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At least 91 records · Page 5

Enhanced Thermal Conductivity in a Diamine-Appended Metal-Organic Framework as a Result of Cooperative CO 2 Adsorption

Using diamine-appended variants of the metal-organic framework M 2 (dobpdc) (M = Mg, Mn, Fe, Co, Zn; dobpdc 4- = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) exhibit exceptional CO 2 capture properties owing to a unique cooperative adsorption mechanism, and thus hold promise for use in the development of energy- and cost-efficient CO 2 separations. Understanding the nature of thermal transport in these materials is essential for such practical applications, however, as temperature rises resulting from exothermic CO 2 uptake could potentially offset the energy savings offered by such cooperative adsorbents. Here, molecular dynamics (MD) simulations are employed in investigating thermal transport in bare and e-2-appended Zn 2 (dobpdc) (e-2 = N -ethylethylenediamine), both with and without CO 2 as a guest. In the absence of CO 2 , the appended diamines function to enhance thermal conductivity in the ab -plane of e-2-Zn 2 (dobpdc) relative to the bare framework, as a result of noncovalent interactions between adjacent diamines that provide additional heat transfer pathways across the pore channel. Upon introduction of CO 2 , the thermal conductivity along the pore channel (the c -axis) increases due to the cooperative formation of metal-bound ammonium carbamates, which serve to create additional heat transfer pathways. In contrast, the thermal conductivity of the bare framework remains unchanged in the presence of zinc-bound CO 2 but decreases in the presence of additional adsorbed CO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improved Rate for the Oxygen Reduction Reaction in a Sulfuric Acid Electrolyte using a Pt(111) Surface Modified with Melamine

The feasible commercialization of alkaline, phosphoric acid and polymer electrolyte membrane fuel cells depends on the development of oxygen reduction reaction (ORR) electrocatalysts with improved activity, stability, and selectivity. The rational design of surfaces to ensure these improved ORR catalytic requirements relies on the so-called "descriptors" (e.g., the role of covalent and noncovalent interactions on platinum surface active sites for ORR). Here, we demonstrate that through the molecular adsorption of melamine onto the Pt(111) surface [Pt(111)-M ad ], the activity can be improved by a factor of 20 compared to bare Pt(111) for the ORR in a strongly adsorbing sulfuric acid solution. Additionally, the M ad moieties act as "surface-blocking bodies," selectively hindering the adsorption of (bi)sulfate anions (well-known poisoning spectator of the Pt(111) active sites) while the ORR is unhindered. This modified surface is further demonstrated to exhibit improved chemical stability relative to Pt(111) patterned with cyanide species (CN ad ), previously shown by our group to have a similar ORR activity increase compared to bare Pt(111) in a sulfuric acid electrolyte, with Pt(111)-M ad retaining a greater than ninefold higher ORR activity relative to bare Pt(111) after extensive potential cycling as compared to a greater than threefold higher activity retained on a CN ad -covered Pt(111) surface. We suggest that the higher stability of the Pt(111)-M ad interface stems from melamine's ability to form intermolecular hydrogen bonds, which effectively turns the melamine molecules into larger macromolecular entities with multiple anchoring sites and thus more difficult to remove.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Controlling the Crystal Packing and Morphology of Metal–Organic Macrocycles through Side-Chain Modification

Supramolecular nanotubes constructed from the self-assembly of conjugated metal–organic macrocycles provide a unique collection of materials properties, including solution processability, porosity, and electrical conductivity. Here we show how small modifications to the macrocycle periphery subtly alter the noncovalent interactions governing self-assembly, leading to large changes in crystal packing, crystal morphology, and materials properties. Specifically, we synthesized five distinct copper-based macrocycles that differ in either the steric bulk, polarity, or hydrogen-bonding ability of the peripheral side chains. We show that the electrical conductivity of these macrocycles is highly sensitive to steric bulk, decreasing by 3 orders of magnitude upon introduction of peripheral neopentyl substituents. Here, we further show that the introduction of hydrogen-bonding groups leads to more ordered packing and a dramatic increase in crystallite size. Together, these results establish side-chain engineering as a rich toolkit for controlling the packing structure, particle morphology, and bulk properties of conjugated metal–organic macrocycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adaptive Pore Opening to Form Tailored Adsorption Sites in a Cooperatively Flexible Framework Enables Record Inverse Propane/Propylene Separation

A proposed low-energy alternative to the separation of alkanes from alkenes by energy-intensive cryogenic distillation is separation by porous adsorbents. Unfortunately, most adsorbents preferentially take up the desired, high-value major component alkene, requiring frequent regeneration. Adsorbents with inverse selectivity for the minor component alkane would enable the direct production of purified, reagent-grade alkene, greatly reducing global energy consumption. However, such materials are exceedingly rare, especially for propane/propylene separation. Here, we report that through adaptive and spontaneous pore size and shape adaptation to optimize an ensemble of weak noncovalent interactions, the structurally responsive metal-organic framework CdIF-13 (sod-Cd(benzimidazolate) 2 ) exhibits inverse selectivity for propane over propylene with record-setting separation performance under industrially relevant temperature, pressure, and mixture conditions. Powder synchrotron X-ray diffraction measurements combined with first-principles calculations yield atomic-scale insight and reveal the induced fit mechanism of adsorbate-specific pore adaptation and ensemble interactions between ligands and adsorbates. Dynamic column breakthrough measurements confirm that CdIF-13 displays selectivity under mixed-component conditions of varying ratios, with a record measured selectivity factor of α ≈ 3 at 95:5 propylene:propane at 298 K and 1 bar. When sequenced with a low-cost rigid adsorbent, we demonstrated the direct purification of propylene under ambient conditions. In conclusion, this combined atomic-level structural characterization and performance testing firmly establishes how cooperatively flexible materials can be capable of unprecedented separation factors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Energetic Origins of Pi–Pi Contacts in Proteins

Accurate potential energy models of proteins must describe the many different types of noncovalent interactions that contribute to a protein’s stability and structure. Pi–pi contacts are ubiquitous structural motifs in all proteins, occurring between aromatic and nonaromatic residues and play a nontrivial role in protein folding and in the formation of biomolecular condensates. Guided by a geometric criterion for isolating pi–pi contacts from classical molecular dynamics simulations of proteins, we use quantum mechanical energy decomposition analysis to determine the molecular interactions that stabilize different pi–pi contact motifs. We find that neutral pi–pi interactions in proteins are dominated by Pauli repulsion and London dispersion rather than repulsive quadrupole electrostatics, which is central to the textbook Hunter–Sanders model. This results in a notable lack of variability in the interaction profiles of neutral pi–pi contacts even with extreme changes in the dielectric medium, explaining the prevalence of pi-stacked arrangements in and between proteins. We also find interactions involving pi-containing anions and cations to be extremely malleable, interacting like neutral pi–pi contacts in polar media and like typical ion–pi interactions in nonpolar environments. Like-charged pairs such as arginine–arginine contacts are particularly sensitive to the polarity of their immediate surroundings and exhibit canonical pi–pi stacking behavior only if the interaction is mediated by environmental effects, such as aqueous solvation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Crystalline Peptoid Nanofibers with a Single-Unit Cell Cross Section

Ultranarrow crystalline one-dimensional nanostructures formed from soft materials facilitate precise structural control in nanomaterial design, which is essential for biomedicine and nanotechnology applications. Systematic control of their hierarchical structure is challenging due to the complexities of simultaneously manipulating multiple noncovalent interactions at such small scales. We employed a polypeptoid crystal motif as a supramolecular synthon to engineer ultranarrow crystalline nanofibers constrained to a single lattice axis by incorporating a single ionizable side chain into the hydrophobic core of a nanosheet-forming peptoid. Cryogenic transmission electron microscopy of the nanofibers revealed detailed molecular arrangements of a unit-cell cross-section and the presence of distinct pH-dependent lattice isoforms that resulted in morphological transformations. Molecular dynamics simulations demonstrated that the ionizable side chain plays a critical role in changing the local conformation of the unit cell, which further impacts the dimensionality of hierarchical structures. Moreover, these fibers were readily functionalized with biological ligands to afford one-dimensional (1D) protein arrays. This approach for the high-precision bottom-up assembly of ultranarrow 1D nanostructures offers significant potential for developing novel biomimetic nanostructures.

Lee, Yen Jea↗

Tuning the Conformations of an M4L4 Cage and Their Impact on Catalysis

Enzymes catalyze chemical reactions with remarkable rate enhancements and selectivity. Supramolecular catalysis seeks to understand and emulate these outcomes, leveraging noncovalent interactions, electric fields, and controlled active site microenvironments to enhance catalysis in an enzyme-like fashion. The effects of conformational dynamics on supramolecular catalysts and assemblies are, however, relatively unexplored, despite their crucial role in enzyme rate enhancement. Here, we elucidate the conformational landscape of a model M4L4 supramolecular host through a rational approach: stabilizing a high-energy conformer through distal ligand modification and a transient intermediate state through symmetry-matched guest encapsulation, as well as tuning the conformer distribution through same-charge metal exchange at the host vertices. Each of these structural modifications induces a substantial shift in the host's conformational landscape, offering insights into the rational design of conformationally dynamic cages and enzymes. Although the thermodynamic properties of the dynamic Ga4L412- cage can be influenced by temperature, solvent, and guest binding, we find that conformational change occurs on a time scale that renders it rate-limiting in a model catalytic reaction, precluding rate enhancement through conformational selection. This concept is illustrated by locking the catalytically inactive conformer to a high-energy conformer that is catalytically competent. These findings demonstrate that precise modulation of the conformational landscape of supramolecular hosts provides an effective strategy for controlling their catalytic activity and binding.

Catalysis↗

Tuning of lattice oxygen reactivity and scaling relation to construct better oxygen evolution electrocatalyst

Developing efficient and low-cost electrocatalysts for oxygen evolution reaction is crucial in realizing practical energy systems for sustainable fuel production and energy storage from renewable energy sources. However, the inherent linear scaling relation for most catalytic materials imposes a theoretical overpotential ceiling, limiting the development of efficient electrocatalysts. Herein, using modeled Na x Mn 3 O 7 materials, we report an effective strategy to construct better oxygen evolution electrocatalyst through tuning both lattice oxygen reactivity and scaling relation via alkali metal ion mediation. Specifically, the number of Na + is linked with lattice oxygen reactivity, which is determined by the number of oxygen hole in oxygen lone-pair states formed by native Mn vacancies, governing the barrier symmetry between O–H bond cleavage and O–O bond formation. On the other hand, the presence of Na + could have specific noncovalent interaction with pendant oxygen in *OOH to overcome the limitation from linear scaling relation, reducing the overpotential ceiling. Combining in situ spectroscopy-based characterization with first-principles calculations, we demonstrate that an intermediate level of Na + mediation (NaMn 3 O 7 ) exhibits the optimum oxygen evolution activity. This work provides a new rational recipe to develop highly efficient catalyst towards water oxidation or other oxidative reactions through tuning lattice oxygen reactivity and scaling relation.

36 MATERIALS SCIENCE↗

AQuaRef: machine learning accelerated quantum refinement of protein structures

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical data, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. Here we present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 machine learned interatomic potential (MLIP) mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data. Notably, AQuaRef aids in determining proton positions, as illustrated in the challenging case of short hydrogen bonds in the parkinsonism-associated human protein DJ-1 and its bacterial homolog YajL.

Zubatyuk, Roman [Carnegie Mellon University, Pitts↗

Supervised learning of a chemistry functional with damped dispersion

Abstract Kohn–Sham density functional theory is widely used in chemistry, but no functional can accurately predict the whole range of chemical properties, although recent progress by some doubly hybrid functionals comes close. Here, we optimized a singly hybrid functional called CF22D with higher across-the-board accuracy for chemistry than most of the existing non-doubly hybrid functionals by using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation that depends on density and occupied orbitals with a damped dispersion term that depends on geometry. We optimized this energy functional by using a large database and performance-triggered iterative supervised training. We combined several databases to create a very large, combined database whose use demonstrated the good performance of CF22D on barrier heights, isomerization energies, thermochemistry, noncovalent interactions, radical and nonradical chemistry, small and large systems, simple and complex systems and transition-metal chemistry.

Liu, Yiwei (ORCID:0000000288126163)↗

Advanced supramolecular design for direct ink writing of soft materials

The exciting advancements in 3D-printing of soft materials are changing the landscape of materials development and fabrication. Among various 3D-printers that are designed for soft materials fabrication, the direct ink writing (DIW) system is particularly attractive for chemists and materials scientists due to the mild fabrication conditions, compatibility with a wide range of organic and inorganic materials, and the ease of multi-materials 3D-printing. Inks for DIW need to possess suitable viscoelastic properties to allow for smooth extrusion and be self-supportive after printing, but molecularly facilitating 3D printability to functional materials remains nontrivial. While supramolecular binding motifs have been increasingly used for 3D-printing, these inks are largely optimized empirically for DIW. Hence, this review aims to establish a clear connection between the molecular understanding of the supramolecularly bound motifs and their viscoelastic properties at bulk. Herein, extrudable (but not self-supportive) and 3D-printable (self-supportive) polymeric materials that utilize noncovalent interactions, including hydrogen bonding, host–guest inclusion, metal–ligand coordination, micro-crystallization, and van der Waals interaction, have been discussed in detail. In particular, the rheological distinctions between extrudable and 3D-printable inks have been discussed from a supramolecular design perspective. Examples shown in this review also highlight the exciting macroscale functions amplified from the molecular design. Challenges associated with the hierarchical control and characterization of supramolecularly designed DIW inks are also outlined. The perspective of utilizing supramolecular binding motifs in soft materials DIW printing has been discussed. Finally, this review serves to connect researchers across disciplines to develop innovative solutions that connect top-down 3D-printing and bottom-up supramolecular design to accelerate the development of 3D-print soft materials for a sustainable future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Layered supramolecular hydrogels from thioglycosides

Low molecular weight hydrogels are made of small molecules that aggregate via noncovalent interactions. Here, comprehensive characterization of the physical and chemical properties of hydrogels made from thioglycolipids of the disaccharides lactose and cellobiose with simple alkyl chains is reported. While thiolactoside hydrogels are robust, thiocellobioside gels are metastable, precipitating over time into fibrous crystals that can be entangled to create pseudo-hydrogels. Rheology confirms the viscoelastic solid nature of these hydrogels with storage moduli ranging from 10–600 kPa. Additionally, thiolactoside hydrogels are thixotropic which is a desirable property for many potential applications. Freeze-fracture electron microscopy of xerogels shows layers of stacked sheets that are entangled into networks. These structures are unique compared to the fibers or ribbons typically reported for hydrogels. Differential scanning calorimetry provides gel-to-liquid phase transition temperatures ranging from 30 to 80 °C. Prodan fluorescence spectroscopy allows assignment of phase transitions in the gels and other lyotropic phases of high concentration samples. Phase diagrams are estimated for all hydrogels at 1–10 wt% from 5 to ≥ 80 °C. In conclusion, these hydrogels represent a series of interesting materials with unique properties that make them attractive for numerous potential applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Volatile fatty acid extraction from fermentation broth using a hydrophobic ionic liquid and in situ enzymatic esterification

Efficient recovery of volatile fatty acids (VFAs) from fermentation broth is a challenge due to low VFA titers and thus limits the commercialization of VFA production using biological routes. Liquid–liquid extraction using hydrophobic ionic liquids (ILs) shows great promise for the extraction and esterification of hydrophilic VFAs. In this study, several ILs were evaluated to select a water-immiscible and efficient extraction solvent. The selected IL, trihexyltetradecyl phosphonium dibutylphosphate ([P 666,14 ][DBP]), gave a cumulative VFA extraction of around 842.8 mg per g IL. The predicted excess enthalpy (H E ) and logarithmic activity coefficients ln(γ) using the COSMO-RS model were validated with the experimentally obtained VFA recovery from fermentation broth. To understand the extraction mechanism of VFAs, quantum theory of atoms in molecules (QTAIM) and noncovalent interaction (NCI) were performed. The results suggest that long chain fatty acids exhibit strong van der Waals interaction with the DBP anion leading to higher VFA extraction. The enzymatic esterification of VFAs with ethanol in [P 666,14 ][DBP] was optimized using the Box–Behnken response surface design of experiment. Under the optimized conditions, up to 83.7% of hexanoic acid was converted to ethyl esters, while other shorter chain VFAs have lower conversion efficiency (38.3–63.2%).

09 BIOMASS FUELS↗

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for groundstate density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetryadapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 10 6 DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R 2 = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R 2 = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2- TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for noncovalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Accurate prediction of global-density-dependent range-separation parameters based on machine learning

In this work, we develop an accurate and efficient XGBoost machine learning model for predicting the global-density-dependent range-separation parameter, ωGDD, for long-range corrected functional (LRC)-ωPBE. This ωGDDML model has been built using a wide range of systems (11 466 complexes, ten different elements, and up to 139 heavy atoms) with fingerprints for the local atomic environment and histograms of distances for the long-range atomic correlation for mapping the quantum mechanical range-separation values. The promising performance on the testing set with 7046 complexes shows a mean absolute error of 0.001 117 a0−1 and only five systems (0.07%) with an absolute error larger than 0.01 a0−1, which indicates the good transferability of our ωGDDML model. In addition, the only required input to obtain ωGDDML is the Cartesian coordinates without electronic structure calculations, thereby enabling rapid predictions. LRC-ωPBE(ωGDDML) is used to predict polarizabilities for a series of oligomers, where polarizabilities are sensitive to the asymptotic density decay and are crucial in a variety of applications, including the calculations of dispersion corrections and refractive index, and surpasses the performance of all other popular density functionals except for the non-tuned LRC-ωPBE. Finally, LRC-ωPBE (ωGDDML) combined with (extended) symmetry-adapted perturbation theory is used in calculating noncovalent interactions to further show that the traditional ab initio system-specific tuning procedure can be bypassed. The present study not only provides an accurate and efficient way to determine the range-separation parameter for LRC-ωPBE but also shows the synergistic benefits of fusing the power of physically inspired density functional LRC-ωPBE and the data-driven ωGDDML model.

Chemistry↗

In vitro investigation of protein assembly by combined microscopy and infrared spectroscopy at the nanometer scale

The nanoscale structure and dynamics of proteins on surfaces has been extensively studied using various imaging techniques, such as transmission electron microscopy and atomic force microscopy (AFM) in liquid environments. These powerful imaging techniques, however, can potentially damage or perturb delicate biological material and do not provide chemical information, which prevents a fundamental understanding of the dynamic processes underlying their evolution under physiological conditions. Here, we use a platform developed in our laboratory that enables acquisition of infrared (IR) spectroscopy and AFM images of biological material in physiological liquids with nanometer resolution in a cell closed by atomically thin graphene membranes transparent to IR photons. In this work, we studied the self-assembly process of S-layer proteins at the graphene-aqueous solution interface. The graphene acts also as the membrane separating the solution containing the proteins and Ca 2+ ions from the AFM tip, thus eliminating sample damage and contamination effects. The formation of S-layer protein lattices and their structural evolution was monitored by AFM and by recording the amide I and II IR absorption bands, which reveal the noncovalent interaction between proteins and their response to the environment, including ionic strength and solvation. Our measurement platform opens unique opportunities to study biological material and soft materials in general.

59 BASIC BIOLOGICAL SCIENCES↗

Mass spectrometry-based technologies for probing the 3D world of plant proteins

Abstract Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions—which are more difficult to study—dynamically regulate the proteome’s 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based “structural” proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function.

Plant Sciences↗