Pathway Selection in Temporal Evolution of Supramolecular Polymers of Ionic π-Systems: Amphiphilic Organic Solvent Dictates the Fate of Water.

Understanding solvent-solute interactions is essential to designing and synthesising soft materials with tailor-made functions. Although the interaction of the solute with the solvent mixture is more complex than the single solvent medium, solvent mixtures are exciting to unfold several unforeseen phenomena in supramolecular chemistry. Here, we report two unforeseen pathways observed during the hierarchical assembly of cationic perylene diimides (cPDIs) in water and amphiphilic organic solvent (AOS) mixtures. When the aqueous supramolecular polymers (SPs) of cPDIs are injected into AOS, initially kinetically trapped short SPs are formed, which gradually transform into thermodynamically stable high aspect ratio SP networks. Using various experimental and theoretical investigations, we found that this temporal evolution follows two distinct pathways depending on the nature of the water-AOS interactions. If the AOS is isopropanol (IPA), water is released from cPDIs into bulk IPA due to strong hydrogen bonding interactions, which further decreases the monomer concentration of cPDIs (Pathway-1). In the case of dioxane AOS, cPDI monomer concentration further increases as water is retained among cPDIs (Pathway-2) due to relatively weak interactions between dioxane and water. Interestingly, these two pathways are accelerated by external stimuli such as heat and mechanical agitation.

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption.

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.

Development of a Systematic and Extensible Force Field for Peptoids (STEPs).

Peptoids (N-substituted glycines) are a class of biomimetic polymers that have attracted significant attention due to their accessible synthesis and enzymatic and thermal stability relative to their naturally occurring counterparts (polypeptides). While these polymers provide the promise of more robust functional materials via hierarchical approaches, they present a new challenge for computational structure prediction for material design. The reliability of calculations hinges on the accuracy of interactions represented in the force field used to model peptoids. For proteins, structure prediction based on sequence and de novo design has made dramatic progress in recent years; however, these models are not readily transferable for peptoids. Current efforts to develop and implement peptoid-specific force fields are spread out, leading to replicated efforts and a fragmented collection of parameterized sidechains. Here, we developed a peptoid-specific force field containing 70 different side chains, using GAFF2 as starting point. The new model is validated based on the generation of Ramachandran-like plots from DFT optimization compared against force field reproduced potential energy and free energy surfaces as well as the reproduction of equilibrium cis/trans values for some residues experimentally known to form helical structures. Equilibrium cis/trans distributions (Kct) are estimated for all parameterized residues to identify which residues have an intrinsic propensity for cis or trans states in the monomeric state.

Biodegradation Studies of Polyhydroxybutyrate and Polyhydroxybutyrate-co-Polyhydroxyvalerate Films in Soil.

Due to increased environmental pressures, significant research has focused on finding suitable biodegradable plastics to replace ubiquitous petrochemical-derived polymers. Polyhydroxyalkanoates (PHAs) are a class of polymers that can be synthesized by microorganisms and are biodegradable, making them suitable candidates. The present study looks at the degradation properties of two PHA polymers: polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-polyhydroxyvalerate (PHBV; 8 wt.% valerate), in two different soil conditions: soil fully saturated with water (100% relative humidity, RH) and soil with 40% RH. The degradation was evaluated by observing the changes in appearance, chemical signatures, mechanical properties, and molecular weight of samples. Both PHB and PHBV were degraded completely after two weeks in 100% RH soil conditions and showed significant reductions in mechanical properties after just three days. The samples in 40% RH soil, however, showed minimal changes in mechanical properties, melting temperatures/crystallinity, and molecular weight over six weeks. By observing the degradation behavior for different soil conditions, these results can pave the way for identifying situations where the current use of plastics can be replaced with biodegradable alternatives.

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations.

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure-property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young's modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young's modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young's modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers.

Diminishing fossil fuel-based resources and ever-growing environmental concerns related to plastic pollution demand for the development of sustainable and biodegradable polymeric material alternatives. Polyhydroxyalkanoates (PHAs) represent an eco-friendly and economically viable class of polymers with a wide range of applications. However, the chemical diversity combined with tunable physical properties available within PHAs poses discovery and optimization challenges with respect to identifying optimal application-specific chemical compositions. Here we use an example of melting temperature (Tm) prediction to demonstrate the promise of machine learning (ML)-based techniques for establishing efficient structure-property mappings in PHA-based chemical space. We employ a manually curated data set of experimentally measured Tm values for a wide range of PHA homo- and copolymer chemistries along with their reported polymer molecular weights and polydispersity indices. Descriptors based on topology, shape, and charge/polarity of specific motifs forming the polymer backbone were then used to numerically represent the polymers. The ML models developed by using available data were used to rapidly predict the property of multicomponent PHA-based copolymers, while estimating uncertainties underlying the predictions. Combined with a previously developed glass transition temperature (Tg) prediction model and an evolutionary algorithm-based search strategy, the approach is demonstrated to address polymer design with multiobjective optimization challenges.

Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers.

Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure-property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers. Towards this aim, here we present and validate the results of our molecular dynamics (MD) simulation based approach aimed at predicting glass transition temperatures (Tg) of PHA-based polymers. Since generally available and widely used polymer forcefields exhibit a relatively poor performance for Tg predictions, we have developed a new forcefield by modifying the polymer consistent force field (PCFF) via refining a selected set of torsion potentials of the polymer backbone using accurate density functional theory (DFT) computations. After carefully assessing the dependence of critical simulation parameters, such as, polymer chain length, number of polymer chains, supercell size, and thermal quenching rate used in the simulation, the applicability and transferability of the modified PCFF (mPCFF) is demonstrated by directly comparing the computed Tg predictions of various polymers with different chemistries, polymer side chain lengths and functional groups forming the polymer side chains against the respective experimentally measured values. Furthermore, the transport properties such as self-diffusion coefficient and viscosity are computationally determined and their well-known correlation with the target properties is demonstrated. Lastly, we have employed the developed approach to predict Tg values for a number of yet-to-be-synthesized PHA-based polymers with a diverse set of functional groups in the polymer side chains. The results are further rationalized by correlating the predicted Tg values with the inter-chain H-bond formation tendencies of the different side chain functional groups. This work represents an important first step towards computationally guided design of PHA-based functional polymers and opens up new directions for a systematic investigation of composition- and configuration-dependent structure-property relationships in more complex binary and ternary copolymer systems.

Solvation dynamics of N-substituted acrylamide polymers and the importance for phase transition behavior.

Functional groups present in thermo-responsive polymers are known to play an important role in aqueous solutions by manifesting their coil-to-globule conformational transition in a specific temperature range. Understanding the role of these functional groups and their interactions with water is of great interest as it may allow us to control both the nature and temperature of this coil-to-globule transition. In this work, polyacrylamide (PAAm), poly(N-isopropylacrylamide) (PNIPAm), and poly(N-isopropylmethacrylamide) (PNIPMAm) solvated in water are studied with the goal of discovering the structure of the solvent and its interaction with these polymers in determining the polymer conformations. Specifically, all-atom molecular dynamics (MD) simulations were performed on polymer chains with 30 monomer units (30-mers) at 295 K, 310 K and 320 K, which is below and above the lower critical solution temperature (LCST) of PNIPAm (LCST = 305 K) and PNIPMAm (LCST = 315 K), respectively. The MD simulation trajectories suggest that changes in the functional groups in the backbone and side-chains alter the water solvation shell around the polymer. This results in a change in the residence time probability and hydrogen bond characteristics of water at simulated temperatures. Specifically, water molecules reside for longer times near PAAm (no LCST) and PNIPMAm (LCST = 315 K) chains as compared to PNIPAm. This might be one of the possible causes for the higher LCST of PNIPMAm as compared to that of PNIPAm. These results can guide experimentalists and theoreticians to design new polymer structures with tailor-made LCST transitions while controlling the water solvation shell around the functional group.

Machine-Learning Based Stacked Ensemble Model for Accurate Analysis of Molecular Dynamics Simulations.

Accurate, faster, and on-the-fly analysis of the molecular dynamics (MD) simulations trajectory becomes very critical during the discovery of new materials or while developing force-field parameters due to automated nature of these processes. Here to overcome the drawbacks of algorithm based analysis approaches, we have developed and utilized an approach that integrates machine-learning (ML) based stacked ensemble model (SEM) with MD simulations, for the first time. As a proof-of-concept, two SEMs were developed to analyze two dynamical properties of a water droplet, its contact angle, and hydrogen bonds. The two SEMs consisted of two layered networks of random forest, artificial neural network, support vector regression, Kernel ridge regression, and k-nearest neighbors ML models. The root-mean-square error values, uncertainty quantification, and sensitivity analysis of both the SEMs suggested that the final result was more accurate as compared to that of the individual ML models. This new computational framework is very general, robust, and has a huge potential in analyzing large size MD simulation trajectories as it can capture critical information very accurately.

Development of Transferable Nonbonded Interactions between Coarse-Grained Hydrocarbon and Water Models.

Interactions between water and hydrocarbons play a significant role in chemical, physical, and biological processes. Here, we present a set of force-field (FF) parameters that define the interactions between coarse-grained (CG) hydrocarbon models ( An , Y. J. Phys. Chem. B , 2018 , 122 , 7143 - 7153 ) and one-site water model ( Bejagam , K. K. J. Phys. Chem. B , 2018 , 122 , 1958 - 1971 ) developed in our recent work. The nonbonded FF interactions between various hydrocarbon beads and the water beads are represented by the 12-6 Lennard-Jones potential. The FF parameters were optimized to reproduce the experimentally measured Gibbs hydration free energies of selected hydrocarbon models (decane and hexadecane with 2:1 mapping scheme and nonane and pentadecane with 3:1 mapping scheme) and the interfacial tensions of decane and nonane models at 300 K. The predicted values of Gibbs hydration free energies of CG decane, hexadecane, nonane, and pentadecane models by the optimized FF parameters were within 8, 12, 11, and 4% of their corresponding experimental values, respectively. These new optimized FF parameters were transferable when used to calculate the Gibbs hydration free energies of different hydrocarbons ranging from pentane to heptadecane at 300 K (minimum error ∼0.5%, and maximum error ∼40.8%). Furthermore, the interfacial tensions of the CG hydrocarbon models calculated by using these new FF parameters showed good agreement with their corresponding experimental values at 300 K. Homogeneous mixtures of CG water and hydrocarbon models were able to exhibit the phase segregation during 1 µs. These new nonbonded interaction parameters were expected to be utilized in modeling the interactions between water and polymer backbones represented with hydrocarbon beads.

Machine-Learning Enabled New Insights into the Coil-to-Globule Transition of Thermosensitive Polymers Using a Coarse-Grained Model.

We present a computational framework that integrates coarse-grained (CG) molecular dynamics (MD) simulations and a data-driven machine-learning (ML) method to gain insights into the conformations of polymers in solutions. We employ this framework to study conformational transition of a model thermosensitive polymer, poly( N-isopropylacrylamide) (PNIPAM). Here, we have developed the first of its kind, a temperature-independent CG model of PNIPAM that can accurately predict its experimental lower critical solution temperature (LCST) while retaining the tacticity in the presence of an explicit water model. The CG model was extensively validated by performing CG MD simulations with different initial conformations, varying the radius of gyration of chain, the chain length, and the angle between the adjacent monomers of the initial configuration of PNIPAM (total simulation time = 90 µs). Moreover, for the first time, we utilize the nonmetric multidimensional scaling (NMDS) method, a data-driven ML approach, to gain further insights into the mechanisms and pathways of this coil-to-globule transition by analyzing CG MD simulation trajectories. NMDS analysis provides entirely new insights and shows multiple metastable states of PNIPAM during its coil-to-globule transition above the LCST.

Machine-Learned Coarse-Grained Models.

Optimizing force-field (FF) parameters to perform molecular dynamics (MD) simulations is a challenging and time-consuming process. We present a novel FF optimization framework that integrates MD simulations with particle swarm optimization (PSO) algorithm and artificial neural network (ANN). This new ANN-assisted PSO framework was used to develop transferable coarse-grained (CG) models for D2O and DMF as a proof of concept. The PSO algorithm was used to generate the set of input FF parameters for the MD simulations of the CG models of these solvents, which were optimized to reproduce their experimental properties. Herein, for the first time, a reverse approach was employed for on-the-fly training of the ANN model, where results (solvent properties) obtained from the MD simulations and their corresponding FF parameters were used as inputs and outputs, respectively. The ANN model was then required to predict a set of new FF parameters, which were tested for their ability to predict the desired experimental properties. This new framework can be extended to integrate any optimization algorithm with ANN and MD simulations to accelerate the FF development.

Development of New Transferable Coarse-Grained Models of Hydrocarbons.

We have utilized an approach that integrates molecular dynamics (MD) simulations with particle swarm optimization (PSO) to accelerate the development of coarse-grained (CG) models of hydrocarbons. Specifically, we have developed new transferable CG beads, which can be used to model the hydrocarbons (C5 to C17) and reproduce their experimental properties with good accuracy. First, the PSO method was used to develop the CG beads of the decane model represented with a 2:1 (2-2-2-2-2) mapping scheme. This was followed by the development of the nonane model described with hybrid 2-2-3-2 and 3:1 (3-3-3) mapping schemes. The force-field parameters for these three CG models were optimized to reproduce four experimentally observed properties including density, enthalpy of vaporization, surface tension, and self-diffusion coefficient at 300 K. The CG MD simulations conducted with these new CG models of decane and nonane, at different timesteps, for various system sizes, and at a range of different temperatures, were able to predict their density, enthalpy of vaporization, surface tension, self-diffusion coefficient, expansibility, and isothermal compressibility with good accuracy. Moreover, a comparison of structural features obtained from the CG MD simulations and the CG beads of mapped all-atom trajectories of decane and nonane showed very good agreement. To test the chemical transferability of these models, we have constructed the models for hydrocarbons ranging from pentane to heptadecane, by using different combinations of the CG beads of decane and nonane. The properties of pentane to heptadecane predicted by these new CG models showed excellent agreement with the experimental data.

Biomimetic temporal self-assembly via fuel-driven controlled supramolecular polymerization.

Temporal control of supramolecular assemblies to modulate the structural and transient characteristics of synthetic nanostructures is an active field of research within supramolecular chemistry. Molecular designs to attain temporal control have often taken inspiration from biological assemblies. One such assembly in Nature which has been studied extensively, for its well-defined structure and programmable self-assembly, is the ATP-driven seeded self-assembly of actin. Here we show, in a synthetic manifestation of actin self-assembly, an ATP-selective and ATP-fuelled, controlled supramolecular polymerization of a phosphate receptor functionalised monomer. It undergoes fuel-driven nucleation and seeded growth that provide length control and narrow dispersity of the resultant assemblies. Furthermore, coupling via ATP-hydrolysing enzymes yielded its transient characteristics. These results will usher investigations into synthetic analogues of important biological self-assembly motifs and will prove to be a significant advancement toward biomimetic temporally programmed materials.

PSO-Assisted Development of New Transferable Coarse-Grained Water Models.

We have employed two-to-one mapping scheme to develop three coarse-grained (CG) water models, namely, 1-, 2-, and 3-site CG models. Here, for the first time, particle swarm optimization (PSO) and gradient descent methods were coupled to optimize the force-field parameters of the CG models to reproduce the density, self-diffusion coefficient, and dielectric constant of real water at 300 K. The CG MD simulations of these new models conducted with various timesteps, for different system sizes, and at a range of different temperatures are able to predict the density, self-diffusion coefficient, dielectric constant, surface tension, heat of vaporization, hydration free energy, and isothermal compressibility of real water with excellent accuracy. The 1-site model is ∼3 and ∼4.5 times computationally more efficient than 2- and 3-site models, respectively. To utilize the speed of 1-site model and electrostatic interactions offered by 2- and 3-site models, CG MD simulations of 1:1 combination of 1- and 2-/3-site models were performed at 300 K. These mixture simulations could also predict the properties of real water with good accuracy. Two new CG models of benzene, consisting of beads with and without partial charges, were developed. All three water models showed good capacity to solvate these benzene models.

Development of non-bonded interaction parameters between graphene and water using particle swarm optimization.

New Lennard-Jones parameters have been developed to describe the interactions between atomistic model of graphene, represented by REBO potential, and five commonly used all-atom water models, namely SPC, SPC/E, SPC/Fw, SPC/Fd, and TIP3P/Fs by employing particle swarm optimization (PSO) method. These new parameters were optimized to reproduce the macroscopic contact angle of water on a graphene sheet. The calculated line tension was in the order of 10-11 J/m for the droplets of all water models. Our molecular dynamics simulations indicate the preferential orientation of water molecules near graphene-water interface with one OH bond pointing toward the graphene surface. Detailed analysis of simulation trajectories reveals the presence of water molecules with ≤∼1, ∼2, and ∼4 hydrogen bonds at the surface of air-water interface, graphene-water interface, and bulk region of the water droplet, respectively. Presence of water molecules with ≤∼1 and ∼2 hydrogen bonds suggest the existence of water clusters of different sizes at these interfaces. The trends observed in the libration, bending, and stretching bands of the vibrational spectra are closely associated with these structural features of water. The inhomogeneity in hydrogen bond network of water at the air-water and graphene-water interface is manifested by broadening of the peaks in the libration band for water present at these interfaces. The stretching band for the molecules in water droplet shows a blue shift as compared to the pure bulk water, which conjecture the presence of weaker hydrogen bond network in a droplet. © 2017 Wiley Periodicals, Inc.

Supramolecular Polymerization of N,N',N″,N‴-tetra-(Tetradecyl)-1,3,6,8-pyrenetetracarboxamide: A Computational Study.

The role of molecular dipole orientations and intermolecular interactions in a derivative of pyrene on its supramolecular self-assembly in solution has been investigated using quantum chemical and force field based computational approaches. Five possible dipole configurations of the molecule have been examined, among which the one in which adjacent dipole vectors are antiparallel to each other is determined to be the ground state, on electrostatic grounds. Self-assembly of this molecule under realistic conditions has been studied using MD simulations. Dipolar relaxation in its liquid crystalline (LC) phase has been investigated and contrasted against that in the well-established benzene-1,3,5-tricarboxamide (BTA) family. The dihedral barrier related to the amide dipole flip is larger in the pyrene system than in BTA which explains the differences in their dipolar relaxation behaviors. The mechanism underlying polarization switching upon the application of an external electric field in the LC phase is investigated. Unlike in BTA, this switching is not associated with a reversal of the helical sense of the hydrogen bonded chains, due to differences in molecular symmetry. The observations enable general conclusions on the relationship between electric field induced chiral enhancement and symmetry to be drawn.

Solvent Clathrate Driven Dynamic Stereomutation of a Supramolecular Polymer with Molecular Pockets.

Control over the helical organization of synthetic supramolecular systems is intensively pursued to manifest chirality in a wide range of applications ranging from electron spin filters to artificial enzymes. Typically, switching the helicity of supramolecular assemblies involves external stimuli or kinetic traps. However, efforts to achieve helix reversal under thermodynamic control and to understand the phenomena at a molecular level are scarce. Here we present a unique example of helix reversal (stereomutation) under thermodynamic control in the self-assembly of a coronene bisimide that has a 3,5-dialkoxy substitution on the imide phenyl groups (CBI-35CH), leading to "molecular pockets" in the assembly. The stereomutation was observed only if the CBI monomer possesses molecular pockets. Detailed chiroptical studies performed in alkane solvents with different molecular structures reveal that solvent molecules intercalate or form clathrates within the molecular pockets of CBI-35CH at low temperature (263 K), thereby triggering the stereomutation. The interplay among the helical assembly, molecular pockets, and solvent molecules is further unraveled by explicit solvent molecular dynamics simulations. Our results demonstrate how the molecular design of self-assembling building blocks can orchestrate the organization of surrounding solvent molecules, which in turn dictates the helical organization of the resulting supramolecular assembly.

Understanding the self-assembly of amino ester-based benzene-1,3,5-tricarboxamides using molecular dynamics simulations.

Amino ester-based benzene-1,3,5-tricarboxamides (BTAs) are widely studied experimentally for their facile self-assembly, which leads to strong three-fold hydrogen bonded supramolecular polymers. Understanding the supramolecular assembly of these BTAs is complicated by the presence of two types of dimers, based on the nature of the intermolecular hydrogen bonding pattern: amide-amide (AA) and amide-carboxylate (AC). AA dimers form three hydrogen bonds between the two molecules, are typical of BTA stacks, and act as a basic building block of assembly. In contrast, AC hydrogen bonding results in six hydrogen bonds between two molecules, and this face-to-face orientation results in a dimer that is more stable than the AA one, however, unfavorable for further assembly. We perform atomistic molecular dynamics (MD) simulations of three derivatives of BTA in order to rationalize the large body of experimental data for these systems, specifically the relative stabilities of AA and AC dimers and oligomers. We find that at zero Kelvin, the AC dimer is more stable than the AA dimer by roughly 20 kcal mol-1. MD simulations of three BTA derivatives (BTA-Met, BTA-Nle, and BTA-Phe) under realistic conditions show that BTA-Met and BTA-Phe can aggregate to form longer assemblies via additional stabilization offered by weak CHS and CHπ hydrogen bonds, respectively. However, the aggregation of BTA-Nle, which is devoid of such functionalities, is limited to that of a dimer. We then employ umbrella sampling to show that oligomers of BTA-Met and BTA-Phe are stable over those of dimers and demonstrate that this results from such weak interactions.

Host-Guest [2+2] Cycloaddition Reaction: Postsynthetic Modulation of CO2 Selectivity and Magnetic Properties in a Bimodal Metal-Organic Framework.

Simultaneous tuning of permanent porosity and modulation of magnetic properties by postsynthetic modification (PSM) with light in a metal-organic framework is unprecedented. With the aim of achieving such a photoresponsive porous magnetic material, a 3D photoresponsive biporous framework, MOF1, which has 2D channels occupied by the guest 1,2-bis(4-pyridyl)ethylene (bpee), H2 O, and EtOH molecules, has been synthesized. The guest bpee in 1 is aligned parallel to pillared bpee with a distance of 3.9 Šbetween the ethylenic groups; this allows photoinduced PSM of the pore surface through a [2+2] cycloaddition reaction to yield MOF2. Such photoinduced PSM of the framework structure introduces enhanced CO2 selectivity over that of N2 . The higher selectivity in MOF2 than that of MOF1 is studied through theoretical calculations. Moreover, MOF2 unveils reversible changes in Tc with response to dehydration-rehydration. This result demonstrates that photoinduced PSM is a powerful tool for fabricating novel functional materials.