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At least 1,225 records · Page 68

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

On-line and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges that include compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time consuming. Here, in this study, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensors cell parameters for targeted gas phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other hazardous processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Iodine capture with mechanically robust heat-treated Ag-Al-Si-O xerogel sorbents

Silver-loaded heat-treated aluminosilicate xerogels (Ag-HTX) were evaluated as sorbents for iodine [I2(g)] capture. The heat treatment step was performed to help increase the mechanical integrity of the gels. The synthesized xerogels were characterized using powder X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, transmission electron microscopy, Brunauer-Emmett-Teller analysis, gravimetric iodine loading, nanoindentation, and dynamic mechanical analysis. The structural and chemical analyses of Ag-HTX showed uniform distribution of Ag throughout the gel network after Ag-exchange. After I2(g) capture, the AgI crystallites were observed in the sorbent verifying chemisorption as the iodine capture mechanism. Iodine loading of this xerogel was 0.43 g g-1 at 150°C over 1 d and 0.52 g g-1 at 22°C over 33 d. The specific surface area of Ag-HTX was 202 m2 g-1 and decreased to 87 m2 g-1 after iodine loading. The hardness of the heat-treated xerogel was 160 times higher than heat-treated aerogel of the same composition. The heat-treatment process increased the modulus value to 40.77 MPa from 6.99 MPa of as-made xerogel, demonstrating the need for this added step in the synthesis process. These results show that Ag-HTX is a promising sorbent for I2(g) capture with good loading capacity and mechanical stability.

xerogels, aerogels, iodine capture, hardness testi↗

Implementation of compound refractive lenses for large field-of-view x-ray phase-contrast imaging during hypervelocity impact experiments

Synchrotron x-ray phase-contrast imaging (XPCI) offers time-resolved visualization of dynamic compression phenomena, but its intrinsically small field-of-view (FOV) limits the time that key features remain in frame. A novel approach to enlarge the FOV is achieved by positioning a two-dimensional parabolic compound refractive lens (CRL) upstream of the sample to deliberately defocus the white beam. Ray-tracing simulations and XPCI measurements show that this CRL configuration can expand the beam by ∼50% vertically and ∼15% horizontally based on the full width at half-maximum of the beam. Implementing the CRL, however, attenuates the photon flux and lowers signal-to-noise ratio (SNR). Task-based analysis using a calibration grid (30 μm dots) showed that both setups fail to consistently meet the Rose criterion (SNR ≥ 5) for features of this size in single-bunch imaging. Extrapolating the measured SNR Rose values suggests that the minimum consistently detectable feature lies closer to 30–40 μm for the standard XPCI setup and above 40 μm for CRL-XPCI. Despite this limitation, the CRL configuration nearly doubles the illuminated area, enabling simultaneous tracking of front and rear observations of boron carbide targets subjected to rod and sphere impacts at 1.0–2.6 km/s. Image tracking algorithms and photonic Doppler velocimetry were used to measure penetration and rear-surface velocity histories. Together, these measurements capture crack fronts, penetration, and material breakout, offering new benchmark data for validating high-strain-rate constitutive models of ceramic materials.

Ceramic materials↗

Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis

This paper uses data-driven operator theoretic approaches to explore the global phase space of a dynamical system. In this work, we defined conditions for discovering new invariant subspaces in the state space of a dynamical system starting from an invariant subspace based on the spectral properties of the Koopman operator. When the system evolution is known locally in several invariant subspaces in the state space of a dynamical system, a phase space stitching result is derived that yields the global Koopman operator. Additionally, in the case of equivariant systems, a phase space stitching result is developed to identify the global Koopman operator using the symmetry properties between the invariant subspaces of the dynamical system and time-series data from any one of the invariant subspaces. Finally, these results are extended to topologically conjugate dynamical systems; in particular, the relation between the Koopman tuple of topologically conjugate systems is established. The proposed results are demonstrated on several second-order nonlinear dynamical systems including a bistable toggle switch. Our method elucidates a strategy for designing discovery experiments: experiment execution can be done in many steps, and models from different invariant subspaces can be combined to approximate the global Koopman operator.

42 ENGINEERING↗

Quantitative Measurement of Li-Ion Concentration and Diffusivity in Solid-State Electrolyte

Here, we present a quantitative method to measure the concentration and diffusivity of Li ion in a solid-state electrolyte with nanoscale depth resolution. We designed a standard sample with different depths of trenches and used time-of-flight secondary ion mass spectroscopy, inductively coupled plasma optical emission spectroscopy, three-dimensional (3D) optical microscopy, and electrochemical strain microscopy to obtain effective Li-ion concentration and the corresponding diffusivity as a function of depth. As a result, we verified the dependence of electromechanical strain induced by ionic oscillation on mobile-ion concentration. We also showed the diffusivity change as a function of effective concentration and discussed the difference in diffusivity near the surface and inside the bulk. Our method can be widely used for electromechanical strain in various ionic conductors, rendering our technique universal for quantitative analysis of ionic dynamics at a multiscale.

36 MATERIALS SCIENCE↗

Study of the light scalar a 0 ( 980 ) through the decay D 0 → a 0 ( 980 ) − e + ν e with a 0 ( 980 ) − → η π −

Using 7.93 fb − 1 of e + e − collision data collected at a center-of-mass energy of 3.773 GeV with the BESIII detector, we present an analysis of the decay D 0 → η π − e + ν e . The branching fraction of the decay D 0 → a 0 ( 980 ) − e + ν e with a 0 ( 980 ) − → η π − is measured to be ( 0.86 ± 0.1 7 stat ± 0.0 5 syst ) × 10 − 4 . The decay dynamics of this process is studied with a single-pole parametrization of the hadronic form factor and the Flatté formula describing the a 0 ( 980 ) line shape in the differential decay rate. The product of the form factor f + a 0 ( 0 ) and the Cabibbo-Kobayashi-Maskawa matrix element | V c d | is determined for the first time with the result f + a 0 ( 0 ) | V c d | = 0.126 ± 0.01 3 stat ± 0.00 3 syst . Published by the American Physical Society 2025

Ablikim, M.↗

A Sensorless Coil Detection Scheme based on Dead-Time Effect in Dynamic Wireless Power Transfer Systems

The detection of electric vehicles in dynamic wireless power transfer (DWPT) systems is important to reduce the standby losses and comply with the electromagnetic-field emission guidelines recommended by the International Commission for Non-Ionizing Radiation Protection. This paper discusses a novel sensorless coil detection scheme, which exploits the phenomenon of voltage-polarity reversal/notches caused by the dead-time effect in the full-bridge inverter. The variations in the system impedance and dead-time effects are collectively exploited to detect the receiver coil in the DWPT system. The proposed coil detection scheme is accomplished at low excitation voltage, which reduces the inverter standby losses. The theoretical analysis of the notch occurrence and open-loop simulation results are presented using a DWPT model developed in the piecewise linear electrical circuit simulation software.

Kavimandan, Utkarsh↗

Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations

Preparing for the deployment of large scientific and engineering codes on upcoming exascale systems with GPU-dense nodes is made challenging by the unprecedented diversity of device architectures and heterogeneous programming models. In this work, we evaluate the process of porting a massively parallel, fluid dynamics code written in CUDA to SYCL, HIP, and Kokkos with a range of backends, using a combination of automated tools and manual tuning. We use a proxy application along with a custom performance model to inform the results and identify additional optimization strategies. At scale performance of the programming model implementations are evaluated on pre-production GPU node architectures for Frontier and Aurora, as well as on current NVIDIA device-based systems Summit and Polaris. Real-world workloads representing 3D blood flow calculations in complex vasculature are assessed. Our analysis highlights critical trade-offs between code performance, portability, and development time.

Martin, Aristotle↗

LiAISON (Life-cycle Assessment Integration into Scalable Open-source Numerical models) [SWR-24-01]

We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE)7. We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming. Scientific publication available here: https://pubs.acs.org/doi/full/10.1021/acs.est.2c04246

Ghosh, Tapajyoti↗

Using multimodal X-ray computed tomography to advance 3D petrography: A non-destructive investigation of olivine inside a carbonaceous chondrite

Rocks form in three dimensions through time and studying them provides information from inside dynamic systems we cannot otherwise observe. Yet how we typically access the interior of the rocks themselves to gain that information may limit our understanding and influence how we reconstruct the processes that formed them. Here, we demonstrate combined non-destructive 3D X-ray imaging techniques that produce quantitative densitometric and crystallographic maps of entire individual grains inside a rock. Olivine grains throughout a sample of the carbonaceous chondrite Northwest Africa (NWA) 11346 were each characterized by size, shape, composition, zoning intensity, and crystallographic orientation. The addition of 3D crystallographic mapping to calibrated 3D densitometric analysis—used to calculate chemical composition—demonstrates a fully non-destructive petrographic method and provides unique insight. For instance, in our case, using crystallographic data to delineate individual grains and then measuring the 3D size, shape, and composition of each distinguishes variably reset relict grains from those later crystallized after a melting event. Intersection in a 2D slice could not have led to this interpretation because the integration of three-dimensional size, rounding, composition, location, and crystallographic orientation measured from each grain forms the key patterns. Multimodal laboratory X-ray imaging has strong potential to advance 3D petrography.

36 MATERIALS SCIENCE↗

CFD-Guided Evaluation of Spark-Assisted Gasoline Compression Ignition for Cold Idle Operation

A closed-cycle, three-dimensional (3D) computational fluid dynamics (CFD) analysis campaign was conducted to evaluate the performance of using spark plugs to assist gasoline compression ignition (GCI) combustion during cold idle operations. A conventional spark plug using single-sided J-strap design was put at a location on the cylinder head to facilitate spray-guided spark assistance. Ignition was modeled with an L-type energy distribution to depict the breakdown and the arc-to-glow phases during the energy discharge process. Several key design parameters were investigated, including injector clocking, number of nozzle holes, spray inclusion angle, number of fuel injections, fuel split ratio, and fuel injection timings. The study emphasized the region around the spark gap, focusing on flame kernel formation and development and local equivalence ratio distribution. Flame kernel development and the ignition process were found to correlate strongly with the fuel stratification and the flow velocity near the spark gap. The analysis results showed that the flame kernel development followed the direction of the local flow field. In addition, the local fuel stratification notably influenced early-stage flame kernel development due to varying injection spray patterns and the fuel injection strategies. Among these design parameters, the number of nozzle holes and fuel injection timing had the most significant effects on the engine combustion performance.

42 ENGINEERING↗

CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)

Abstract. Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes (i.e., phenomena) such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.

58 GEOSCIENCES↗

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees C or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗

Solvent-dependent complex reaction pathways of bromoform revealed by time-resolved X-ray solution scattering and X-ray transient absorption spectroscopy

The photochemical reaction pathways of CHBr3 in solution were unveiled using two complementary X-ray techniques, time-resolved X-ray solution scattering (TRXSS) and X-ray transient absorption spectroscopy, in a wide temporal range from 100 ps to tens of microseconds. By performing comparative measurements in protic (methanol) and aprotic (methylcyclohexane) solvents, we found that the reaction pathways depend significantly on the solvent properties. In methanol, the major photoproducts are CH3OCHBr2 and HBr generated by rapid solvolysis of iso-CHBr2-Br, an isomer of CHBr3. In contrast, in methylcyclohexane, iso-CHBr2-Br returns to CHBr3 without solvolysis. In both solvents, the formation of CHBr2 and Br is a competing reaction channel. From the structural analysis of TRXSS data, we determined the structures of key intermediate species, CH3OCHBr2 and iso-CHBr2-Br in methanol and methylcyclohexane, respectively, which are consistent with the structures from density functional theory calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers

The concentration of dissolved oxygen (DO), an important measure of water quality and river metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as regulated by interacting and competing drivers (light, temperature and flow) that define rivers’ climate. Its continental-scale drivers, however, have remained elusive, partly due to the scarcity and spatio-temporal inconsistency of water quality data. Here we show, via a deep learning model (long short-term memory) trained using data from 580 rivers, that temperature predominantly drives daily DO dynamics in the contiguous United States. Light comes a close second, whereas flow imparts minimal influence. This work showcases the promise of using deep learning models for data filling that enables large-scale systematic analysis of patterns and drivers. Results show fairly accurate prediction of DO by temperature alone, and declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate.

54 ENVIRONMENTAL SCIENCES↗

Optimum power analysis of a self-reactive wave energy point absorber with mechanically-driven power take-offs

This paper studies a self-reactive ocean wave energy converter (WEC) where energy is absorbed through the relative motion between a floating buoy on the ocean surface and a submerged body. Two types of direct-drive power take off (PTO) systems are examined. One adopts a ball screw system in inverse driving mode with mechanical motion rectifier (MMR) that converts the oscillating bi-directional input heave motion into unidirectional rotation to drive an electromagnetic generator, and another one uses a ball screw system to directly drive the generator (called non-MMR). Dynamic models for both types of PTOs are established and integrated with the overall WEC system model in both the time and frequency domains. Numerical simulation is used to investigate the dynamic performance of both PTOs. The influences of the PTO inerter, which is from the rotational inertia of the flywheel and generator, and the WEC drag damping coefficient are discussed and explored. The analytical closed-form solution for the optimum condition of the non-MMR system is derived based on the criterion of maximum power absorption, and numerical simulation yields the optimum condition for the MMR system. Overall, the results under regular and irregular waves show that the PTO inerter in the MMR system can improve the power absorption for small wave periods and maintain the same performance for large wave periods. The PTO inerter in the non-MMR system shifts the peak frequency. The WEC drag damping negatively influences the power absorption in both PTO systems, especially at large wave periods.

16 TIDAL AND WAVE POWER↗

Single electron in a storage ring: a probe into the fundamental properties of synchrotron radiation and a powerful diagnostic tool

An experimental study of undulator radiation generated by a single electron was carried out at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. Photons were detected with a single-photon avalanche diode (SPAD) at an average rate of 20 kHz, corresponding to one detection every few hundred revolutions. Events were continuously recorded by a picosecond event timer for as long as 5 minutes at a time. The collected data were used to directly test the predictions of classical Poisson photostatistics. The study was motivated in part by a previous observation of sub-Poissonian statistics in a similar experiment with synchrotron radiation. In addition, we show that the time series of recorded events can be used to study the synchrotron motion of a single electron, yielding dynamical parameters such as the phase jitter of the radiofrequency cavity and the dependence of the synchrotron period on amplitude. Research plans for further experiments are also discussed.

47 OTHER INSTRUMENTATION↗