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An alkene-terminated ligand self-assembles with rare-earth ions, yielding polymerizable cages and subsequently rare-earth-binding polymer networks.
Ammonia protonic ceramic fuel cells (NH 3 -PCFCs) are promising and attractive energy-conversion devices owing to their high energy density, zero-carbon emission, and safety. The development of NH 3 -PCFCs, however, depends largely on the insufficient activity and poor durability of typical Ni-based anodes for ammonia decomposition, especially at low temperatures such as 550 °C. Herein, we report a self-assembled heterostructured Ru 0.95 Cu 0.05 Ni x (RCN) catalyst obtained through an in situ reaction between the surface-decorated Ru 0.95 Cu 0.05 nanoparticles and the Ni grain in the anode under typical processing conditions. At 550 °C, Ni–BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3 anode-supported PCFCs with RCN catalysts exhibit a high peak power density of 0.732 W cm -2 and a significantly enhanced durability of 100 h in NH 3 . Moreover, the cells demonstrate improved thermal stability compared with the bare cell during a 31-cycle thermal cycling test in NH 3 between 550 and 700 °C. In conclusion, the enhanced performance is likely attributed to the synergistic effects of Ru and Cu in RCN for NH 3 decomposition, resulting in a more vital interaction of NH 3 than that of the bare anode surfaces, as confirmed by NH 3 thermal conversion, electrochemical performance, and theoretical simulations.
Multiscale modeling requires the linking of models at different levels of detail, with the goal of gaining accelerations from lower fidelity models while recovering fine details from higher resolution models. Communication across resolutions is particularly important in modeling soft matter, where tight couplings exist between molecular-level details and mesoscale structures. While multiscale modeling of biomolecules has become a critical component in exploring their structure and self-assembly, backmapping from coarse-grained to fine-grained, or atomistic, representations presents a challenge, despite recent advances through machine learning. A major hurdle, especially for strategies utilizing machine learning, is that backmappings can only approximately recover the atomistic ensemble of interest. We demonstrate conditions for which backmapped configurations may be reweighted to exactly recover the desired atomistic ensemble. By training separate decoding models for each sidechain type, we develop an algorithm based on normalizing flows and geometric algebra attention to autoregressively propose backmapped configurations for any protein sequence. Critical for reweighting with modern protein force fields, our trained models include all hydrogen atoms in the backmapping and make probabilities associated with atomistic configurations directly accessible. We also demonstrate, however, that reweighting is extremely challenging despite state-of-the-art performance on recently developed metrics and generation of configurations with low energies in atomistic protein force fields. Through detailed analysis of configurational weights, we show that machine-learned backmappings must not only generate configurations with reasonable energies, but also correctly assign relative probabilities under the generative model. These are broadly important considerations in generative modeling of atomistic molecular configurations.
NbN thin films are considered as a promising alternative candidate to conventional Al superconducting electrodes for Josephson junction devices, superconducting qubits and quantum logic circuits. Herein, by co-growing ferromagnetic Co with superconducting NbN, self-assembled NbN–Co vertically aligned nanocomposite (VAN) thin films have been successfully deposited on a MgO substrates, with NbN in its cubic superconducting phase. The obtained VAN metamaterials exhibit epitaxial growth of NbN with uniformly dispersed Co nanoplates. Further physical property characterization reveals that the NbN–Co VAN presents ferromagnet properties with strong out-of-plane magnetic anisotropy and optical anisotropy. This hybrid metamaterial system could find future applications in superconducting spintronic devices and quantum computing devices.
The CLP5 self-assembles into spherical aggregates and encapsulates doxorubicin to form the CLP5@DOX nanomedicine, which exerts a combined antitumor effect by targeting caspase-3 and penetrating the cell membrane.
A series of archaeal bipolar glycerol dialkyl glycerol tetraether (GDGT) lipids were synthesized. Structural properties of self-assembled structures of GDGTs were studied and related to functional behavior such as membrane fusion.
It is imperative to understand how the design of perovskite solar modules (PSMs) affects their degradation mechanisms and byproducts under environmental stressors to enable their long-term reliability. This work reports on the impact of the hole-transport layer (HTL) on thermal cyclability and degradation mechanisms of p–i–n PSMs by comparing 3 HTLs: NiO x , MeO-2PACz (a self-assembled monolayer), and a bilayer HTL of MeO-2PACz on NiO x . We observe surprising thermal cyclability from a performance standpoint despite generating clear degradation products. We find that without a fully dense HTL, seemingly insignificant moisture/oxygen ingress through the edge of the PSMs can lead to rapid destabilization of the metal halide perovskite (MHP) layer due to reactions between MHP degradation byproducts and the indium-tin oxide layer. We also show that illumination-induced cation phase segregation is dependent on the illumination of an inactive area (regions of the substrate uncovered by the rear electrode). As such, we find that a dense interface between the MHP and ITO is necessary for chemically robust PSMs and highlight criteria for testing field-relevant configurations in lab-scale architectures.
The experimental realization of kagome lattices that exhibit the predicted coexistence of topological states with high electron kinetics and non-dispersing quantum states remains challenging. Additionally, the robustness of these states against structural perturbations has rarely been explored. Here, we report on the formation of an analogue kagome structure via the electrostatic self-assembly of 4,7-dibromobenzo[c]-1,2,5-thiadiazole (2Br-BTD) molecules on Au(111). Local spectroscopic measurements, supported by theoretical calculations, reveal that the weak molecular coupling reshapes the topological-induced Shockley surface state of Au(111) by imposing a (7 × 7) periodicity, resulting in new band crossings. The molecular overlayer favours the opening of electron gaps at these positions manifested as sharp peaks in dI/dV spectra and electron localization in either the hexagonal or triangular sublattices of the kagome structure. To explore the robustness of these topological states, we monitored their stability under varied conditions, including different temperatures, the unaltered herringbone reconstruction of the Au(111) surface and local structural relaxation of the molecular assembly. These results demonstrate the degree of topological protection of these states, which holds potential for fundamental and applied research.
Owing to their low flammability, solid-state and quasi-solid-state electrolytes are safer alternatives to liquid organic electrolytes for energy storage applications. Metal–organic frameworks (MOFs), with facile functional tunability, long-range order, and rich host–guest interactions, have been implemented as electrolyte materials in a wide range of energy storage applications. In this work, we investigate a class of MOFs called polyMOFs as quasi-solid-state electrolyte materials. Unlike MOF–polymer composites, which are physical mixtures of MOF particles and polymers, polyMOFs are composed of polymeric linkers and metal ion nodes that self-assembled into crystalline and porous framework materials. PolyMOFs thus marry the ionic transport properties of liquid electrolyte and polymers with the synthetic versatility and host–guest interactions of MOFs. We demonstrate that the functionality of the polymer backbone of the polyMOF linker can improve room-temperature ion transport in the material. The polyMOF based on poly(ethylene glycol) (PEG) exhibits greater ionic conductivity, lithium transference number, and lower activation energy than its polyethylene (PE) analog. Supported by solid-state 7 Li nuclear magnetic resonance spectroscopy, we propose these improvements are due to stronger coordination of Li + to oxide sites in PEG, allowing for dissociation of Li and its associated anion. DFT studies further reveal that the confined solvent molecule mediates Li + transport in PEG-functionalized UiO-66 via a metastable adsorption and hopping mechanism. This work lies at the interface of inorganic and polymer electrolytes, unveiling fundamental insights into the design of next-generation ion conductive materials for energy technologies.
Polyoxometalate (POM) supramolecular gels are a growing family of materials, both for understanding fundamental self-assembly and fabricating flexible monolithic materials that retain the function of metal oxides. Here, we exploited the pH-dependent speciation of polyoxoniobates (PONbs), targeting the formation of PONb-containing supramolecular gels that contain other low molecular weight components (silicate, germanate, phosphate, and carbonate). The introduction of gaseous CO 2 into aqueous hexaniobate solutions resulted in the formation of both new crystalline phases and highly transparent gels. The crystalline phases, formulated Cs 24 [Nb 7 O 22 (NbO(CO 3 ) 2 ) 9 (Si 3 O 9 )]·19.6H 2 O and Cs 21 Na 3 [Nb 7 O 22 (NbO(CO 3 ) 2 ) 9 (Ge 3 O 9 )]·33.6H 2 O are templated by a rare planar [X 3 O 9 ] 6− (X = Si, Ge) ring. Crystalline phases were not obtained with phosphate; instead, the gels contain a mixture of phosphate-centred PONbs and network-forming phosphate. POM speciation within the gels, physical properties, and assembly mechanisms were benchmarked by solution and solid-state nuclear magnetic resonance (NMR) spectroscopy, vibrational spectroscopies, small-angle X-ray scattering (SAXS), and thermogravimetry-mass spectroscopy. Optical analysis and dielectric behavior of the gels confirmed that they are highly transparent ionic and electronic conductors. The alkali and hydroxide concentration controls the formation of crystalline materials or supramolecular gels while maintaining the same network building blocks, providing a rare opportunity to describe the molecular-level structure of inorganic amorphous materials.
Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions.
Small-angle x-ray scattering is used for the structure determination of superlattice for its superior resolution, nondestructive nature, and high penetration power of x rays. With the advent of high brilliance x-ray sources and innovative computing algorithms, there have been notable advances in small angle x-ray scattering analysis of superlattices. High brilliance x-ray beams have made data analyses less model-dependent. Additionally, novel data acquisition systems are faster and more competitive than ever before, enabling a more accurate mapping of the superlattices' reciprocal space. Fast and high-throughput computing systems and algorithms also make possible advanced analysis methods, including iterative phasing algorithms, non-parameterized fitting of scattering data with molecular dynamics simulations, and the use of machine learning algorithms. As a result, solving nanoscale structures with high resolutions has become an attainable task. In this review, we highlight new developments in the field and introduce their applications for the analysis of nanoscale ordered structures, including nanoparticle supercrystals, nanoscale lithography patterns, and supramolecular self-assemblies. Particularly, we highlight the reciprocal space mapping techniques and the use of iterative phase retrieval algorithms. We also cover coherent-beam-based small angle x-ray scattering techniques such as ptychography and ptycho-tomography in view of the traditional small angle x-ray scattering perspective.
The performance of catalysts depends on their nanoscale properties, and local variations in structure and composition can have a dramatic impact on the catalytic reactivity. Therefore, probing the localized reactivity of catalytic surfaces using high spatial resolution vibrational spectroscopy, such as infrared (IR) nanospectroscopy and tip-enhanced Raman spectroscopy, is essential for mapping their reactivity pattern. Two fundamentally different scanning probe IR nanospectroscopy techniques, namely, scattering-type scanning near-field optical microscopy (sSNOM) and atomic force microscopy-infrared spectroscopy (AFM-IR), provide the capabilities for mapping the reactivity pattern of catalytic surfaces with a spatial resolution of ~20 nm. Herein, we compare these two techniques with regard to their applicability for probing the vibrational signature of reactive molecules on catalytic nanoparticles. For this purpose, we use chemically addressable self-assembled molecules on Au nanoparticles as model systems. We identified significant spectral differences depending on the measurement technique, which originate from the fundamentally different working principles of the applied methods. While AFM-IR spectra provided information from all the molecules that were positioned underneath the tip, the s-SNOM spectra were more orientation-sensitive. Due to its field-enhancement factor, the s-SNOM spectra showed higher vibrational signals for dipoles that were perpendicularly oriented to the surface. The s-SNOM sensitivity to the molecular orientation influenced the amplitude, position, and signal-to-noise ratio of the collected spectra. Ensemble-based IR measurements verified that differences in the localized IR spectra stem from the enhanced sensitivity of s-SNOM measurements to the adsorption geometry of the probed molecules.
Characterizing the novel properties of layered van der Waals materials is key for their application in functional devices. A better understanding of this type of material requires correlative imaging of diverse nanoscale material properties. Within this class of materials, CuInP 2 S 6 (CIPS) has received a significant degree of interest due to its ionically mediated room temperature ferroelectricity. Moreover, it is possible to form stable self-assembled heterostructures of ferroelectric CuInP 2 S 6 (CIPS) and non-ferroelectric (i.e., lacking Cu) In 4/3 P 2 S 6 (IPS) phases, by controlling the targeted composition and kinetics of synthesis. In this work, we present a correlative nanometric imaging study of the phononic modes and piezoelectricity of the phase-separated thin heteroepitaxial CIPS/IPS flakes. Here, we show that it is possible to isolate the different phononic modes of the two phases by spatially correlating them with their distinct ferroelectric behavior. The coupling of our experimental data with unsupervised learning statistical methods enables unraveling specific Raman peaks that are characteristic of each chemical phase (CIPS and IPS) present in the composite sample, discarding the less significant ones.
We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model’s predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Furthermore, our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.
The self-assembly of colloidal nanoparticles into ordered superlattices typically uses dynamic interactions to govern particle crystallization, as these non-permanent bonds prevent the formation of kinetically trapped, disordered aggregates. However, while the use of reversible bonding is critical in the formation of highly ordered particle arrangements, dynamic interactions also inherently make the structures more prone to disassembly or disruption when subjected to different environmental stimuli. Thus, there is typically a trade-off between the ability to initially form an ordered colloidal material and the ability of that material to retain its order under different conditions. Here, we present a method for embedding colloidal nanoparticle superlattices into a polymer gel matrix. This encapsulation strategy physically prevents the nanoparticles from dissociating upon heating, drying, or the introduction of chemicals that would normally disrupt the lattice. However, the use of a gel as the embedding medium still permits further modification of the colloidal nanoparticle lattice by introducing stimuli that deform the gel network (as this deformation in turn alters the nanoparticle lattice structure in a predictable manner). Moreover, encapsulation of the lattice within a gel permits further stabilization into fully solid materials by removing the solvent from the gel or by replacing the solvent with a liquid monomer that can be photopolymerized. This embedding method therefore makes it possible to incorporate ordered colloidal arrays into a polymer matrix as either dynamic or static structures, expanding their potential for use in responsive materials.
Nanostructured molybdenum disulfide (MoS 2 ) thin films were grown on a nanohole-patterned silicon substrate using plasma-enhanced atomic layer deposition. A nanoscale hole-patterned silicon substrate was fabricated for the growth of MoS 2 film using the self-assembly-based nanofabrication method. The nanoscale holes can significantly increase the surface area of the substrate while the formation and growth of nanostructures normally start at the surface of the substrate. Hydrogen sulfide (H 2 S) gas was used as the S source in the growth of molybdenum disulfide (MoS 2 ) while molybdenum (V) chloride (MoCl 5 ) powder was used as the Mo source. The MoS 2 film had a stoichiometric ratio of 1 (Mo) to 2 (S), and had peaks of E 1 2g and A 1g , which represent the in-plane and out-plane vibration modes of the Mo–S bond, respectively. It was found that the MoS 2 film grown in the nanoscale hole, especially at the wall of the hole, has more hexagonal-like structures due to the effects of nanoscale space confinement and the nanoscale interface although the film shows an amorphous structure. Post-growth high-temperature annealing ranging from 800 to 900 °C produced local crystalline structures in the film, which are compatible with those reported by other researchers.
We present herein a custom-made, in situ, multimodal spin coater system with an integrated heating stage that can be programmed with spinning and heating recipes and that is coupled with synchrotron-based, grazing-incidence wide- and small-angle x-ray scattering. The spin coating system features an adaptable experimental chamber, with the ability to house multiple ancillary probes such as photoluminescence and visible optical cameras, to allow for true multimodal characterization and correlated data analysis. This system enables monitoring of structural evolutions such as perovskite crystallization and polymer self-assembly across a broad length scale (2 Å–150 nm) with millisecond temporal resolution throughout a complete thin film fabrication process. The use of this spin coating system allows scientists to gain a deeper understanding of temporal processes of a material system, to develop ideal conditions for thin film manufacturing.