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NASA NTRS2015

High Volume Fraction Carbon Nanotube Composites for Aerospace Applications

Reported nanoscale mechanical properties of carbon nanotubes (CNTs) suggest that their use may enable the fabrication of significantly lighter structures for use in space applications. To be useful in the fabrication of large structures, however, their attractive nanoscale properties must be retained as they are scaled up to bulk materials and converted into practically useful forms. Advances in CNT production have significantly increased the quantities available for use in manufacturing processes, but challenges remain with the retention of nanoscale properties in larger assemblies of CNTs. This work summarizes recent progress in producing carbon nanotube composites with tensile properties approaching those of carbon fiber reinforced polymer composites. These advances were achieved in nanocomposites with CNT content of ~70% by weight. The processing methods explored to yield these CNT composite properties will be discussed, as will the characterization and test methods that were developed to provide insight into the factors that contribute to the enhanced tensile properties. Technology maturation was guided by parallel advancements in computational modeling tools that aided in the interpretation of experimental data.

Siochi, Emilie J.↗

NASA NTRS2018

The Importance of Nightside Magnetometer Observations for Electromagnetic Sounding of the Moon

Understanding the structure and composition of the lunar interior is a fundamental goal in furthering our knowledge of the formation and subsequent evolution of the Earth-Moon system. Among various methods, electromagnetic sounding is a valuable approach to constraining lunar interior structure. Recent analyses of plasma and field observations provide a wealth of understanding about the dynamics of the lunar plasma environment. To perform Time Domain EM (TDEM) Sounding at the Moon, the first step is to characterize the dynamic plasma environment, and to be able to isolate geophysically induced currents from concurrently present plasma currents. The TDEM Sounding transfer function method focuses on analysis of the nightside observations when the Moon is immersed in the solar wind. This method requires two simultaneous observations: an upstream reference measuring the pristine solar wind, and one downstream at or near the lunar surface. This method was last performed during Apollo and assumed the induced fields on the nightside of the Moon expand as in an undisturbed vacuum within the wake cavity. Our results indicate that EM sounding of airless bodies in the solar wind must be interpreted via self-consistent plasma models in order to untangle plasma and induced field contributions, with implications not only at the Moon but at all airless bodies exposed to the solar wind. Nightside TDEM sounding has the capability to advance the state of knowledge of the field of lunar science. This requires magnetometer operations to withstand the harsh conditions of the lunar night.

Haviland, H. Fuqua↗

NASA NTRS2019

Active and Passive Radiative Transfer Simulations for GPM-Related Field Campaigns

Radiative transfer modeling is an important tool for interpreting remote sensing observations. It allows us to determine how sensor characteristics will impact observations, and it gives us a framework for us to test assumptions about the phenomena we are attempting to observe. In this work, we use cloud simulations for precipitation events observed during various GPM-related field campaigns. The simulations show how various properties of clouds and precipitation affect the measurements.

Adams, Ian S.↗

NASA NTRS2020

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

Corbetta, Matteo↗

NASA NTRS2020

Mantle-melt Partitioning of the Highly Siderophile Elements: New Results and Application to Mars

Trace elements and extant and extinct isotopic attributes in martian meteorites have been used to argue that Mars accreted quickly, differentiated into core and mantle, and established several mantle reservoirs, possibly within 10 Ma of T0. The partitioning of trace elements in the deep mantle has been relatively unstudied, despite the need for such knowledge in understanding magma ocean crystallization and the origin of depleted and enriched mantle reservoirs. The siderophile element composition of the martian mantle, and lithophile isotopic systems such as Sr, Hf, and Nd, are thought to record evidence for early metal-silicate equilibrium and deep magma ocean at an intermediate depth and pressure of 800 km or 14 GPa. We have carried out experiments across this pressure range to better understand the mineral/melt partitioning of a wide range of elements. These new data are used to evaluate differentiation models for Mars and to help interpret the available isotopic data. The relatively incompatible nature of Re compared to mildly compatible Os means that the crystallization of a deep magma ocean will lead to residual liquids with super chondritic Re/Os, and solids with sub-chondritic Re/Os. Such material available in the mantle could be the source of enriched isotopic reservoir that produced shergottites with + Os values. On the other hand, slightly sub-chondritic Re/Os ratios in the crystallizing solids would provide a reservoir that could produce - Os values. Melting of mixtures of these two enriched and depleted end members could explain the Nd-Os isotopic correlations and systematics of shergottites.

K Righter↗

NASA NTRSDate not supplied

Exploring the Elevated Water Vapor Signal Associated With the Free Tropospheric Biomass Burning Plume Over the Southeast Atlantic Ocean

In southern Africa, widespread agricultural fires produce substantial biomass burning (BB) emissions over the region. The seasonal smoke plumes associated with these emissions are then advected westward over the persistent stratocumulus cloud deck in the southeast Atlantic (SEA) Ocean, resulting in aerosol effects which vary with time and location. Much work has focused on the effects of these aerosol plumes, but previous studies have also described an elevated free tropospheric water vapor signal over the SEA. Water vapor influences climate in its own right, and it is especially important to consider atmospheric water vapor when quantifying aerosol–cloud interactions and aerosol radiative effects. Here we present airborne observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign over the SEA Ocean. In observations collected from multiple independent instruments on the NASA P-3 aircraft (from near-surface to 6–7 km), we observe a strongly linear correlation between pollution indicators (carbon monoxide (CO) and aerosol loading) and atmospheric water vapor content, seen at all altitudes above the boundary layer. The focus of the current study is on the especially strong correlation observed during the ORACLES-2016 deployment (out of Walvis Bay, Namibia), but a similar relationship is also observed in the August 2017 and October 2018 ORACLES deployments. Using reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and specialized WRF-Chem simulations, we trace the plume–vapor relationship to an initial humid, smoky continental source region, where it mixes with clean, dry upper tropospheric air and then is subjected to conditions of strong westward advection, namely the southern African easterly jet (AEJ-S). Our analysis indicates that air masses likely left the continent with the same relationship between water vapor and carbon monoxide as was observed by aircraft. This linear relationship developed over the continent due to daytime convection within a deep continental boundary layer (up to ∼5–6 km) and mixing with higher-altitude air, which resulted in fairly consistent vertical gradients in CO and water vapor, decreasing with altitude and varying in time, but this water vapor does not originate as a product of the BB combustion itself. Due to a combination of conditions and mixing between the smoky, moist continental boundary layer and the dry and fairly clean upper-troposphere air above (∼6 km), the smoky, humid air is transported by strong zonal winds and then advected over the SEA (to the ORACLES flight region) following largely isentropic trajectories. Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) back trajectories support this interpretation. This work thus gives insights into the conditions and processes which cause water vapor to covary with plume strength. Better understanding of this relationship, including how it varies spatially and temporally, is important to accurately quantify direct, semi-direct, and indirect aerosol effects over this region.

Kristina Pistone↗

NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗

NASA NTRSDate not supplied

Interpretable ML Approaches for Novel Solid State Electrolyte Design

Shreyas J Honrao↗

DOE OSTI2025

Glimpsing Physics of Nano-Hz Gravitational Waves in Neutrinos from Core-Collapse Supernovae

The growing evidence for nano-hertz gravitational waves, from NANOGrav and other observations, may be pointing to a cosmological first-order phase transition at temperatures of $\mathcal{O}(10-100)\;\mathrm{MeV}$. Such an interpretation requires dynamics beyond the Standard Model in this energy range. If so, it may well be the case that core-collapse supernova explosions would recreate the first-order phase transition leaving a unique imprint on the spectrum of neutrinos emitted in the initial few seconds. This scenario is also suggestive of a low-mass seesaw mechanism to explain neutrino masses. We outline the prospects for future observations of Galactic supernovae to uncover the signals of this scenario, which could get further confirmation with additional pulsar timing array data establishing the primordial origin of the observed nano-hertz gravitational waves.

Davoudiasl, Hooman [Brookhaven] (ORCID:00000003348↗

DOE OSTI2026

Optimization of the light detection system of the ICARUS detector

The ICARUS detector, a key component of the Short Baseline Neutrino (SBN) Program at Fermi National Acelerator Laboratory (FNAL), is a 600-ton Liquid Argon Time Projection Chamber (LArTPC) equipped with a Light Detection System (LDS) that uses 360 Hamamatsu R5912-MOD 8-inch photomultiplier tubes (PMTs), specifically designed to operate under cryogenic conditions ($\sim 87 \ K$). These PMTs feed the trigger signal to the readout, improve the spatial and timing resolution of the events, and contribute to cosmic rays mitigation. During operation at FNAL, a progressive degradation in the PMT gain was observed. We developed an experimental setup to investigate the temperature dependence of PMT performance. Gain measurements were carried out from room temperature to $-70 ^\circ C$ using an environmental chamber. The results show that, while the PMTs exhibit stable performance at room temperature, a significant and irreversible reduction in gain emerges at lower temperatures. Al though $-70 ^\circ C$ remains above the liquid argon temperatures, the trend clearly reveals a gain-sensitive degradation mechanism. A simplified physical model was developed to reproduce and interpret the observed behavior. Based on these findings, a series of mitigation strategies were implemented in the ICARUS detector to preserve PMT performance and ensure reliable operation under cryogenic conditions.

Saia, C. [Catania Astrophys. Observ.]↗

DOE OSTI2026

Pre-Equilibrium De-Excitations for Neutrino-Nucleus Interactions

The Deep Underground Neutrino Experiment (DUNE) is sensitive to MeV-scale energy depositions from low-energy astrophysical neutrinos, including those from core-collapse supernovae. Interpreting these detector signals requires accurate modeling of the nuclear de-excitation that follows from the neutrino-nucleus interaction. The MARLEY (Model of Argon Reaction Low-Energy Yields) event generator specializes in the low-energy regime. MARLEY currently assumes the residual nucleus equilibrates immediately after the primary interaction. This omits the intermediate pre-equilibrium stage in which energy redistributes among nucleons until statistical equilibrium is reached. While pre-equilibrium effects are well established for nucleon-induced reactions, they have not previously been studied for neutrino-nucleus interactions. This work addresses that gap by implementing a two-component exciton model, which is the first dedicated treatment of pre-equilibrium de-excitation for neutrino-nucleus interactions, restructured around MARLEY's existing class hierarchy to prepare for direct integration, including particle-hole state densities, internal transition rates, and pre-equilibrium particle emission. The calculations show encouraging agreement with the TALYS-2.2 nuclear reaction code for neutron-nucleus interactions. We further propose a concrete integration path into the full MARLEY event generator, including derived class structure and an extended event record for pre-equilibrium vertices in support of future reweighting. Remaining work focuses on refining the emission width calculation, adding $\gamma$-ray emission, and completing this integration to quantify the impact of pre-equilibrium effects on the expected low-energy neutrino signals in DUNE and similar experiments.

Visser, Erin [Michigan State U., East Lansing (mai↗

DOE OSTI2026

Interpreting AI for fusion: An application to plasma profile analysis for tearing mode stability

Artificial intelligence models have demonstrated strong predictive capabilities for various instabilities in fusion devices such as Tokamaks, including tearing modes (TM), edge localized modes, and disruptive events, but their opaque nature raises concerns about safety and trustworthiness when applied to fusion power plants. Here, we present a physics-based interpretation framework using a TM prediction model as a demonstration that is validated through a dedicated DIII-D TM avoidance experiment. By applying Shapley analysis, we identify how profiles such as rotation, temperature, and density contribute to the model's prediction of TM stability. Our analysis shows that in our experimental scenario, core electron temperature and rotation peaking play the primary role in TM stability, while density changes have smaller effects on stability. We show that off-axis ion temperature stabilizes TMs, suggesting that off-axis neutral beam heating can further stabilize this scenario. This work presents a generalizable ML-based event prediction methodology, from training to physics-driven interpretation, bridging the gap between physics understanding and opaque ML models.

Farre-Kaga, Hiro J. [Princeton Univ., NJ (United S↗

NASA NTRS2018

Weber Number Tests in the NASA Icing Research Tunnel

A study of the Weber Number effects on droplets in the NASA Icing Research Tunnel is described. The work focuses on examining the droplet Weber Number effects observed for droplets accelerated by air flow in the contraction section of the Icing Research Tunnel to the test section. These results will aid in Supercooled Large Drop facility design studies. Measurements acquired with the Phase Doppler Interferometer and High Speed Imaging Dual Range Flight Probes at a series of locations through the contraction are presented alongside a 1D numerical model developed during this study to aid interpretation of the experimental results. An estimate of the maximum Weber Number observed in the Icing Research Tunnel for varying drop sizes up to 1000 m is presented and provided for incorporation into future design studies. Finally, experimental results coupled with a numerical model indicate that breakup of drops up to 1000 m is not occurring in the NASA Icing Research Tunnel up to 129 m/s.

Icing Wind Tunnel↗

NASA NTRS2019

Weber Number Tests in the NASA Icing Research Tunnel

A study of the Weber Number effects on droplets in the NASA Icing Research Tunnel is described. The work focuses on examining the droplet Weber Number effects observed for droplets accelerated by air flow in the contraction section of the Icing Research Tunnel to the test section. These results will aid in Supercooled Large Drop facility design studies. Measurements acquired with the Phase Doppler Interferometer and High Speed Imaging Dual Range Flight Probes at a series of locations through the contraction are presented alongside a 1D numerical model developed during this study to aid interpretation of the experimental results. An estimate of the maximum Weber Number observed in the Icing Research Tunnel for varying drop sizes up to 1000 μm is presented and provided for incorporation into future design studies. Finally, experimental results coupled with a numerical model indicate that breakup of drops up to 1000 μm is not occurring in the NASA Icing Research Tunnel up to 129 m/s.

Icing Wind Tunnel↗

DOE OSTI2020

A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Geothermal Play-Fairway Analysis of Washington State Prospects: Final Report

The Washington State Geothermal Play-Fairway Analysis overcomes the exploration challenges posed by dense vegetation, glacial deposits, and extreme precipitation. The geothermal play-fairways we target are locations where heat, permeability, and saturated porosity are present in sufficient volume to provide adequate heat exchange at depths accessible by modern drilling technology. The three study areas lie along the Cascade Range magmatic arc and are near Mount Baker, Mount St. Helens, and the Wind River Valley. The seven-year project is divided into three phases. In Phase 1 we build on a previous statewide assessment of geothermal resources and develop an initial modeling approach. The results are a series of favorability, uncertainty, and risk maps for three targeted study areas. Based on these initial results, we collect new geologic and geophysical data to further refine our modeling and reduce exploration uncertainty in Phase 2. We improve the modeling method to handle the new data and update the favorability, uncertainty, and risk maps. We also update the conceptual geothermal resource models. In Phase 3 we validate our modeling approach by drilling two temperature-gradient holes and collecting and analyzing core, image logs, and new geochemistry. Our modeling approach improves on an earlier statewide method through a more-rigorous and detailed assessment of heat and permeability. Permeability potential is assessed through geomechanical modeling of the deformation that can generate and maintain reservoir porosity and permeability. Metrics to inform heat potential include temperature-gradient wells, which are sparse in Washington; proximity of Quaternary volcanic vents and young intrusive rock; spring temperature; and reservoir temperature inferred from geothermometry. We weight the individual components using an expert-guided approach known as the Analytical Hierarchy Process. During Phase 2 we also develop a fluid-filled fracture model, and an infrastructure model that helps to delineate areas which are more favorable for geothermal development based on proximity to transmission lines, elevation, land ownership and use restrictions, and availability of process water. New geologic and geophysical data is collected during Phase 2 in each of our three main study areas. At Mount Baker and north of Mount St. Helens we conduct 1:24,000-scale geologic mapping and lidar analysis to better constrain the location and character of surface faults; detailed mapping in the Wind River Valley was completed just prior to the start of this project. Ages of intrusive rocks are determined with 40 Ar/ 39 Ar geochronology, though all of our samples are Miocene or older. We collect ground based gravity observations (a total of 1,580 new stations) in all of our study areas and ground-based magnetic lines (a total of 93 km) at Mount Baker. These data are combined with existing gravity and aeromagnetic data and used to constrain fault locations and geometry. Two to three cross sections are constructed at each study area using the mapped surface geology and forward-modeling of the gravity and magnetic data; these cross sections form the basis for our updated conceptual models. We collect magnetotelluric surveys at Mount Baker and Mount St. Helens and these data are inverted to form a resistivity model from the surface to about 10 km depth; each model shows conductive zones that can be interpreted as upwelling geothermal fluids. At Mount St. Helens we deploy a passive seismic array and use the newly detected events to refine the location of the Saint Helens seismic zone. We also employ ambient-noise tomography to develop a detailed seismic-velocity model for the study area and use this model to help constrain our cross sections and conceptual model. Based on the new data collected during Phase 2—and our updated models—we develop a campaign of temperature-gradient holes and core analysis to validate our modeling in Phase 3. Drill hole MB76-31 is located near Little Park Creek, 11 km west-southwest of the summit of Mount Baker, and is 1,471 ft deep. About 410 ft of core from the lower portion of the hole—and image logs from ~175 ft below ground surface to the bottom—are collected and analyzed. Water samples are collected and processed for geothermometry. Drill hole MSH17-24 is located along upper Schultz Creek, 16 km north-northeast of Mount St. Helens and has core from 470 ft to the bottom at 1,053 ft. We did not collect image logs due to borehole stability concerns, but water samples are collected and analyzed for geothermometry. Repeat temperature-gradient measurements are made at both sites and thermal conductivity is measured from core samples. At MB76-31, the equilibrated temperature gradient of 64°C/km and calculated heat flow of 141–159 mW/m 2 is more than twice the regional average. Detailed mapping and analysis of the core, coupled with correlation to the image logs, indicates a history of permeability generation consistent with our predictions of high permeability. Because the site has high favorability in the Phase 2 model, we consider the results a positive validation of the modeling. At site MSH17-24, the equilibrated temperature gradient of ~15°C/km and calculated heat flow of 41–43 mW/m 2 are similar to regional. Geochemical analysis of the water samples indicates a meteoric source without any geothermal component. Detailed outcrop-based mapping of fault exposures near the drill site and analysis of image logs from nearby boreholes indicates a history of permeability generation consistent with our predictions. Because the site has low favorability in the Phase 2 model, we consider the results a positive validation of the modeling. Together, the two sites provide a reasonably positive validation of the Phase 2 modeling and should encourage future use of this modeling approach.

15 GEOTHERMAL ENERGY↗

NASA NTRS1999

Investigating Galactic Structure with COBE/DIRBE and Simulation

In this work I applied the current version of the SKY model of the point source sky to the interpretation of the diffuse all-sky emission observed by COBE/DIRBE (Cosmic Background Explorer Satellite/Diffuse Infrared Background Experiment). The goal was to refine the SKY model using the all-sky DIRBE maps of the Galaxy, in order that a search could be made for an isotropic cosmic background."Faint Source Model" [FSM] was constructed to remove Galactic fore ground stars from the ZSMA products. The FSM mimics SKY version 1 but it was inadequate to seek cosmic background emission because of the sizeable residual emission in the ZSMA products after this starlight subtraction. At this point I can only support that such models are currently inadequate to reveal a cosmic background. Even SKY5 yields the same disappointing result.

Cohen, Martin↗

DOE OSTI2020

Linear two-pool models are insufficient to infer soil organic matter decomposition temperature sensitivity from incubations

Abstract Terrestrial carbon (C)-climate feedbacks depend strongly on how soil organic matter (SOM) decomposition responds to temperature. This dependency is often represented in land models by the parameter Q 10 , which quantifies the relative increase of microbial soil respiration per 10 °C temperature increase. Many studies have conducted paired laboratory soil incubations and inferred “active” and “slow” pool Q 10 values by fitting linear two-pool models to measured respiration time series. Using a recently published incubation study (Qin et al. in Sci Adv 5(7):eaau1218, 2019) as an example, here we first show that the very high parametric equifinality of the linear two-pool models may render such incubation-based Q 10 estimates unreliable. In particular, we show that, accompanied by the uncertain initial active pool size, the slow pool Q 10 can span a very wide range, including values as high as 100, although all parameter combinations are producing almost equally good model fit with respect to the observations. This result is robust whether or not interactions between the active and slow pools are considered (typically these interactions are not considered when interpreting incubation data, but are part of the predictive soil carbon models). This very large parametric equifinality in the context of interpreting incubation data is consistent with the poor temporal extrapolation capability of linear multi-pool models identified in recent studies. Next, using a microbe-explicit SOM model (RESOM), we show that the inferred two pools and their associated parameters (e.g., Q 10 ) could be artificial constructs and are therefore unreliable concepts for integration into predictive models. We finally discuss uncertainties in applying linear two-pool (or more generally multiple-pool) models to estimate SOM decomposition parameters such as temperature sensitivities from laboratory incubations. We also propose new observations and model structures that could enable better process understanding and more robust predictive capabilities of soil carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗

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