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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials

Machine learning and data mining coupled with molecular modeling have become powerful tools for materials discovery. Metal-organic frameworks (MOFs) are a rich area for this due to their modular construction and numerous applications. Here, we make data from several previous large-scale studies in MOFs and zeolites from our groups (and new data for N 2 and Ar adsorption in MOFs) easily accessible in one place. The database includes over 3 million simulated adsorption data points for H 2 , CH 4 , CO 2 , Xe, Kr, Ar, and N 2 in over 160 000 MOFs and zeolites, textural properties like pore sizes and surface areas, and the structure file for each material. We include metadata about the Monte Carlo simulations to enable reproducibility. The database is searchable by MOF properties, and the data are stored in a standardized JSON format that that is interoperable with the NIST adsorption database. We also identify several MOFs that meet high performance targets for multiple applications, such as high storage capacity for both hydrogen and methane or high CO 2 capacity plus good Xe/Kr selectivity. Here, by providing this data publicly, we hope to facilitate machine learning studies on these materials, leading to new insights on adsorption in MOFs and zeolites.

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

Hierarchically porous and single Zn atom-embedded carbon molecular sieves for H2 separations

Abstract Hierarchically porous materials containing sub-nm ultramicropores with molecular sieving abilities and microcavities with high gas diffusivity may realize energy-efficient membranes for gas separations. However, rationally designing and constructing such pores into large-area membranes enabling efficient H 2 separations remains challenging. Here, we report the synthesis and utilization of hybrid carbon molecular sieve membranes with well-controlled nano- and micro-pores and single zinc atoms and clusters well-dispersed inside the nanopores via the carbonization of supramolecular mixed matrix materials containing amorphous and crystalline zeolitic imidazolate frameworks. Carbonization temperature is used to fine-tune pore sizes, achieving ultrahigh selectivity for H 2 /CO 2 (130), H 2 /CH 4 (2900), H 2 /N 2 (880), and H 2 /C 2 H 6 (7900) with stability against water vapor and physical aging during a continuous 120-h test.

42 ENGINEERING↗
DOE OSTI2025

Sequential Infiltration Synthesis of Bilayer Porous Alumina Nanostructures for Broad-Angle, Broadband Antireflective Coatings

Antireflective coatings (ARCs) are thin films engineered to reduce light reflections. Delivering broadband, wide-angle performance is essential for photovoltaics, imaging, and sensing, yet truly omnidirectional antireflection remains difficult due to angle-dependent optical paths and a narrow palette of suitable refractive indices. Here, in this study, we systematically investigate an emerging class of multilayer inorganic ARCs based on conformally coated nanoporous alumina templated by intrinsically microporous polymers (PIMs) and block copolymers (BCPs). We establish a design framework that maps thickness reflectance relationships to identify thickness pairs minimizing reflection across wavelength and incidence angle. We show that deliberately separating the local reflectance minima of the top and bottom layers in bilayer nanostructures broadens the antireflective bandwidth and angular range. We show that 235-nm single-side bilayer porous alumina nanostructures achieves under 2 % reflectance from 380–750 nm for incidence angles up to 45° with less than 0.6% reflectance for incidence angles under 20°. The approach is readily extensible to additional layers or materials with refractive indices tuned via templated nanoporosity and composition, enabling practical, etch-free ARC fabrication without HF or fluorinated precursors and advancing straightforward design of broadband, wide-angle (quasi-omnidirectional) ARCs for next-generation optical systems.

antireflective coatings↗
DOE OSTI2025

Enhancement of Uranium Ionization Efficiencies Using Zn-MOF-74 Derived Nanoporous Ion Emitters for Thermal Ionization Mass Spectrometry

The recent introduction of nano-porous ion emitters (nano-PIEs) formed from metal organic frameworks (MOFs) has demonstrated the potential to enhance sensitivity for thermal ionization mass spectrometry (TIMS). Nano-PIEs take advantage of the parent MOF’s chemical and structural tunability to form scaffolds for ion emitters. A study by Barpaga et al. 2023 on MOF-74 as the parent material found that with high volatility metals in their framework uranium sample utilization efficiency (SUE) increases by up to nine-fold (e.g., Zn-MOF-74) compared to that of the analyte on a bare filament (~0.05%). Here, in this study, we investigate the performance of Zn-MOF-74 to maximize uranium efficiencies at the trace level (= 10 -12 g) by altering the parent MOF morphology and chemistry (i.e., MOF crystal sizes and thermal degradation) and optimizing its integration with TIMS (i.e., MOF mass on a filament and ramp conditions). We observed improvement in SUE up to 20 times (≤1.0%) that of a bare filament load when nano-PIEs derived from nanocrystals of Zn-MOF-74 were heated under a specific current ramp condition. This demonstrates that rates of nano-PIE structural collapse during TIMS analysis and the subsequently formed nanomaterials (and their features) can be tuned to control analyte ionization.

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

Exploitation of Pore Structure for Increased CO 2 Selectivity in Type 3 Porous Liquids

CO 2 capture requires materials with high adsorption selectivity and an industrial ease of implementation. To address these needs, a new class of porous materials was recently developed that combines the fluidity of solvents with the porosity of solids. Type 3 porous liquids (PLs) composed of solvents and metal–organic frameworks (MOFs) offer a promising alternative to current liquid carbon capture methods due to the inherent tunability of the nanoporous MOFs. However, the effects of MOF structural features and solvent properties on CO 2 –MOF interactions within PLs are not well understood. Herein experimental and computational data of CO 2 gas adsorption isotherms were used to elucidate both solvent and pore structure influences on ZIF-based PLs. The roles of the pore structure including solvent size exclusion, structural environment, and MOF porosity on PL CO 2 uptake were examined. A comparison of the pore structure and pore aperture was performed using ZIF-8, ZIF-L, and amorphous-ZIF-8. Adsorption experiments here have verified our previously proposed solvent size design principle for ZIF-based PLs (1.8× ZIF pore aperture). Furthermore, the CO 2 adsorption isotherms of the ZIF-based PLs indicated that judicious selection of the pore environment allows for an increase in CO 2 selectivity greater than expected from the individual PL components or their combination. This nonlinear increase in the CO 2 selectivity is an emergent behavior resulting from the complex mixture of components specific to the ZIF-L + 2'-hydroxyacetophenone-based PL.

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

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti3C2TX MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures-from isolated vacancies to nanopores-revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

2D materials↗
DOE OSTI2018

Energy Efficient Formaldehyde Synthesis by Direct Hydrogenation of Carbon Monoxide in Functionalized Metal–Organic Frameworks

Formaldehyde (CH 2 O) is a highly versatile platform chemical with an annual production of over 30 megatons. The current most widely used industrial method for formaldehyde synthesis is very energy intensive and plagued with significant exergy losses. We propose a highly efficient heterogeneous nanoporous catalyst capable of direct hydrogenation of CO to CH 2 O followed by condensation of CH 2 O from a mixture of CO and H 2 , which are non-condensable gases. The key to this synthesis is a Lewis Pair (LP) functionalized metal-organic framework (MOF) heterogeneous porous catalyst. We have computed reaction pathways, barriers, and uncertainties for CO hydrogenation on a LP functionalized MOF using various density functional theory (DFT) methods and high-level ab initio methods. We have assessed the uncertainties in the energetics inherent in the DFT functionals. Here, we predict that the thermodynamics of CH 2 O synthesis will be enhanced relative to gas phase synthesis and more importantly, will not be equilibrium limited because the barrier to product desorption from the MOF is much lower than the barrier for the reverse reaction. Hence, the product will be continuously removed from the process, facilitating very high overall conversion rates. Our proposed process will dramatically improve the sustainability of CH 2 O synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Fundamentals of hydrogen storage in nanoporous materials

Abstract Physisorption of hydrogen in nanoporous materials offers an efficient and competitive alternative for hydrogen storage. At low temperatures (e.g. 77 K) and moderate pressures (below 100 bar) molecular H 2 adsorbs reversibly, with very fast kinetics, at high density on the inner surfaces of materials such as zeolites, activated carbons and metal–organic frameworks (MOFs). This review, by experts of Task 40 ‘Energy Storage and Conversion based on Hydrogen’ of the Hydrogen Technology Collaboration Programme of the International Energy Agency, covers the fundamentals of H 2 adsorption in nanoporous materials and assessment of their storage performance. The discussion includes recent work on H 2 adsorption at both low temperature and high pressure, new findings on the assessment of the hydrogen storage performance of materials, the correlation of volumetric and gravimetric H 2 storage capacities, usable capacity, and optimum operating temperature. The application of neutron scattering as an ideal tool for characterising H 2 adsorption is summarised and state-of-the-art computational methods, such as machine learning, are considered for the discovery of new MOFs for H 2 storage applications, as well as the modelling of flexible porous networks for optimised H 2 delivery. The discussion focuses moreover on additional important issues, such as sustainable materials synthesis and improved reproducibility of experimental H 2 adsorption isotherm data by interlaboratory exercises and reference materials.

08 HYDROGEN↗
DOE OSTI2018

Nutrient transport suggests an evolutionary basis for charged archaeal surface layer proteins

Surface layers (S-layers) are two-dimensional, proteinaceous, porous lattices that form the outermost cell envelope component of virtually all archaea and many bacteria. Despite exceptional sequence diversity, S-layer proteins (SLPs) share important characteristics such as their ability to form crystalline sheets punctuated with nano-scale pores, and their propensity for charged amino acids, leading to acidic or basic isoelectric points. However, the precise function of S-layers, or the role of charged SLPs and how they relate to cellular metabolism is unknown. Nano-scale lattices affect the diffusion behavior of low-concentration solutes, even if they are significantly smaller than the pore size. Here, we offer a rationale for charged S-layer proteins in the context of the structural evolution of S-layers. Using the ammonia-oxidizing archaea (AOA) as a model for S-layer geometry, and a 2D electrodiffusion reaction computational framework to simulate diffusion and consumption of the charged solute ammonium (NH 4 + ), we find that the characteristic length scales of nanoporous S-layers elevate the concentration of NH 4 + in the pseudo-periplasmic space. Our simulations suggest an evolutionary, mechanistic basis for S-layer charge and shed light on the unique ability of some AOA to oxidize ammonia in environments with nanomolar NH 4 + availability, with broad implications for comparisons of ecologically distinct populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

In situ Gas Cell TEM Investigation of Nanoporous and Zeolite-based Nanocatalysts

A crucial application of in situ transmission electron microscopy is the understanding of nanocatalysts and the effect of the environment on their structure. Pre-treatments, such as calcination or annealing, can dramatically impact the catalytic properties by modifying the morphology and composition of nanostructures. Zeolites and nanoporous structures are particularly sensitive to reactive environments as the porosity or the chemical state can substantially change during the interaction with gases at elevated temperatures.[1] For instance, it has been shown that heating in air causes redispersion of sintered Cu clusters on zeolite, improving the catalytic properties.[2] These observations are possible though innovative in situ TEM gas holders, where the samples are enclosed into a small cell with SiNx windows, isolating the reactive environment from the rest of the column. [3] Here, we present in situ gas cell experiments of sensitive nanoporous structure and zeolite-based nanocatalysts. Scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy were used to understand migration of Al during calcination. In situ diagnostics also help distinguish Al as Bronsted sites, extra-framework Lewis sites, or bulk alumina. More broadly, in situ gas-heating TEM experiments are useful to determine chemical changes and modification of morphology of sensitive nanocatalysts upon pre-treatment (Figure 1).[4] Detailed in situ STEM and energy-dispersive X-ray spectroscopy (EDS) demonstrated compositional changes in nanoporous CuAlTi structures for hydrogen-deuterium exchange (H2-D2) reaction. Coarsening due to annealing at high temperature, a necessary steps for the preparation of catalysts, can be reversed by applying a redox cycle. Using the wide range of gases and temperature, the diagnostics are helpful to derive fundamental understanding of these catalysts at the atomic scale and also provide general guidelines to improve their design.

Foucher, Alexandre [ORNL] (ORCID:0000000150424002)↗
DOE OSTI2023

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites

Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Data-driven design of metal–organic frameworks for wet flue gas CO 2 capture

Limiting the increase of CO 2 in the atmosphere is one of the largest challenges of our generation. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO 2 emissions, much effort is focused on developing solid adsorbents that can efficiently capture CO 2 from flue gases emitted from anthropogenic sources. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO 2 from nitrogen do not perform well when using realistic flue gas that contains water, because water competes with CO 2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO 2 -binding sites-which we term 'adsorbaphores'-that endow MOFs with CO 2 /N 2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO 2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

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

Ideal conductor/dielectric model (ICDM): A generalized technique to correct for finite-size effects in molecular simulations of hindered ion transport

Molecular simulations serve as indispensable tools for investigating the kinetics and elucidating the mechanism of hindered ion transport across nanoporous membranes. In particular, recent advancements in advanced sampling techniques have made it possible to access translocation timescales spanning several orders of magnitude. In our prior study [Shoemaker et al., J. Chem. Theory Comput. 18, 7142 (2022)], we identified significant finite size artifacts in simulations of pressure-driven hindered ion transport through nanoporous graphitic membranes. We introduced the ideal conductor model, which effectively corrects for such artifacts by assuming the feed to be an ideal conductor. In the present work, we introduce the ideal conductor dielectric model (Icdm), a generalization of our earlier model, which accounts for the dielectric properties of both the membrane and the filtrate. Using the Icdm model substantially enhances the agreement among corrected free energy profiles obtained from systems of varying sizes, with notable improvements observed in regions proximate to the pore exit. Moreover, the model has the capability to consider secondary ion passage events, including the transport of a co-ion subsequent to the traversal of a counter-ion, a feature that is absent in our original model. We also investigate the sensitivity of the new model to various implementation details. The Icdm model offers a universally applicable framework for addressing finite size artifacts in molecular simulations of ion transport. It stands as a significant advancement in our quest to use molecular simulations to comprehensively understand and manipulate ion transport processes through nanoporous membranes.

Chemistry↗
DOE OSTI2026

Discovery of nanopore filling by gypsum in wellbore cement exposed to 17 MPa CO 2 under geologic carbon storage conditions

Here, this study investigates the pore structure evolution of the reaction zones in wellbore cement samples exposed to a CO 2 -rich solution in equilibrium with 17 MPa supercritical CO 2 over 14 days. Through advanced characterization methods of field emission SEM, Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN), and micro-CT, a new mechanism of CO 2 -cement reaction involving filling of nanopores in the interior of cement by gypsum was revealed. Gypsum was formed by the liberation of SO 4 2− from ettringite (AFt) and monosulfate (AFm) caused by a decrease in pH. Based on these experimental observations, a new CO 2 -cement reaction model that incorporates four distinct reaction zones is developed. This model provides a comprehensive framework for understanding the spatial and temporal distribution of minerals in cement due to high pressure CO 2 —cement reactions. This study demonstrates that the major damage induced by high pressure CO 2 alteration occurs in the most exterior region of the cement. The interior region of the cement maintains its integrity due to nanopore filling by gypsum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Simplifying computational workflows with the Multiscale Atomic Zeolite Simulation Environment (MAZE)

Zeolites, an important class of 3-dimensional nanoporous materials, have been widely explored for a variety of applications including gas storage, separations, and catalysis. As the properties of these aluminosilicate materials depend on a number of factors (e.g., framework topology, Si/Al ratio, extra-framework cations etc.), detailed experiments (e.g., catalytic properties, adsorption capacities etc.) are often limited to only a handful of materials. Computational methods have played an important role in (1) providing molecular level insights to rationalize experimental observations, and (2) screening large libraries of zeolites to identify promising candidates for experimental synthesis and validation. Different levels of theory and computational chemistry codes are necessary to describe the range of relevant phenomena such as adsorption (e.g., grand canonical Monte Carlo), diffusion (e.g., molecular dynamics), and chemical reactions (e.g., density functional theory). Manipulation of atomic structures, handling of input files, and developing robust workflows becomes quite cumbersome. To mitigate these challenges, we describe the development of the Multiscale Atomic Zeolite Simulation Environment (MAZE) – a Python package that simplifies zeolite-specific calculation workflows by providing a user-friendly interface for systematically manipulating zeolite structures

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Tuning Superhydrophilic Nanostructured Surfaces to Maximize Water Droplet Evaporation Heat Transfer Performance

Spraying water droplets on air fin surfaces is often used to augment the performance of air-cooled Rankine power plant condensers and wet cooling tower heat exchangers for building air-conditioning systems. To get the best performance in such processes, the water droplets delivered to the surface should spread rapidly into an extensive, thin film and evaporate with no liquid leaving the surface due to recoil or splashing. This paper presents predictions of theoretical/computational modeling and results of experimental studies of droplet spreading on thin-layer, nanostructured, superhydrophilic surfaces that exhibit very high wicking rates (wickability) in the porous layer. Analysis of the experimental data in the model framework illuminates the key aspects of the physics of the droplet-spreading process and evaporation heat transfer. This analysis also predicts the dependence of droplet-spreading characteristics on the nanoporous surface morphology and other system parameters. The combined results of this investigation indicate specific key strategies for design and fabrication of surface coatings that will maximize the heat transfer performance for droplet evaporation on heat exchanger surfaces. The implications regarding wickability effects on pool boiling processes are also discussed.

Engineering↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

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

Bridging interfacial properties and cell performance: A multiscale model for proton-exchange-membrane fuel cells

Here, to elucidate the impact of local interfaces on mass-transport resistance and overall cell performance of low-loaded proton-exchange-membrane fuel cells (PEMFCs), we present a multiscale modeling framework incorporating a novel modified agglomerate model. The model considers three distinct Pt-electrolyte interfaces: Pt on the carbon surface covered by either ionomer or water film and Pt inside carbon nanopores. Detailed mass-transport voltage-loss breakdowns reveal that coupled agglomerate-interface-scale mass transport dominates the mass-transport loss. The ionomer poisons the exterior-Pt surface through suppressing O 2 adsorption and intrinsic ORR activity, leading to low current-density performance. Conversely, interior-Pt interface enhances the kinetic performance but limits high current-density performance due to its low interfacial permeability. The exterior-Pt/water interface demonstrates superior kinetic performance and mass transport, though its practical implementation requires ensuring proton transport. By coupling the multiscale CL properties with ink parameters, the model identifies an optimal I to C ratio of approximately 0.5, a moderate value where the ionomer content is sufficient to guarantee proton transport without fully covering the Pt surface and forming large agglomeration, thus allowing the utilization of the Pt-water interface and avoiding high mass-transport loss. Overall, the model helps unravel limiting phenomena across different operating regimes and provides routes for optimizing performance.

Cell diagnostic↗