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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Structure of a PKA RIα Recurrent Acrodysostosis Mutant Explains Defective cAMP-Dependent Activation

Most disease related mutations that impair PKA signaling are present within the regulatory PKA RI alpha-subunit (RIα). Although mutations in the PRKAR1A gene are linked to Carney complex disease (CNC) and more recently to acrodysostosis-1 (ACRDYS1), the two diseases show contrasting phenotypes. While CNC mutations cause increased PKA activity, ACRDYS1 mutations result in decreased PKA activity and cAMP resistant holoenzymes. Mapping the ACRDYS1 disease mutations reveals their localization to the second of two tandem cAMP binding domains (CNB-B) and here we characterize a recurrent deletion mutant where the last 14 residues are missing. The crystal structure of a monomeric form of this mutant (RIα92-365) bound to the C subunit reveals the dysfunctional regions of the RIα-subunit. Beyond the missing residues, the entire capping motif is disordered (residues 357-379) and explains the disrupted cAMP binding. Moreover, the effects of the mutation extend far beyond the CNB-B domain and include the active site and N-lobe of the C-subunit, which is in a partially open conformation with the C-tail disordered. A key residue that contributes to this crosstalk, D267, is altered in our structure and we confirmed its functional importance by mutagenesis. In particular, the D267 interaction with Arg241, a residue shown earlier to be important for allosteric regulation, is disrupted thereby strengthening the interaction of D267 with the C-subunit residue Arg194 at the R:C interface. Here, we see here how the switch between active (cAMP-bound) and inactive (holoenzyme) conformations is perturbed and how the dynamically controlled crosstalk between the helical domains of the two CNB-domains is necessary for functional regulation of PKA activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy

Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.

2D materials↗
DOE OSTI2022

Explainable machine learning of the underlying physics of high-energy particle collisions

We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the final state particles. We demonstrate the proof-of-concept of our White Box AI approach using a Generative Adversarial Network (GAN) which learns from a DGLAP-based parton shower Monte Carlo event generator. The constrained generator network architecture mimics the structure of a parton shower exhibiting similarities with Recurrent Neural Networks (RNNs). We show, for the first time, that our approach leads to a network that is able to learn not only the final distribution of particles, but also the underlying parton branching mechanism, i.e. the Altarelli-Parisi splitting function, the ordering variable of the shower, and the scaling behavior. While the current work is focused on perturbative physics of the parton shower, we foresee a broad range of applications of our framework to areas that are currently difficult to address from first principles in QCD. Examples include nonperturbative and collective effects, factorization breaking and the modification of the parton shower in heavy-ion, and electron-nucleus collisions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Explaining the GeV excess with exploding black holes

Black holes may form in present-day collapse of microscopic structures of dark matter. We show that, if microstructure black holes (MSBH) with mass m ~ 10 13 g are produced, the spectrum of gamma rays from their evaporation agrees remarkably well with the GeV excess observed by Fermi Gamma-ray Space Telescope, while still avoiding all observational constraints. We also discuss the generic requirements for MSBHs to explain the GeV excess.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

An Application of ASP in Nuclear Engineering: Explaining the Three Mile Island Nuclear Accident Scenario

Abstract The paper describes an ongoing effort in developing a declarative system for supporting operators in the Nuclear Power Plant (NPP) control room. The focus is on two modules: diagnosis and explanation of events that happened in NPPs. We describe an Answer Set Programming (ASP) representation of an NPP, which consists of declarations of state variables, components, their connections, and rules encoding the plant behavior. We then show how the ASP program can be used to explain the series of events that occurred in the Three Mile Island, Unit 2 (TMI-2) NPP accident, the most severe accident in the USA nuclear power plant operating history. We also describe an explanation module aimed at addressing answers to questions such as “why an event occurs?” or “what should be done?” given the collected data.

Computer Science↗
DOE OSTI2025

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI

A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. Here, in this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.

Harwood, Thomas V. [USDOE Joint Genome Institute (↗
DOE OSTI2024

Microbial Proxies for Anoxic Microsites Vary with Management and Partially Explain Soil Carbon Concentration

Anoxic microsites are potentially important but unresolved contributors to soil organic carbon (C) storage. How anoxic microsites vary with soil management and the degree to which anoxic microsites contribute to soil C stabilization remain unknown. Sampling from four long-term agricultural experiments in the central United States, we examined how anoxic microsites varied with management (e.g., cultivation, tillage, and manure amendments) and whether anoxic microsites determine soil C concentration in surface (0–15 cm) soils. We used a novel approach to track anaerobe habitat space and, hence, anoxic microsites using DNA copies of anaerobic functional genes over a confined volume of soil. No-till practices inconsistently increased anoxic microsite extent compared to conventionally tilled soils, and within one site organic matter amendments increased anaerobe abundance in no-till soils. Across all long-term tillage trials, uncultivated soils had ~2–4 times more copies of anaerobic functional genes than their cropland counterparts. Finally, anaerobe abundance was positively correlated to soil C concentration. Even when accounting for other soil C protection mechanisms, anaerobe abundance, our proxy for anoxic microsites, explained 41% of the variance and 5% of the unique variance in soil C concentration in cropland soils, making anoxic microsites the strongest management-responsive predictor of soil C concentration. Our results suggest that careful management of anoxic microsites may be a promising strategy to increase soil C storage within agricultural soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Coadsorbed Species Explain the Mechanism of Methanol Temperature-Programmed Desorption on CeO 2 (111)

Here, we have used density functional theory calculations to investigate the temperature-programmed desorption (TPD) of methanol from CeO 2 (111). For the first time, low-temperature water formation and high-temperature methanol desorption are explained by our calculations. High coverages of methanol, which correspond to experimental conditions, are required to properly describe these features of the TPD spectrum. We identify a mechanism for the low-temperature formation of water involving the dissociation of two methanol molecules on the same surface O atom and filling of the resulting surface vacancy with one of the methoxy products. After water desorption, methoxy groups are stabilized on the surface and react at higher temperatures to form methanol and formaldehyde by a disproportionation mechanism. Alternatively, the stabilized methoxy groups undergo sequential C–H scission reactions to produce formaldehyde. Calculated energy requirements and methanol/formaldehyde selectivity agree with the experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Distinct Chemistries Explain Decoupling of Slip and Wettability in Atomically Smooth Aqueous Interfaces

Despite essentially identical crystallography and equilibrium structuring of water, nanoscopic channels composed of hexagonal boron nitride and graphite exhibit an order-of-magnitude difference in fluid slip. We investigate this difference using molecular dynamics simulations, demonstrating that its origin is in the distinct chemistries of the two materials. In particular, the presence of polar bonds in hexagonal boron nitride, absent in graphite, leads to Coulombic interactions between the polar water molecules and the wall. We demonstrate that this interaction is manifested in a large typical lateral force experienced by a layer of oriented hydrogen atoms in the vicinity of the wall, leading to the enhanced friction in hexagonal boron nitride. The fluid adhesion to the wall is dominated by dispersive forces in both materials, leading to similar wettabilities. Finally, our results rationalize recent observations that the difference in frictional characteristics of graphite and hexagonal boron nitride cannot be explained on the basis of the minor differences in their wettabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Single Nanoflake Photoelectrochemistry Reveals Intrananoflake Doping Heterogeneity That Explains Ensemble-Level Photoelectrochemical Behavior

Transition metal dichalcogenide (TMD) nanoflake thin films are attractive electrode materials for photoelectrochemical (PEC) solar energy conversion and sensing applications, but their photocurrent quantum yields are generally lower than those of bulk TMD electrodes. The poor PEC performance has been primarily attributed to enhanced charge carrier recombination at exposed defect and edge sites introduced by the exfoliation process. Here, a single nanoflake PEC approach reveals how an alternative effect, doping heterogeneity, limits ensemble-level PEC performance. Photocurrent mapping and local photocurrent–potential (i–E) measurements of MoS 2 nanoflakes exfoliated from naturally occurring bulk crystals revealed the presence of n- and p-type domains within the same nanoflake. Interestingly, the n- and p-type domains in the natural MoS 2 nanoflakes were equally efficient for iodide oxidation and tri-iodide reduction (IQE values exceed 80%). At the single domain-level, the natural MoS 2 nanoflakes were nearly as efficient as nanoflakes exfoliated from synthetic n-type MoS 2 crystals. Single domain-level i–E measurements explain why natural MoS 2 nanoflakes exhibit an n-type to p-type photocurrent switching effect in ensemble-level measurements: the n- and p-type diode currents from individual domains oppose each other upon illuminating the entire nanoflake, resulting in zero photocurrent at the switching potential. Furthermore, the doping heterogeneity effect is likely due to nonideal stoichiometry, where p-type domains are S-rich according to XPS measurements. Although this doping heterogeneity effect limits photoanode or photocathode performance, these findings open the possibility to synthesize efficient TMD nanoflake photocatalysts with well-defined lateral p- and n-type domains for enhanced charge separation.

14 SOLAR ENERGY↗
DOE OSTI2023

Biphasic Janus Particles Explain Self-Healing in Pt–Pd Diesel Oxidation Catalysts

The addition of Pd to Pt-based diesel oxidation catalysts is known to enhance performance and restrict the anomalous growth of Pt nanoparticles when subjected to aging at high temperatures in oxidative environments. To gain a mechanistic understanding, we studied the transport of the mobile Pt and Pd species to the vapor phase, since vapor phase transport is the primary route for sintering in these catalysts. The results are surprising: there is a 30-fold drop in the effective vapor pressure of Pt in the Pt-Pd catalysts compared to monometallic Pt. At the same time, there is a significant enhancement in the vapor pressure of Pd, compared to PdO, which otherwise has a negligible vapor pressure at the aging temperature. Such behavior cannot be explained simply by alloying Pt and Pd in the metallic phase, or a core-shell morphology where a PdO shell covers a Pt core. Transmission electron microscopic examination of catalysts aged up to 50 h in air at 800 °C shows that the particles exhibit a biphasic “Janus”-like structure. The metal and oxide phases are conjoined, exposing a metal and an oxide face to the gas phase. The high mobility of the Pt and Pd allows them to be partitioned into the metal and oxide phases, in apparent thermodynamic equilibrium. The PdO helps to trap mobile PtO2 and as a result contains high concentrations of Pt oxide, consistent with its role in mitigating the transport of Pt to the vapor phase and preventing the growth of anomalously large particles. In turn, Pt allows Pd to remain metallic, allowing the catalyst to retain both metal and oxide functionality for catalysis. The regeneration of deactivated catalysts typically requires an external input, such as a change in the working environment from reducing to oxidizing or vice-versa. Here, we show that the mobile species, which are primary contributors to catalyst sintering are effectively returned to the active site, hence our use of the term “selfhealing”. The detailed insights into the inner workings of the Pt-Pd diesel oxidation catalysts can help provide clues to the design of robust and durable heterogeneous catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A Simple Bond-Additivity Model Explains Large Decreases in Heats of Adsorption in Solvents Versus Gas Phase: A Case Study with Phenol on Pt(111) in Water

Previously, we reported the low-coverage heat of adsorption of phenol on Pt(111) facets of a Pt wire in aqueous phase to be approximately 21 kJ/mol (relative to aqueous phenol) on the basis of measurements of the adsorption equilibrium constant. This is much smaller than the heat we reported for gas-phase phenol adsorption at this same low coverage on single-crystal Pt(111) under ultrahigh vacuum (200 kJ/mol) on the basis of adsorption calorimetry measurements. Here we quantitatively analyze the individual contributions that give rise to this 179 kJ/mol difference using a simple pairwise bond-additivity model, taking advantage of experimental data from the literature to estimate the bond energies. The dominant contribution to the lowering in heat when phenol is adsorbed in water is the energy cost to break the strong bond of liquid water to Pt(111) (~116 kJ per mole of phenol area). The water–phenol bonding is lost on one face of the phenol, and this costs ~50 kJ/mol, but this is nearly compensated by the new water–water bonding (~53 kJ/mol of phenol area). The results indicate that the intrinsic bond energy between phenol and Pt(111) is not very different in the gas versus the aqueous phase, provided one takes into consideration the expectation that water forces phenol into islands of high local coverage even at low average coverage (for the same reason that phenol has limited solubility in water). Furthermore, this explains the lack of a strong coverage dependence in the heat of adsorption when it is measured in aqueous phase, whereas it decreases by ~57 kJ/mol with coverage when it is measured in gas phase. This bond-additivity analysis presented here can be easily generalized to other adsorbates, surfaces, and solvents. It clarifies why catalysis with molecules such as phenol which have very strong bonding to Pt-group metals can proceed rapidly at room temperature in liquid solvents such as water but would never proceed in the gas phase at room temperature due to irreversible site poisoning.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Can Isoprene Oxidation Explain High Concentrations of Atmospheric Formic and Acetic Acid over Forests?

Formic and acetic acid concentrations are particularly high over forested areas of the world. However, the gas-phase mechanisms for producing these acids are poorly understood even for isoprene, the globally dominant biogenic hydrocarbon. We quantified formic and acetic acid production from reactions of hydroxyl radical (OH) (between high and low ranges of nitric oxide (NO) levels) with isoprene, methacrolein (MACR), isoprene epoxydiol (IEPOX), isoprene hydroxy hydroperoxide (ISOPOOH), and α-pinene from the focused isoprene experiments at California Institute of Technology (FIXCIT) laboratory chamber study. We find that (i) OH oxidation of MACR, IEPOX, and ISOPOOH are sources of formic acid, (ii) isoprene peroxy radical isomerization and associated photolysis oxidation products are potentially important sources of organic acids, and (iii) high levels of NO generally suppress organic acid formation from OH oxidation of isoprene. We modified existing chemical mechanisms for isoprene oxidation to account for organic acid production pathways observed in the FIXCIT study. We simulated organic acid production during the Southeastern Oxidant and Aerosol Study using the updated chemical mechanisms and represented acetic acid within a factor of 2 but still underpredicted formic acid by a factor of 6. While we cannot explain ambient formic acid with explicit chemical mechanisms, the FIXCIT results suggest that the oxidation of isoprene could account for as much as 70% of the global annual production of formic acid from gas-phase reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Silver Adsorption on Calcium Niobate(001) Nanosheets: Calorimetric Energies That Explain Sinter-Resistant Support

Metal nanoparticles deposited on oxide supports are essential to many technologies, including catalysts, fuel cells and electronics. Therefore, understanding the chemical bonding strength between metal nanoparticles and oxide surfaces is of great interest. The adsorption energetics, adhesion energy and adsorbate structure of Ag on dehydrated HCa 2 Nb 3 O 10 (001) nanosheets at 300 K have been studied using metal adsorption calorimetry and surface spectroscopies. These dehydrated (“dh”) calcium niobate nanosheets “(dh-HCa 2 Nb 3 O 10 (001))” have stoichiometry Ca 4 Nb 6 O 19 . They impart unusual stability to metal nanoparticles when used as catalyst supports and are easy-to-prepare by Langmuir-Blodgett (LB) techniques, highly ordered, and essentially single-crystal surfaces of mixed oxides with a huge ratio of terrace to edge sites. Below monolayer coverage, Ag grows on dh-HCa 2 Nb 3 O 10 (001) as 2D islands of thickness ~2 layers. The differential heat of Ag adsorption is initially ~303 kJ/mol, increasing slowly to ~338 kJ/mol by 0.8 ML. At higher coverages, Ag atoms mainly add on top of these 2D islands, growing 3D nanoparticles of increasing thickness, as the heat decreases asymptotically towards silver’s heat of sublimation (285 kJ/mol). The adhesion energy of Ag(s) to this Ca niobate surface is estimated to be 4.33 J/m 2 , larger than on any oxide surface previously measured. This explains the sinter resistance reported for metal nanoparticles on this support. Electron transfer from Ag into the calcium niobate is also measured. Furthermore, these results demonstrate an easy way to do singlecrystal-type surface science studies - and especially thermochemical measurements - on the complex surfaces of mixed oxides: using LB-deposited perovskite nanosheets and ultrahigh vacuum annealing in O 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Explaining the Temperature Dependence of Spirilloxanthin’s S* Signal by an Inhomogeneous Ground State Model

We investigate the nature of the S* excited state in carotenoids by performing a series of pump–probe experiments with sub-20 fs time resolution on spirilloxanthin in a polymethyl-methacrylate matrix varying the sample temperature. Following photoexcitation, we observe sub-200 fs internal conversion of the bright S2 state into the lower-lying S1 and S* states, which in turn relax to the ground state on a picosecond time scale. Upon cooling down the sample to 77 K, we observe a systematic decrease of the S*/S1 ratio. This result can be explained by assuming two thermally populated ground state isomers. The higher lying one generates the S* state, which can then be effectively frozen out by cooling. These findings are supported by quantum chemical modeling and provide strong evidence for the existence and importance of ground state isomers in the photophysics of carotenoids.

14 SOLAR ENERGY↗
DOE OSTI2021

Sub-Cloud Turbulence Explains Cloud-Base Updrafts for Shallow Cumulus Ensembles: First Observational Evidence

Sub-cloud turbulent kinetic energy has been used to parameterize the cloud-base updraft velocity (w b ) in cumulus parameterizations. The validity of this idea has never been proved in observations. Instead, it was challenged by recent Doppler Lidar observations showing a poor correlation between the two. We argue that the low correlation is likely caused by the difficulty of a fixed-point Lidar to measure ensemble properties of cumulus fields. Taking advantage of the stationarity and ergodicity of early afternoon convection, we developed a Lidar sampling methodology to measure w b of a shallow cumulus (ShCu) ensemble (not a single ShCu). By analyzing 128 ShCu ensembles over the Southern Great Plains, we show that the ensemble properties of sub-cloud turbulence explain nearly half of the variability in ensemble-mean wb, demonstrating the ability of sub-cloud turbulence to dictate w b . Lastly, the derived empirical formulas will be useful for developing cumulus parameterizations and satellite inference of w b .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mineral Protection and Resource Limitations Combine to Explain Profile-Scale Soil Carbon Persistence

The fate of soil carbon (C) is largely controlled by microbial oxidation of organic matter (OM), which is constrained by a variety of mechanisms. OM association with soil minerals provides pronounced protection against microbial decomposition. However, factors such as climate, occlusion, and resource limitations also contribute to OM preservation. Here, we explore the factors explaining C distribution and age within an upland rainforest soil in Hawai'i, a site with abundant preferential flow paths (PFPs) and high short-range order (SRO) mineral content. We characterized lateral and vertical changes in Δ 14 C, SRO mineral content, C-functional group chemistry, and microbial community composition to elucidate the contributions of multiple protection mechanisms to OM preservation. Consistent with our expectation, SRO mineral content and Δ 14 C were strongly correlated (R 2 = 0.95), indicating strong mineral protection of OM throughout the profile. Surprisingly, distance from PFP was also a significant predictor of Δ 14 C and improved model fit, particularly in the shallow horizons (R 2 = 0.97). Elevated C/N ratios, decreased microbial abundance, and greater SRO mineral content suggest nitrogen limitations and enhanced mineral protection constrain OM turnover with distance from PFPs in deep, subsurface mineral horizons. Steady microbial abundance, increasing putative anaerobe abundance, and changes in C-functional group chemistry indicate oxygen limitations constrain OM turnover in the matrix of shallow mineral horizons. Given that oxygen and nutrient limitations contribute to OM preservation in this high SRO system—an exemplar of mineral protection—resource limitations may play an even more important role in OM preservation in other well-structured soils.

59 BASIC BIOLOGICAL SCIENCES↗