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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

CAMIS: A Cylindrical Active Mask Imaging System

Detecting and locating radiological and nuclear materials at distances of 10 m or more in urban and cluttered environments continues to pose challenges in nuclear security and proliferation detection. Previous approaches have focused on large-area radiation imaging using planar configurations of detectors and passive masks. However, these approaches suffer from limitations such as limited field-of-view (FOV) and reduced detection efficiency due to absorption in the mask. To address these limitations, we have developed the cylindrical active mask imaging system (CAMIS). This system comprises 128 NaI(Tl) (10 cm)3 detectors. These detectors are arranged in a cylindrical configuration, enabling gamma-ray imaging with a full 360° azimuthal FOV. The active mask elements within the system are arranged in a pseudorandom configuration, providing unique encoding for all incident directions within the FOV. CAMIS offers an effective detection area of approximately 1 m2 across the entire 360° FOV in the horizontal plane, achieving a mean angular resolution of 10.9° in the azimuthal direction and 12.9° in the polar direction measured by taking the full-width at half-maximum (FWHM) of a cross-section at the maximum reconstructed intensity.

Lamb, C↗
DOE OSTI2026

Clean Cities and Communities Partnership 2024 Activity Report

Clean Cities and Communities (CC&C) is a U.S. Department of Energy (DOE) partnership that fosters collaboration and innovation to advance transportation energy choices nationwide. More than 75 DOE-designated CC&C coalitions work in urban, suburban, and rural areas to deliver objective technical expertise based on a unique understanding of local markets. As partners with DOE's Transportation Technologies Office (TTO), coalitions build bridges between national priorities and local needs to create transportation energy systems that are affordable, reliable, and secure. Together, coalitions create compounding impacts nationwide that support locally driven energy choices and benefit regional economic development and job growth. This report summarizes the success and impact of partnership activities based on data and information provided in their annual reports.

2024↗
DOE OSTI2025

Equitable Urban Electric Vehicle Charging: Feasibility and Benefits of Streetlight Charging in Kansas City Right-of-Way

With an increasing global emphasis on sustainability, electric vehicles (EVs) play a crucial role in reducing urban pollution and carbon emissions. For EVs to be widely adopted, equitable and convenient access to charging infrastructure is essential. Equity in this paper refers to the proactive engagement with the community to ensure that the benefits of streetlight charging are distributed equitably across diverse neighborhoods in Kansas City, providing fair charging opportunities and resources to all community members. This research explores the potential of utilizing streetlights—ubiquitous elements of urban electrical infrastructure—as low-cost, equitable EV charging solutions. Compared with conventional chargers, streetlight charging offers several notable advantages, including proximity to roadways, potential boosts to the local economy, and easier usage due to city ownership. These chargers also leverage existing power setups to minimize costs and maximize efficiency by reutilizing existing structures. In this manuscript, we introduce a systematic framework to develop, analyze, and evaluate a scalable and cost-effective streetlight charging solution. Initially, we employ a two-tiered site selection framework that accounts for both charging demand and equity considerations to identify optimal locations for streetlight chargers. We then assess the feasibility of these chargers through observational data from 23 units installed in Kansas City, Missouri, comparing their performance with traditional chargers. Here, our evaluation extends to the environmental impact, comparing reduced gasoline consumption and greenhouse gas (GHG) emission reductions between streetlights and regular charging stations. Our findings reveal that streetlight charging stations offer faster charging speeds than conventional stations, validating their viability for EV charging. Additionally, the duration of stay at streetlight chargers tends to be shorter, likely due to parking constraints or associated costs. Furthermore, streetlight-powered chargers demonstrate considerable environmental advantages, achieving 11.94% greater avoided gasoline consumption and 11.24% higher GHG reductions compared with regular charging stations, highlighting their substantial environmental benefits.

42 ENGINEERING↗
DOE OSTI2025

Life-cycle cost-benefit analysis of a novel self-heating pavement made from coal-derived solid carbon

Winter weather events challenge the safety, efficiency, and sustainability of roadway networks, particularly road bridges vulnerable to climate impacts. Although effective, conventional de-icing methods incur high expenses and cause significant environmental contamination. To address these longstanding issues, a low-cost novel coal-derived carbon enabled smart pavement (CDC-SP) de-icing system was developed, aiming to alleviate traffic delays, severe corrosion, compromised safety, and environmental pollution. However, the economic performance of CDC-SP has not been quantified to guide decision-making. To this end, this paper presents a Life-Cycle Cost-Benefit Analysis (LCCBA) to assess the economic feasibility of the CDC-SP de-icing system for road bridges in five representative urban and rural areas across cool humid and cold humid climate zones. Given the variations and uncertainties of several factors that determine the costs and benefits, a sensitivity analysis was conducted to determine the system’s economic reliability. Additionally, a Monte Carlo simulation (MCS) was performed to quantify the impacts of important factors and identify the most beneficial scenarios. Furthermore, the CDC-SP de-icing systems have demonstrated substantial economic benefits and short payback periods, showing great applicability for rural and urban road bridges with different service levels across multiple climate zones as compared to conventional de-icing methods.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Climatic and Socioeconomic Drivers of Water Use and Their Spatio‐Temporal Patterns for Small and Mid‐Sized Cities in the Contiguous United States

This study explores the drivers of urban water use and their spatial-temporal patterns in 142 small and mid-sized cities across the Contiguous United States (CONUS) by analyzing the data directly collected from these cities and using advanced machine learning techniques. We identify five distinguished clusters across CONUS, each showing unique trends of the impact of drivers on water use. We find that socioeconomic factors significantly influence water use in eastern and southwestern cities, while climatic variables such as precipitation and temperature range dominate in central and northwestern regions. Temporal analysis reveals the impacts of major socioeconomic and climatic disruptions on urban water use in the period 2011–2021, including the COVID lockdown, the rapid growth of data centers, and the drought of 2012. In addition, our analysis suggests that economic growth in small and mid-sized US cities continues to be accompanied by rising water use, contrasting with the opposite trend observed in large cities in prior studies. This implies that as smaller cities develop, their water use may increase above current levels until incomes reach a higher threshold, highlighting the need to improve water use efficiency. This study also presents useful insights for developing effective water demand management strategies in response to climatic variability and socioeconomic growth in small and mid-sized cities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

AmeriFlux US-DUF Denver Urban Field Station

This is the AmeriFlux version of the carbon flux data for the site US-DUF Denver Urban Field Station. Site Description - This site was established on a 25m freestanding former radio tower at the Forest Service's Rocky Mountain Research Station headquarters in Fort Collins, CO. The tower is situated next to a parking lot and several one and two story buildings. To the north and west is the campus of Colorado State University, and to the north and east is low density residential development. To the south and east are a busy intersection and rapid transit bus line, and to the south is green space surrounding the City of Fort Collins' Spring Creek Trail.

Frank, John [US Forest Service, Rocky Mountain Res↗
DOE OSTI2024

A global urban heat island intensity dataset: Generation, comparison, and analysis

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Further, based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2026

Non‐Equilibrium Synthesis Methods to Create Metastable and High‐Entropy Nanomaterials

Stabilizing multiple elements within a single phase enables the creation of advanced materials with exceptional properties arising from their complex composition. However, under equilibrium conditions, the Hume–Rothery rules impose strict limitations on solid-state miscibility, restricting combinations of elements with mismatched crystal structures, atomic radii, valence states, or electronegativities. This severely narrows the accessible compositional space for creating new inorganic materials. In this review, we highlight how non-equilibrium synthesis methods, featuring ultrafast heating and quenching, can overcome these thermodynamic barriers, enabling integration of immiscible elements into metastable and high-entropy nanostructures. The resulting materials benefit from both kinetic trapping and stabilization by high configurational entropy, leading to enhanced phase stability. These materials can exhibit unique structural and functional properties that are needed for advancing catalysis, energy storage, thermoelectrics, and sensing. Furthermore, the ability of non-equilibrium methods to generate unconventional compositions and structures expands the material design space dramatically, offering rich datasets for AI-guided materials discovery. When combined with their inherent high-throughput and scalable characteristics, these approaches enable rapid, iterative optimization and accelerate the development and industrial production of next-generation inorganic materials.

high-entropy materials↗
DOE OSTI2026

Improving Building Footprint Extraction Using NAIP and 3DEP Lidar Derived Features with Deep Learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Liu, Jung Kuan [United States Geological Survey (U↗
DOE OSTI2026

2020 Can Do Colorado E-Bike Mini Pilot Program Study

### The Colorado Energy Office conducted a mini pilot program study as part of the Can Do Colorado initiative, providing e-bikes to 13 low-income participants. The program aimed to encourage energy-efficient transportation during the COVID-19 pandemic as transit services were reduced and people were concerned about exposure. The insights garnered from this small-scale pilot study informed the design of a full-scale, 2-year pilot in locations across Colorado. For more information about the mini pilot program, see NLR's [Preliminary Results Report](https://www.nlr.gov/docs/fy21osti/79657.pdf). Micromobility options such as e-bikes offer a solution for improving energy efficiency for short-distance trips, especially in urban areas. Pedal-assist e-bikes use an electric motor and battery to help power the bike. The motor amplifies the power behind each pedal stroke, augmenting the energy you put into the bike. #### Data Collection Agency The Colorado Energy Office conducted the survey. #### Survey Methodology Participants in the program received a Momentum LaFree E+ e-bike (Class 1) and accessories at no cost and manually submitted travel data and feedback for 3 months using the CanBikeCo App. The smartphone app, developed in partnership with NLR, used a customized version of the [NLR OpenPATH platform](https://www.nlr.gov/transportation/openpath.html). #### Survey Records and Data Survey records include a total of 13 participants. This dataset contains 3 months of end-to-end, multimodal travel data manually submitted via smartphone app by 13 low-income essential workers in the greater Denver area. The data includes distance, mode (e.g., e-bike, car, transit), trip purpose, and demographic information.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Enriching OpenStreetMap network data for transportation applications: Insights into the impact of urban congestion on accessibility

OpenStreetMap (OSM) data is a valuable open-source resource for various transportation, traffic, and planning applications. However, OSM network data lack operating traffic speed information, which is critical for transport planning and operations. Addressing this shortcoming, this study leverages commercial vendor data (to serve as ground truth) with exogenous, open-source variables characterizing local transport infrastructure, land use, and demographic information to predict average congested traffic speeds on OSM networks. Three machine-learning models were tested and estimated for OSM links with and without speed limit information in the Denver metropolitan region. Among these, XGBoost performed best, with mean absolute errors of 3.27 and 3.62 mph for links with and without speed limits, respectively. The developed models accurately predicted traffic speeds for different hours and days of the week compared to ground truth data. Using these predicted speeds, drive accessibility scores were computed for the Denver region for different time periods using the Mobility Energy Productivity (MEP) metric to understand the impact of congestion on energy-efficient accessibility. Results show that congestion-adjusted drive accessibility can be significantly lower compared to accessibility calculated using free flow speeds. Specifically, weekday evening hours saw a 42 % drop in accessibility due to reduced speeds, particularly around downtown Denver. Across the Denver metro region, approximately half as many opportunities and jobs are accessible in under 20 min by car during the evening peak period relative to free flow conditions. These findings underscore the importance of using congestion-adjusted operating speeds rather than speed limits in accessibility calculations, as reliance on speed limits can substantially overestimate energy-efficient drive accessibility in large, car-centric cities susceptible to significant congestion. In conclusion, the methodology presented here could further enrich OSM network data, making them useful for an even broader range of transportation applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Inappropriate antibiotic access practices at the community level in Eastern Ethiopia

Access to antibiotic medications is critical to achieving the Sustainable Development Goal for good health and well-being. However, non-prescribed and informal sources are implicated as the most common causes of inappropriate antibiotic access practices, resulting in untargeted therapy, which leads to antibiotic resistance. Hence, knowing antibiotic access practices at the community level is essential to target misuse sources. In this study, 2256 household representatives were surveyed between July and September 2023 to examine their antibiotic access practices. Of 1245 household members who received antibiotics, 45.6% did so inappropriately. Non-prescribed antibiotic access was more common among urban residents and individuals not enrolled in health insurance schemes. This means of antibiotic access was also more common among individuals concerned about distance, drug availability, and healthcare convenience at public facilities. In addition, women and rural individuals were more likely to get antibiotics from unauthorized sources. Unrestricted antibiotic dispensing practices in urban areas enabled their non-prescribed access, while unlicensed providers prevailed with this access practice in rural areas. In this regard, personal behaviors and healthcare-related gaps such as the lack of health insurance, inconvenience, and drug unavailability have led community members to seek antibiotics from unofficial and non-prescribed sources. Targeting the identified behavioral and institutional factors can enhance antibiotic access through prescriptions, hence reducing antibiotic resistance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Adaptive continuity-preserving simplification of street networks

Street network data is widely used to study human-based activities and urban structure. Often, these data are geared towards transportation applications, which require highly granular, directed graphs that capture the complex relationships of potential traffic patterns. While this level of network detail is critical for certain fine-grained mobility models, it represents a hindrance for studies concerned with the morphology of the street network. For the latter case, street network simplification — the process of converting a highly granular input network into its most simple morphological form — is a necessary, but highly tedious preprocessing step, especially when conducted manually. In this manuscript, we develop and present a novel adaptive algorithm for simplifying street networks that is both fully automated and able to mimic results obtained through a manual simplification routine. The algorithm — available in the neatnet Python package — outperforms current state-of-the-art procedures when comparing those methods to manually, human-simplified data, while preserving network continuity.

Python↗
DOE OSTI2026

RDPP: Accelerating Diversity in DOE Climate Science and Resilience Research (Final Report)

The scope of the project was set out to accelerate the inclusion of diversity into the Department of Energy (DOE) Earth and Environmental Systems Sciences Division (EESSD) relevant climate science and resilience research to inclusively advance solutions. The Project Objectives were to usher in equitable use-inspired climate-related research with underrepresented Minorities of which this project helped fund 7 HU graduate students work with the DOE (three of which will graduate in Spring 2025). The two key aims underpinning that core goal were AIM1: developing partnerships (18 organized engaged, see partners list) and AIM2: Developing capabilities (3 visits to DOE facilities, 5 DOE partners visits to HU, increased visiting faculty participation in BNL-DOE lab, secured 5 grants together totaling 1.2 million in funds for HU). The project objectives were highly successful as they were designed to ambitiously pull together DOE lab researchers with the long-standing and successful transdisciplinary climate science research programs of the PI and local DC groups. The major outcomes of this RDPP program will be in the new fundamentally inclusive partnerships with DOE and HU tasked to understand the urban-rural impacts due to climate change in the US, Eastern South Atlantic (ESA) Region, related to energy issues driven by heat stress and the water cycle. Overall, this project contributes to the DOE and science community vision for catalyze connections for project-ready underrepresented minorities (URMs) at a prominent HBCU to DOE projects supported by the Biological and Environmental research (BER) Program; particularly, the Earth and Environmental Systems Sciences Division (EESSD).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Demographic evolution↗
DOE OSTI2025

Influence of Water, Vacuum, and Temperature on Surface Conditions of a Zeolite‐based Molecular Sieve

Molecular sieves such as zeolite-based materials are ubiquitous in industrial separation processes. However, there is a significant gap in understanding the surface properties and adsorption mechanisms for commercial zeolites, as most research focuses on pure zeolite powders rather than industrially relevant forms. Here, this work addresses this gap in understanding by employing advanced characterization techniques, including positron annihilation spectroscopy, X-ray diffraction, scanning electron microscopy, X-ray fluorescence spectroscopy, X-ray photoelectron spectroscopy, liquid nitrogen sorption, and Fourier-transform infrared spectroscopy, to investigate the adsorption and desorption behavior of water in commercial zeolite 13X. Our research reveals insights into the pore-filling mechanisms, the impact of material binders on adsorption properties, and the dynamics of hydration and drying processes for zeolites. Monitoring changes on a minute scale allowed the distinction between fast and slow processes leading to sample drying. The identification of positronium bound to Na + ions indicated that water molecules remain in the vicinity of Na + ions after air-drying zeolite 13X. These findings highlight the importance of various environmental conditions in restoring zeolite properties to baseline after hydration, with significant implications for optimizing industrial processes. This work sets the direction for further research aimed at developing more efficient and robust separation techniques.

Beads Binder↗