Data-Driven Imputation of Miscibility of Aqueous Solutions via Graph-Regularized Logistic Matrix Factorization.

Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications.

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels.

Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees.

PoreMatMod.jl: Julia Package for Postsynthetic Modification of Crystal Structure Models.

PoreMatMod.jl is a free, open-source, user-friendly, and documented Julia package for modifying crystal structure models of porous materials such as metal-organic frameworks (MOFs). PoreMatMod.

Non-injective gas sensor arrays: identifying undetectable composition changes.

Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF.

Evaluating the Fitness of Combinations of Adsorbents for Quantitative Gas Sensor Arrays.

Robust, high-performance gas-sensing technology has applications in industrial process monitoring and control, air quality monitoring, food quality assessment, medical diagnosis, and security threat detection. Nanoporous materials (NPMs) could be utilized as recognition elements in a gas sensor because they selectively adsorb gas. Imitating mammalian olfaction, sensor arrays of NPMs use measurements of the adsorbed mass of gas in a set of distinct NPMs to infer the gas composition.

Statistical Mechanical Model of Gas Adsorption in a Metal-Organic Framework Harboring a Rotaxane Molecular Shuttle.

Metal-organic frameworks (MOFs) are modular and tunable nanoporous materials with applications in gas storage, separations, and sensing. Integrating flexible/dynamic, gas-responsive components into MOFs can give them unique or enhanced adsorption properties. Here, we explore the adsorption properties that could be imparted to a MOF by a rotaxane molecular shuttle (RMS) in its pores.

Curating Metal-Organic Frameworks To Compose Robust Gas Sensor Arrays in Dilute Conditions.

Metal-organic frameworks (MOFs), tunable, nanoporous materials, are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions.

The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation.

Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery.

Understanding Gas Storage in Cuboctahedral Porous Coordination Cages.

Porous molecular solids are promising materials for gas storage and gas separation applications. However, given the relative dearth of structural information concerning these materials, additional studies are vital for further understanding their properties and developing design parameters for their optimization. Here, we examine a series of isostructural cuboctahedral, paddlewheel-based coordination cages, M(Bu-bdc) (M = Cr, Mo, Ru; Bu-bdc = 5--butylisophthalate), for high-pressure methane storage.

Multi- and instabilities in gas partitioning between nanoporous materials and rubber balloons.

In the two-balloon experiment, two rubber balloons are connected and allowed to exchange gas. Owing to the non-monotonic relationship between the radius of the balloon and the pressure of gas inside it, the two-balloon system presents multi- and in-stabilities. Herein, we consider a two-adsorbent system, where two different adsorbents are allowed to exchange gas.

Eigencages: Learning a Latent Space of Porous Cage Molecules.

Porous organic cage molecules harbor nanosized cavities that can selectively adsorb gas molecules, lending them applications in separations and sensing. The geometry of the cavity strongly influences their adsorptive selectivity. For comparing cages and predicting their adsorption properties, we embed/encode a set of 74 porous organic cage molecules into a low-dimensional, latent "cage space" on the basis of their intrinsic porosity.

High-Throughput Computational Screening of Multivariate Metal-Organic Frameworks (MTV-MOFs) for CO Capture.

Multivariate metal-organic frameworks (MTV-MOFs) contain multiple linker types within a single structure. Arrangements of linkers containing different functional groups confer structural diversity and surface heterogeneity and result in a combinatorial explosion in the number of possible structures. In this work, we carried out high-throughput computational screening of a large number of computer-generated MTV-MOFs to assess their CO capture properties using grand canonical Monte Carlo simulations.

Effect of ring rotation upon gas adsorption in SIFSIX-3-M (M = Fe, Ni) pillared square grid networks.

Dynamic and flexible metal-organic frameworks (MOFs) that respond to external stimuli, such as stress, light, heat, and the presence of guest molecules, hold promise for applications in chemical sensing, drug delivery, gas separations, and catalysis. A greater understanding of the relationship between flexible constituents in MOFs and gas adsorption may enable the rational design of MOFs with dynamic moieties and stimuli-responsive behavior. Here, we detail the effect of subtle structural changes upon the gas sorption behavior of two "SIFSIX" pillared square grid frameworks, namely SIFSIX-3-M (M = Ni, Fe).

Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage.

The Materials Genome is in action: the molecular codes for millions of materials have been sequenced, predictive models have been developed, and now the challenge of hydrogen storage is targeted. Renewably generated hydrogen is an attractive transportation fuel with zero carbon emissions, but its storage remains a significant challenge. Nanoporous adsorbents have shown promising physical adsorption of hydrogen approaching targeted capacities, but the scope of studies has remained limited.

Adsorbate-induced lattice deformation in IRMOF-74 series.

IRMOF-74 analogues are among the most widely studied metal-organic frameworks (MOFs) for adsorption applications because of their one-dimensional channels and high metal density. Most studies involving the IRMOF-74 series assume that the crystal lattice is rigid. This assumption guides the interpretation of experimental data, as changes in the crystal symmetry have so far been ignored as a possibility in the literature.

Statistical mechanical model of gas adsorption in porous crystals with dynamic moieties.

Some nanoporous, crystalline materials possess dynamic constituents, for example, rotatable moieties. These moieties can undergo a conformation change in response to the adsorption of guest molecules, which qualitatively impacts adsorption behavior. We pose and solve a statistical mechanical model of gas adsorption in a porous crystal whose cages share a common ligand that can adopt two distinct rotational conformations.

Noria: A Highly Xe-Selective Nanoporous Organic Solid.

Separation of xenon and krypton is of industrial and environmental concern; the existing technologies use cryogenic distillation. Thus, a cost-effective, alternative technology for the separation of Xe and Kr and their capture from air is of significant importance. Herein, we report the selective Xe uptake in a crystalline porous organic oligomeric molecule, noria, and its structural analogue, PgC-noria, under ambient conditions.

Metal-organic framework with optimally selective xenon adsorption and separation.

Nuclear energy is among the most viable alternatives to our current fossil fuel-based energy economy. The mass deployment of nuclear energy as a low-emissions source requires the reprocessing of used nuclear fuel to recover fissile materials and mitigate radioactive waste. A major concern with reprocessing used nuclear fuel is the release of volatile radionuclides such as xenon and krypton that evolve into reprocessing facility off-gas in parts per million concentrations.

Kinetically tuned dimensional augmentation as a versatile synthetic route towards robust metal-organic frameworks.

Metal-organic frameworks with high stability have been pursued for many years due to the sustainability requirement for practical applications. However, researchers have had great difficulty synthesizing chemically ultra-stable, highly porous metal-organic frameworks in the form of crystalline solids, especially as single crystals. Here we present a kinetically tuned dimensional augmentation synthetic route for the preparation of highly crystalline and extremely robust metal-organic frameworks with a preserved metal cluster core.

In silico design of porous polymer networks: high-throughput screening for methane storage materials.

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal-organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials.

Optimizing nanoporous materials for gas storage.

In this work, we address the question of which thermodynamic factors determine the deliverable capacity of methane in nanoporous materials. The deliverable capacity is one of the key factors that determines the performance of a material for methane storage in automotive fuel tanks. To obtain insights into how the molecular characteristics of a material are related to the deliverable capacity, we developed several statistical thermodynamic models.

The role of dendritic spine morphology in the compartmentalization and delivery of surface receptors.

Since AMPA receptors are major molecular players in both short- and long-term plasticity, it is important to identify the time-scales of and factors affecting the lateral diffusion of AMPARs on the dendrite surface. Using a mathematical model, we study how the dendritic spine morphology affects two processes: (1) compartmentalization of the surface receptors in a single spine to retain local chemistry and (2) the delivery of receptors to the post-synaptic density (PSD) of spines via lateral diffusion following insertion onto the dendrite shaft. Computing the mean first passage time (MFPT) of surface receptors on a sample of real spine morphologies revealed that a constricted neck and bulbous head serve to compartmentalize receptors, consistent with previous works.

Pattern formation of Rho GTPases in single cell wound healing.

The Rho GTPases-Rho, Rac, and Cdc42-control an enormous variety of processes, many of which reflect activation of these GTPases in spatially confined and mutually exclusive zones. By using mathematical models and experimental results to establish model parameters, we analyze the formation and segregation of Rho and Cdc42 zones during Xenopus oocyte wound repair and the role played by Abr, a dual guanine nucleotide exchange factor-GTPase-activating protein, in this process. The Rho and Cdc42 zones are found to be best represented as manifestations of spatially modulated bistability, and local positive feedback between Abr and Rho can account for the maintenance and dynamic properties of the Rho zone.