Electrostatic coupling and water bridging in adsorption hierarchy of biomolecules at water-clay interfaces.

Clay minerals are implicated in the retention of biomolecules within organic matter in many soil environments. Spectroscopic studies have proposed several mechanisms for biomolecule adsorption on clays. Here, we employ molecular dynamics simulations to investigate these mechanisms in hydrated adsorbate conformations of montmorillonite, a smectite-type clay, with ten biomolecules of varying chemistry and structure, including sugars related to cellulose and hemicellulose, lignin-related phenolic acid, and amino acids with different functional groups. Our molecular modeling captures biomolecule-clay and biomolecule-biomolecule interactions that dictate selectivity and competition in adsorption retention and interlayer nanopore trapping, which we determine experimentally by NMR and X-ray diffraction, respectively. Specific adsorbate structures are important in facilitating the electrostatic attraction and Van der Waals energies underlying the hierarchy in biomolecule adsorption. Stabilized by a network of direct and water-bridged hydrogen bonds, favorable electrostatic interactions drive this hierarchy whereby amino acids with positively charged side chains are preferentially adsorbed on the negatively charged clay surface compared to the sugars and carboxylate-rich aromatics and amino acids. With divalent metal cations, our model adsorbate conformations illustrate hydrated metal cation bridging of carboxylate-containing biomolecules to the clay surface, thus explaining divalent cation-promoted adsorption from our experimental data. Adsorption experiments with a mixture of biomolecules reveal selective inhibition in biomolecule adsorption, which our molecular modeling attributes to electrostatic biomolecule-biomolecule pairing that is more energetically favorable than the biomolecule-clay complex. In sum, our findings highlight chemical and structural features that can inform hypotheses for predicting biomolecule adsorption at water-clay interfaces.

Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling.

Understanding the intracellular dynamics of brain cells entails performing three-dimensional molecular simulations incorporating ultrastructural models that can capture cellular membrane geometries at nanometer scales. While there is an abundance of neuronal morphologies available online, e.g. from NeuroMorpho.Org, converting those fairly abstract point-and-diameter representations into geometrically realistic and simulation-ready, i.e. watertight, manifolds is challenging. Many neuronal mesh reconstruction methods have been proposed; however, their resulting meshes are either biologically unplausible or non-watertight. We present an effective and unconditionally robust method capable of generating geometrically realistic and watertight surface manifolds of spiny cortical neurons from their morphological descriptions. The robustness of our method is assessed based on a mixed dataset of cortical neurons with a wide variety of morphological classes. The implementation is seamlessly extended and applied to synthetic astrocytic morphologies that are also plausibly biological in detail. Resulting meshes are ultimately used to create volumetric meshes with tetrahedral domains to perform scalable in silico reaction-diffusion simulations for revealing cellular structure-function relationships. Availability and implementation: Our method is implemented in NeuroMorphoVis, a neuroscience-specific open source Blender add-on, making it freely accessible for neuroscience researchers.

Driving and characterizing nucleation of urea and glycine polymorphs in water.

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework "reweighted autoencoded variational Bayes for enhanced sampling (RAVE)." We study two molecular systems-urea and glycine-in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.

Free energies at QM accuracy from force fields via multimap targeted estimation.

Accurate predictions of ligand binding affinities would greatly accelerate the first stages of drug discovery campaigns. However, using highly accurate interatomic potentials based on quantum mechanics (QM) in free energy methods has been so far largely unfeasible due to their prohibitive computational cost. Here, we present an efficient method to compute QM free energies from simulations using cheap reference potentials, such as force fields (FFs). This task has traditionally been out of reach due to the slow convergence of computing the correction from the FF to the QM potential. To overcome this bottleneck, we generalize targeted free energy methods to employ multiple maps-implemented with normalizing flow neural networks (NNs)-that maximize the overlap between the distributions. Critically, the method requires neither a separate expensive training phase for the NNs nor samples from the QM potential. We further propose a one-epoch learning policy to efficiently avoid overfitting, and we combine our approach with enhanced sampling strategies to overcome the pervasive problem of poor convergence due to slow degrees of freedom. On the drug-like molecules in the HiPen dataset, the method accelerates the calculation of the free energy difference of switching from an FF to a DFTB3 potential by three orders of magnitude compared to standard free energy perturbation and by a factor of eight compared to previously published nonequilibrium calculations. Our results suggest that our method, in combination with efficient QM/MM calculations, may be used in lead optimization campaigns in drug discovery and to study protein-ligand molecular recognition processes.

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience.

Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE: There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations.

Electric fields control water-gated proton transfer in cytochrome c oxidase.

Aerobic life is powered by membrane-bound enzymes that catalyze the transfer of electrons to oxygen and protons across a biological membrane. Cytochrome c oxidase (CcO) functions as a terminal electron acceptor in mitochondrial and bacterial respiratory chains, driving cellular respiration and transducing the free energy from O2 reduction into proton pumping. Here we show that CcO creates orientated electric fields around a nonpolar cavity next to the active site, establishing a molecular switch that directs the protons along distinct pathways. By combining large-scale quantum chemical density functional theory (DFT) calculations with hybrid quantum mechanics/molecular mechanics (QM/MM) simulations and atomistic molecular dynamics (MD) explorations, we find that reduction of the electron donor, heme a, leads to dissociation of an arginine (Arg438)-heme a3 D-propionate ion-pair. This ion-pair dissociation creates a strong electric field of up to 1 V Å-1 along a water-mediated proton array leading to a transient proton loading site (PLS) near the active site. Protonation of the PLS triggers the reduction of the active site, which in turn aligns the electric field vectors along a second, "chemical," proton pathway. We find a linear energy relationship of the proton transfer barrier with the electric field strength that explains the effectivity of the gating process. Our mechanism shows distinct similarities to principles also found in other energy-converting enzymes, suggesting that orientated electric fields generally control enzyme catalysis.

From data to noise to data for mixing physics across temperatures with generative artificial intelligence.

Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical and theoretical relevance. Here we develop a framework based on statistical mechanics and generative artificial intelligence that allows solving this problem. Specifically, we work with denoising diffusion probabilistic models and show how these models in combination with replica exchange molecular dynamics achieve superior sampling of the biomolecular energy landscape at temperatures that were never simulated without assuming any particular slow degrees of freedom. The key idea is to treat the temperature as a fluctuating random variable and not a control parameter as is usually done. This allows us to directly sample from the joint probability distribution in configuration and temperature space. The results here are demonstrated for a chirally symmetric peptide and single-strand RNA undergoing conformational transitions in all-atom water. We demonstrate how we can discover transition states and metastable states that were previously unseen at the temperature of interest and even bypass the need to perform further simulations for a wide range of temperatures. At the same time, any unphysical states are easily identifiable through very low Boltzmann weights. The procedure while shown here for a class of molecular simulations should be more generally applicable to mixing information across simulations and experiments with varying control parameters.

Structural basis of mammalian complex IV inhibition by steroids.

The mitochondrial electron transport chain maintains the proton motive force that powers adenosine triphosphate (ATP) synthesis. The energy for this process comes from oxidation of reduced nicotinamide adenine dinucleotide (NADH) and succinate, with the electrons from this oxidation passed via intermediate carriers to oxygen. Complex IV (CIV), the terminal oxidase, transfers electrons from the intermediate electron carrier cytochrome c to oxygen, contributing to the proton motive force in the process. Within CIV, protons move through the K and D pathways during turnover. The former is responsible for transferring two protons to the enzyme's catalytic site upon its reduction, where they eventually combine with oxygen and electrons to form water. CIV is the main site for respiratory regulation, and although previous studies showed that steroid binding can regulate CIV activity, little is known about how this regulation occurs. Here, we characterize the interaction between CIV and steroids using a combination of kinetic experiments, structure determination, and molecular simulations. We show that molecules with a sterol moiety, such as glyco-diosgenin and cholesteryl hemisuccinate, reversibly inhibit CIV. Flash photolysis experiments probing the rapid equilibration of electrons within CIV demonstrate that binding of these molecules inhibits proton uptake through the K pathway. Single particle cryogenic electron microscopy (cryo-EM) of CIV with glyco-diosgenin reveals a previously undescribed steroid binding site adjacent to the K pathway, and molecular simulations suggest that the steroid binding modulates the conformational dynamics of key residues and proton transfer kinetics within this pathway. The binding pose of the sterol group sheds light on possible structural gating mechanisms in the CIV catalytic cycle.

Functional enzyme-polymer complexes.

SignificanceThe use of biological enzyme catalysts could have huge ramifications for chemical industries. However, these enzymes are often inactive in nonbiological conditions, such as high temperatures, present in industrial settings. Here, we show that the enzyme PETase (polyethylene terephthalate [PET]), with potential application in plastic recycling, is stabilized at elevated temperature through complexation with random copolymers. We demonstrate this through simulations and experiments on different types of substrates. Our simulations also provide strategies for designing more enzymatically active complexes by altering polymer composition and enzyme charge distribution.

The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes.

The nature of ion-ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion-ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene's electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes.

Y225A induces long-range conformational changes in human prion protein that are protective in Drosophila.

Prion protein (PrP) misfolding is the key trigger in the devastating prion diseases. Yet the sequence and structural determinants of PrP conformation and toxicity are not known in detail. Here, we describe the impact of replacing Y225 in human PrP with A225 from rabbit PrP, an animal highly resistant to prion diseases. We first examined human PrP-Y225A by molecular dynamics simulations. We next introduced human PrP in Drosophila and compared the toxicity of human PrP-WT and Y225A in the eye and in brain neurons. Y225A stabilizes the ß2-α2 loop into a 310-helix from six different conformations identified in WT and lowers hydrophobic exposure. Transgenic flies expressing PrP-Y225A exhibit less toxicity in the eye and in brain neurons and less accumulation of insoluble PrP. Overall, we determined that Y225A lowers toxicity in Drosophila assays by promoting a structured loop conformation that increases the stability of the globular domain. These findings are significant because they shed light on the key role of distal α-helix 3 on the dynamics of the loop and the entire globular domain.

"Boundary residues" between the folded RNA recognition motif and disordered RGG domains are critical for FUS-RNA binding.

Fused in sarcoma (FUS) is an abundant RNA-binding protein, which drives phase separation of cellular condensates and plays multiple roles in RNA regulation. The RNA-binding ability of FUS protein is crucial to its cellular function. Here, our molecular simulation study on the FUS-RNA complex provides atomic resolution insights into the observations from biochemical studies and also illuminates our understanding of molecular driving forces that mediate the structure, stability, and interaction of the RNA recognition motif (RRM) and RGG domains of FUS with a stem-loop junction RNA. We observe clear cooperativity and division of labor among the ordered (RRM) and disordered domains (RGG1 and RGG2) of FUS that leads to an organized and tighter RNA binding. Irrespective of the length of RGG2, the RGG2-RNA interaction is confined to the stem-loop junction and the proximal stem regions. On the other hand, the RGG1 interactions are primarily with the longer RNA stem. We find that the C terminus of RRM, which make up the "boundary residues" that connect the folded RRM with the long disordered RGG2 stretch of the protein, plays a critical role in FUS-RNA binding. Our study provides high-resolution molecular insights into the FUS-RNA interactions and forms the basis for understanding the molecular origins of full-length FUS interaction with RNA.

Allosteric pathways of SARS and SARS-CoV-2 spike protein identified by neural relational inference.

The receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein must undergo a crucial conformational transition to invade human cells. It is intriguing that this transition is accompanied by a synchronized movement of the entire spike protein. Therefore, it is possible to design allosteric regulators targeting non-RBD but hindering the conformational transition of RBD. To understand the allosteric mechanism in detail, we establish a computational framework by integrating coarse-grained molecular dynamic simulations and a state-of-the-art neural network model called neural relational inference. Leveraging this framework, we have elucidated the allosteric pathway of the SARS-CoV-2 spike protein at the residue level and identified the molecular mechanisms involved in the transmission of allosteric signals. The movement of D614 is coupled with that of Q321. This interaction subsequently influences the movement of K528/K529, ultimately coupling with the movement of RBD during conformational changes. Mutations that weaken the interactions within this pathway naturally block the allosteric signal transmission, thereby modulating the conformational transitions. This observation also offers a rationale for the distinct allosteric patterns observed in the SARS-CoV spike protein. Our result provides a useful method for analyzing the dynamics of potential viral variants in the future.

Glycans modulate lipid binding in Lili-Mip lipocalin protein: insights from molecular simulations and protein network analyses.

The unique viviparous Pacific Beetle cockroaches provide nutrition to their embryo by secreting milk proteins Lili-Mip, a lipid-binding glycoprotein that crystallises in-vivo. The resolved in-vivo crystal structure of variably glycosylated Lili-Mip shows a classical Lipocalin fold with an eight-stranded antiparallel beta-barrel enclosing a fatty acid. The availability of physiologically unaltered glycoprotein structure makes Lili-Mip a very attractive model system to investigate the role of glycans on protein structure, dynamics, and function. Towards that end, we have employed all-atom molecular dynamics simulations on various glycosylated stages of a bound and free Lili-Mip protein and characterised the impact of glycans and the bound lipid on the dynamics of this glycoconjugate. Our work provides important molecular-level mechanistic insights into the role of glycans in the nutrient storage function of the Lili-Mip protein. Our analyses show that the glycans stabilise spatially proximal residues and regulate the low amplitude opening motions of the residues at the entrance of the binding pocket. Glycans also preserve the native orientation and conformational flexibility of the ligand. However, we find that either deglycosylation or glycosylation with high-mannose and paucimannose on the core glycans, which better mimic the natural insect glycosylation state, significantly affects the conformation and dynamics. A simple but effective distance- and correlation-based network analysis of the protein also reveals the key residues regulating the barrel's architecture and ligand binding characteristics in response to glycosylation.

Amidation-Reaction Strategy Constructs Versatile Mixed Matrix Composite Membranes towards Efficient Volatile Organic Compounds Adsorption and CO2 Separation.

Mixed matrix composite membranes (MMCMs) have shown advantages in reducing VOCs and CO2 emissions. Suitable composite layer, substrate, and good compatibility between the filler and the matrix in the composite layer are critical issues in designing MMCMs. This work develops a high-performance UiO-66-NA@PDMS/MCE for VOCs adsorption and CO2 permea-selectivity, based on a simple and facile fabrication of composite layer using amidation-reaction approach on the substrate. The composite layer shows a continuous morphological appearance without interface voids. This outstanding compatibility interaction between UiO-66-NH2 and PDMS is confirmed by molecular simulations. The Si─O functional group and UiO-66-NH2 in the layer leads to improved VOCs adsorption via active sites, skeleton interaction, electrostatic interaction, and van der Waals force. The layer and ─CONH─ also facilitate CO2 transport. The MMCMs show strong four VOCs adsorption and high CO2 permeance of 276.5 GPU with a selectivity of 36.2. The existence of VOCs in UiO-66-NA@PDMS/MCE increases the polarity and fine-tunes the pore size of UiO-66-NH2, improving the affinity towards CO2 and thus promoting the permea-selectivity for CO2, which is further verified by GCMC and EMD methods. This work is expected to offer a facile composite layer manufacturing method for MMCMs with high VOC adsorption and CO2 permea-selectivity.

Biophysical and structural characterization of tetramethrin serum protein complex and its toxicological implications.

Tetramethrin (TMT) is a commonly used insecticide and has a carcinogenic and neurodegenerative effect on humans. The binding mechanism and toxicological implications of TMT to human serum albumin (HSA) were examined in this study employing a combination of biophysical and computational methods indicating moderate binding affinity and potential hepato and renal toxicity. Fluorescence quenching experiments showed that TMT binds to HSA with a moderate affinity, and the binding process was spontaneous and predominantly enthalpy-driven. Circular dichroism spectroscopy revealed that TMT binding did not induce any significant conformational changes in HSA, resulting in no changes in its alpha-helix content. The binding site and modalities of TMT interactions with HSA as computed by molecular docking and molecular dynamics simulations revealed that it binds to Sudlow site II of HSA via hydrophobic interactions through its dimethylcyclopropane carboxylate methyl propanyl group. The structural dynamics of TMT induce proper fit into the binding site creating increased and stabilizing interactions. Additionally, molecular mechanics-Poisson Boltzmann surface area calculations also indicated that non-polar and van der Waals were found to be the major contributors to the high binding free energy of the complex. Quantum mechanics (QM) revealed the conformational energies of the binding confirmation and the degree of deviation from the global minimum energy conformation of TMT. The results of this study provide a comprehensive understanding of the binding mechanism of TMT with HSA, which is important for evaluating the toxicity of this insecticide in humans.

Molecular-Level Insight into the Chlorofluorocarbons Adsorption by Defective Covalent Organic Polymers.

Halocarbons have important industrial applications, but because of their contribution to global warming and the fact that they can cause ozone depletion, they are considered highly toxic. Hence, the techniques that can capture and recover the used halocarbons with energy-efficient methods have been recently received greater attention. In this contribution, we report the capture of dichlorodifluoromethane (R12), which has high global warming and ozone depletion potential, using covalent organic polymers (COPs). The defect-engineered COPs were synthesized and demonstrated outstanding sorption capacities, ~226 wt % of R12 combined with linear-shaped adsorption isotherms. We further identified the plausible microscopic adsorption mechanism of the investigated COPs via grand canonical Monte Carlo simulations applied to non-defective and a collection of atomistic models of the defective COPs. The modeling work suggests that significant R12 adsorption performance is attributed to a gradual increment of porosities due to isolated/interconnected micro-/meso-pore channels and the change of the long-range ordering of both COPs. The successive hierarchical-pore-filling mechanism promotes R12 molecular adsorption via moderate van der Waals adsorbate-adsorbent interactions in the micropores of both COPs at low pressure followed by adsorbate-adsorbate interactions in the extra-voids created at moderate to high pressure ranges. This continuous pore-filling mechanism makes defective COPs as promising sorbents for halocarbon adsorption.

Exploring the potential of beta-cyclodextrin-based MIL-101(Cr) for pharmaceutical removal from wastewater: A combined density functional theory and molecular simulations study.

Pharmaceutical contaminants pose significant risks to ecosystems and human health, necessitating effective removal strategies. This research focuses on developing advanced adsorbents for removing pharmaceutical pollutants from the environment. Metal-organic frameworks (MOFs), specifically MIL-101(Cr) functionalized with biodegradable beta-cyclodextrin (ß-CDex), were investigated as potential nanocomposite adsorbents for the removal of ketorolac (KTRK), naproxen (NPXN), and tramadol (TRML). The study employed molecular simulations and density functional theory (DFT) calculations to explore the interactions between the pollutants and adsorbents. Analyses of DFT results, including electrostatic potential, ionization energy, density of states, and molecular orbital analysis, provided insights into the reactivity of pollutants and adsorbents. Additionally, the structural properties of the adsorbents, such as fractional free volume, radius of gyration, and system energies, were thoroughly examined. Molecular dynamics (MD) and Monte Carlo (MC) simulations were used to evaluate the adsorption capacities of MIL-101(Cr) for the target pharmaceutical pollutants. The results demonstrated the superior adsorption performance of the nanocomposite adsorbent, particularly for KTRK, with an adsorption energy of -1934 kcal/mol, compared to the pristine MIL-101(Cr), which had an adsorption energy of -1916 kcal/mol. This enhanced adsorption is attributed to the optimal molecular fit, guest-host solid interactions, and the selective encapsulation capabilities of ß-CDex. This research highlights the potential of MOF-based nanocomposites as effective and sustainable solutions for pharmaceutical pollution. By advancing the understanding of molecular interactions through simulations, this study contributes to developing innovative adsorbents for wastewater treatment and the protection of water resources.

Tackling antibiotic contaminations in wastewater with novel Modified-MOF nanostructures: A study of molecular simulations and DFT calculations.

The contamination of wastewater with antibiotics has emerged as a critical global challenge, with profound implications for environmental integrity and human well-being. Adsorption techniques have been meticulously investigated and developed to mitigate and alleviate their effects. In this study, we have investigated the adsorption behaviour of Erythromycin (ERY), Gentamicin (GEN), Levofloxacin (LEVO), and Metronidazole (MET) antibiotics as pharmaceutical contaminants (PHCs) on amide-functionalized (RC (=O)NH2)/MIL-53 (Al) (AMD/ML53A), using molecular simulations and density functional theory (DFT) calculations. Based on our DFT calculations, it becomes apparent that the adsorption tendencies of antibiotics are predominantly governed by the presence of AMD functional groups on the adsorbent surface. Specifically, hydrogen bonding (HB) and van der Waals (vdW) interactions between antibiotics and AMD groups serve as the primary mechanisms facilitating adsorption. Furthermore, we have observed that the adsorption behaviors of these antibiotics are influenced by their respective functional groups, molecular shapes, and sizes. Our molecular simulations delved into how the AMD/ML53A surfaces interact with antibiotics as PHCs. Moreover, various chemical quantum descriptors based on Frontier Molecular Orbitals (FMO) were explored to elucidate the extent of AMD/ML53A adsorption and to assess potential alterations in their electronic properties throughout the adsorption process. Monte Carlo simulation showed that ERY molecules adsorb stronger to the adsorbent in acidic and basic conditions than other contaminants, with high energies: -404.47 kcal/mol in acidic and -6375.26 kcal/mol in basic environments. Molecular dynamics (MD) simulations revealed parallel orientation for the ERY molecule's adsorption on AMD/ML53A with 80% rejection rate. In conclusion, our study highlighted the importance of modeling in developing practical solutions for removing antibiotics as PHCs from wastewater. The insights gained from our calculations can facilitate the design of more effective adsorption materials, ultimately leading to a more hygienic and sustainable ecosystem.

Unveiling the mechanisms behind high CO2 adsorption by the selection of suitable ionic liquids incorporated into a ZIF-8 metal organic framework: A computational approach.

There is growing focus on the crucial task of effectively capturing carbon dioxide (CO2) from the atmosphere to mitigate environmental consequences. Metal-organic frameworks (MOFs) have been used to replace many conventional materials in gas separation, and the incorporation of ionic liquids (ILs) into porous MOFs has shown promise as a new technique for improving CO2 capture and separation. However, the driving force underlying the electronic modulation of MOF nanostructures and the mechanisms behind their high CO2 adsorption remain unclear. This study reports the effect of encapsulating different imidazolium ILs in porous ZIF-8, to clarify the adsorption mechanism of CO2 using density functional theory (DFT)-based approaches. For this purpose, a range of anions, including bis(trifluoromethylsulfonyl)imide [NTf2], methanesulfonate [MeSO3], and acetate [AC], were combined with the 1-ethyl-3-methylimidazolium [EMIM]+ cation. [EMIM]+-based ILs@ZIF-8 composites were computationally investigated to identify suitable materials for CO2 capture. First, the intermolecular and intramolecular interactions between [EMIM]+ and different anions were examined in detail, and their effects on CO2 adsorption were explored. Subsequently, the integration of these ILs into the ZIF-8 solid structure was studied to reveal how their interactions influenced the CO2 adsorption behavior. Our results demonstrate that the incorporation of ILs strongly affects the adsorption capability of CO2, which is highly dependent on the nature of the ILs inside the ZIF-8 framework. DFT simulations further confirmed that the incorporation of ILs into ZIF-8 led to superior CO2 capture compared to isolated ILs and pristine ZIF-8. This improvement was attributed to the mutual interactions between the ILs and ZIF-8, which effectively fine-tuned CO2 adsorption within the composite structure. This understanding may act as a general guide for gaining more insight into the interfacial interactions between ILs and ZIFs structures and how these molecular-level interactions can help predict the selection of ILs for CO2 adsorption and separation, thereby addressing environmental challenges with greater precision and effectiveness.