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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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Structural basis of sequence-specific cytosine deamination by double-stranded DNA deaminase toxin DddA
The interbacterial deaminase toxin DddA catalyzes cytosine-to-uracil conversion in double-stranded (ds) DNA and enables CRISPR-free mitochondrial base editing, but the molecular mechanisms underlying its unique substrate selectivity have remained elusive. Here, we report crystal structures of DddA bound to a dsDNA substrate containing the 5'-TC target motif. These structures show that DddA binds to the minor groove of a sharply bent dsDNA and engages the target cytosine extruded from the double helix. DddA Phe1375 intercalates in dsDNA and displaces the 5' (-1) thymine, which in turn replaces the target (0) cytosine and forms a noncanonical T–G base pair with the juxtaposed guanine. This tandem displacement mechanism allows DddA to locate a target cytosine without flipping it into the active site. Biochemical experiments demonstrate that DNA base mismatches enhance the DddA deaminase activity and relax its sequence selectivity. On the basis of the structural information, we further identified DddA mutants that exhibit attenuated activity or altered substrate preference. Our studies may help design new tools useful in genome editing or other applications.
AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems
Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.
Balancing Water Sustainability and Productivity Objectives in Microalgae Cultivation: Siting Open Ponds by Considering Seasonal Water-Stress Impact Using AWARE-US
Microalgae have great potential as an energy crop. Scaling-up algal biofuel production in the United States (US) should be done with careful attention to water stress. This study evaluates the regional and seasonal water-stress impact of potential algae-pond deployments in the US. Three site-selection strategies focusing on biomass yield, water-use efficiency (WUE), and water-stress impact, respectively, are applied and compared to meet a US algae biomass production target of 30 million metric tons/yr ash-free dry weight, which converts to 20.8 billion L renewable diesel, via hydrothermal liquefaction. Ranking algae ponds based on biomass yield leads to freshwater consumption of 2.66 km3/yr, resulting in the highest water-stress impact (39.1 US equivalent km3). Under the WUE scenario, water consumption is reduced by 81%, but biomass yield is reduced by 12%. In contrast, adding a water-stress constraint to the biomass-yield ranking reduces water consumption by 50% and water-stress impact by 97%, with a small yield reduction (1.7%). Results show that pond location has a significant effect on water stress and that water stress is not proportional to water consumption or yield. Furthermore, capturing seasonal water patterns is critical for planning because sites in water-abundant regions can have short-term but significant water-stress impacts.
Shifting Left for Machine Learning: An Empirical Study of Security Weaknesses in Supervised Learning-based Projects
Context: Supervised learning-based projects (SLPs), i.e., software projects that use supervised learning algorithms, such as decision trees are useful for performing classification-related tasks. Yet, security weaknesses, such as the use of hard-coded passwords in SLPs, can make SLPs susceptible to security attacks. A characterization of security weaknesses in SLPs can help practitioners understand the security weaknesses that are frequent in SLPs and adopt adequate mitigation strategies. Objective: The goal of this paper is to help practitioners se-curely develop supervised learning-based projects by conducting an empirical study of security weaknesses in supervised learning-based projects. Methodology: We conduct an empirical study by quantifying the frequency of security weaknesses in 278 open source SLPs. Results: We identify 22 types of security weaknesses that occur in SLPs. We observe ‘use of potentially dangerous function’ to be the most frequently occurring security weakness in SLPs. Of the identified 3,964 security weaknesses, 23.79 % and 40.49 % respectively, appear for source code files used to train and test models. We also observe evidence of co-location, e.g., instances of command injection co-locates with instances of potentially dangerous function. Conclusion: Based on our findings, we advocate for a shift left approach for SLP development with security-focused code reviews, and application of security static analysis.
Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks
MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.
Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks
MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.
Fast response, optical radiance measurements of low intensity impurity emission in WEST plasmas with staggered wavelength filters
Fast response acquisition of atomic line emission through the use of a filterscope has been implemented on the WEST tokamak at multiple poloidal locations. Filterscopes consist of a fiber-optic-based transmission from the tokamak to specifically engineered optical bandpasses and photomultiplier tubes (PMT) that collect the emission intensity to measure a radiance, L [W/cm2/str], over the specified bandpass. This diagnostic records plasma-wall interaction (PWI) properties (e.g. impurity emission and recycling of main fuel ions) up to a max acquisition rate of 100 kHz. These PMT radiometric measurements are calibrated into a line-normalized radiance, LN [photons/sec/cm2/str], similar to traditional spectrometers, which can later be converted to a particle flux. Low intensity emission peaks are difficult to quantify due to often comparable continuum levels, thus a secondary filter shifted to a judiciously-selected, line-free region (~1 nm away) allows for background subtraction. The system currently installed on WEST targets tungsten (W) gross sputtering specifically by monitoring, with a filter pair, the spectral region near the neutral W line emission (W I 400.9 nm). The line and background filters of the pair are centered at 400.6 nm and 403.1 nm, respectively. Through a set beam splitters on each sight line, the filter pair are measuring from the same location. The two L signals near W I are then subtracted from each other yielding only the W I LN. W I LN data from a recent WEST experimental campaign is presented and compared with plasma parameters and traditional spectrometer measurements of W I line emission to demonstrate the capabilities of this staggered-filter filterscope method. Specifically, the choice of WEST-specific bandpass curves for the W I LN will be illustrated based on this latter comparison.
The Illumination of Thunderclouds by Lightning: 4. Volumetric Thunderstorm Imagery
Optical instruments such as the Geostationary Lightning Mapper (GLM) detect lightning based on transient changes in cloud illumination. The horizontal location of lightning is determined from the coordinates of the pixels on the imaging array illuminated during the flash. However, the vertical position of the lightning pulses (approximated by GLM “groups”) below the cloud top cannot be routinely measured from a single space-based instrument. In our prior work, we have developed a machine learning algorithm that can infer optical source altitude for a given pulse based on how the optical energy is distributed across the group footprint and the local Advanced Baseline Imager Cloud-Top Height (CTH). In this fourth part of our thundercloud illumination study, we leverage these source altitudes to generate volumetric GLM imagery of a Colombia thunderstorm. We find that 3D versions of the current GLM meteorological imagery products (that describe thunderstorm kinematics) and thundercloud imagery products (that depict how the flashes appear from space) provide additional insights into lightning activity in the thunderstorm that are lost in the vertical integration used to generate the current 2D GLM gridded products. This new volumetric imaging capability provides a more comprehensive picture of where lightning occurs in the storm, how its physical characteristics vary across three-dimensional space, and how its optical emissions interact with surrounding the cloud medium.
UT-GOM2-2 Drilling Fluid (Technical Note)
It is proposed that a salt-saturated, water-based mud might improve borehole stability for UT-GOM2-2 relative to a fresh-water-based mud. However, the primary objective for UT-GOM2-2 is to safely and successfully acquire uncompromised hydrate-bearing sediment cores. This can only be done if conditions stay within the hydrate stability zone, and well away from the hydrate stability boundary. The addition of salt to the drilling mud, shifts the hydrate stability boundary closer to estimated conditions. This shift, by some estimates of pressure and temperature, where conditions now fall outside of the hydrate stability zone, may result in borehole enlargement and the release of free gas into the borehole. This shift by all estimates, even when coring conditions stay inside the hydrate stability zone, shrinks the window between the estimated conditions and the hydrate stability boundary. This shift is likely to compromise the hydrate-bearing cores while they are being recovered from the bottom-hole to the rig floor. In this report we present two examples: 1) Assuming temperature and pressure from in-situ estimates: Hydrate within the target reservoirs (Orange and Blue) at Walker Ridge Block 313 (WR 313) will be outside the hydrate stability zone in the presence of a drilling mud with 9.5 wt.% salinity (the salinity of the proposed 10.5 ppg salt-based mud) at in situ pressure and temperature. Thus, the hydrate will be unstable. A 10.5 ppg salt-based mud may result in dissociation of the hydrate into its components: water and gas. 10.5 ppg salt-based mud may enlarge the borehole, release free gas into the borehole, and compromise the cores. 2) Assuming temperatures equal to the measured LWD borehole temperatures at this location: Hydrate within the target reservoirs will be stable with a salinity of 9.5 wt.% (the salinity of the proposed 10.5 ppg salt-based mud), but possibly unstable (just at the methane hydrate stability boundary) for a salinity of 17.2 wt.% (the salinity of the proposed 13.5 ppg salt-based kill mud). if the borehole has the same temperature as recorded during previous LWD drilling at this location. A 13.5 ppg salt-based kill mud may destabilize the borehole. More importantly, a 10.5 ppg salt-based mud does not provide a large enough window between the estimated conditions and the hydrate stability boundary. Core temperatures and pressures fluctuate from the bottom-hole conditions as the core is brought up from the bottom-hole to the rig floor. These fluctuations are likely to cause the hydrate in the cores to touch or cross the stability boundary. Therefore, cores captured using a 10.5 ppg salt-based mud are likely to be compromised during core recovery to the rig floor.
Representative identification of spectra and environments (RISE) using k-means
Not provided.
Modeling Framework for the Assessment of a Sustainable Hydrogen Production and Supply Chain Network in California
The cost-effective and sustainable deployment of hydrogen supply and demand networks, especially in large economic regions like California, can be challenging considering the spatial-temporal availability and variability of the different actors across the network such as production processes, distribution modes, and end-users. In this presentation, we will provide an overview and demonstration of a modeling framework used to assess the environmental, economic, and human health impacts of plausible hydrogen production and supply chain networks in California. Scenarios focus on green hydrogen production pathways using water electrolysis and biomass gasification. End-use applications included in the model are transit, medium and heavy-duty trucking, port authorities, and power and aviation companies that currently consume natural gas, diesel, and aviation fuel for their day-to-day operation. Representative locations for hydrogen production and end-use are based on recent projections of the hydrogen economy in California. All mass and energy flows, as well as estimated emissions, are based on H2A process model designs and projections of technology performance, literature review, and LBNL process, economic and life cycle modeling, and not on company data for the sake of this presentation. Human health impacts are included following methodologies developed for the University of California Irvine HyDeal project. Life cycle phases associated with hydrogen production include feedstock preparation (water and biomass), energy production and consumption (renewable, grid, and combination of renewable and grid electricity), maintenance (chemical utilization in electrolysis and natural gas combustion in gasification), carbon sequestration, hydrogen storage (compression and liquefaction), and distribution (truck and pipeline). We apply the framework utilizing California specific emission factors, financial data, and human health damages and explore the impact of network characteristics on results. Example variations include: the inclusion of policy incentives or not, different representations of the electricity grid and source, electrolysis versus gasification versus combinations of both for production, liquefaction versus compression based on producer capacity cutoffs, transportation truck versus pipeline based on existing infrastructure, and ultimate end use. Comparison of these different scenarios can help inform future projects by demonstrating the trade-offs among environmental, economic, and human health impacts. This model, automated in R, is a starting platform upon which new analysis, modeling capabilities, locations, and emission factors can be rapidly tested and integrated.
Self-Powered Autonomous Sensing System for Arctic Ocean using a Frequency-multiplied Cylindrical Triboelectric Nanogenerator
An autonomous sensing system for collecting environmental observations in the Arctic region is critical for the estimation and prediction of climate change. Ocean waves are a great source of energy for these sensing systems but there has been limited research done on small-scale energy harvesting applications in the Arctic Ocean (subsea or surface). The available wave energy in the Arctic Ocean is lower than the typical ocean wave energy due to the low wave frequency, height, and operating months. Although the available wave energy depends on the specific location in the Arctic Ocean, the wave height decreases everywhere during the winter (Jan-March) [1]. Our target location in this work is the Beaufort and Chukchi Seas, which are ice covered for about 195 days per year leaving only a few months (June-November) for wave energy harvesting [2]. During these few months, the average wave frequency is 0.2 Hz and the most common wave frequency is around 0.15 Hz. Many energy harvesting methods are unsuitable for use in the Arctic Ocean because of to the cold temperature, low wave frequency, and low wave height. Triboelectric nanogenerators (TENG) are one of the few energy harvesting methods that excel in these conditions. Zhong et al. [3] designed a stacked pendulum-structured TENG for low-frequency ocean wave energy and reported the device generated a peak power density of 11.2 W/m3 under the low wave frequency of 0.2 Hz. The autonomous sensing system we developed for the Arctic Ocean, the Arctic-TENG, is based on a frequency-multiplied cylindrical TENG (FMC-TENG) which is an optimized TENG configuration for Arctic Ocean conditions due to the high power density under low-frequency wave conditions [4]. Figure 1 shows an FMC-TENG with multiple pairs of free-standing triboelectric-layer mode materials (Aluminum and FEP). The mass and magnet attached to the rotor store gravitational potential energy which is released as kinetic energy when the potential energy overcomes the repulsive magnetic force generated by the opposing magnet attached to the stator. This force unbalance triggers a sudden rotation and swinging motion of the mass which increases the angular velocity of the system and therefore enhances the output power of the TENG system. The main components of the Arctic-TENG are a 3D-printed rotor and stator, electric and dielectric material adhesive tape, and bearings. All these materials were cold-soaked and tested at -40 °C in a chest freezer. Both the 3D-printed parts and adhesives were confirmed to have a minimal effect from the cold temperature. Multiple bearings were tested and the starting torque of each one was compared both at room temperature and -40 °C. The bearing with the lowest starting torque at -40 °C was selected for use in the Arctic-TENG. The Arctic-TENG was tested using an out-of-water motor-driven wave simulator (Figure 2). The wave simulator allows for controlled testing at a wave height of 0.2 m and frequencies between 0.1 Hz to 0.5 Hz. This wave simulator was used for both room temperature and -40 °C testing. The Arctic-TENG generated significantly more power at -40 °C compared to room temperature for each frequency tested. The system stored energy in a supercapacitor via a power management circuit, and the amount of energy stored per day was calculated at different wave frequencies. Based on the conditions at the proposed deployment location (days of non-ice-covered ocean and wave frequency), the amount of stored energy per year from Arctic-TENG was calculated to be enough energy for two transmissions every day. Durability testing was completed on the Arctic-TENG at room temperature to determine the lifetime. The rotor of the Arctic-TENG was attached to a DC motor and spun continuously for several millions of cycles without degradation of the electrical output, demonstrating the feasibility for long-term operation.
Memory page access counts based on page refresh
A processing system tracks counts of accesses to memory pages using a set of counters located at the memory module that stores the pages, wherein the counts are adjusted at least in part based on refreshes of the memory pages. This approach allows a processing system to efficiently maintain the counts with relatively small counters and with relatively low overhead. Furthermore, the rate at which the counters are adjusted, relative to the page refreshes, is adjustable, so that the access counts are useful for a wide variety of application types.
Electrochemically induced metal- vs. ligand-based redox changes in mackinawite: identification of a Fe 3+ - and polysulfide-containing intermediate
Under anaerobic conditions, ferrous iron reacts with sulfide producing FeS, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases, such as greigite or pyrite. A greater understanding of this maturation process holds promise for the development of iron-sulfide catalysts, which are known to promote diverse chemical reactions, such as H + , CO 2 and NO 3 - reduction processes. Hampering the full realization of the catalytic potential of FeS, however, is an incomplete knowledge of the molecular and redox processess ocurring between mineral and nanoparticulate phases. Here, we investigated the chemical properties of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Tracing oxidative maturation pathways by varying electrode potential, nanoparticulate n(Fe 2+ S 2- ) (s) was found to oxidize to a Fe 3+ containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7). In a subsequent oxidation, polysulfides are proposed to give a material that is composed of Fe 2+ , Fe 3+ , S 2- and polysulfide (S n 2- ) species, with its composition described as Fe 2+ 1-3x Fe 3+ 2x S 2- 1-y (S n 2- ) y . Thermodynamic properties of model compounds calculated by density functional theory indicate that ligand oxidation occurs in conjunction with structural rearrangements, whereas metal oxidation may occur prior to structural rearrangement. These findings together point to the existence of a metastable FeS phase located at the junction of a metal-based oxidation path between FeS and greigite (Fe 2+ Fe 3+ 2 S 2- 4 ) and a ligand-based oxidation path between FeS and pyrite (Fe 2+ (S 2 ) 2- ).
Subsidence Measurements at the Teller 27 Field Site, Seward Peninsula, Alaska, 2021
In September 2021, differential GPS (dGPS) points were collected in areas of visual subsidence in the Teller watershed near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway on the Seward Peninsula of Alaska. This study aimed to understand how structural permafrost loss is affecting meter-scale ground elevation. As ice thaws, heaving and slumping of the landscape creates new microtopographical features on the landscape that may alter surface hydrology and soil moisture and thus plant community composition and biogeochemical cycling. The Teller 27 field site is underlain with discontinuous permafrost, so the landscape features a variety of permafrost features. We collected ground elevations from eight subsidence areas in two forms: 1) Transects were sampled laterally through areas of ground subsidence to capture the high edges and low spots where the earth slumped. 2) The circumference of the subsidence area was sampled in order to measure the ground area affected by subsidence. These data were collected with an Emlid Reach RS2 dGPS and a base station. This dataset includes one *.csv of dGPS locations of the eight subsidence locations and one *.kml of measurement locations.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
Investigating a Renewable-Resource-Targeting Mobile Aquaculture System Using Route Optimization Based on Optimal Foraging Theory
Aquaculture systems require careful consideration of location, which determines water conditions, pollution impacts, and hazardous conditions. Mobility may be able to address these factors while also supporting the targeting of renewable energy sources such as wind, wave, and solar power throughout the year. In this paper, a purpose-built mobile aquaculture ship is identified and modeled with a combination of renewable energy harvesting capabilities as a case study with the objective of assessing the potential benefits of targeting high renewable energy potentials to power aquaculture operations. A route optimization algorithm is created and tuned to simulate the mobility of the aquaculture platform and cost-basis comparisons are made to a stationary system. The small spatial variability in renewable energy potential when combining multiple resources significantly limits the benefits of a mobile, renewable-targeting aquaculture system. On the other hand, the consistent energy harvest from a blend of renewable energy types (13 kW installed wind capacity, 661 m 2 installed solar, and 1 m characteristic width wave-energy converter) suggests that the potential benefits of a mobile platform for offshore aquaculture (mitigation of environmental and social concerns, any potential positive impact on yields, hazard avoidance, etc.) can likely be pursued without significant increases in energy harvester costs.
Vertical distribution of boundary layer new particle formation and implications for nanoparticle growth mechanisms (Final Report)
New particles can form high above ground in the atmosphere before such formation events are observed by ground-based instruments. Once formed, these particles can take up significant amounts of water vapor, which has implications on the mechanisms by which nanoparticles grow and, ultimately, impact cloud formation processes. These observations motivated the research performed in this project using a combination of modeling, laboratory experiments, and the analysis of field observations. We hypothesized is that ground-based measurements do not always accurately represent the location and timing of new particle formation events, nor do they adequately characterize the dominant physical and chemical processes that are responsible for the subsequent growth of newly formed particles. Essentially, we wanted to study the new particle formation process under conditions that we expect may be more relevant to the actual location in the atmosphere where such events take place, which are often characterized by higher relative humidities, lower temperatures, and a broader range of precursors and oxidants compared to those observed at ground level. This project had two main objectives, each of which directly addressed our hypothesis. Our first is to investigate the timing, distribution, meteorological conditions, and nanoparticle properties associated with boundary layer new particle formation through the analysis of ARM field campaigns such as HI-SCALE and long-term observations and observations performed in other locales such as in China and India where atmospheric conditions could play a crucial role in determining the mechanisms of new particle formation. This activity provided a comprehensive description, both in time and in space, of the gases and nanoparticle properties that are responsible for these events. We also performed modeling studies to explore the vertical profile and timing of new particle formation events from these campaigns. Our second objective was to perform laboratory and process-level modeling studies to investigate the role that conditions such as low ambient temperature, high relative humidity, and different oxidants such as nitrate radical play in determining unique chemical pathways for nanoparticle growth. In doing so, we revisited the representation of nanoparticle growth in global models and updated assumptions of formation and growth based on the findings of the field, laboratory, and process-level-model work. Our research provides crucial insights into the most important regions of the atmospheric boundary layer for future observational and modeling studies. It also identifies the most relevant environmental conditions, precursors, and oxidants that are responsible for nanoparticle growth, which will aid in the design of laboratory studies and process-level model experiments. The unique pathways that we discovered can be incorporated into regional and global models, thereby improving predictions and attribution of the role of new particle formation in, e.g., air pollution formation and cloud properties.