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DOE OSTI2024

Hardware-in-the-Loop Evaluation for Potential High Limit Estimation-Based PV Plant Active Control

This paper validates the efficacy of an artificial intelligence (AI)-based photovoltaic (PV) plant control and optimization approach in enabling PV plants as accountable grid reliability service providers. The validation is performed in a realistic laboratory controller-hardware-in-the-loop environment, leveraging accurate PV plant modeling and standard industrial communication protocols. Through simulations that account for diverse weather conditions and active control scenarios, the results highlight the superior performance of the AI-based solution in comparison to a state-of-the-art reference-control grouping-based approach. Such a finding contributes to mitigating the risk of overcurtailment and uninstructed deviations of active PV plant controls, and offers practical guidance for its field deployment. Furthermore, it establishes a standardized testing framework for comparing various PV active control strategies.

hardware-in-the-loop↗

DOE OSTI2025

Performance Evaluation of Intelligent Solar Control Software Through Hardware-in-the-Loop (CRADA Final Report)

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.

14 SOLAR ENERGY↗

DOE OSTI2024

Effective Corrosion-Resistant Single-Atom Alloy Catalyst on HfO 2 -Passivated BiVO 4 Photoanode for Durable (≈800 h) Solar Water Oxidation

Green hydrogen (H 2 ) production from solar water splitting necessitates photoelectrodes with superior photoelectrochemical (PEC) activity and durability. However, surface defects and photocorrosion instability—especially at high potentials—limit PEC performance and stability. Herein, the prototypical bismuth vanadate (BiVO 4 ) photoanode is used to demonstrate a holistic approach to improve photocurrent density and long-term stability. In this approach, high surface-area nanostructuring of BiVO 4 is combined with barium (Ba) doping with semi-crystalline hafnium oxide (HfO 2 ) surface passivation and single-atom nickel platinum (NiPt) catalysts. The introduction of Ba 2+ ions into BiVO 4 increases the concentration of conductive V 4+ ions or the ratio of V 4+ ions to oxygen vacancies, avoiding V 5+ dissolution during water oxidation. The semi-crystalline HfO 2 , which serves as a passivation layer, prevents BiVO 4 photocorrosion by suppressing harmful chemical reactions when holes are transferred to the electrolyte. The synergistic use of isolated single-atom and Ni-Pt coordination improves charge transfer at the photoanode/electrolyte interface, leading to enhanced PEC kinetics and stability. As a result, a photoelectrode is demonstrated with ≈6.5 mA cm -2 at 1.23 V versus a reversible hydrogen electrode (RHE) and continuous operation for 800 h with a negligible degradation rate. This work provides a promising approach to improve photoanodes for PEC H 2 production.

08 HYDROGEN↗

DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

closed-loop validation↗

DOE OSTI2024

Integrate Latimer Controls' Solution into RTAC (CRADA Final Report, CRD-23-24672)

Latimer Controls, Inc. was awarded two vouchers under the Department of Energy's American-Made Solar Prize Round 6 to conduct collaborative research at a national laboratory. The National Renewable Energy Laboratory (NREL) was selected as a partner to assist Latimer Controls in the performance evaluation of its photovoltaic (PV) control software. This collaboration focuses on developing a hardware-in-the-loop (HIL) testbed at NREL, which will be used to test and validate the Latimer PV control technology in a realistic yet de-risked environment. Both Latimer and NREL teams will work together to analyze the collected test data, derive insights, and disseminate the scientific findings. Recent studies underscore the potential of solar energy as a zero-marginal-cost and zero-emission flexibility resource within the bulk power system, particularly when integrated with advanced control systems. To enhance the performance of such systems, Latimer Controls has developed leading-edge technologies, including machine learning (ML) algorithms and hierarchical inverter set-point allocation methods. These innovations are designed to estimate the operational headroom of large PV plants for grid integration and control. However, comprehensive validation under real-world conditions remains necessary. To address this gap, the concurrent CRADA project proposes the real-world application and validation of the Latimer Control solution within a HIL environment. Initially, the Latimer algorithm was developed and tested within MATLAB Simulink, a platform suitable for research-level simulations and iterative development. However, transitioning this technology to a real solar site as an industry-ready solution necessitates implementation in a format compatible with widely used solar power plant controllers. In this additional CRADA work, the MATLAB Simulink-based logic will be translated into Structured Text, a programming language compliant with IEC 61131 standards, which is commonly used for custom logic implementations in industry-leading programmable logic controllers (PLCs), such as the Schweitzer SEL real-time automation controller (RTAC). This transition will facilitate the deployment of the Latimer Control solution in real-world solar power plants, thereby advancing the technology towards commercialization.

14 SOLAR ENERGY↗

DOE OSTI2025

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

14 SOLAR ENERGY↗

DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services: Preprint

closed-loop validation↗

DOE OSTI2026

Dispersion Suppression for Wedge-Based Final Cooling at a 10 TeV Muon Collider

Achieving a luminosity of $\gtrsim 10^{34} cm^{-2} s^{-1}$ in a $10 \text{ } TeV$ Muon Collider, given the short lifetime of a muon, requires reducing the 6D emittance of the muon beam through a process known as ionization cooling. In the final stage of this cooling process, the transverse emittance must be reduced to $22 \text{ } μm$, typically by allowing longitudinal emittance growth up to downstream acceptance limits. While the current International Muon Collider Collaboration designs involve $40 \text{ } T$ solenoids to reach the transverse emittance target, such high-field solenoids come with several challenges, including mechanical stress management, quench protection, and potential limitations in relying on High Temperature Superconductor technology. Designed as an alternative to using such solenoids while simultaneously reaching target transverse emittance, the previously proposed wedge-based, reverse emittance-exchange cooling scheme requires excellent dispersion suppression. In this study, we design and simulate a dispersion suppressor channel for the wedge-based final cooling design that reduces dispersion in the target direction to a target value of $D_x \sim 0.001 \text{ } m$.

Karaaslan, I. [Chicago U.] (ORCID:0000000337849879↗

DOE OSTI2025

Magnesium‐Mediated Electrochemical Synthesis of Ammonia

Metal-mediated electrochemical synthesis of ammonia (NH3) is a promising method to activate N2 at room temperature. While a Li-mediated approach has been optimized to produce NH3 at high current density and selectivity, Li's scarcity and its highly negative plating potential limit scalability and energy efficiency. Alternative mediators have been proposed, but only Ca has shown some promise, achieving ≈50% Faradaic efficiency (FE), though requiring voltages beyond -3 V. Here, we report a Mg-mediated nitrogen reduction reaction (Mg-NRR), where N2 is activated on Mg to form Mg3N2, followed by protolysis to release NH3 and regenerate Mg. A notable NH3 FE of 25.28 ± 3.80% is achieved at a current density of -45 mA cm-2, corresponding to an NH3 partial current density of -11.30 ± 1.77 mA cm-2 under 6 bar N2. Isotope-labeled experiments confirm that NH3 originates from N2, with similar FE (25.15 ± 1.01%). Importantly, NH3 production is demonstrated at a total cell potential as low as -3 V. This Li-free Mg-NRR system offers key advantages, including lower energy input and use of earth-abundant materials, making it a scalable route for sustainable NH3 synthesis.

Goyal, Ishita↗

DOE OSTI2025

A High-Speed, High-Resolution Transition Edge Sensor Spectrometer for Soft X-Rays at the Advanced Photon Source

This project explores the design and development of a transition edge sensor (TES) spectrometer for resonant soft X-ray scattering (RSXS) measurements developed in collaboration between Argonne National Laboratory (ANL) and the National Institute of Standards and Technology (NIST). Soft X-ray scattering is a powerful technique for studying the electronic and magnetic properties of materials on a microscopic level. However, the lack of high-performance soft X-ray spectrometers has limited the potential of this technique. TES spectrometers have the potential to overcome these limitations due to their high energy resolution, high efficiency, and broad energy range. This project aims to optimize the design of a TES spectrometer for RSXS measurements and more generally soft X-ray spectroscopy at the Advanced Photon Source (APS) 29-ID, leading to improved understanding of advanced materials. We will present a detailed description of the instrument design and implementation. The spectrometer consists of a large array of approximately 250 high-speed and high-resolution pixels. The pixels have saturation energies of approximately 1 keV, sub-ms pulse duration and energy resolution of approximately 1 eV. The array is read out using microwave multiplexing chips with MHz bandwidth per channel, enabling efficient data throughput. To facilitate measurement of samples in situ under ultra-high vacuum conditions at the beamline, the spectrometer is integrated with an approximately 1 m long snout.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Cysteine Rich Intestinal Protein 2 is a copper-responsive regulator of skeletal muscle differentiation and metal homeostasis

Copper (Cu) is essential for respiration, neurotransmitter synthesis, oxidative stress response, and transcription regulation, with imbalances leading to neurological, cognitive, and muscular disorders. Here we show the role of a novel Cu-binding protein (Cu-BP) in mammalian transcriptional regulation, specifically on skeletal muscle differentiation using murine primary myoblasts. Utilizing synchrotron X-ray fluorescence-mass spectrometry, we identified murine cysteine-rich intestinal protein 2 (mCrip2) as a key Cu-BP abundant in both nuclear and cytosolic fractions. mCrip2 binds two to four Cu + ions with high affinity and presents limited redox potential. CRISPR/Cas9-mediated deletion of mCrip2 impaired myogenesis, likely due to Cu accumulation in cells. CUT&RUN and transcriptome analyses revealed its association with gene promoters, including MyoD1 and metallothioneins, suggesting a novel Cu-responsive regulatory role for mCrip2. Our work describes the significance of mCrip2 in skeletal muscle differentiation and metal homeostasis, expanding understanding of the Cu-network in myoblasts. Copper (Cu) is essential for various cellular processes, including respiration and stress response, but imbalances can cause serious health issues. This study reveals a new Cu-binding protein (Cu-BP) involved in muscle development in primary myoblasts. Using unbiased metalloproteomic techniques and high throughput sequencing, we identified mCrip2 as a key Cu-BP found in cell nuclei and cytoplasm. mCrip2 binds up to four Cu + ions and has a limited redox potential. Deleting mCrip2 using CRISPR/Cas9 disrupted muscle formation due to Cu accumulation. Further analyses showed that mCrip2 regulates the expression of genes like MyoD1, essential for muscle differentiation, and metallothioneins in response to copper supplementation. This research highlights the importance of mCrip2 in muscle development and metal homeostasis, providing new insights into the Cu-network in cells.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Precision Labeling of Native Antibodies with Lock Coupling

The formation of stable protein complexes enables much of biotechnology, but even high-affinity complexes can dissociate, limiting potential applications in biomaterials, bioimaging, nanomedicine, and other protein-based technologies. Here, in this study, we describe lock coupling, a simple and selective one-step reaction between interfacial lysine and glutamate or aspartate side chains to form stable isopeptide bonds and be used for the precise labeling of native antibodies. We identify conditions in which short-lived activated esters formed by the aqueous carbodiimide EDC promote isopeptide bond formation specifically at preassociated amine-acid pairs. Indiscriminate cross-linking is minimized by formation of protein complexes before addition of catalyst, use of acidic pH to suppress exposed Lys reactivity, and limiting the aqueous stability of activated esters. For native antibody (Ab) labeling, we show that the small IgG-binding protein GB1 can be covalently attached to the Ab Fc domain and that introduction of Cys into GB1 loops allows for facile conjugation of fluorophores, micelles, or inorganic nanocrystals for imaging in live cells and animals. By varying Cys substituents and protein stoichiometry, a defined number of probes can be uniformly attached without the need for extensive purification. In live-cell confocal microscopy, labeled GB1 serves as a stable replacement for secondary Abs, enabling simple multicolor immunostaining and imaging. Lock coupling requires just a single reagent in aqueous buffer and leverages both the innate ability of proteins to form high-affinity complexes and the widespread presence of Lys-Glu/Asp pairs at their interfaces, with the potential for precision synthesis of protein-based probes for imaging, biomaterials, biophysics, and medicine.

antibody↗

DOE OSTI2025

Electrochemical Corrosion and Catalysis Dynamics of Tin Oxide during Water Oxidation

Metal oxide corrosion severely limits anodic electrocatalysis, particularly at high potentials in acidic environments, where degradation pathways remain poorly defined. This study establishes explicit connections between corrosion and electrocatalysis on tin oxide during water oxidation by examining the roles of lattice defects, reactive oxygen species, interfacial pH variations, and speciation of corroded tin in acid. We first demonstrate the presence of structural defects such as oxygen vacancies and substoichiometric Sn(II) species by integrating electron paramagnetic resonance spectroscopy, ultraviolet photoelectron spectroscopy, and Mott–Schottky analysis. Kohn–Sham density functional theory calculations reveal that explicit water structures thermodynamically stabilize reaction intermediates and lower reaction overpotentials. Moreover, we propose that water dissociation leads to hydrogen-bonding networks formed by H* and OH* intermediates, which may span the entire catalyst surface and decrease the interfacial pH to drive corrosion. In contrast, the electrochemical generation of reactive oxygen species is shown to play a minor role in catalyst corrosion during water oxidation using inductively coupled plasma mass spectrometry coupled with selective chemical scavengers. Square-wave voltammetry combined with rotating ring-disk electrodes is used to reveal that under open-circuit conditions, only Sn(IV) cations chemically dissolve from tin oxide, while both Sn(IV) and Sn(II) species electrochemically corrode during water oxidation. Our results unveil a dynamic and complicated interplay between corrosive and catalytic pathways on metal oxide electrocatalysts: a decrease in interfacial pH due to water oxidation exacerbates Sn(II)/Sn(IV) corrosion. Subsequently, the electrochemical corrosion of Sn(II)/Sn(IV) facilitates product formation from lattice oxygen, while the redeposition of corroded Sn(II) as Sn(IV) can enable oxygen exchange with water. By elucidating the roles of defects and interfacial chemistry, this work provides a roadmap for engineering improved electrocatalysts that balance activity and stability, a critical step toward scalable and durable energy technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Reactive flash sintering and characterization of bulk high entropy nitrides

Over the past decade, numerous high-entropy ceramics have been synthesized, often displaying attractive properties. However, the study on facile preparation of bulk high entropy nitrides (HEN) are limited, despite its broad potential applications. This research demonstrates for the first time rapid fabrication (within ∼6 min) of bulk high-entropy nitrides, especially (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N, from binary nitride powder mixtures using a highly efficient reactive flash sintering (RFS) technique. X-ray diffraction (XRD) shows the HENs from RFS are near single-phase solid solutions with a rock salt crystal structure, while in situ synchrotron study carried out during RFS captured in real time the formation of HEN, which was preserved upon cooling, suggesting thermodynamic stability of the HEN phase, even up to extreme pressure (∼35.6 GPa). Microscopic analyses using SEM, STEM, and EDS reveal decent uniformity for HEN with no obvious segregation of elements, even to submicron scale. Some properties of the obtained bulk HENs are consistent with expectations. For example, their hardness and bulk modulus are close to estimates based on rule-of-mixture (ROM) values from the constituent binary nitrides. Meanwhile, some other measured properties seem to show surprises. For example, the fracture toughness for the HENs (e.g., 7.81 ± 1.40 MPa•m 1/2 or higher) turns out to be more than double of the expected ROM estimates. The significantly improved fracture toughness is attributed to the observed nano-layered structure of the HENs, despite the HEN’s cubic crystal structure and high hardness. In addition, the oxidation resistance shows improvement up till ∼800°C, possibly due to Ta doping that suppress oxygen vacancy formation in the oxide shell, while the 5-metal HEN of (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N displays superconductivity (T c of ∼5–7 K from magnetism and resistivity measurements, slightly lower than ROM estimate), despite insulating property of starting AlN. Furthermore, future study combining experimental investigation using larger samples to confirm the observed increase in fracture toughness and oxidation resistance, theoretical modeling at different length scale, and more detailed structural/chemical characterization, especially at the atomic scale, are all needed to fully understand the inter-relationships between composition, processing, structure, and novel properties for these HENs and the development of related new materials for different applications.

Flash sintering↗

DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗

DOE OSTI2025

Combined TDLAS and chemiluminescence imaging in a flat flame burner operated with NH3/H2 blends

Ammonia is a promising hydrogen carrier due to its favorable storage and transport characteristics. However, its direct use in combustion systems is limited by low flammability and potential for high nitrogen oxide emissions. To better understand ammonia combustion, researchers conducted experiments using a flat flame burner and measured species profiles using tunable-diode-laser-absorption-spectroscopy and chemiluminescence imaging. They tested three flame conditions with different ammonia-hydrogen blends and oxygen levels. The results were compared to simulations using various kinetic mechanisms, including one that accounts for excited species chemistry. The goal is to provide direct information about species profiles in a simple system, isolating chemical kinetics from fluid dynamic effects, which can inform the development of more efficient and low-emission combustion systems using ammonia.

ammonia combustion↗

DOE OSTI2025

Deterministic fabrication of highly reproducible monochromatic quantum emitters in hexagonal boron nitride

Quantum emitters in hexagonal boron nitride are important room temperature single-photon sources. However, conventional fabrication methods yield quantum emitters with dispersed and inconsistent spectral profiles, limiting their potential for practical quantum applications, which demand reproducible high quality single-photon sources. Here, we report the deterministic creation of highly reproducible monochromatic quantum emitters by applying carbon-ion implantation on freestanding hexagonal boron nitride flakes, while a carbon mask with suitable thickness was adapted to optimize the implantation results. Quantum emitters fabricated using this approach exhibited thermally limited monochromaticity, with an emission center wavelength of 590.7 ± 2.7 nm, a narrow full width at half maximum of 7.1 ± 1.7 nm, an emission rate of 1 MHz without optical engineering, and exceptional stability under ambient conditions. Density functional theory calculations and scanning transmission electron microscopy suggest that these emitters are comprised of boron centered carbon tetramers. This method provides a reliable single-photon source for optical quantum computing and potential future industry-scale applications.

Hua, Muchuan [Argonne National Laboratory (ANL), A↗

DOE OSTI2025

Investigation of kinetic inductance and microwave loss in thin-film TaC x N 1−x superconducting resonators

The promise of tantalum for realizing superconducting quantum devices has generated interest in its compound films, particularly nitrides. Among these, cubic-phase tantalum carbonitride (TaC x N 1−x ) offers reduced susceptibility to oxidation and a high critical temperature, yet its microwave properties remain largely unexplored. Here, in this study, we investigate plasma-enhanced atomic layer deposition of cubic-phase TaC x N 1−x thin films for superconducting microwave circuits. Structural and transport measurements reveal nanocrystalline morphology with sub-10 nm grains and superconductivity in the dirty limit. Coplanar waveguide resonators exhibit moderately high kinetic inductance (16.8 pH/sq for 30 nm films) with potential for enhancement through dimensional scaling. The films also support high internal quality factors exceeding 10 5 at 50 mK in the single-photon regime, comparable to granular aluminum. Loss analysis identifies the two-level systems as the dominant limiting mechanism, with potential for further reduction through interface engineering. These results establish atomic layer deposited TaC x N 1−x as a promising material for scalable, low-loss, high-inductance superconducting circuits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗