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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sr 2 MnO 4 as a reactive CO 2 sorbent for sorption-enhanced steam reforming of biogas to green hydrogen

Sorption-enhanced steam biogas reforming is an attractive approach for hydrogen production from renewable resources, with the performance of the CO 2 sorbents being a critical factor. In this study, Sr 2 MnO 4 was investigated as a redox-activated CO 2 sorbent for sustainable hydrogen production from biogas. The Sr 2 MnO 4 sorbents exhibited a CO 2 sorption capacity of over 26 g per 100 g of sorbents, along with excellent cyclic stability in thermogravimetric analysis. Complete regeneration of the sorbent was achieved with a relatively small temperature swing (100 °C). Fixed-bed reactor experiments further demonstrated the application of Sr 2 MnO 4 sorbents in sorption-enhanced steam biogas reforming. Biogas simulants with varying CO 2 contents were converted to ~94 vol% H 2 before CO 2 breakthrough. Stable CO 2 capacity and hydrogen production were maintained over 20 cycles. In addition, optimization of the regeneration duration enabled the generation of highly pure CO 2 and more efficient use of O 2 . These results support the feasibility of biogas-to‑hydrogen conversion with net-negative carbon emissions through integration with CO 2 capture and sequestration.

09 BIOMASS FUELS↗
DOE OSTI2023

Urban mining from biomass, brine, sewage sludge, phosphogypsum and e-waste for reducing the environmental pollution: Current status of availability, potential, and technologies with a focus on LCA and TEA

We discuss how rapid industrialization, improved standards of living, growing economies and ever-increasing population has led to the unprecedented exploitation of the finite and non-renewable resources of minerals in past years. It was observed that out of 100 BMT of raw materials processed annually only 10% is recycled back. This has resulted in a strenuous burden on natural or primary resources of minerals (such as ores) having limited availability. Moreover, severe environmental concerns have been raised by the huge piles of waste generated at landfill sites. To resolve these issues, ‘Urban Mining’ from waste or secondary resources in a Circular Economy’ concept is the only sustainable solution. The objective of this review is to critically examine the availability, elemental composition, and the market potential of the selected secondary resources such as lignocellulosic/algal biomass, desalination water, sewage sludge, phosphogypsum, and e-waste for minerals sequestration. This review showed that, secondary resources have potential to partially replace the minerals required in different sectors such as macro and microelements in agriculture, rare earth elements (REEs) in electrical and electronics industry, metals in manufacturing sector and precious elements such as gold and platinum in ornamental industry. Further, inputs from the selected life cycle analysis (LCA) & techno economic analysis (TEA) were discussed which showed that although, urban mining has a potential to reduce the greenhouse gaseous (GHG) emissions in a sustainable manner however, process improvements through innovative, novel and cost-effective pathways are essentially required for its large-scale deployment at industrial scale in future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Decomposition-Based Learn-To-Optimize Approach with Feasibility Layer Assistance for Sub-Hourly Unit Commitment

Sub-hourly unit commitment (UC) with 15-min intervals is gaining significant attention as a way to respond rapidly to the fluctuations in electricity supply and demand introduced by renewable resources. However, the increased temporal resolution and complex inter-temporal dependencies pose substantial computational challenges for traditional optimization methods. To this end, this paper explores a decomposition-based learn-to-optimize approach. Building on recent advances in machine learning, our method revisits the long- overlooked Lagrangian relaxation framework, which is a classical decomposition technique that enables tractable subproblem solving. These smaller subproblems are inherently well-suited for machine learning, as their reduced dimensionality and structural regularity allow predictive models to efficiently learn and generalize solution patterns. We thus propose a generic predictive model, which embeds Gated Recurrent Units (GRUs) and Attention in the encoder-decoder structure, and integrate a rule-based feasibility layer to capture temporal dependencies, reduce training effort, and improve feasibility w.r.t. unit-level constraints. Our method has been validated on the IEEE 118-bus system, demonstrating promising performance in solving sub-hourly UC problems efficiently and feasibly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reviewing flexibility in industrial electrification: Focusing on green ammonia and steel in the United States

The renewable energy transition in the power sector involves a paradigm shift for flexibility. Flexible demand is more attainable than before, while supply flexibility faces new constraints due to the increased share of variable renewable resources. Increased demand flexibility can allow less use of peaking power plants and avoid need for additional capacity. Industrial flexibility could be especially interesting in this regard, as industrial customers are larger on average than other customers and have typically provided the largest share of demand response in the United States. We consider industrial demand, studying characteristics of flexible industrial loads. We then examine the dynamics of change occurring around industrial load flexibility by focusing on two case studies: green ammonia and steel production via electric arc furnaces. Electric arc furnace steel production is an important component of current demand response programs, whereas green ammonia and green fuels offer new paradigms for flexibility. We analyze the structure and functions of the technological innovation systems of load flexibility in those two industries via interviews with twenty-two stakeholders. Here, we conclude that in the United States these technological innovation systems are not well-functioning for industry in general or for steel, but do seem to be present for green ammonia. Additionally, explicit connections between scope two greenhouse gas emissions (those from purchased energy) and flexibility are lacking, and likewise industry stakeholders do not appear to make a connection between decarbonization and load flexibility, thus flexible demand is not viewed as a tool in industrial decarbonization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

GPU-resident sparse direct linear solvers for alternating current optimal power flow analysis

Integrating renewable resources within the transmission grid at a wide scale poses significant challenges for economic dispatch as it requires analysis with more optimization parameters, constraints, and sources of uncertainty. This motivates the investigation of more efficient computational methods, especially those for solving the underlying linear systems, which typically take more than half of the overall computation time. In this paper, we present our work on sparse linear solvers that take advantage of hardware accelerators, such as graphical processing units (GPUs), and improve the overall performance when used within economic dispatch computations. We treat the problems as sparse, which allows for faster execution but also makes the implementation of numerical methods more challenging. We present the first GPU-native sparse direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate significant performance improvements when using high-performance linear solvers within alternating current optimal power flow (ACOPF) analysis. Furthermore, we demonstrate the feasibility of getting significant performance improvements by executing the entire computation on GPU-based hardware. Finally, we identify outstanding research issues and opportunities for even better utilization of heterogeneous systems, including those equipped with GPUs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Dynamic operation of metal-supported solid oxide electrolysis cells

Symmetric-structure metal-supported solid oxide fuel cells and electrolysis cells (MS-SOFCs, MS-SOECs) offer several advantages over conventional solid oxide cells, including the use of inexpensive materials, high mechanical strength, and rapid ramp-up ability. Aggressive operation of MS-SOCs in fuel cell mode is well-established, including extremely fast start-up, redox tolerance, and imbalanced pressure. Here, we extend dynamic operation to MS-SOCs in SOEC mode with high steam content for both small button cells and a large rectangular cell, including: steam cycling, thermal cycling, redox cycling and power cycling. Steam cycling entailed switching between 3:97 and 50:50 steam:hydrogen ratio. For thermal cycling, the temperature was rapidly varied between 150°C and 700°C for 50 cycles. Redox cycling involved switching the steam side gas between 50 % humidified H 2 and 50 % humidified N 2 for 5 cycles. Power cycling was performed by operating the cell under variable current density, resulting in cell voltage between 1.3V and 2.8V. Degradation rates for each testing strategy were compared to a baseline cell, and found to be similar. In conclusion, the excellent tolerance to dynamic operation increases confidence that MS-SOECs will be compatible with dynamic or intermittent renewable resources.

08 HYDROGEN↗
DOE OSTI2023

Towards functionalized lignin and its derivatives for high-value material applications

In pursuit of low-carbon development, the production of diverse functional materials from renewable resources, especially lignocellulosic biomass, is of vital importance. Lignin is a major component of lignocellulose, and it is nature's only true high-volume aromatic polymer, which has the advantages of biodegradability, biocompatibility, and low acquisition cost. After modification by a variety of physicochemical strategies, it can be applied as an alternative to basic industrial materials, including polyurethanes, phenolic, and epoxy resins, which stands comparably to conventional synthetic polymers in terms of both performance and reduced cost. To further unlock the potential of lignin, an alluring opportunity is the exploitation of lignin for high-value materials, especially nanomaterials, that find extensive applications in energy and environment areas. This review provides a systematic summary and perspective of research that has been devoted to lignin transformation to high-value materials, ranging from industrially well-established engineering materials to the recently emerged nanomaterials that were used for energy and environment applications. Latest cutting-edge innovations on lignin modification, controlled depolymerization, and assembly during the last five years are summarized. Structure-function relationships of lignin materials in terms of their specific applications are analyzed. Furthermore, challenges and future opportunities for lignin conversion to high-value materials are also provided. We wish this review will stimulate further advances in lignin based high-value materials, and promote “waste” into “wealth”.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Radiation-induced dry reforming: A negative emission process

The reaction between the most abundant greenhouse gases (GHG) to produce hydrogen might represent the most powerful and effective decarbonizing opportunity, if a low-carbon energy source is used to drive it. This is the case of methane (CH 4 ) dry reforming (MDR) where its reaction with carbon dioxide (CO 2 ) produces synthesis gas (syngas, a mixture of carbon monoxide and hydrogen). Here this study explores the feasibility of using ionizing radiation to induce the MDR reaction, at low temperatures and/or less energy demanding conditions. Additionally, the ionizing radiation is proposed to be supplied by nuclear power plants (NPPs), which are low-carbon reliable energy generation sources. Thus, the radiolysis of CO 2 , CH 4 and their mixtures, under ?-irradiation was evaluated in the absence and presence of nickel catalysts. The radiation-induced MDR reaction and radiation-induced catalytic promotion were proven to take place at temperatures close to ambient though at low conversion, with yields below 1%. Since irradiation and heat can be provided by a nuclear power plant, this radiation-induced reaction establishes a connection between nuclear energy to renewable resources and enables a pathway for a decarbonized cleaner chemical industry, for producing green chemicals.

08 HYDROGEN↗
DOE OSTI2025

Co nanoclusters derived from zinc-trimesic acid fiber for efficient levulinic acid hydrogenation

The hydrogenation of levulinic acid (LA) to γ-valerolactone (GVL) is significant for producing chemicals and fuels from renewable resources, a promising direction in biomass refining. Currently, non-precious metal catalysts suffer from low activity due to a single electronic structure, which is incapable of effectively activating both CO in LA and H 2 . Herein we report in situ fabricated Co nanoclusters within positive and metallic Co sites on N-doped carbon, which can simultaneously activate LA's CO and H 2 , enhancing the activity and selectivity for GVL production. Urea-assisted Co dispersion coupled with variation of pyrolysis temperature, Co nanoclusters were formed via direct conversion of Co-containing zinc trimesic acid fibers. The resulting Co nanoclusters possess dual active sites of Co-N x and metallic Co, with adjustable electronic structures. Under the reaction conditions of 200 °C and 4.5 MPa H 2 for 4 h, LA was completely converted, achieving 95 % yield of GVL. The outstanding catalytic activity is attributed to the Co-N x and metallic Co active sites, which facilitate the activation of CO and H 2 , respectively. In conclusion, this research provides a new concept for converting N-free metal-organic frameworks into non-precious metal nanoclusters, offering valuable insights for designing high-performance non-precious metal catalysts for biomass-derived chemical and fuel production.

Co nanoclusters↗
DOE OSTI2022

Methods and system for siting advanced nuclear reactors and evaluating energy policy concerns

There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

An Ab initio based OH initiated oxidation kinetics of glycerol carbonate: A promising biofuel component

The global energy demand is steadily increasing because of the population explosion and economic growth. Fossil fuels supply around 85 % of global primary energy demand. On one hand, fulfilling the increasing energy demands is a big challenge for the next few decades. On the other hand, the continued burning of fossil fuels leads to higher CO2 emissions, severely impacting global warming. Therefore, the policymakers vow to shift from conventional fuels to renewable resources for economic, environmental, and future energy security reasons. In this context, biofuels from lignocellulosic biomass and/or carbon-neutral fuels produced in the sustainable carbon cycle can close the carbon cycle and reach net zero-carbon emission. Recently, glycerol carbonate has been proposed as a promising fuel or fuel additive for future sustainability. Therefore, we investigated the hydrogen abstraction reactions of glycerol carbonate (GC) by OH radicals using high-level ab initio and variational transition state theory calculations. We mapped out the potential energy surface using the CCSD(T)/cc-pV(D, T)Z//MP2/cc-pVTZ level of theory. Here we used the ab initio parameters to obtain the site-specific rate coefficients by employing the variational transition state theory. We observed that every hydrogen atom in GC displays a unique reactivity with OH radicals. We derived branching ratio of each channel that are difficult to access experimentally. The overall rate coefficients exhibit a strong non-Arrhenius behaviour, which can be represented as: $k^{CVT/SCT}_{ov}$ (T) = 3.39 x 10 -20 x T 2.659 x e$\frac{-750.0 Jmol^{-1}}{RT}$ $\frac{cm^{3}}{molecule s}$ This is the first reported rate data for the glycerol carbonate and OH radicals reaction.

09 BIOMASS FUELS↗
DOE OSTI2026

Control co-design under uncertainty for offshore wind farms: Optimizing grid integration, energy storage, and market participation

Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. Here, this paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2% and provide flexibility in managing wind resource uncertainties.

Control Co-design↗
DOE OSTI2025

Dynamic Life Cycle Assessment for Evaluating the Global Warming Potential of Geothermal Energy Production Using Inactive Oil and Gas Wells for District Heating in Tuttle, Oklahoma

Repurposing abandoned oil and gas infrastructure for geothermal energy production has great potential to reduce greenhouse gas (GHG) emissions. This study quantified the life cycle global warming potential of geothermal energy production using four inactive oil and gas wells repurposed for district heating in Tuttle, Oklahoma. A cradle-to-grave prospective life cycle assessment was performed to compare GHG emissions between the geothermal district heating system and conventional natural gas-fired heating system from 2020 to 2050. For initial implementation of the geothermal system, we investigated two approaches: 1) repurposing abandoned infrastructure from a nearby oil and gas well site, and 2) production and injection well drillings including new construction of a central heat exchange station. Environmental impacts from the geothermal system were estimated for five scenarios where a natural gas peaking boiler is incorporated to supply peak heat demand. The prospective results indicated that cumulative reduction in GHG emissions from transitioning to the geothermal district heating system increase over time as a function of future renewable resource penetration and technological advancements within electricity, fuel, and steel production. Over 30 years, the global warming potential associated with the district heating demand will have been reduced by up to 24 % with the repurposed system. These results imply that repurposing existing oil and gas infrastructure for geothermal energy systems of district heating will bring future climate benefits.

abandoned oil and gas wells↗
DOE OSTI2023

Diffusion in intact secondary cell wall models of plants at different equilibrium moisture content

Secondary plant cell walls are composed of carbohydrate and lignin polymers, and collectively represent a significant renewable resource. Leveraging these resources depends in part on a mechanistic understanding for diffusive processes within plant cell walls. Common wood protection treatments and biomass conversion processes to create biorefinery feedstocks feature ion or solvent diffusion within the cell wall. X-ray fluorescence microscopy experiments have determined that ionic diffusion rates are dependent on cell wall hydration as well as the ionic species through non-linear relationships. In this work, we use classical molecular dynamics simulations to map the diffusion behavior of different plant cell wall components (cellulose, hemicellulose, lignin), ions (Na + , K + , Cu2 + , Cl - ) and water within a model for an intact plant cell wall at various hydration states (3-30 wt% water). From these simulations, we analyze the contacts between different plant cell wall components with each other and their interaction with the ions. Generally, diffusion increases with increasing hydration, with lignin and hemicellulose components increasing diffusion by an order of magnitude over the tested hydration range. Ion diffusion depends on charge. Positively charged cations preferentially interact with hemicellulose components, which include negatively charged carboxylates. As a result, positive ions diffuse more slowly than negatively charged ions. Measured diffusion coefficients are largely observed to best fit piecewise linear trends, with an inflection point between 10 and 15% hydration. These observations shed light onto the molecular mechanisms for diffusive processes within secondary plant cell walls at atomic resolution.

09 BIOMASS FUELS↗
DOE OSTI2020

Metabolic engineering of β-oxidation to leverage thioesterases for production of 2-heptanone, 2-nonanone and 2-undecanone

Medium-chain length methyl ketones are potential blending fuels due to their cetane numbers and low melting temperatures. Biomanufacturing offers the potential to produce these molecules from renewable resources such as lignocellulosic biomass. Here, we designed and tested metabolic pathways in Escherichia coli to specifically produce 2-heptanone, 2-nonanone and 2-undecanone. We achieved substantial production of each ketone by introducing chain-length specific acyl-ACP thioesterases, blocking the β-oxidation cycle at an advantageous reaction, and introducing active β-ketoacyl-CoA thioesterases. Using a bioprospecting approach, we identified fifteen homologs of E. coli β-ketoacyl-CoA thioesterase (FadM) and evaluated the in vivo activity of each against various chain length substrates. The FadM variant from Providencia sneebia produced the most 2-heptanone, 2-nonanone, and 2-undecanone, suggesting it has the highest activity on the corresponding β-ketoacyl-CoA substrates. We further tested enzyme variants, including acyl-CoA oxidases, thiolases, and bi-functional 3-hydroxyacyl-CoA dehydratases to maximize conversion of fatty acids to β-keto acyl-CoAs for 2-heptanone, 2-nonanone, and 2-undecanone production. In order to address the issue of product loss during fermentation, we applied a 20% (v/v) dodecane layer in the bioreactor and built an external water cooling condenser connecting to the bioreactor heat-transferring condenser coupling to the condenser. Finally, using these modifications, we were able to generate up to 4.4 g/L total medium-chain length methyl ketones.

2-Heptanone↗
DOE OSTI2021

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with up 85 compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Altogether, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

59 BASIC BIOLOGICAL SCIENCES↗