we predicted would lead to varied biogeochemical characteristics and hydrological interactions. At each marsh position, we continuously monitored porewater NO$^{–}_{3}$ concentrations at depths of 10, 30, and 50 cm and porewater levels measured at 70 cm depth over seven deployments of ~10 days each that spanned seasonal wet/dry periods common to Mediterranean climates. We quantified tidal event hysteresis between NO$^{–}_{3}$ and water level to understand how NO$^{–}_{3}$ concentrations and sources fluctuate across tidal cycles. In dry periods, the NO$^{–}_{3}$-porewater level relationship indicated that the NO$^{–}_{3}$ source was likely estuarine surface water that flooded the transect during high tides and the salt marsh was a NO$^{–}_{3}$ sink. In wet periods, the NO$^{–}_{3}$-porewater level relationship suggested the salt marsh was a source of NO$^{–}_{3}$. Finally, these findings suggest that tidal and seasonal hydrologic fluxes together control NO$^{–}_{3}$ porewater dynamics and export and influence ecological processes in coastal environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

River–aquifer interactions enhancing evapotranspiration in a semiarid riparian zone: A modelling study

The hydrologic flows across the river–aquifer interface play an important role in groundwater dynamics and biogeochemical reactions within the subsurface; however, little is known about the effects of river–aquifer interactions on land surface processes. In this study, we developed a fully coupled three-dimensional (3D) land surface and subsurface model at a high resolution (~1 km) that accounts for high-frequency hydrologic exchange flow conditions to investigate how river–aquifer interactions modulate surface water budgets in the Upper Columbia-Priest Rapids watershed, a typical semiarid watershed located in the northwestern United States where river stage fluctuates in response to reservoir releases changing. Our results show that the spatiotemporal dynamics of river–aquifer interactions are highly heterogeneous, driven mainly by river-stage fluctuations. Adding 6.64 × 10 6 m 3 year –1 of water over the watershed from the river to groundwater owing to the lateral flow, river–aquifer interactions led to an increase in soil evaporation and transpiration supplied by higher soil moisture content, particularly in deeper subsurface. In a hypothetic future scenarios where a 5-m rise in river stage was assumed, the hydrologic flow exchange rates were intensified, resulting in higher surface water over the entire watershed. Overall, lateral flow induced by river–aquifer exchanges leads to an increase in evapotranspiration of ~75% in the historical period and of ~83% in the hypothetical future scenario. Finally, our study demonstrates the potential of coupled model as an effective tool for understanding river–aquifer–land surface interactions, and indicates that river–aquifer interactions fundamentally alter the water balance of the riparian zone for the semiarid watershed and will likely become more frequent and intense in the future under the effects of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evapotranspiration Partitioning Using Flux Tower Data in a Semi-Arid Ecosystem

Information about evapotranspiration (ET) and its components, that is, evaporation and transpiration, is crucial for a wide range of water and ecosystem management applications. However, partitioning ET into its two components is often challenging because of their spatiotemporal variabilities and lack of process understanding. This study developed a machine learning (ML) framework to shed light on ET processes and assess the relative importance of different drivers by incorporating hydrometeorology and biomass productivity variables. The Shapley Additive Explanations (SHAP) approach was applied to enhance explainability and rank the importance of ET drivers and their components. A total of 62 variables covering hydrometeorological and biomass productivity dimensions were considered from the Reynolds Creek Critical Zone Observatory (CZO) station in Idaho. The variable importance assessment identified the leading drivers individually for evaporation, transpiration and ET (soil water content for evaporation, vapour pressure deficit for transpiration and soil water content for ET). The results further highlighted the value of combining hydrometeorological and biomass productivity variables to achieve better predictability of ET processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensitivity of Fine‐Resolution Urban Heat Island Simulations to Soil Moisture Parameterization

ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Talebpour, Mahdad↗
DOE OSTI2022

Integrating phytoplankton pigment and DNA meta-barcoding observations to determine phytoplankton composition in the coastal ocean

Quantifying phytoplankton composition is critical to predicting marine ecosystem structure and function. DNA meta-barcoding and high-performance liquid chromatography (HPLC) pigment analysis are two widely used methods for assessing phytoplankton composition; however, comparing their performance has been done only rarely. Here, we integrate DNA meta-barcoding and HPLC pigment observations to determine eukaryotic phytoplankton composition in the Santa Barbara Channel, California. We find that both methods identify the same four dominant eukaryotic phytoplankton taxa (diatoms, dinoflagellates, chlorophytes, and prymnesiophytes), but inter- and intra-lineage variability in biomarker pigmentation (associated with both a lack of taxonomic specificity of biomarker pigments and intrinsic differences in accessory pigmentation) drives substantial disagreement between the methods. Covariation network analysis circumvents this disagreement and reveals that diverse assemblages of phytoplankton and other protists covary with distinct suites of biomarker pigments. Our results highlight the strengths and weaknesses of each method in characterizing phytoplankton composition and reveal novel insights into phytoplankton physiology that could only be gained by integrating the two methods. Finally, we suggest a path to monitor eukaryotic plankton communities on unprecedented spatiotemporal scales based on the covariation of unique phytoplankton and protistan assemblages with remotely sensible phytoplankton pigment concentrations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Seasonal drivers of dissolved oxygen across a tidal creek–marsh interface revealed by machine learning

Abstract Dissolved oxygen (DO) is a key biogeochemical control in coastal systems, and its concentration and drivers vary markedly through time and space. This makes it difficult to accurately represent coastal DO and associated biogeochemical processes in models, limiting our ability to predict how these systems will respond to global change. We obtained high‐frequency (5‐min) in situ measurements of DO collected at three locations across the interface of a tidal creek and coastal marsh in the Pacific Northwest, USA. Random Forest machine learning models quantified the importance of three categories of environmental drivers (Aquatic, Climatic, and Terrestrial) of DO variability across the creek–marsh interface. We selected two 4‐month datasets representing Summer and Winter seasonal periods to test two hypotheses on the dominant drivers of DO at the coastal interface. We found that the Terrestrial driver—characterized by long periods of anaerobic conditions and episodic pulses in DO after floods—was most important during the Winter, whereas the Aquatic driver—characterized by variability over tidal, diel, and lunar cycles—was most important during the Summer. We explored how future climate change scenarios could alter the drivers of DO variability using a cumulative sums driver–response framework. Our results suggest that under climate change, Aquatic and Climatic drivers may increase in importance during the Summer, potentially linked to changing metabolic regimes and sea level, with Terrestrial driver importance potentially increasing during the Winter. Our approach highlights useful methods for understanding the spatiotemporal complexity of oxygen across coastal interfaces and quantifying the relative importance of distinct environmental drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Manipulation of Scattering Spectra with Topology of Light and Matter

Structured lights, including beams carrying spin and orbital angular momenta, radially and azimuthally polarized vector beams, as well as spatiotemporal optical vortices, have attracted significant interest due to their unique amplitude, phase front, polarization, and temporal structures, enabling a variety of applications in optical and quantum communications, micromanipulation, and super-resolution imaging. In parallel, structured optical materials, metamaterials, and metasurfaces consisting of engineered unit cells—meta-atoms, opened new avenues for manipulating the flow of light and optical sensing. While several studies explored structured light effects on the individual meta-atoms, their shapes are largely limited to simple spherical geometries. However, the synergy of the structured light and complex-shaped meta-atoms has not been fully explored. Here, in this paper, the role of the helical wavefront of Laguerre–Gaussian beams in the excitation and suppression of higher-order resonant modes inside all-dielectric meta-atoms of various shapes, aspect ratios, and orientations, is demonstrated and the excitation of various multipolar moments that are not accessible via unstructured light illumination is predicted. The presented study elucidates the role of the complex phase distribution of the incident light in shape-dependent resonant scattering, which is of utmost importance in a wide spectrum of applications ranging from remote sensing to spectroscopy.

42 ENGINEERING↗
DOE OSTI2023

Supramolecular polymers: Dynamic assemblies of “dancing” monomers

The objective of this perspective on supramolecular polymers is to highlight how their dynamic nature based on noncovalent bonding among monomers can have a profound impact on their functions. We focus here on peptide amphiphile supramolecular polymers developed in our laboratory and use recent results on their dynamic behavior to reflect on the exciting functions that might emerge in these systems. We are greatly motivated by recent results that demonstrate unprecedented bioactivity in supramolecular polymers to address the enormous scientific challenge of finding strategies to reverse paralysis caused by traumatic injuries or disease. We suggest that future opportunities exist for novel functions in supramolecular polymers by tuning dynamic behavior through the chemical design of monomers, and also by gaining an understanding of the precise spatiotemporal mechanisms linked to supramolecular motion in these emerging polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Physics-aware deep learning (PADL) has gained popularity for use in spatiotemporal dynamics simulations, such as those in computational modeling of energetic materials (EM). We show that the challenge PADL methods face while learning complex field evolution problems can be simplified and accelerated by decoupling it into two tasks: learning complex geometric features in evolving fields and modeling dynamics over these features in a lower-dimensional feature space. We build upon our previous work on physics-aware recurrent convolutional neural networks (PARC). PARC embeds knowledge of underlying physics into its neural network architecture for more robust and accurate prediction of evolving physical fields. PARC was shown to effectively learn complex nonlinear features such as the formation of hotspots and coupled shock fronts in various initiation scenarios of EMs, as a function of microstructures, serving effectively as a microstructure-aware burn model. Here, we further accelerate PARC and reduce its computational cost by projecting the original dynamics onto a lower-dimensional invariant manifold, or “latent space.” The projected latent representation encodes the complex geometry of evolving fields (e.g., temperature and pressure) in a set of data-driven features. The reduced dimension of this latent space allows us to learn the dynamics during the initiation of EM with a lighter and more efficient model. We observe a significant decrease in training and inference time while maintaining results comparable to PARC at inference. This work takes steps towards enabling rapid prediction of EM thermomechanics at larger scales and characterization of EM structure–property–performance linkages at a full application scale.

Mathematics and Computing↗
DOE OSTI2020

What can we learn from N 2 O isotope data? – Analytics, processes and modelling

The isotopic composition of nitrous oxide (N 2 O) provides useful information for evaluating N 2 O sources and budgets. Due to the co–occurrence of multiple N 2 O transformation pathways, it is, however, challenging to use isotopic information to quantify the contribution of distinct processes across variable spatiotemporal scales. Here, we present an overview of recent progress in N 2 O isotopic studies and provide suggestions for future research, mainly focusing on: analytical techniques; production and consumption processes; and interpretation and modelling approaches. Comparing isotope–ratio mass spectrometry (IRMS) with laser absorption spectroscopy (LAS), we conclude that IRMS is a precise technique for laboratory analysis of N 2 O isotopes, while LAS is more suitable for in situ/inline studies and offers advantages for site–specific analyses. When reviewing the link between the N 2 O isotopic composition and underlying mechanisms/processes, we find that, at the molecular scale, the specific enzymes and mechanisms involved determine isotopic fractionation effects. In contrast, at plot–to–global scales, mixing of N 2 O derived from different processes and their isotopic variability must be considered. We also find that dual isotope plots are effective for semi–quantitative attribution of co–occurring N 2 O production and reduction processes. More recently, process–based N 2 O isotopic models have been developed for natural abundance and 15 N–tracing studies, and have been shown to be effective, particularly for data with adequate temporal resolution. Here, despite the significant progress made over the last decade, there is still great need and potential for future work, including development of analytical techniques, reference materials and inter–laboratory comparisons, further exploration of N 2 O formation and destruction mechanisms, more observations across scales, and design and validation of interpretation and modelling approaches. Synthesizing all these efforts, we are confident that the N 2 O isotope community will continue to advance our understanding of N 2 O transformation processes in all spheres of the Earth, and in turn to gain improved constraints on regional and global budgets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Using Explainable Artificial Intelligence to Predict Perovskite Solar Cell Electrical Metastability from Operando Photoluminescence Images in Accelerated Stress Testing

Metal halide perovskite (MHP) solar cells exhibit a metastable response to bias governed by coupled ionic–electronic processes, complicating the conventional reciprocity relation between luminescence intensity and device open-circuit voltage (V oc ). This limits the use of luminescence as a diagnostic for device screening or accelerated stress testing, motivating new approaches that can interpret photoluminescence (PL) signals under nonequilibrium conditions. From the artificial intelligence perspective, we develop an explainable deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) layers, and an attention mechanism to learn spatiotemporal features from operando photoluminescence PL image sequences. The model achieves a mean absolute error of ±0.027 V in predicting open-circuit voltage transients and reduces extreme-tail errors by up to 78% compared to physics-based reciprocity calculations. Gradient-weighted Class Activation Mapping (Grad-CAM) provides interpretability by highlighting physically meaningful regions such as electrode edges and emergent defect features. From the engineering application perspective, this framework enables accurate, contactless prediction of device V oc and identification of degradation-relevant features during accelerated aging of perovskite solar cells. This approach demonstrates how explainable AI can enhance operando diagnostics and reliability analysis in photovoltaic devices under nonequilibrium conditions.

14 SOLAR ENERGY↗
DOE OSTI2021

Root water uptake of biofuel crops revealed by coupled electrical resistivity and soil water content measurements

Abstract Biofuel crops, including annuals such as maize ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and canola ( Brassica napus L.), as well as high‐biomass perennial grasses such as miscanthus ( Miscanthus × giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz), are candidates for sustainable alternative energy sources. However, large‐scale conversion of croplands to perennial biofuel crops could have substantial impacts on regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales, these differences in crop water use are not well known. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. In this study, we demonstrate the use of a novel coupled hydrogeophysical approach with both time domain reflectometry soil water content and ER measurements to compare root water uptake and soil properties of an annual crop rotation with the perennial grass miscanthus, across three growing seasons (2009–2011) in southwest Michigan, USA. We estimated maximum root depths to be between 1.2 and 2.2 m, with the vertical distribution of roots being notably deeper in 2009 relative to 2010 and 2011, likely due to the drought conditions during that first year. Modeled cumulative ET of both crops was underestimated (2–34%) relative to estimates obtained from soil water drawdown in prior studies but was found to be greater in the perennial grass than the annual crops, despite shallower modeled rooting depths in 2010 and 2011.

Kuhl, Alexandria S.↗
DOE OSTI2024

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data.

Yang, Yi↗
DOE OSTI2021

Characterization of Mammalian In Vivo Enhancers Using Mouse Transgenesis and CRISPR Genome Editing [Book Chapter]

Embryonic morphogenesis is strictly dependent on tight spatiotemporal control of developmental gene expression, which is typically achieved through the concerted activity of multiple enhancers driving cell type-specific expression of a target gene. Mammalian genomes are organized in topologically associated domains, providing a preferred environment and framework for interactions between transcriptional enhancers and gene promoters. While epigenomic profiling and three-dimensional chromatin conformation capture have significantly increased the accuracy of identifying enhancers, assessment of subregional enhancer activities via transgenic reporter assays in mice remains the gold standard for assigning enhancer activity in vivo. Once this activity is defined, the ideal method to explore the functional necessity of a transcriptional enhancer and its contribution to target gene dosage and morphological or physiological processes is deletion of the enhancer sequence from the mouse genome. Here we present detailed protocols for efficient introduction of enhancer-reporter transgenes and CRISPR-mediated genomic deletions into the mouse genome, including a step-by-step guide for pronuclear microinjection of fertilized mouse eggs. We provide instructions for the assembly and genomic integration of enhancer-reporter cassettes that have been used for validation of thousands of putative enhancer sequences accessible through the VISTA enhancer browser, including a recently published method for robust site-directed transgenesis at the H11 safe-harbor locus. Together, these methods enable rapid and large-scale assessment of enhancer activities and sequence variants in mice, which is essential to understand mammalian genome function and genetic diseases.

cis-regulatory elements↗