Thermogravimetric Analysis of Moisture in Natural and Thermally Treated Clay Materials.

Clays are a class of porous materials; their surfaces are naturally covered by moisture. Weak thermal treatment may be considered practical to remove the water molecules, changing the surface properties and making the micro- and/or mesoporosities accessible to interact with other molecules. Herein, a modulated thermogravimetric analysis (MTGA) study of the moisture behavior on the structures of five, both fibrous and laminar, clay minerals is reported. The effect of the thermal treatment at 150 °C, which provokes the release of weakly adsorbed water molecules, was also investigated. The activation energies for the removal of the adsorbed water (Ea) were calculated, and they were found to be higher, namely, from 160 to 190 kJ mol-1, for fibrous clay minerals compared to lamellar structures, ranging in this latter case from 80 to 100 kJ mol-1. The thermal treatment enhances the rehydration in Na-montmorillonite, stevensite, and sepiolite structures with a decrease in the energy required to remove it, while Ea increases significantly in palygorskite (from 164 to 273 kJ mol-1). As a proof of concept, the MTGA results are statistically correlated, together with a full characterization of the physico-chemical properties of the five clay minerals, with the adsorption of two molecules, i.e., aflatoxin B1 (AFB1) and ß-carotene. Herein, the amount of adsorbed molecules ranges from 12 to 97% for the former and from 22 to 35% for the latter, depending on the particular clay. The Ea was correlated with AFB1 adsorption with a Spearman score of -0.9. When the adsorbed water is forcibly removed, e.g., under vacuum conditions and high temperatures, the structure becomes the most important, decreasing the Spearman score between ß-carotene and Ea to -0.6.

Machine learning-aided design of composite mycotoxin detoxifier material for animal feed.

The development of food and feed additives involves the design of materials with specific properties that enable the desired function while minimizing the adverse effects related with their interference with the concurrent complex biochemistry of the living organisms. Often, the development process is heavily dependent on costly and time-consuming in vitro and in vivo experiments. Herein, we present an approach to design clay-based composite materials for mycotoxin removal from animal feed. The approach can accommodate various material compositions and different toxin molecules. With application of machine learning trained on in vitro results of mycotoxin adsorption-desorption in the gastrointestinal tract, we have searched the space of possible composite material compositions to identify formulations with high removal capacity and gaining insights into their mode of action. An in vivo toxicokinetic study, based on the detection of biomarkers for mycotoxin-exposure in broilers, validated our findings by observing a significant reduction in systemic exposure to the challenging to be removed mycotoxin, i.e., deoxynivalenol (DON), when the optimal detoxifier is administrated to the animals. A mean reduction of 32% in the area under the plasma concentration-time curve of DON-sulphate was seen in the DON + detoxifier group compared to the DON group (P = 0.010).

High-Performance Mg-Li Hybrid Batteries Based on Pseudocapacitive Anatase Ti1-x Cox O2-y Nanosheet Cathodes.

Despite the proposed safety, performance, and cost advantages, practical implementation of Mg-Li hybrid batteries is limited due to the unavailability of reliable cathodes compatible with the dual-ion system. Herein, a high-performance Mg-Li dual ion battery based upon cobalt-doped TiO2 cathode was developed. Extremely pseudocapacitance-type Ti1-x Cox O2-y nanosheets consist of an optimum 3.57 % Co-atoms. This defective cathode delivered exceptional pseudocapacitance (maximum of 93 %), specific capacities (386 mAh g-1 at 25 mA g-1 ), rate performance (191 mAh g-1 at 1 A g-1 ), cyclability (3000 cycles at 1 A g-1 ), and coulombic efficiency (≈100 %) and fast charging (≈11 min). This performance was superior to the TiO2 -based Mg-Li dual-ion battery cathodes reported earlier. Mechanistic studies revealed dual-ion intercalation pseudocapacitance with negligible structural changes. Excellent electrochemical performance of the cation-doped TiO2 cathode was credited to the rapid pseudocapacitance-type Mg/Li-ion diffusion through the disorder generated by lattice distortions and oxygen vacancies. Ultrathin nature, large surface area, 2D morphology, and mesoporosity also contributed as secondary factors facilitating superior electrode-electrolyte interfacial kinetics. The demonstrated method of pseudocapacitance-type Mg-Li dual-ion intercalation by introducing lattice distortions/oxygen vacancies through selective doping can be utilized for the development of several other potential electrodes for high-performance Mg-Li dual-ion batteries.

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials.

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R 2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter-property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures.

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal-organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure-property relationships for materials design.

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal-Organic Frameworks.

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal-organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO2. Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal-Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. A strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

Simultaneous Improvement of Mechanical and Fire-Safety Properties of Polymer Composites with Phosphonate-Loaded MOF Additives.

Flame-retardant (FR) additives are commonly used to improve the fire safety of synthetic polymers, which are widely employed in manufactured consumer goods. Incorporation of an FR in a polymer typically leads to deterioration of its mechanical properties. It also manifests itself in non-negligible volatile organic compound (VOC) release, which in turn increases environmental risks carried by both the application and disposal of the corresponding consumer goods. Herein, we present a hierarchical strategy for the design of composite materials, which ensures simultaneous improvement of both mechanical and fire-safety properties of polymers while limiting the VOC release. Our strategy employs porous metal-organic framework (MOF) particles to provide a multifunctional interface between the FR molecules and the polymer. Specifically, we demonstrate that the particles of environmentally friendly HKUST-1 MOF can be infused by a modern FR-dimethyl methylphosphonate (DMMP)-and then embedded into widely used unsaturated polyesters. The DMMP-HKUST-1 additive endows the resulting composite material with improved processability, flame retardancy, and mechanical properties. Single-crystal X-ray diffraction, thermogravimetric analysis, and computational modeling of the additive suggest the complete pore filling of HKUST-1 with DMMP molecules being bound to the open metal sites of the MOF.

Capturing chemical intuition in synthesis of metal-organic frameworks.

We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal-organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.

Toward Automated Tools for Characterization of Molecular Porosity.

The emerging advanced porous materials, e.g. extended framework materials and porous molecular materials, offer an unprecedented level of control of their structure and function. The enormous possibilities for tuning these materials by changing their building blocks mean that, in principle, optimally performing materials for a variety of applications can be systematically designed. However, the process of finding a set of optimal structures for a given application requires computational high-throughput tools to analyze and sieve through many candidate materials. In particular, in the case of porous molecular materials, the analysis and selection of a molecule is one of the key aspects as the structure of the molecule determines the structure of the resulting material, and very often the porosity of the molecule significantly contributes to the porous properties of the resulting material. In this work, we introduce definitions and algorithms to characterize porosity at the molecular level, along with a software implementation of these algorithms. We demonstrate applications of the software tool in the discovery and characterization of porous molecules among ca. 94 million molecules currently enlisted in the PubChem database.

Cutting Materials in Half: A Graph Theory Approach for Generating Crystal Surfaces and Its Prediction of 2D Zeolites.

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. We hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area can yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal-organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc.

Electrostatic Estimation of Intercalant Jump-Diffusion Barriers Using Finite-Size Ion Models.

We report on a scheme for estimating intercalant jump-diffusion barriers that are typically obtained from demanding density functional theory-nudged elastic band calculations. The key idea is to relax a chain of states in the field of the electrostatic potential that is averaged over a spherical volume using different finite-size ion models. For magnesium migrating in typical intercalation materials such as transition-metal oxides, we find that the optimal model is a relatively large shell. This data-driven result parallels typical assumptions made in models based on Onsager's reaction field theory to quantitatively estimate electrostatic solvent effects. Because of its efficiency, our potential of electrostatics-finite ion size (PfEFIS) barrier estimation scheme will enable rapid identification of materials with good ionic mobility.

Computationally-Guided Synthetic Control over Pore Size in Isostructural Porous Organic Cages.

The physical properties of 3-D porous solids are defined by their molecular geometry. Hence, precise control of pore size, pore shape, and pore connectivity are needed to tailor them for specific applications. However, for porous molecular crystals, the modification of pore size by adding pore-blocking groups can also affect crystal packing in an unpredictable way. This precludes strategies adopted for isoreticular metal-organic frameworks, where addition of a small group, such as a methyl group, does not affect the basic framework topology. Here, we narrow the pore size of a cage molecule, CC3, in a systematic way by introducing methyl groups into the cage windows. Computational crystal structure prediction was used to anticipate the packing preferences of two homochiral methylated cages, CC14-R and CC15-R, and to assess the structure-energy landscape of a CC15-R/CC3-S cocrystal, designed such that both component cages could be directed to pack with a 3-D, interconnected pore structure. The experimental gas sorption properties of these three cage systems agree well with physical properties predicted by computational energy-structure-function maps.

Xenon Recovery at Room Temperature using Metal-Organic Frameworks.

Xenon is known to be a very efficient anesthetic gas, but its cost prohibits the wider use in medical industry and other potential applications. It has been shown that Xe recovery and recycling from anesthetic gas mixtures can significantly reduce its cost as anesthetic. The current technology uses series of adsorbent columns followed by low-temperature distillation to recover Xe; this method is expensive to use in medical facilities. Herein, we propose a much simpler and more efficient system to recover and recycle Xe from exhaled anesthetic gas mixtures at room temperature using metal-organic frameworks (MOFs). Among the MOFs tested, PCN-12 exhibits unprecedented performance with high Xe capacity and Xe/O2 , Xe/N2 and Xe/CO2 selectivity at room temperature. The in situ synchrotron measurements suggest that Xe is occupies the small pockets of PCN-12 compared to unsaturated metal centers (UMCs). Computational modeling of adsorption further supports our experimental observation of Xe binding sites in PCN-12.

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials.

Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable, and it can also be obtained from the refined unit cell by a number of computational techniques. In this work, we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium, and probe center pore volumes, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe-accessible and -occupiable pore volume. We show that, unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.

Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C60 fullerene using Nano-QSPR.

Many technological implementations in the field of nanotechnology have involved carbon nanomaterials, including fullerenes such as the buckminsterfullerene, C60. The unprecedented properties of such organic nanomaterials (in particular their large surface area) gained extensive attention for their potential use as organic pollutant sorbents. Sorption interactions can be very hazardous and useful at the same time. This work investigates the influence of halogenation by bromine and/or chlorine in dibenzo-p-dioxins on their sorption ability on the C60 fullerene surface. Halogenated dibenzo-p-dioxins (PXDDs, where X = Br or Cl) are ever-present in the environment and accidently produced in many technological processes in only approximately known quantities. If all combinatorial Br and/or Cl dioxin substitution possibilities are present in the environment, the experimental characterization and investigation of sorbent effectiveness is more than difficult. In this work, we have developed a quantitative structure-property relationship (QSPR) model (R2 = 0.998), predicting the adsorption energy [kcal/mol] for 1,701 PXDDs adsorbed on C60 (PXDD@C60). Based on the QSPR model reported herein, we concluded that the lowest energy PXDD@C60 complexes are those that the World Health Organization (WHO) considers to be less dangerous with respect to the aryl hydrocarbon receptor (AhR) toxicity mechanism. Therefore, the effectiveness of fullerenes as sorbent agents may be underestimated as sorption could be less effective for toxic congeners than previously believed.

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. Here the Nanoporous Materials Genome, containing over 850 000 materials, is analyzed with a variety of computational tools to explore the limits of hydrogen storage. Optimal features that maximize net capacity at room temperature include pore sizes of around 6 Šand void fractions of 0.1, while at cryogenic temperatures pore sizes of 10 Šand void fractions of 0.5 are optimal. Our top candidates are found to be commercially attractive as "cryo-adsorbents", with promising storage capacities at 77 K and 100 bar with 30% enhancement to 40 g/L, a promising alternative to liquefaction at 20 K and compression at 700 bar.

The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials.

For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation, we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high-performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other nonoptimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

Rational Design of a Low-Cost, High-Performance Metal-Organic Framework for Hydrogen Storage and Carbon Capture.

We present the in silico design of a MOF-74 analogue, hereon known as M2(DHFUMA) [M = Mg, Fe, Co, Ni, Zn], with enhanced small-molecule adsorption properties over the original M2(DOBDC) series. Constructed from 2,3-dihydroxyfumarate (DHFUMA), an aliphatic ligand which is smaller than the aromatic 2,5-dioxidobenzene-1,4-dicarboxylate (DOBDC), the M2(DHFUMA) framework has a reduced channel diameter, resulting in higher volumetric density of open metal sites and significantly improved volumetric hydrogen (H2) storage potential. Furthermore, the reduced distance between two adjacent open metal sites in the pore channel leads to a CO2 binding mode of one molecule per two adjacent metals with markedly stronger binding energetics. Through dispersion-corrected density functional theory (DFT) calculations of guest-framework interactions and classical simulation of the adsorption behavior of binary CO2:H2O mixtures, we theoretically predict the M2(DHFUMA) series as an improved alternative for carbon capture over the M2(DOBDC) series when adsorbing from wet flue gas streams. The improved CO2 uptake and humidity tolerance in our simulations is tunable based upon metal selection and adsorption temperature which, combined with the significantly reduced ligand expense, elevates this material's potential for CO2 capture and H2 storage. The dynamical and elastic stabilities of Mg2(DHFUMA) were verified by hybrid DFT calculations, demonstrating its significant potential for experimental synthesis.

Scanning electron microscopy image representativeness: morphological data on nanoparticles.

A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca3 (PO4 )2 , and calcium hydroxyphosphate, Ca5 (PO4 )3 (OH), both naturally occurring minerals with a wide range of biomedical applications.