Engineered Nanoporous Frameworks for Adsorption Cooling Applications

The energy demand for traditional vapor-compressed technology for space cooling continues to soar year after year due to global warming and the increasing human population's need to improve living and working conditions. Thus, there is a growing demand for eco-friendly technologies that use sustainable or waste energy resources. This review discusses the properties of various refrigerants used for adsorption cooling applications followed by a brief discussion on the thermodynamic cycle. Next, sorbents traditionally used for cooling are reviewed to emphasize the need for advanced capture materials with superior properties to improve refrigerant sorption. The remainder of the review focus on studies using engineered nanoporous frameworks (ENFs) with various refrigerants for adsorption cooling applications. The effects of the various factors that play a role in ENF-refrigerant pair selection, including pore structure/dimension/shape, morphology, open-metal sites, pore chemistry and possible presence of defects, are reviewed. Next, in-depth insights into the sorbent-refrigerant interaction, and pore filling mechanism gained through a combination of characterization techniques and computational modeling are discussed. Finally, we outline the challenges and opportunities related to using ENFs for adsorption cooling applications and provide our views on the future of this technology.

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO₂ and N₂ adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms.

Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74

Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO₂ uptake and CO₂/N₂ selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO₂ adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO₂ in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO₂ in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.

A Theoretical Perspective to Decipher the Origin of High Hydrogen Storage Capacity in Mn(II) Metal-Organic Framework

Herein, we report a detailed periodic DFT investigation of Mn(II)-based [(Mn₄ Cl)₃ (BTT)₈ ]^3- (BTT^3- =1,3,5-benzenetristetrazolate) metal-organic framework (MOF) to explore various hydrogen binding pockets, nature of MOF…H₂ interactions, magnetic coupling and, H₂ uptake capacity. Earlier experiments found an uptake capacity of 6.9 wt % of H_2, with the heat of adsorption estimated to be ∼10 kJ/mol, which is one among the highest for any MOFs reported. Our calculations unveil different binding sites with computed binding energy varying from -6 to -15 kJ/mol. The binding of H₂ at the Mn²⁺ site is found to be the strongest (site I), with H₂ found to bind Mn²⁺ ion in a η² fashion with a distance of 2.27 Å and binding energy of -15.4 kJ/mol. The bonding analysis performed using NBO and AIM reveal a strong donation of σ (H₂ ) to the d_z ² orbital of the Mn²⁺ ion responsible for such large binding energy. The other binding pockets, such as -Cl (site II) and BTT ligands (site III and IV) were found to be weaker, with the binding energy decreasing in the order I>II>III>IV. The average binding energy computed for these four sites put together is 9.6 kJ/mol, which is in excellent agreement with the experimental value of ∼10 kJ/mol. We have expanded our calculations to compute binding energy for multiple sites simultaneously, and in this model, the binding energy per site was found to decrease as we increased the number of H₂ molecules suggesting electronic and steric factors controlling the overall uptake capacity. The calculated adsorption isotherm using the GCMC method reproduces the experimental observations. Further, the magnetic coupling computed for the unbound MOF reveals moderate ferromagnetic and strong antiferromagnetic coupling within the tetrameric {Mn₄ } unit leading to a three-up-one-down spin configuration as the ground state. These were then coupled ferromagnetically to other tetrameric units in the MOF network. The magnetic coupling was found to alter only marginally upon gas binding, suggesting that both exchange interaction and the spin-states are unlikely to play a role in the H₂ uptake. This is contrary to the O₂ uptake studied lately, where strong dependence on exchange-coupling/spin state was witnessed, suggesting exchange-coupling/magnetic field dependent binding as a viable route for gas separation.

Computational Characterization of Zr-Oxide MOFs for Adsorption Applications

Zr-oxide secondary building units construct metal-organic framework (MOF) materials with excellent gas adsorption properties and high mechanical, thermal, and chemical stability. These attributes have led Zr-oxide MOFs to be well-recognized for a wide range of applications, including gas storage and separation, catalysis, as well as healthcare domain. Here, we report structure search methods within the Cambridge Structural Database (CSD) to create a curated subset of 102 Zr-oxide MOFs synthesized to date, bringing a unique record for all researchers working in this area. For the identified structures, we manually corrected the proton topology of hydroxyl and water molecules on the Zr-oxide nodes and characterized their textural properties, Brunauer-Emmett-Teller (BET) area, and topology. Importantly, we performed systematic periodic density functional theory (DFT) calculations comparing 25 different combinations of basis sets and functionals to calculate framework partial atomic charges for use in gas adsorption simulations. Through experimental verification of CO₂ adsorption in selected Zr-oxide MOFs, we demonstrate the sensitivity of CO₂ adsorption predictions at the Henry's regime to the choice of the DFT method for partial charge calculations. We characterized Zr-MOFs for their CO₂ adsorption performance via high-throughput grand canonical Monte Carlo (GCMC) simulations and revealed how the chemistry of the Zr-oxide node could have a significant impact on CO₂ uptake predictions. We found that the maximum CO₂ uptake is obtained for structures with the heat of adsorption values >25 kJ/mol and the largest cavity diameters of ca. 6-7 Å. Finally, we introduced augmented reality (AR) visualizations as a means to bring adsorption phenomena alive in porous adsorbents and to dynamically explore gas adsorption sites in MOFs.

Eleven NanoHUB Simulation Tools Using RASPA Software To Demonstrate Classical Atomistic Simulations of Fluids and Nanoporous Materials

Eleven interactive simulation tools were created on nanoHUB to help users learn how to perform classical atomistic simulations. These tools enable users to perform classical Monte Carlo and molecular dynamics simulations using RASPA software. These tools use comparatively small numbers of production cycles to keep the runtimes short, so that users will not be discouraged by long wait times to see results. Here, we show that these tools produce results of sufficient accuracy and reproducibility for learning purposes. The 11 tools developed were as follows: (1) calculation of the self-diffusion constant of gas molecules in metal-organic frameworks (MOFs), (2) gas adsorption in MOFs using the grand canonical ensemble, (3) Henry's coefficient calculator for gas molecules in MOFs and a zeolite, (4) adsorption of a gas mixture in a MOF, (5) self-diffusion of a gas mixture in a MOF, (6) void fraction calculation for several MOFs and zeolites, (7) surface area calculation for several MOFs and zeolites, (8) calculation of radial distribution function and self-diffusion constant for several pure gases, (9) energy distribution of adsorption sites using a probe molecule in MOFs, (10) molecular dynamics simulation of pure fluids in the NPT ensemble, and (11) gas adsorption in MOFs using the Gibbs ensemble.

Confinement Effects on Distribution and Transport of Neutral Solutes in a Small Hydrophobic Nanopore

Molecular dynamics simulations are used to study confinement effects in small cylindrical silica pores with extended hydrophobic surface functionalization as realized, for example, in reversed-phase liquid chromatography (RPLC) columns. In particular, we use a 6 nm cylindrical and a 10 nm slit pore bearing the same C₁₈ stationary phase to compare the conditions inside the smaller-than-average pores within an RPLC column to column-averaged properties. Two small, neutral, apolar to moderately polar solutes are used to assess the consequences of spatial confinement for typical RPLC analytes with water (W)-acetonitrile (ACN) mobile phases at W/ACN ratios between 70/30 and 10/90 (v/v). The simulated data show that true bulk liquid behavior, as observed over an extended center region in the 10 nm slit pore, is not recovered within the 6 nm cylindrical pore. Instead, the ACN-enriched solvent layer around the C₁₈ chain ends (the ACN ditch), a general feature of hydrophobic interfaces equilibrated with aqueous-organic liquids, extends over the entire pore lumen of the small cylindrical pore. This renders the entire pore a highly hydrophobic environment, where, contrary to column-averaged behavior, neither the local nor the pore-averaged sorption and diffusion of analytes scales directly with the W/ACN ratio of the mobile phase. Additionally, the solute polarity-related discrimination between analytes is enhanced. The consequences of local ACN ditch overlap in RPLC columns are reminiscent of ion transport in porous media with charged surfaces, where electrical double-layer overlap occurring locally in smaller pores leads to discrimination between co- and counterionic species.

Extended Wertheim theory predicts the anomalous chain length distributions of divalent patchy particles under extreme confinement

Colloidal patchy particles with divalent attractive interaction can self-assemble into linear polymer chains. Their equilibrium properties in 2D and 3D are well described by Wertheim's thermodynamic perturbation theory which predicts a well-defined exponentially decaying equilibrium chain length distribution. In experi- mental realizations, due to gravity, particles sediment to the bottom of the suspension forming a monolayer of particles with a gravitational height smaller than the particle diameter. In accordance with experiments, an anomalously high monomer concentration is observed in simulations which is not well understood. To account for this observation, we interpret the polymerization as taking place in a highly confined quasi-2D plane and extend the Wertheim thermodynamic perturbation theory by defining addition reactions constants as functions of the chain length. We derive the theory, test it on simple square well potentials, and apply it to the experimental case of synthetic colloidal patchy particles immersed in a binary liquid mixture that are described by an accurate effective critical Casimir patchy particle potential. The important interaction parameters entering the theory are explicitly computed using the integral method in combination with Monte Carlo sampling. Without any adjustable parameter, the predictions of the chain length distribution are in excellent agreement with explicit simulations of self-assembling particles. We discuss generality of the approach, and its application range.

Parametrization of Nonbonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach

The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques as often as experiments. MOFs are widely known for their outstanding adsorption properties, so a precise description of the host-guest interactions is essential for high-throughput screening aimed at ranking the most promising candidates. However, highly accurate ab initio calculations cannot be routinely applied to model thousands of structures due to the demanding computational costs. Furthermore, methods based on force field (FF) parametrization suffer from low transferability. To resolve this accuracy-efficiency dilemma, we applied a machine learning (ML) approach: extreme gradient boosting. The trained models reproduced the atom-in-material quantities, including partial charges, polarizabilities, dispersion coefficients, quantum Drude oscillator, and electron cloud parameters, with accuracy similar to the reference data set. The aforementioned FF precursors make it possible to thoroughly describe noncovalent interactions typical for MOF-adsorbate systems: electrostatic, dispersion, polarization, and short-range repulsion. The presented approach can also readily facilitate hybrid atomistic simulation/ML workflows.

Performance-Based Screening of Porous Materials for Carbon Capture

Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

Assessment of Performance of Density Functionals for Predicting Potential Energy Curves in Hydrogen Storage Applications

The availability of accurate computational tools for modeling and simulation is vital to accelerate the discovery of materials capable of storing hydrogen (H₂) under given parameters of pressure swing and temperature. Previously, we compiled the H2Bind275 data set consisting of equilibrium geometries and assessed the performance of 55 density functionals over this data set (Veccham, S. P.; Head-Gordon, M. J. Chem. Theory Comput. 2020, 16, 4963-4982). As it is crucial for computational tools to accurately model the entire potential energy curve (PEC), in addition to the equilibrium geometry, we extended this data set with 389 new data points to include two compressed and three elongated geometries along 78 PECs for H₂ binding, forming the H2Bind78 × 7 data set. By assessing the performance of 55 density functionals on this significantly larger and more comprehensive H2Bind78 × 7 data set, we identified the best performing density functionals for H₂ binding applications: PBE0-DH, ωB97X-V, ωB97M-V, and DSD-PBEPBE-D3(BJ). The addition of Hartree-Fock exchange improves the performance of density functionals, albeit not uniformly throughout the PEC. We recommend the usage of ωB97X-V and ωB97M-V density functionals as they offer good performance for both geometries and energies. In addition, we also identified B97M-V and B97M-rV as the best semilocal density functionals for predicting H₂ binding energy at its equilibrium geometry.

Molecular simulations of the adsorption and separation of hydrogen sulfide, carbon dioxide, methane, and nitrogen and their binary mixtures (H2S/CH4), (CO2/CH4) on NUM-3a metal-organic frameworks

In this work, the adsorptions of carbon dioxide, methane, nitrogen, and hydrogen sulfide and the separation of their binary mixtures into NUM-3a Metal-Organic Framework (MOF) were studied through Grand Canonical Monte Carlo (GCMC) simulation method. The simulated pure gas uptakes using three generic force fields (UFF, Dreiding, and OPLS) at 298 K were compared with the experimental values. The accuracy of the applied force fields for each gas was compared with the experimental isotherms and discussed. Our results show that OPLS has the best accuracy in the case of methane while Dreiding was the best for CO₂ and N₂. Simulated gas uptakes indicated that H₂S was more adsorbed by NUM-3a than CO₂, CH₄, and N₂. The calculated adsorption selectivity of NUM-3a for the binary mixtures of CH₄ with H₂S is larger than that of CO₂. NUM-3a possess more affinity for H₂S and CO₂ than for CH₄, where it may be a promising adsorbent material for separating carbon dioxide and hydrogen sulfide from methane. Furthermore, the most probable sites for the adsorption of the studied gases on the NUM-3a were investigated. The heats of adsorptions, as well as Henry's law constants, were also calculated, and it was in line with the observed gas adsorptions. The most preferred sites for the adsorption of carbon dioxide and hydrogen sulfide are the carboxyl groups and inside the channels and around the metal centers. However, methane and nitrogen are mainly accumulating in the channels' s apexes of NUM-3a around the metal center.

Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids

Nanoporous solids are ubiquitous in chemical, energy, and environmental processes, where controlled transport of molecules through the pores plays a crucial role. They are used as sorbents, chromatographic or membrane materials for separations, and as catalysts and catalyst supports. Defined as materials where confinement effects lead to substantial deviations from bulk diffusion, nanoporous materials include crystalline microporous zeotypes and metal–organic frameworks (MOFs), and a number of semi-crystalline and amorphous mesoporous solids, as well as hierarchically structured materials, containing both nanopores and wider meso- or macropores to facilitate transport over macroscopic distances. The ranges of pore sizes, shapes, and topologies spanned by these materials represent a considerable challenge for predicting molecular diffusivities, but fundamental understanding also provides an opportunity to guide the design of new nanoporous materials to increase the performance of transport limited processes. Remarkable progress in synthesis increasingly allows these designs to be put into practice. Molecular simulation techniques have been used in conjunction with experimental measurements to examine in detail the fundamental diffusion processes within nanoporous solids, to provide insight into the free energy landscape navigated by adsorbates, and to better understand nano-confinement effects. Pore network models, discrete particle models and synthesis-mimicking atomistic models allow to tackle diffusion in mesoporous and hierarchically structured porous materials, where multiscale approaches benefit from ever cheaper parallel computing and higher resolution imaging. Here, we discuss synergistic combinations of simulation and experiment to showcase theoretical progress and computational techniques that have been successful in predicting guest diffusion and providing insights. We also outline where new fundamental developments and experimental techniques are needed to enable more accurate predictions for complex systems.

High-Pressure Insertion of Dense H2 into a Model Zeolite

Our combined high-pressure synchrotron X-ray diffraction and Monte Carlo modeling studies show super-filling of the zeolite, and computational results suggest an occupancy by a maximum of nearly two inserted H₂ molecules per framework unit, which is about twice that observed in gas hydrates. Super-filling prevents amorphization of the host material up to at least 60 GPa, which is a record pressure for zeolites and also for any group IV element being in full 4-fold coordination, except for carbon. We find that the inserted H₂ forms an exotic topologically constrained glassy-like form, otherwise unattainable in pure hydrogen. Raman spectroscopy on confined H₂ shows that the microporosity of the zeolite is retained over the entire investigated pressure range (up to 80 GPa) and that intermolecular interactions share common aspects with bulk hydrogen, while they are also affected by the zeolite framework.

ParaMol: A Package for Automatic Parameterization of Molecular Mechanics Force Fields

The ensemble of structures generated by molecular mechanics (MM) simulations is determined by the functional form of the force field employed and its parameterization. For a given functional form, the quality of the parameterization is crucial and will determine how accurately we can compute observable properties from simulations. While accurate force field parameterizations are available for biomolecules, such as proteins or DNA, the parameterization of new molecules, such as drug candidates, is particularly challenging as these may involve functional groups and interactions for which accurate parameters may not be available. Here, in an effort to address this problem, we present ParaMol, a Python package that has a special focus on the parameterization of bonded and nonbonded terms of druglike molecules by fitting to ab initio data. We demonstrate the software by deriving bonded terms' parameters of three widely known drug molecules, viz. aspirin, caffeine, and a norfloxacin analogue, for which we show that, within the constraints of the functional form, the methodologies implemented in ParaMol are able to derive near-ideal parameters. Additionally, we illustrate the best practices to follow when employing specific parameterization routes. We also determine the sensitivity of different fitting data sets, such as relaxed dihedral scans and configurational ensembles, to the parameterization procedure, and discuss the features of the various weighting methods available to weight configurations. Owing to ParaMol's capabilities, we propose that this software can be introduced as a routine step in the protocol normally employed to parameterize druglike molecules for MM simulations.

Probing the potential of ureasil-poly(ethylene oxide) as a glyphosate scavenger in aqueous milieu: force-field parameterization and MD simulations

The intensive use of glyphosate in conventional agriculture and its high solubility in water have led to contamination of aqueous systems worldwide.

Adsorption of Chiral [5]-Aza[5]helicenes on DNA Can Modify Its Hydrophilicity and Affect Its Chiral Architecture: A Molecular Dynamics Study

Helicenes are interesting chiral molecules without asymmetric carbon atoms but with intrinsic chirality. Functionalized 5-Aza[5]helicenes can form non-covalent complexes with anticancer drugs and therefore be potential carriers. The paper highlights the different structural selectivity for DNA binding for two enantiopure compounds and the influence of concentration on their adsorption and self-aggregation process. In this theoretical study based on atomistic molecular dynamics simulations the interaction between (M)- and (P)-5-Aza[5]helicenes with double helix B-DNA is investigated. At first the interaction of single pure enantiomer with DNA is studied, in order to find the preferred site of interaction at the major or minor groove. Afterwards, the interaction of the enantiomers at different concentrations was investigated considering both competitive adsorption on DNA and possible helicenes self-aggregation. Therefore, racemic mixtures were studied. The helicenes studied are able to bind DNA modulating or locally modifying its hydrophilic surface into hydrophobic after adsorption of the first helicene layer partially covering the negative charge of DNA at high concentration. The (P)-enantiomer shows a preferential binding affinity of DNA helical structure even during competitive adsorption in the racemic mixtures. These DNA/helicenes non-covalent complexes exhibit a more hydrophobic exposed surface and after self-aggregation a partially hidden DNA chiral architecture to the biological environment.

Combined Adsorption and Reaction in the Ternary Mixture N2, N2O4, NO2 on MIL-127 Examined by Computer Simulations

A high selectivity of NO _x over N₂ (simulating air) is found in silico when studying the adsorption of the ternary mixture N₂O₄/NO₂/N₂ on the metal-organic framework MIL-127(Fe) by molecular simulations under consideration of the recombination reaction N₂O₄ ↔ 2NO₂. The number of N atoms in nitrogen oxides NO _x and that in N₂ is used to define a selectivity of the combined adsorption and chemical recombination that can reach values of about 1000.