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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Drift kinetic electrostatic simulations of the edge localized mode heat pulse

In the present work, electrostatic drift kinetic simulations of parallel plasma transport within the tokamak scrape-off layer (SOL) are conducted using the COGENT code. The SOL configuration is represented in one-dimensional slab geometry, incorporating a heat source localized in the midplane. The heat source parameters correspond to those characterizing edge-localized modes observed in the Joint European Torus (JET) tokamak. The numerical model includes kinetic treatment of both ions and electrons, a simplified model for the gyrokinetic Poisson equation that allows one to step over short time scales associated with fast electrostatic shear Alfvèn waves, and the logical sheath boundary condition (LSBC) that enforces global system quasineutrality. A third-order accurate LSBC is derived to be consistent with the third-order accurate upwind advection scheme utilized in the code, and it was shown to noticeably impact the simulation results, especially parallel heat flux at the target plate. The findings of this study are in agreement with results from preceding fluid and kinetic simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Recovery of Scandium from Bauxite Residue Using Sulfation Roasting-Assisted Water Leaching Process

Here, this study presents a method for recovering scandium from Indonesian bauxite residue (red mud) through a sulfation roasting-assisted water leaching process. Ammonium sulfate, (NH 4 ) 2 SO 4 , was chosen as the sulfate source for its lower melting point, less corrosivity, decreased toxicity, and the potential for ammonia recovery. A modified mixing system was introduced to improve the distribution of (NH 4 ) 2 SO 4 on red mud particles by combining them in water. A series of experiments were conducted to optimize the sulfation roasting process by examining the effects of the (NH 4 ) 2 SO 4 /red mud ratio, temperature, and roasting time. Subsequently, water leaching was optimized while considering the effect of leaching time and liquid-to-solid (L/S) ratio. The leaching of scandium from the roasted red mud showed rapid kinetics, even when using only water. Under the roasting conditions of 1/1 (g/g) (NH 4 ) 2 SO 4 /red mud ratio, 700°C, 60 min roasting time, and the leaching conditions of 65°C, 60 min, and 10 mL/g (L/S), over 90% of scandium was recovered, along with 64.4% Al leaching and below 8% Fe leaching. These results were further corroborated through comprehensive analyses such as x-ray diffraction (XRD) and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). This study offers insights into an alternative, more environmentally friendly method for Sc recovery from red mud, promoting the sustainable use of hazardous waste like red mud.

ammonium sulfate↗
DOE OSTI2025

Preparation and electrochemical properties of high-entropy oxide Li x (CrMnCoNiZn) 3-x O 4

High-entropy oxides (HEOs) have gained significant attention as anode materials for lithium-ion batteries (LIBs) due to their high theoretical specific capacity, synergistic effects of constituent elements, and enhanced structural stability induced by high entropy. Here, this study investigates the electrochemical properties of a novel series of HEOs, Li x (CrMnCoNiZn) 3-x O 4 , synthesized via the sol-gel method. As lithium content increases, the electrochemical lithium storage performance of the HEOs improves, attributed to the rise in high-valence states and oxygen vacancies. The high valence state of elements facilitates greater electron transfer, enhancing the specific capacity during lithium insertion. Additionally, a higher concentration of oxygen vacancies boosts ionic conductivity and lithium ion diffusion kinetics, leading to superior electrochemical performance. Among the synthesized materials, Li 0.11 (CrMnCoNiZn) 2.89 O 4 exhibited the best performance, delivering a discharge capacity of 365 mAh·g −1 after 100 cycles at 100 mA·g −1 , 257 mAh·g −1 after 1000 cycles at 1 A·g −1 , and a rate capacity of 196 mAh·g −1 at 2 A·g −1 . The Li + diffusion coefficient reached 2.78 × 10 −15 cm 2 ·s −1 . This work not only deepens the understanding of HEOs' electrochemical behavior in LIBs but also provides insights into the impact of lithium ion substitution on their performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Universal salt-assisted assembly of MXene from suspension on polymer substrates

Abstract Two-dimensional carbides and nitrides, known as MXenes, are promising for water-processable coatings due to their excellent electrical, thermal, and optical properties. However, depositing hydrophilic MXene nanosheets onto inert or hydrophobic polymer surfaces requires plasma treatment or chemical modification. This study demonstrates a universal salt-assisted assembly method that produces ultra-thin, uniform MXene coatings with exceptional mechanical stability and washability on various polymers, including high-performance polymers for extreme temperatures. The salt in the Ti 3 C 2 T x colloidal suspension reduces surface charges, enabling electrostatically hydrophobized MXene deposition on polymers. A library of salts was used to optimize assembly kinetics and coating morphology. A 170 nm MXene coating can reduce radiation temperature by ~200 °C on a 300 °C PEEK substrate, while the coating on Kevlar fabric provides comfort in extreme conditions, including outer space and polar regions.

Science & Technology - Other Topics↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Oxidative Dehydrogenation of N ‐Heteroaromatic Alkyl Alcohols and Amines Facilitated by Dearomative Tautomerization

The oxidation of alcohols, amines, and halides is a fundamental transformation in organic chemistry with significant applications in the synthesis of fine chemicals, pharmaceuticals, and natural products. Here we show that a broad variety of N-heteroarenes bearing hydroxymethyl, aminomethyl, or halomethyl groups are oxidatively dehydrogenated to their respective aldehydes by simply heating them in acidic or basic aqueous solution under ambient atmosphere. The quantitative oxidation of 9-acridinemethanol to 9-acridinecarboxaldehyde serves as an illustrative example, proceeding to completion within 3 hours in refluxing 5% aqueous acetic acid or even household vinegar. Quinoline derivatives may be similarly oxidized but require higher temperatures and longer reaction times, while indole derivatives are oxidized under basic conditions. Based on comprehensive regioselectivity screens, internal kinetic isotope competition, and density functional theory (DFT) calculations, we propose a mechanism in which migration of a methylene hydrogen to the pyridinic nitrogen by acid-catalyzed dearomative tautomerization yields an unstable enol or enamine intermediate that then irreversibly loses two hydrogen atoms to atmospheric oxygen. In addition to the simplicity and environmentally benign nature of our method, we observe no indication of any over-oxidation to carboxylic acids. Finally, we demonstrate the synthetic utility of this reaction through two different one-pot formylations of acridine.

Chemistry↗
DOE OSTI2024

Multiscale Modeling of Nanoparticle Precipitation in Oxide Dispersion-Strengthened Steels Produced by Laser Powder Bed Fusion

Laser Powder Bed Fusion (LPBF) enables the efficient production of near-net-shape oxide dispersion-strengthened (ODS) alloys, which possess superior mechanical properties due to oxide nanoparticles (e.g., yttrium oxide, Y-O, and yttrium-titanium oxide, Y-Ti-O) embedded in the alloy matrix. To better understand the precipitation mechanisms of the oxide nanoparticles and predict their size distribution under LPBF conditions, we developed an innovative physics-based multiscale modeling strategy that incorporates multiple computational approaches. These include a finite volume method model (Flow3D) to analyze the temperature field and cooling rate of the melt pool during the LPBF process, a density functional theory model to calculate the binding energy of Y-O particles and the temperature-dependent diffusivities of Y and O in molten 316L stainless steel (SS), and a cluster dynamics model to evaluate the kinetic evolution and size distribution of Y-O nanoparticles in as-fabricated 316L SS ODS alloys. The model-predicted particle sizes exhibit good agreement with experimental measurements across various LPBF process parameters, i.e., laser power (110–220 W) and scanning speed (150–900 mm/s), demonstrating the reliability and predictive power of the modeling approach. The multiscale approach can be used to guide the future design of experimental process parameters to control oxide nanoparticle characteristics in LPBF-manufactured ODS alloys. Additionally, our approach introduces a novel strategy for understanding and modeling the thermodynamics and kinetics of precipitation in high-temperature systems, particularly molten alloys.

Wang, Zhengming (ORCID:0000000241627112)↗
DOE OSTI2024

Active site remodeling in tumor-relevant IDH1 mutants drives distinct kinetic features and potential resistance mechanisms

Abstract Mutations in human isocitrate dehydrogenase 1 (IDH1) drive tumor formation in a variety of cancers by replacing its conventional activity with a neomorphic activity that generates an oncometabolite. Little is understood of the mechanistic differences among tumor-driving IDH1 mutants. We previously reported that the R132Q mutant unusually preserves conventional activity while catalyzing robust oncometabolite production, allowing an opportunity to compare these reaction mechanisms within a single active site. Here, we employ static and dynamic structural methods and observe that, compared to R132H, the R132Q active site adopts a conformation primed for catalysis with optimized substrate binding and hydride transfer to drive improved conventional and neomorphic activity over R132H. This active site remodeling reveals a possible mechanism of resistance to selective mutant IDH1 therapeutic inhibitors. This work enhances our understanding of fundamental IDH1 mechanisms while pinpointing regions for improving inhibitor selectivity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Integrated CO 2 Capture and Conversion to Formate with a Molecular Platinum Bis(diphosphine) Electrocatalyst

Carbon dioxide is a potentially valuable feedstock for carbon-based fuels or commodities but is only available in dilute streams. Many studies have focused on either the capture and concentration of CO 2 or the reduction of pure CO 2 streams. The direct reduction of sorbent-captured CO 2 in an integrated process would skip the energy-intensive CO 2 concentration and sorbent regeneration step. Herein, we report the electrocatalytic reduction of 1,3-bis(2,6-diisopropylphenyl)imidazolium-2-carboxylate (IPr·CO 2 ), which forms quantitatively from the reaction of sorbent 1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene (IPr) with 10% and 0.04% CO 2 streams, by catalyst [Pt(dmpe) 2 ](PF 6 ) 2 (dmpe = 1,2-bis(dimethylphosphino)ethane) to formate with >70% Faradaic efficiencies. Unexpectedly, experimental studies indicate that the proton source phenol facilitates rapid decarboxylation of IPr·CO 2 to release CO 2 , which is the substrate for reduction. Kinetic studies determined the rate of hydride transfer from a catalytic intermediate [HPt(dmpe) 2 ](PF 6 ) to form the C–H bond in formate to be 0.22 M –1 s –1 . Further details on the mechanism, transition state energy, and structure for hydride transfer to CO 2 , a common step in CO 2 reduction, were explored using computational methods.

Chemistry↗
DOE OSTI2024

Mapping the Microstructure During the Historic U-Mo Monolithic Fuel Foil Fabrication Process

Since 2004, there has been extensive effort towards the development of a uranium molybdenum monolithic fuel system to convert high performance research and test reactors. The RERTR-6 experiment was the first to attempt a monolithic fuel instead of a dispersed fuel form. The fabrication methods evolved overtime and provided the basis for current fabrication methods. The various steps in the process inevitably tailor the fuel alloy microstructure which is known to influence irradiation behavior. This document aims to present and discuss the fabrication evolution that transpired through a recounting of historical data from the various fabrication campaigns. By overlaying this data with basic science studies on the U-Mo that explored transformation kinetics, it is possible to estimate a measure of the impact heat treatments have on the final as-fabricated microstructure.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Theoretical studies of chemical reactions related to the formation and growth of polycyclic aromatic hydrocarbons (PAH) and molecular properties of their key intermediates (Final Progress Report)

The formation mechanisms of polycyclic aromatic hydrocarbons, (PAHs) – organic molecules carrying fused benzene rings – are of great interest to scientists and engineers due to their importance in combustion chemistry and astrochemistry. On Earth, PAHs are largely produced in incomplete combustion of fossil fuel and are considered as critical precursors to unwanted soot particles leading to combustion inefficiency and causing air pollution along with detrimental health effects. Simple PAH molecules initially formed in the gas phase, are further involved in a build-up process in combustion flames leading to larger PAH, bowl-shaped nanostructures, fullerenes, and solid-phase species including carbonaceous dust, graphene particles, and soot. In deep space, PAH and their derivatives are potential key intermediates and nucleation sites leading eventually to carbonaceous nanoparticles (“interstellar grains”). Therefore, the understanding of the key processes in the synthesis of PAHs along with their precursors and their degradation mechanisms in combustion systems and in interstellar, circumstellar, and planetary atmospheric environments will provide critical insights into how complex aromatic structures, carbonaceous nanoparticles, and fullerenes are formed and destroyed. Achieving this understanding is an important step in the development of the efficient combustion processes and of the ecofriendly devices with reduced environmental pollution as well as technological strategies for the production of hydrogen and solid carbon through thermal or plasma-assisted pyrolysis of natural gas and biomass. Also, the understanding of the key processes of PAH and soot growth will help in our comprehension of chemical evolution in the universe. Detailed information on the mechanisms and reliable rate constants of the key elementary chemical reactions involved in PAH formation and destruction processes and in inception of soot particles is often missing, with the main deficiencies being the absence of temperature- and pressure-dependent rate constants for the broad range of conditions occurring in various terrestrial and interstellar processes and the lack of data on the reaction products and their branching ratios. Complementary to experimental studies, these gaps in knowledge can be filled by using quantum chemical calculations of reaction potential energy surfaces providing us with accurate energies of reaction products, intermediates, and transition states, revealing the reaction mechanism, and giving the molecular properties required to compute rate constants for relevant reaction steps and product branching ratios using the RRKM-Master Equation (ME) method. Molecular dynamics (MD) simulations can be used in cases when a reaction rate cannot be properly described by statistical theories. During the terminal renewal project period we employed these ab initio/RRKM-ME and MD approaches to complete our studies on several key reactions relevant to the formation/growth of PAH and inception of soot particles including (1) the reaction mechanism and kinetics of the resonance stabilized fulvenallenyl radical with propargyl and C 3 H 4 isomers; (2) the reaction mechanism and kinetics for the C + indene and C 2 + styrene reactions producing naphthyl or azulenyl radicals in low-temperature environments; (3) the MD study of non-equilibrium dimerization of acepyrene and coronene and its radical. The information derived from our theoretical calculations contributed to a better fundamental understanding of the reaction mechanisms and provide missing critical kinetic data to improve combustion models of hydrocarbon fuels and astrochemical models of the growth of carbonaceous molecules and particles in cold molecular clouds, circumstellar envelopes, and planetary atmospheres.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Kinetic Plasma Simulation Capabilities in the MOOSE Framework: Verification of Particle-Particle Collisions

High-fidelity simulations of complex plasma systems allow researchers to gain key insights into and understanding of these systems. To facilitate massively parallel high-fidelity plasma simulations, finite-element-based particle-in-cell capabilities are being developed within the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) based framework called Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER). While SALAMANDER’s primary objective is modeling edge plasmas and plasma-facing components in fusion devices, the particle-in-cell capabilities being developed are general and will support modeling low-temperature plasmas as well. Previously, collisionless magnetostatic simulation capabilities have been verified with the two-stream and Dorey-Guest-Harris instabilities, and single particle motion. Collisions were implemented using the direct simulation Monte Carlo method, and verification of this capability will be presented here several verification problems: relaxation of a randomly initialized gas to a Maxwellian distribution, Fourier heat flow, and comparison of reaction rates to both analytic calculations and those calculated using a multi-term Boltzmann solver.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Chemical screening by time-resolved X-ray scattering to discover allosteric probes

Abstract Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis ( k VR ), capture structure–activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF–aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF–aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Wake-Resolving Acoustic Tomography: Advances through Numerical Covariance Methods

Acoustic tomography offers path-integrated measurements of atmospheric velocity and temperature fluctuations with high spatial resolution. Classical implementations of time-dependent stochastic inversion rely on homogeneous, isotropic covariance models that are poorly suited to the anisotropic structure of wind turbine wakes. By directly estimating heterogeneous covariances from large-eddy simulations (LESs) into the time-dependent stochastic inversion operator, we relax implicit assumptions in the analytical models used historically. Retrievals using these LES-informed models improve agreement with true fields in variance, turbulent kinetic energy, and spectral content compared to analytical and precursor-based covariance models. The results indicate that LES-informed covariance models can enhance the accuracy of acoustic tomography retrievals in complex, anisotropic flows such as wind turbine wakes in some cases and highlight instances where analytical models still offer competitive performance, despite their simplifying assumptions.

17 WIND ENERGY↗
DOE OSTI2024

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CALPHAD-based ICME design of single-step aging to enhance mechanical strength of WAAM Haynes 282

To match the strength of wire-arc additive manufactured Haynes 282 to its wrought counterpart via a single-step aging heat treatment, the CALPHAD (Calculation of Phase Diagrams) method is integrated with physics-based process-structure-property models and experimental validation. The integrated computational materials engineering (ICME) framework simulates the effects of aging on γ′ and M 23 C 6 precipitation and the resulting yield strength. To improve simulation reliability, the interfacial energies between γ/γ′ and γ/M 23 C 6 carbides were estimated by comparison with precipitation kinetic modeling and measured precipitate sizes. γ′ and M23C6 were found to precipitate simultaneously between 640 and 860 °C, producing microstructures similar to those produced by two-step aging. The optimal γ′ size for peak yield stress was calculated to be 20–23 nm. WAAM Haynes 282 aged at 780 °C for 50 h exceeded the mechanical performance of its wrought counterpart subjected to two-step aging, though desired properties can also be achieved at 800 °C for 16 h or less. The error in yield strength is less than 20 MPa, demonstrating good agreement between the modeling framework and experiments. Creep studies showed that WAAM Haynes 282 exceeded the calculated rupture time, reaching 481 h. This proposed methodology can accelerate the design of aging heat treatments for any γ′-strengthened nickel-base alloy, minimizing the resources required for trial-and-error experiments.

CALPHAD↗