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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impact of developmental methylmercury exposure on avian embryonic development, hatchling growth, and survival

Abstract Methylmercury (MeHg) is a globally ubiquitous and persistent environmental toxicant that negatively affects animal behavior, health, and reproduction. In birds, MeHg is transferred from female to egg, contaminating embryos during sensitive stages of early embryonic development and growth. This toxic exposure in the prenatal environment not only induces mortality but also possible lasting impacts on physiology, health, and survival, even once hatched. The purpose of our study was to further elucidate the negative effects of MeHg exposure during avian embryonic development and explore how such exposure can impact offspring development, growth, and survival, both in ovo and posthatch. To assess this, we experimentally dosed fertile mallard and wood duck eggs with MeHg II chloride and reared developing embryos and ducklings to various endpoints. We found that embryos not only readily accumulated MeHg throughout incubation, but they also displayed varying dose-dependent disparities in body mass and morphometrics, with control individuals being larger during early and late embryonic stages of development (p < 0.05). Furthermore, hatched offspring exposed to MeHg exhibited increasingly slower growth rates between 7 to 10 and 10 to 15 days posthatch (p < 0.05), and were found to have lower survival probabilities both under controlled laboratory conditions (p < 0.005), and in the natural environment (p < 0.05). Our findings on the detrimental effects of MeHg on avian embryos and hatchlings emphasize the need for more proactive means of environmental protection and remediation to protect vulnerable avian populations and the ecosystems they inhabit.

Leaphart, James C.↗
DOE OSTI2023

Methods to Account for CAP-88 PC-Omitted Nuclides in Radioactive Air Emissions From DOE Facilities

Routine research and development activities at US Department of Energy facilities can result in the release of radioactive emissions, potentially exposing the public and the environment. Such emissions are subject to certain Clean Air Act regulations, specifically those outlined in 40 CFR Part 61, Subpart H, which are enforced by the US Environmental Protection Agency. Compliance is determined in part with a dose standard. Doses to members of the public from most US Department of Energy facility airborne emissions are modeled using CAP-88 PC software, the latest version of which contains more than 1,200 radionuclides. Radioactive effluents from US Department of Energy facilities may contain radioisotopes that are not available in CAP-88 PC, and those radioisotopes must still be evaluated to determine whether doses to members of the public are below established limits. CAP-88 PC-omitted radionuclides can be accounted for using alternate methods, including the use of surrogate radionuclides. Here, this paper elucidates the process of accounting for CAP-88 PC-omitted radionuclides by providing criteria and rationale for surrogate selection, a compilation of surrogate radionuclides used in the past by US Department of Energy facilities, a discussion of alternate methods used to account for CAP-88 PC-omitted radionuclides and a comparison of methods and impacts on receptor doses. Ultimately, this paper aims to aid in the process of surrogate selection and consequently to simplify and expedite compliance with Clean Air Act regulations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Modification in Applying Appendix D of 40 CFR Part 61 to Heated Solid Radionuclide Materials With High Melting and Boiling Points

Appendix D of Title 40 Part 61 of the US Code of Federal Regulations (CFR) provides a procedure that US Department of Energy (US DOE) facility owners and operators can use to estimate radionuclide emissions to the atmosphere for dose calculations instead of measuring emissions for minor sources under the 40 CFR Part 61, Subpart H, National Emission Standards for Emissions of Radionuclides Other Than Radon From Department of Energy Facilities, regulation. The procedure assumes that any radioactive material heated above 100 °C is completely vaporized and emitted to the atmosphere. In 1991, the US DOE Oak Ridge Reservation (ORR) requested approval to use different release fractions (RFs) for uranium because of its high melting and boiling points. In response to the request, the US Environmental Protection Agency (US EPA) Region IV approved the use of modified RFs for elemental uranium provided no reaction had taken place to alter its chemical form. In 2015, the ORR requested approval to use different RFs for tungsten, again because of its high melting and boiling points. EPA Region IV approved the use of modified RFs for heated radioactive tungsten metal. In accordance with the two precedents set for heating uranium and radioactive tungsten metals, in 2016, the ORR requested approval to use modified RFs in a similar fashion for other radioactive solid metals and compounds with melting and boiling points above 500 °C that might be heated above 100 °C in future research projects and experiments. EPA Region IV again granted approval to use modified RFs for the list of compounds. This note discusses the proposed modified RFs and their development.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Evaluating the Environmental Impacts of Connected and Automated Vehicles: Potential Shortcomings of a Binned-Based Emissions Model

In addition to providing safety and mobility benefits, Connected and Automated Vehicles (CAVs) have the potential to reduce fuel consumption and emissions. As new CAV applications are developed, it is valuable to estimate these potential environmental benefits, typically using vehicle activity data and emissions models. To date, most researchers in the U.S. have used the MOVES vehicle emissions model, developed and maintained by the U.S. Environmental Protection Agency (EPA). However, because MOVES uses a binning approach, it is likely underestimating the true energy and emissions savings that occur when CAV applications smooth traffic flow. To illustrate this problem, we measure and model the fuel consumption and CO 2 emissions for a real-world CAV application: Eco-Approach and Departure (EAD) at signalized intersections. Real-world measurements are compared to a MOVES-based estimate, as well as to an estimate provided by the physical-based Comprehensive Modal Emissions Model (CMEM). Results show that MOVES consistently underestimates the energy and emissions benefits of the CAV application, primarily since the bin sizes in MOVES are too large to catch the nuances of traffic smoothing. On the other hand, CMEM provided a more accurate energy and emissions estimate, primarily since it uses analytical functions to model emissions and does not suffer from the same binning problem.

42 ENGINEERING↗
DOE OSTI2024

Evaluation of Autonomous Vehicle Sensing and Compute Load on a Chassis Dynamometer

The sensing and compute load auxiliary energy consumption in autonomous vehicles may be significant due to the large number of sensors and the high compute load from sensor processing and route planning. To understand this issue, this study investigates the top-down energy usage of an electric 2015 Kia Soul fully instrumented with state sensors and a state-specific computer for path planning and sensor processing. A chassis dynamometer was then used to evaluate the cases of (1) no sensors or computation, (2) only sensors operating, and (3) sensors plus compute load. The vehicle was operated autonomously on the dynamometer using a PolySync drive-kit with drive-by-wire longitudinal control. The DynoJet model 224xLC was used to adapt the eddy current dynamometer's road load parameters to comply with an Environmental Protection Agency drive schedule and to evaluate performance against the Argonne National Laboratory Digital Dynamometer Dataset. On the UDDS-HWFET combined driving cycle, the stock battery's range was reduced by 5.6% for sensors alone and 12.2% for sensors and compute load. These results show that the added sensing and compute auxiliary load from automated and autonomous systems is significant and that research efforts need to be spent investigating new energy efficient systems.

Brown, Nicholas E.↗
DOE OSTI2023

Wildfire Risk Evaluation Framework for Grid Operations and Planning

The United States (US) environmental protection agency's (EPA's) climate change indicators for wildfires show a long-term trend of increased annual wildfire activity, larger wildfire size, and more variable dynamics in wildfire behavior. This has caused more frequent preemptive public safety power shutoff (PSPS) events in the regions with recognized high wildfire risk. These preemptive power shutoffs attempt to prevent the ignition of a wildfire but it nonetheless renders the transmission line non-operational, which often sheds load of downstream communities. As wildfires and PSPS events become more frequent, it is crucial to find the communities most at risk of load shedding. To that end, this paper proposes the wildfire risk evaluation of the system (WiRES) framework, which is a performance-based framework that translates extreme weather-related and PSPS event probabilities into a cumulative probability of a non-operational transmission line. This study also provides a geospatial visualization tool that breaks down the entire western electrical coordinating council (WECC) region into 50 km grid cells which can be used to 1) filter out transmission lines with higher than a threshold outage probability, and 2) graphically discover the affected regions and their biophysical and socioeconomic metrics. %such as the social vulnerability index, population density, gross domestic product, etc. Lastly, an impact assessment study is conducted which connects the results of the proposed framework to python and powerworld-based contingency analysis to highlight the applicability of the framework.

Wildfire, Resilience, Extreme Events, Grid Reslien↗
DOE OSTI2023

Human Health Risk Assessment for Improper Landfill Disposal of End-of-Life CdTe PV

The present work is a continuation of the 2020 IEA PVPS Task 12 Human Health Risk Assessment Methods for PV Part 3: Module Disposal Risks. The 2020 report performed a human health risk assessment (HHRA) for disposal of a cadmium telluride (CdTe) PV module in an unlined landfill, focusing solely on risks from cadmium. This study extends the 2020 HHRA on CdTe PV, analyzing eleven constituent elements: Cd, Se, Te, Cu, Si, Cr(III), Mo, Sn, Zn, Ni, and Al. The present HHRA was performed through two methods: utilization of the U.S. Environmental Protection Agency's (USEPA) Delisting Risk Assessment Software (DRAS V.4.0) on eight exposure pathways for cancer risk and non-cancer hazards; and comparison of exposure point concentrations to federal standards for groundwater, surface water, air, and soil exposure pathways. Cancer risks and non-cancer hazards posed by elemental leaching through all evaluated exposure pathways, using both methods, were found to be several orders of magnitude below USEPA health-protective thresholds. Cadmium exhibited both the highest risks and lowest uncertainty considering data availability on chemical content, leachate, and federal screening levels.

chemicals↗
DOE OSTI2025

Crash–and–dash: a new era in tree genome editing

In less than a decade since the first demonstrations of CRISPR genome editing of agronomic genes in several tree species (Zhou et al., 2015; Jia et al., 2016; Ren et al., 2016), this disruptive technology has been deployed for a growing number of traits in both basic and applied research. The precision and efficiency of CRISPR editing allow for the recovery of null mutants in the first generation (Zhou et al., 2015; Elorriaga et al., 2018; Muhr et al., 2018), which is a significant benefit for perennial trees with long generation times. The stability of editing outcomes over multiple years or clonal propagation cycles (Bewg et al., 2022; Chen et al., 2023; Goralogia et al., 2024) is another key advantage since most woody perennials are vegetatively propagated in commercial operations. However, in many countries, gene-edited trees with stably integrated T-DNA face the same regulatory hurdles as traditional transgenics, slowing field trial characterization and the integration of transgenesis with conventional breeding (Boerjan & Strauss, 2024). In an article recently published in New Phytologist, Hoengenaert et al. (2025; doi: 10.1111/nph.20415) demonstrate transgene-free editing in poplar (Populus tremula × alba) that shows promise for wide adoption. The CRISPR-edited, transgene-free canker-resistant citrus (Citrus sinensis) trees (Su et al., 2023) have recently been approved by USDA-APHIS and are exempt from regulation by the US Environmental Protection Agency (EPA) for commercial production. The work by Hoengenaert et al. (2025) suggests a similar path could be followed for purpose-grown plantations for bioenergy, bioproducts, and biomaterials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Three Way Catalyst-Selective Catalytic Reduction Aftertreatment System Evaluation for a Lean Burn Gasoline Engine Operating in Homogenous Charge Compression Ignition, Spark-Assisted Compression Ignition, and Spark-Ignited Combustion Modes

Low temperature and dilute homogenous charge compression ignition (HCCI) and spark-assisted compression ignition (SACI) can improve fuel efficiency and reduce engine-out NO x emissions, especially during lean operation. However, under lean operation, these combustion modes are unable to achieve Environmental Protection Agency (EPA) Tier 3 emissions standards without the use of lean aftertreatment. The three way catalyst (TWC)-SCR lean aftertreatment concept investigated in this work uses periodic-rich operation to produce NH 3 over a TWC to be stored on a selective catalytic reduction (SCR) catalyst for NO x conversion during subsequent lean operation. Experiments were performed with a modified 2.0 L gasoline engine that was cycled between lean HCCI and rich SACI operation and between lean and rich spark-ignited (SI) combustion to evaluate NO x conversion and fuel efficiency benefits. Different lambda values during rich operation and different times held in rich operation were investigated. Results are compared to a baseline case in which the engine is always operated at stoichiometric conditions. SCR system calculations are also presented to allow for comparisons of system performance for different levels of stored NH 3 . With the configuration used in this study, lean/rich HCCI/SACI operation resulted in a maximum NO x conversion efficiency of only 10%, while lean/rich SI operation resulted in a maximum NO x conversion efficiency of 60%. If the low conversion efficiency of HCCI/SACI operation could be improved through higher brick temperatures or additional SCR bricks, calculations indicate that TWC-SCR aftertreatment has the potential to provide attractive fuel efficiency benefits and near-zero tailpipe NO x . Calculated potential fuel efficiency improvement relative to stoichiometric SI is 7–17% for lean/rich HCCI/SACI with zero tailpipe NO x and -1 to 5% for lean/rich SI with zero tailpipe NO x emissions. Although the previous work indicated that the use of HCCI/SACI increases the time for NH 3 to start forming over the TWC during rich operation, reduces NH 3 production over the TWC per fuel amount, and increases NH 3 slip over the SCR catalyst, if NO x conversion efficiency could be enhanced, improvements in fuel efficiency could be realized while meeting stringent tailpipe NO x standards.

Engineering↗
DOE OSTI2016

Atomic layer deposition on polymer fibers and fabrics for multifunctional and electronic textiles

Textile materials, including woven cotton, polymer knit fabrics, and synthetic nonwoven fiber mats, are being explored as low-cost, flexible, and light-weight platforms for wearable electronic sensing, communication, energy generation, and storage. The natural porosity and high surface area in textiles is also useful for new applications in environmental protection, chemical decontamination, pharmaceutical and chemical manufacturing, catalytic support, tissue regeneration, and others. These applications raise opportunities for new chemistries, chemical processes, biological coupling, and nanodevice systems that can readily combine with textile manufacturing to create new “multifunctional” fabrics. Atomic layer deposition (ALD) has a unique ability to form highly uniform and conformal thin films at low processing temperature on nonuniform high aspect ratio surfaces. Recent research shows how ALD can coat, modify, and otherwise improve polymer fibers and textiles by incorporating new materials for viable electronic and other multifunctional capabilities. This article provides a current overview of the understanding of ALD coating and modification of textiles, including current capabilities and outstanding problems, with the goal of providing a starting point for further research and advances in this field. After a brief introduction to textile materials and current textile treatment methods, the authors discuss unique properties of ALD-coated textiles, followed by a review of recent electronic and multifunctional textiles that use ALD coatings either as direct functional components or as critical nucleation layers for active materials integration. The article concludes with possible future directions for ALD on textiles, including the challenges in materials, manufacturing, and manufacturing integration that must be overcome for ALD to reach its full potential in electronic and other emerging multifunctional textile systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Water Network Tool for Resilience (WNTR)

The Water Network Tool for Resilience (WNTR) is an open source Python package designed to simulate and analyze resilience of water distribution networks. The United States Environmental Protection Agency, in partnership with Sandia National Laboratories, developed WNTR to integrate critical aspects of resilience modeling for water distribution networks into a single software framework. The software includes capability to: • Generate water network models • Modify network structure and operations • Assign fragility and survival curves to network components • Model disruptive events such as power outages, earthquakes, fires, pipe breaks, and contamination incidents • Model response and repair strategies • Simulate hydraulics and water quality • Evaluate resilience using a wide range of metrics • Integrate dependency with other critical infrastructure and supply chains • Analyze results and generate graphics SAND2019-450 M

Villa, Daniel↗
DOE OSTI2017

Water Energy Simulation Toolset

The Water-Energy Simulation Toolset (WEST) is an interactive simulation model that helps visualize impacts of different stakeholders on water quantity and quality of a watershed. The case study is applied for the Snake River Basin with the fictional name Cutthroat River Basin. There are four groups of stakeholders of interest: hydropower, agriculture, flood control, and environmental protection. Currently, the quality component depicts nitrogen-nitrate contaminant. Users can easily interact with the model by changing certain inputs (climate change, fertilizer inputs, etc.) to observe the change over the entire system. Users can also change certain parameters to test their management policy.

Nguyen, Thuy↗
DOE OSTI2024

Organochlorine pesticides and risk of papillary thyroid cancer in U.S. military personnel: a nested case-control study

Background: The effects of organochlorine pesticide (OCP) exposure on the development of human papillary thyroid cancer (PTC) are not well understood. A nested case-control study was conducted with data from the U.S. Department of Defense Serum Repository (DoDSR) cohort between 2000 and 2013 to assess associations of individual OCPs serum concentrations with PTC risk. Methods: This study included 742 histologically confirmed PTC cases (341 females, 401 males) and 742 individually-matched controls with pre-diagnostic serum samples selected from the DoDSR. Associations between categories of lipid-corrected serum concentrations of seven OCPs and PTC risk were evaluated for classical PTC and follicular PTC using conditional logistic regression, adjusted for body mass index category and military branch to compute odds ratios (OR) and 95% confidence intervals (CIs). Effect modification by sex, birth cohort, and race was examined. Results: There was no evidence of associations between most of the OCPs and PTC, overall or stratified by histological subtype. Overall, there was no evidence of an association between hexachlorobenzene (HCB) and PTC, but stratified by histological subtype HCB was associated with significantly increased risk of classical PTC (third tertile above the limit of detection (LOD) vs. <LOD, OR = 1.61, 95% CI, 1.09, 2.38; p for trend = 0.05) and significantly decreased risk of follicular variant PTC (third tertile above the limit of detection (LOD) vs. <LOD, OR = 0.38, 95% CI, 0.16, 0.91; p for trend = 0.04). Further stratified by sex, risk of classical PTC was higher for females (third tertile above LOD vs. <LOD, OR = 2.23, 95% CI: 1.23, 4.06; p-trend = 0.02) than for males (OR = 1.22, 95%CI: 0.72–2.08; p-trend = 0.56), though the test for interaction by sex was not statistically significant (p-interaction = 0.30). Similarly, β-hexachlorocyclohexane (β-HCCH) was associated with a higher risk for classical PTC for women with concentrations ≥LOD versus <LOD (OR = 1.76, 95% CI: 1.07, 2.89), while the effects were null for men. There were no consistent trends when stratified by race or birth year. Conclusions: The U.S. Environmental Protection Agency has classified HCB and other OCPs we studied here as probable human carcinogens. Our findings of increased risks for classical PTC associated with increased concentrations of HCB and β-HCCH, which were stronger among females, should be replicated in future studies of other populations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

CATMoS: Collaborative Acute Toxicity Modeling Suite

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different endpoints: LD50 value, U.S. Environmental Protection Agency hazard categories, Globally Harmonized System for Classification and Labelling hazard categories, very toxic chemicals (LD50 =50 mg/kg), and non-toxic chemicals (LD50 >2000 mg/kg). Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared to in vivo results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for over 800,000 chemicals have been made available via the NTP’s Integrated Chemical Environment. The models are also implemented in a free, standalone open-source tool, OPERA, which allows predictions of new and untested chemicals to be made.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Intense infrared lasers for strong-field science

The advent of chirped-pulse amplification in the 1980s and femtosecond Ti:sapphire lasers in the 1990s enabled transformative advances in intense laser–matter interaction physics. Whereas most of experiments have been conducted in the limited near-infrared range of 0.8–1 μm, theories predict that many physical phenomena such as high harmonic generation in gases favor long laser wavelengths in terms of extending the high-energy cutoff. Significant progress has been made in developing few-cycle, carrier-envelope phase-stabilized, high-peak-power lasers in the 1.6–2 μm range that has laid the foundation for attosecond X ray sources in the water window. Even longer wavelength lasers are becoming available that are suitable to study light filamentation, high harmonic generation, and laser–plasma interaction in the relativistic regime. Long-wavelength lasers are suitable for sub-bandgap strong-field excitation of a wide range of solid materials, including semiconductors. In the strong-field limit, bulk crystals also produce high-order harmonics. In this review, we first introduce several important wavelength scaling laws in strong-field physics, then describe recent breakthroughs in short- (1.4–3 μm), mid- (3–8 μm), and long-wave (8–15 μm) infrared laser technology, and finally provide examples of strong-field applications of these novel lasers. Furthermore, some of the broadband ultrafast infrared lasers will have profound effects on medicine, environmental protection, and national defense, because their wavelengths cover the water absorption band, the molecular fingerprint region, as well as the atmospheric infrared transparent window.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling

Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral data as proportions with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture.

Superfund↗
DOE OSTI2018

Uncertainty quantification in ToxCast high throughput screening

High throughput screening (HTS) projects like the U.S. Environmental Protection Agency’s ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.

59 BASIC BIOLOGICAL SCIENCES↗