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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Visual Modeling for Complex System Valuation: Implementation Guidance

Within the Transactive Systems Program (TSP) at Pacific Northwest National Laboratory (PNNL) the need to incorporate a valuation analysis design early within the research process of transactive energy systems led to development of a valuation methodology. This methodology allows the modeling of economic exchanges within a complex system and supports the evaluation of individual stakeholder economic outcomes in addition to systemwide costs and benefits. The use of visual modeling practices enables the research team to reach common understanding and agreement on the analysis design within the complex system. While this methodology was developed for the valuation of transactive energy systems, it can be applied to any complex system where a granular economic analysis is desired. It allows for the inclusion of equity analyses and ties individual activities and microeconomic outcomes with the systemwide macroeconomic impacts. This document serves as implementation guidance for analysts planning to deploy the methodology within a research study. The appendixes provide specific guidance on how this methodology is deployed within the TSP at PNNL for analysts seeking guidance for deployment within that context.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Coming to Grips with Ambiguity: Ion Mobility-Mass Spectrometry for Protein Quaternary Structure Assignment

Multiprotein complexes are central to our understanding of cellular biology, as they play critical roles in nearly every biological process. Despite many impressive advances associated with structural characterization techniques, large and highly-dynamic protein complexes are too often refractory to analysis by conventional, high-resolution approaches. To fill this gap, ion mobility-mass spectrometry (IM-MS) methods have emerged as a promising approach for characterizing the structures of challenging assemblies due in large part to the ability of these methods to characterize the composition, connectivity, and topology of large, labile complexes. In this Critical Insight, we present a series of bioinformatics studies aimed at assessing the information content of IM-MS datasets for building models of multiprotein structure. Our computational data highlights the limits of current coarse-graining approaches, and compelled us to develop an improved workflow for multiprotein topology modeling, which we benchmark against a subset of the multiprotein complexes within the PDB. This improved workflow has allowed us to ascertain both the minimal experimental restraint sets required for generation of high-confidence multiprotein topologies, and quantify the ambiguity in models where insufficient IM-MS information is available. We conclude by projecting the future of IM-MS in the context of protein quaternary structure assignment, where we predict that a more complete knowledge of the ultimate information content and ambiguity within such models will undoubtedly lead to applications for a broader array of challenging biomolecular assemblies. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Two-Coordinate Coinage Metal Complexes as Solar Photosensitizers

Generating a sustainable fuel from sunlight plays an important role in meeting the energy demands of the modern age. Herein we report two-coordinate carbene-metal-amide (cMa, M = Cu(I) and Au(I)) complexes can be used as sensitizers to promote the light driven reduction of water to hydrogen. The cMa complexes studied here absorb visible photons (ε vis > 10 3 M -1 cm -1 ), maintain long excited state lifetimes (τ ~ 0.2-1 μs) and perform stable photo-induced charge transfer to a target substrate with high photoreducing potential (E +/* up to 2.33 V vs. Fc +/o based on a Rehm-Weller analysis). We pair these coinage metal complexes with a cobalt-glyoxime electrocatalyst to photocatalytically generate hydrogen and compare the performance of the copper- and gold-based cMa complexes. We also find that these two-coordinate complexes presented can perform photo-driven hydrogen production from water without the addition of the cobalt-glyoxime electrocatalyst. In this “catalyst free” system the cMa sensitizer partially decomposes to give metal nanoparticles that catalyze water reduction. Furthermore, this work identifies two-coordinate coinage metal complexes as promising abundant metal, solar fuels photosensitizers that offer exceptional tunability and photoredox properties.

14 SOLAR ENERGY↗
DOE OSTI2021

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling

Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Frontiers in divalent $f$-block chemistry

Divalent f-block chemistry has undergone rapid expansion in recent years, driven by advances in the stabilization of low-valent lanthanide and actinide complexes. Although f-elements were historically accessed in the +3 or higher oxidation states, the isolation of +2 species has revealed unusual spectroscopic signatures, distinct bonding motifs, and multiple accessible electronic configurations that influence their chemical and physical properties. These developments have generated opportunities in areas including quantum information science, molecular magnetism, catalysis, and luminescence. Computational chemistry has played a pivotal role in interpreting the electronic structure and reactivity of these systems. However, accurately modeling divalent f-block complexes remains challenging because of strong electronic correlation, multiconfigurational character, and the presence of close-in-energy competing electronic states. As experimental capabilities and theoretical methodologies continue to advance, a comprehensive assessment of divalent chemistry is both timely and needed. In this Review, we integrate experimental and theoretical perspectives to provide a systematic analysis of divalent lanthanide and actinide complexes across the f-block series. We critically assess the role of ligands in determining competing ground-state configurations (f n d 1 vs. f n+1 ) resulting in their distinct spectroscopic, magnetic, and bonding properties. Finally, we discuss emerging strategies for predictive modeling and rational design of low-valent f-block molecular systems, with potential applications ranging from dinitrogen reduction to quantum technologies.

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

Conformational communication mediates the reset step in t6A biosynthesis

Abstract The universally conserved N6-threonylcarbamoyladenosine (t6A) modification of tRNA is essential for translational fidelity. In bacteria, t6A biosynthesis starts with the TsaC/TsaC2-catalyzed synthesis of the intermediate threonylcarbamoyl adenylate (TC–AMP), followed by transfer of the threonylcarbamoyl (TC) moiety to adenine-37 of tRNA by the TC-transfer complex comprised of TsaB, TsaD and TsaE subunits and possessing an ATPase activity required for multi-turnover of the t6A cycle. We report a 2.5-Å crystal structure of the T. maritima TC-transfer complex (TmTsaB2D2E2) bound to Mg2+-ATP in the ATPase site, and substrate analog carboxy-AMP in the TC-transfer site. Site directed mutagenesis results show that residues in the conserved Switch I and Switch II motifs of TsaE mediate the ATP hydrolysis-driven reactivation/reset step of the t6A cycle. Further, SAXS analysis of the TmTsaB2D2-tRNA complex in solution reveals bound tRNA lodged in the TsaE binding cavity, confirming our previous biochemical data. Based on the crystal structure and molecular docking of TC–AMP and adenine-37 in the TC-transfer site, we propose a model for the mechanism of TC transfer by this universal biosynthetic system.

Luthra, Amit↗
DOE OSTI2023

Machine-learning-assisted automation of single-crystal neutron diffraction

Neutron scattering is a powerful but expensive technique to study materials and discover new matter. Advanced detector technology has significantly improved the efficiency of neutron experiments, increasing the complexity of neutron data reduction and analysis. Machine learning (ML) brings new directions for neutron diffraction data reduction and experiment operation. Here, this work presents an ML-assisted data reduction and analysis method for precise recognition of Bragg peaks and the corresponding regions of interest; it can then automatically screen and align a measured crystal using the recognized peaks, and subsequently plan and optimize the data collection with user-provided information and uncertainty quantification values of detected peaks. This method shows robust performance in different complex sample environments and enables automated single-crystal neutron diffraction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Understanding Complex Magnetic Spin Textures with Simulation-Assisted Lorentz Transmission Electron Microscopy

There is an increased interest in topologically nontrivial magnetic spin textures such as skyrmions and chiral domain-wall solitons, both from a point of fundamental physics understanding as well as potential technological interest in low-power memory applications. In order to control their behavior, it is necessary to understand their complex spin texture at the nanoscale. Lorentz transmission electron microscopy (LTEM) is a suitable technique for studying these systems due to its high spatial resolution and capability to simultaneously characterize magnetic texture and microstructure. In this work, we present the application of PyLorentz, an open-source software suite that we have developed, for quantitative image analysis of Neel-type skyrmions in thin-film heterostructures. PyLorentz enhances LTEM capabilities by enabling reconstruction of magnetic induction maps from experimental images, as well as simulating LTEM images using micromagnetic simulation data. We demonstrate this for simulated Neel skyrmions as well as experimental data from [Pt/Co/W] multilayer heterostructures. Finally, we also show how simulation-assisted LTEM analysis is crucial for understanding these complex magnetic spin textures, in which the reconstructed magnetic induction map (seen in the LTEM images) differs significantly from the magnetization configuration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

BlueCRAB Domain Overlapping Coupling: Theory & Verification

Coupling low- and high-fidelity codes is a useful way to model complex engineering systems. Thanks to the high-fidelity code, complex phenomena can be resolved in areas of the system where this is required, yet the efficiency of the lower-fidelity code is still retained in modeling the rest of the system. This document details the theory and implementation of coupling two different thermal-hydraulic codes: the system thermal-hydraulics (STH) code System Analysis Module (SAM) and the coarse-mesh computational fluid dynamics (CFD) code Pronghorn. Both applications are included in the Comprehensive Reactor Analysis Bundle (BlueCRAB) code suite and are based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. The domain overlapping (DO) coupling approach was adopted, as it offers proven advantages over more conventional domain decomposition methods. In the DO coupling, SAM provides Pronghorn with boundary conditions that depend on the system-level simulation of the entire plant. In return, the overlapping coupled SAM components–termed “surrogate components”–receive friction factors and source terms computed online based on the Pronghorn simulation. The framework is designed to be generic and enable coupling regardless of geometry and the number of inlet/outlet boundaries in the DO coupled domain. The developed method leads to consistent pressure drops, enthalpies, and scalar concentrations when comparing the coupled SAM and Pronghorn simulations. This document presents the DO coupling approach, along with two verification and two demonstration cases. The proposed problems explore different physical aspects relevant to nuclear reactor analysis, including buoyancy-driven flows, complex flow patterns, and multiple inlets and outlets. Periodically, new versions of this “BlueCRAB Domain Overlapping Coupling: Theory & Verification” report will be issued to reflect future developments and verification tests.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Understanding nanoscale structural distortions in Pb(Zr 0.2 Ti 0.8 )O 3 by utilizing X-ray nanodiffraction and clustering algorithm analysis

Hard X-ray nanodiffraction provides a unique nondestructive technique to quantify local strain and structural inhomogeneities at nanometer length scales. However, sample mosaicity and phase separation can result in a complex diffraction pattern that can make it challenging to quantify nanoscale structural distortions. In this work, a k-means clustering algorithm was utilized to identify local maxima of intensity by partitioning diffraction data in a three-dimensional feature space of detector coordinates and intensity. This technique has been applied to X-ray nanodiffraction measurements of a patterned ferroelectric PbZr 0.2 Ti 0.8 O 3 sample. The analysis reveals the presence of two phases in the sample with different lattice parameters. A highly heterogeneous distribution of lattice parameters with a variation of 0.02 Å was also observed within one ferroelectric domain. This approach provides a nanoscale survey of subtle structural distortions as well as phase separation in ferroelectric domains in a patterned sample.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Analysis of the Wtb vertex from the measurement of triple-differential angular decay rates of single top quarks produced in the t -channel at $ \sqrt{s}=8 $ TeV with the ATLAS detector

The electroweak production and subsequent decay of single top quarks in the t-channel is determined by the properties of the Wtb vertex, which can be described by the complex parameters of an effective Lagrangian. We use an analysis of a triple-differential decay rate in t-channel production to simultaneously determine five generalised helicity fractions and phases, as well as the polarisation of the produced top quark. The complex parameters are then constrained. This analysis is based on 20.2 fb -1 of proton-proton collision data at a centre-of-mass energy of 8 TeV collected with the ATLAS detector at the LHC. The fraction of decays containing transversely polarised W bosons is measured to be f 1 = 0.30 ± 0.05. The phase between amplitudes for transversely and longitudinally polarised W bosons recoiling against left-handed b-quarks is measured to be δ - = 0.002π + 0.017π + 0.016π , giving no indication of CP violation. The fractions of longitudinal or transverse W bosons accompanied by right-handed b-quarks are also constrained. Based on these measurements, limits are placed at 95% CL on the ratio of the complex coupling parameters Re [g R /V L ϵ [-0.12, 0.17] and Im [g R /V L ϵ [-0.07, 0.06]. Constraints are also placed on the ratios |V R /V L | and |g L /V L |. Additionally, the polarisation of single top quarks in the t-channel is constrained to be P > 0.72 (95% CL). None of the above measurements make assumptions about the value of any of the other parameters or couplings and all of them are in agreement with the Standard Model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Fractal dimension analysis reveals skeletal muscle disorganization in mdx mice

Duchenne Muscular Dystrophy (DMD) is characterized by muscle extracellular matrix disorganization due to the increased collagen deposition leading to fibrosis that significantly exacerbates disease progression. Fractal dimension analysis is a method that quantifies tissue/cellular disorganization and characterizes complex structures. The first objective of the present study was use fractal analysis to evaluate extracellular matrix disorganization in mdx mice soleus muscle. Next, we mimic a hyper-proliferation of fibrogenic cells by co-culturing NIH3T3 fibroblasts and C2C12 myoblasts to test whether fibroblasts induce disorganization in myoblast arrangement. Here, we show mdx presented high skeletal muscle disorganization as revealed by fractal analysis. Similarly, this method revealed that myoblasts co-cultured with fibroblast also presented cellular arrangement disorganization. We also reanalyzed skeletal muscle microarrays transcriptomic data from mdx and DMD patients that revealed transcripts related to extracellular matrix organization. This analysis also identified Osteoglycin, which was validated as a potential regulator of ECM organization in mdx dystrophic muscles. Our results demonstrate that fractal dimension is useful tool for the analysis of skeletal muscle disorganization in DMD and also reveal a fibroblast-myoblast cross-talk that contributes to “in vitro” myoblast disarrangement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Rational Design of Orthogonal Multipolar Interactions with Fluorine in Protein–Ligand Complexes

Multipolar interactions involving fluorine and the protein backbone have been frequently observed in protein–ligand complexes. Such fluorine–backbone interactions may substantially contribute to the high affinity of small molecule inhibitors. Here we found that introduction of trifluoromethyl groups into two different sites in the thienopyrimidine class of menin–MLL inhibitors considerably improved their inhibitory activity. In both cases, trifluoromethyl groups are engaged in short interactions with the backbone of menin. In order to understand the effect of fluorine, we synthesized a series of analogues by systematically changing the number of fluorine atoms, and we determined high-resolution crystal structures of the complexes with menin. Here, we found that introduction of fluorine at favorable geometry for interactions with backbone carbonyls may improve the activity of menin–MLL inhibitors as much as 5- to 10-fold. In order to facilitate the design of multipolar fluorine–backbone interactions in protein–ligand complexes, we developed a computational algorithm named FMAP, which calculates fluorophilic sites in proximity to the protein backbone. We demonstrated that FMAP could be used to rationalize improvement in the activity of known protein inhibitors upon introduction of fluorine. Furthermore, FMAP may also represent a valuable tool for designing new fluorine substitutions and support ligand optimization in drug discovery projects. Analysis of the menin–MLL inhibitor complexes revealed that the backbone in secondary structures is particularly accessible to the interactions with fluorine. Lastly, considering that secondary structure elements are frequently exposed at protein interfaces, we postulate that multipolar fluorine–backbone interactions may represent a particularly attractive approach to improve inhibitors of protein–protein interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exploring Classification of Topological Priors With Machine Learning for Feature Extraction

In many scientific endeavors, increasingly abstract representations of data allow for new interpretive methodologies and conceptualization of phenomena. For example, moving from raw imaged pixels to segmented and reconstructed objects allows researchers new insights and means to direct their studies toward relevant areas. Thus, the development of new and improved methods for segmentation remains an active area of research. With advances in machine learning and neural networks, scientists have been focused on employing deep neural networks such as U-Net to obtain pixel-level segmentations, namely, defining associations between pixels and corresponding/referent objects and gathering those objects afterward. Topological analysis, such as the use of the Morse-Smale complex to encode regions of uniform gradient flow behavior, offers an alternative approach: first, create geometric priors, and then apply machine learning to classify. This approach is empirically motivated since phenomena of interest often appear as subsets of topological priors in many applications. Using topological elements not only reduces the learning space but also introduces the ability to use learnable geometries and connectivity to aid the classification of the segmentation target. Here, in this article, we describe an approach to creating learnable topological elements, explore the application of ML techniques to classification tasks in a number of areas, and demonstrate this approach as a viable alternative to pixel-level classification, with similar accuracy, improved execution time, and requiring marginal training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Continuous Fluorescence Assays for Reactions Involving Adenine

5'-Methylthioadenosine phosphorylase (MTAP) and 5'-methylthioadenosine nucleosidase (MTAN) catalyze the phosphorolysis and hydrolysis of 5'-methylthioadenosine (MTA), respectively. Both enzymes have low KM values for their substrates. Kinetic assays for these enzymes are challenging, as the ultraviolet absorbance spectra for reactant MTA and product adenine are similar. We report a new assay using 2-amino-5'-methylthioadenosine (2AMTA) as an alternative substrate for MTAP and MTAN enzymes. Hydrolysis or phosphorolysis of 2AMTA forms 2,6-diaminopurine, a fluorescent and easily quantitated product. We kinetically characterize 2AMTA with human MTAP, bacterial MTANs and use 2,6-diaminopurine as a fluorescent substrate for yeast adenine phosphoribosyltransferase. 2AMTA was used as the substrate to kinetically characterize the dissociation constants for three-transition-state analogue inhibitors of MTAP and MTAN. Kinetic values obtained from continuous fluorescent assays with MTA were in good agreement with previously measured literature values, but gave smaller experimental errors. Chemical synthesis from ribose and 2,6-dichloropurine provided crystalline 2AMTA as the oxalate salt. Chemo-enzymatic synthesis from ribose and 2,6-diaminopurine produced 2-amino-S-adenosylmethionine for hydrolytic conversion to 2AMTA. Interaction of 2AMTA with human MTAP was also characterized by pre-steady-state kinetics and by analysis of the crystal structure in a complex with sulfate as a catalytically inert analogue of phosphate. Furthermore, this assay is suitable for inhibitor screening by detection of fluorescent product, for quantitative analysis of hits by rapid and accurate measurement of inhibition constants in continuous assays, and pre-steady-state kinetic analysis of the target enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Mechanism of Ti-Catalyzed Oxidative Nitrene Transfer in [2+2+1] Pyrrole Synthesis from Alkynes and Azobenzene

A combined computational and experimental study on the mechanism of Ti-catalyzed formal [2 + 2 + 1] pyrrole synthesis from alkynes and aryl diazenes is reported. This reaction proceeds through a formally TiII/TiIV redox catalytic cycle as determined by natural bond orbital (NBO) and intrinsic bond orbital (IBO) analysis. Kinetic analysis of the reaction of internal alkynes with azobenzene reveals a complex equilibrium involving Ti=NPh monomer/dimer equilibrium and Ti=NPh + alkyne [2 + 2] cycloaddition equilibrium along with azobenzene and pyridine inhibition equilibria prior to rate-determining second alkyne insertion. Computations support this kinetic analysis, provide insights into the structure of the active species in catalysis and the roles of solvent, and provide a new mechanism for regeneration of the Ti imido catalyst via disproportionation. Reductive elimination from a 6-membered azatitanacyclohexadiene species to generate pyrrole-bound Ti π is surprisingly facile and occurs through a unique electrocyclic reductive elimination pathway similar to a Nazarov cyclization. The resulting Ti π species are stabilized through backbonding into the $π*$ of the pyrrole framework, although solvent effects also significantly stabilize free Ti π species that are required for pyrrole loss and catalytic turnover. Further computational and kinetic analysis reveals that in complex reactions with unysmmetric alkynes the resulting pyrrole regioselectivity is driven primarily by steric effects for terminal alkynes and inductive effects for internal alkynes.

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

A novel framework for hosting capacity analysis with spatio-temporal probabilistic voltage sensitivity analysis

Smart grids are envisioned to accommodate high penetration of distributed photovoltaic (PV) generation, which may cause adverse grid impacts in terms of voltage violations. Therefore, PV Hosting capacity is being used as a planning tool to determine the maximum PV installation capacity that causes the first voltage violation and above which would require infrastructure upgrades. Additionally, traditional methods of Hosting capacity analysis are scenario based and computationally complex as they rely on iterative load flow algorithms that require investigating a large number of scenarios for accurate assessment of PV impacts. Therefore, this paper presents a computationally efficient analytical approach to compute the probability distribution of voltage change due to random behavior of randomly located multiple distributed PVs. The proposed approach is based on Spatio-temporal probabilistic voltage sensitivity analysis that exploits both spatial and temporal uncertainties associated with PV injections. Thereafter, the derived distribution is used to quantify voltage violations for various PV penetration levels and subsequently determine the hosting capacity of the system without the need to examine large number of scenarios. Results of the proposed framework are validated via conventional load flow based simulation approach on the IEEE 37 and IEEE 123 node test systems.

42 ENGINEERING↗
DOE OSTI2022

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

36 MATERIALS SCIENCE↗