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Here, homogeneous and heterogeneous catalysts have advantages and disadvantages. Molecular catalysts supported on heterogeneous materials have the potential to combine the advantages of both fields. Jabbour et al. report a detailed study of the 3D structure of an Ir-catalyst on silica.
The ubiquity of sulfur–metal connections in nature inspires the design of bi- and multimetallic systems in synthetic inorganic chemistry. Common motifs for biocatalysts developed in evolutionary biology include the placement of metals in close proximity with flexible sulfur bridges as well as the presence of π-acidic/delocalizing ligands. This Account will delve into the development of a (NO)Fe(N 2 S 2 ) metallodithiolate ligand that harnesses these principles. The Fe(NO) unit is the centroid of a N 2 S 2 donor field, which as a whole is capable of serving as a redox-active, bidentate S-donor ligand. Its paramagnetism as well as the ν(NO) vibrational monitor can be exploited in the development of new classes of heterobimetallic complexes. We offer four examples in which the unpaired electron on the {Fe(NO)} 7 unit is spin-paired with adjacent paramagnets in proximal and distal positions. First, the exceptional stability of the (NO)Fe(N 2 S 2 )-Fe(NO) 2 platform, which permits its isolation and structural characterization at three distinct redox levels, is linked to the charge delocalization occurring on both the Fe(NO) and the Fe(NO) 2 supports. This accommodates the formation of a rare nonheme {Fe(NO)} 8 triplet state, with a linear configuration. A subsequent FeNi complex, featuring redox-active ligands on both metals (NO on iron and dithiolene on nickel), displayed unexpected physical properties. Our research showed good reversibility in two redox processes, allowing isolation in reduced and oxidized forms. Various spectroscopic and crystallographic analyses confirmed these states, and Mössbauer data supported the redox change at the iron site upon reduction. Oxidation of the complex produced a dimeric dication, revealing an intriguing magnetic behavior. The monomer appears as a spin-coupled diradical between {Fe(NO)} 7 and the nickel dithiolene monoradical, while dimerization couples the latter radical units via a Ni 2 S 2 rhomb. Magnetic data (SQUID) on the dimer dication found a singlet ground state with a thermally accessible triplet state that is responsible for magnetism. A theoretical model built on an H 4 chain explains this unexpected ferromagnetic low-energy triplet state arising from the antiferromagnetic coupling of a four-radical molecular conglomerate. For comparison, two (NO)Fe(N 2 S 2 ) were connected through diamagnetic group 10 cations producing diradical trimetallic complexes. Antiferromagnetic coupling is observed between {Fe(NO)} 7 units, with exchange coupling constants (J) of -3, -23, and -124 cm –1 for Ni II , Pd II , and Pt II , respectively. This trend is explained by the enhanced covalency and polarizability of sulfur-dense metallodithiolate ligands. A central paramagnetic trans-Cr(NO)(MeCN) receiver unit core results in a cissoid structural topology, influenced by the stereoactivity of the lone pair(s) on the sulfur donors. This {Cr(NO)} 5 radical bridge, unlike all previous cases, finds the coupling between the distal Fe(NO) radicals to be ferromagnetic (J = 24 cm –1 ). The stability and predictability of this S = 1/2 moiety and the steric/electronic properties of the bridging thiolate sulfurs suggest it to be a likely candidate for the development of novel molecular (magnetic) compounds and possibly materials. The role of synthetic inorganic chemistry in designing synthons that permit connections of the (NO)Fe(N 2 S 2 ) metalloligand is highlighted as well as the properties of the heterobi- and polymetallic complexes derived therefrom.
Hierarchical materials in the natural world are often made through the self-assembly of amphiphilic molecules. Achieving similar structural complexity in synthetic materials requires understanding how various molecular parameters affect assembly behavior. In recent years, inverse surfactants─molecules with hydrophobic head groups and hydrophilic macromolecular tails─have been shown to self-assemble into supramolecular assemblies in aqueous solutions that show promise for a number of applications, including drug delivery. Here, we build an understanding of the morphological phase diagram of inverse surfactants using insights from scattering experiments, computer simulations, and statistical mechanics. The scattering and simulation results reveal that changing the headgroup size is an important molecular knob in controlling morphological transitions. The molecular size ratio of the hydrophobic group to the hydrophilic group emerges as a crucial dimensionless quantity in our theory and plays a determining role in setting the micelle structure and the transition from mesoscale to macroscale aggregates. Our minimal theory is able to qualitatively explain the key features of the morphological phase diagram, including the prevalence of fiber-like structures in comparison to spherical and planar micelles. Together, these findings provide a more complete picture of the molecular dependencies of assemblies of inverse surfactants, which we hope may aid in the de novo design of supramolecular structures.
A photocontrolled isomerization reaction of oxabenzonorbornadiene (OBNBD) derivatives with a photo acid generator (PAG) has been developed. Under irradiation, the PAG releases an acid that catalyzes the heterolytic cleavage of the benzyl ether, triggering an aromatization process accompanied by substantial heat release that accelerates the transformation. Large exotherms can further push low reactivity substrates to achieve full conversion within minutes. In conclusion, this approach not only illuminates practical and environmentally benign advantages in organic synthesis but also opens new avenues for developing photoresponsive molecular architectures and advanced functional materials.
Polycyclic aromatic hydrocarbons such as perylene, pyrene, and their derivatives are highly emissive fluorophores in solution. Furthermore, the practical applications of these materials in the field of molecular electronic and light emitting devices are often hindered by self-quenching effects due to the formation of non-fluorescent aggregates in concentrated solutions or in the solid state. Herein, we demonstrate that aggregation caused quenching of perylenes can be minimalized by molecular incorporation into metalorganic frameworks (MOFs). This study utilized a stable Zr 6 cluster-based MOF, UiO-67, as a matrix. Linear linkers containing the photo-responsive moieties were designed and incorporated into the parent UiO-67 scaffold through the partially replacement of the non-fluorescent linkers of a similar length, forming mixed-linker MOFs. Here, the average distance between perylene moieties was tuned by changing the linker ratios, thus controlling the fluorescence intensity, emission wavelength, and quantum yield. Molecular modeling was further adopted to correlate the number of isolated perylene linkers within the framework to the ratio between the two linkers, therefore rationalizing the change in the observed fluorescent properties. Taking advantage of the tunable fluorescence, inherent porosity, and high chemical stability of this MOF platform, it was applied as a fluorescent sensor for oxygen detection in the gas phase, a model reaction, showing fast response and good recyclability.
This paper evaluates the potential of algae-derived biobinders as sustainable alternatives for pavement construction. It specifically examines the physicochemical and rheological properties of biomodified binders and their potential to offset carbon emissions when used as partial replacements for conventional petroleum-based asphalt binders. Biosequestration of CO 2 using microalgal cell factories is a promising way of recycling CO 2 into biomass via photosynthesis. Our study demonstrates that incorporating algae-derived binders into asphalt can significantly reduce carbon emissions. Each 1% increase in algae-based biobinder leads to an approximate 4.5% decrease in net carbon emissions. This indicates that a blend containing about 22% biobinder has the potential to achieve carbon neutrality. Blends with higher proportions may even result in net-negative emissions, highlighting a promising strategy for environmentally responsible road construction. In terms of performance, the study shows that certain algae-derived biobinders significantly enhance the cracking resistance of asphalt, particularly under subzero temperatures, by improving its stress-relief capacity. A key contribution of this work is the introduction of polarizability as a novel molecular-level parameter for assessing the compatibility of algae-derived bio-oils with asphalt. By capturing the electronic responsiveness of bio-oil molecules, polarizability serves as a predictive indicator of their interaction potential with asphalt components, providing a new dimension for evaluating the binder performance at the molecular scale. Among the tested materials, the biobinder derived from Haematococcus pluvialis demonstrated particularly strong improvements in resistance to permanent deformation under repeated loading conditions analogous to traffic-induced stress, as well as enhanced resistance to moisture-induced damage. In conclusion, these findings advance the chemistry-driven design of biomass-based binders and highlight a promising pathway toward the development of low-carbon, high-performance, and sustainable infrastructure materials.
Halide perovskites have attracted a great amount of attention owing to their unique materials chemistry, excellent electronic properties, and low-cost manufacturing. Two-dimensional (2D) halide perovskites, originating from three-dimensional (3D) perovskite structures, are structurally more diverse and therefore create functional possibilities beyond 3D perovskites. The much less restrictive size constraints on the organic component of these hybrid materials particularly provide an exciting platform for designing unprecedented materials and functionalities at the molecular level. In this Perspective, we discuss the concept and recent development of a sub-class of 2D perovskites, namely, organic semiconductor-incorporated perovskites (OSiPs). OSiPs combine the electronic functionality of organic semiconductors with the soft and dynamic halide perovskite lattice, offering opportunities for tailoring the energy landscape, lattice and carrier dynamics, and electron/ion transport properties for various fundamental studies, as well as device applications. Specifically, we summarize recent advances in the design, synthesis, and structural analysis of OSiPs with various organic conjugated moieties as well as the application of OSiPs in photovoltaics, light-emitting devices, and transistors. As a result, challenges and further opportunities for OSiPs in molecular design, integration of novel functionality, film quality, and stability issues are addressed.
While the discovery of two-dimensional (2D) magnets opens the door for fundamental physics and next-generation spintronics, it is technically challenging to achieve the room-temperature ferromagnetic (FM) order in a way compatible with potential device applications. Here in this paper, we report the growth and properties of single- and few-layer CrTe 2 , a van der Waals (vdW) material, on bilayer graphene by molecular beam epitaxy (MBE). Intrinsic ferromagnetism with a Curie temperature (TC) up to 300 K, an atomic magnetic moment of ~0.21 μ B /Cr and perpendicular magnetic anisotropy (PMA) constant (K u ) of 4.89 × 10 5 erg/cm 3 at room temperature in these few-monolayer films have been unambiguously evidenced by superconducting quantum interference device and X-ray magnetic circular dichroism. This intrinsic ferromagnetism has also been identified by the splitting of majority and minority band dispersions with ~0.2 eV at Г point using angle-resolved photoemission spectroscopy. The FM order is preserved with the film thickness down to a monolayer (T C ~ 200 K), benefiting from the strong PMA and weak interlayer coupling. The successful MBE growth of 2D FM CrTe 2 films with room-temperature ferromagnetism opens a new avenue for developing large-scale 2D magnet-based spintronics devices.
Nanoscale spin textures, especially magnetic skyrmions, have attracted intense interest as candidate high-density and power-efficient information carriers for spintronic devices. Facilitating a deeper understanding of sub-100 nm to atomic-scale spin textures requires more advanced magnetic imaging techniques. Here, we demonstrate a Lorentz electron ptychography (LEP) method which can enable high-resolution, high-sensitivity magnetic field imaging for widely-available electron microscopes. The resolution of LEP is not limited by the usual diffraction limit of the lens optics, but instead is determined by the maximum scattering angle at which a statistically meaningful dose can still be recorded – this can be an improvement of up to 2 to 6 times depending on the allowable dose. Using FeGe as a model system, we realize a more accurate magnetic field measurement of skyrmions with an improved spatial resolution and sensitivity by also correcting the probe damping effects from the imaging optics via LEP. This allows us to directly resolve subtle internal structures of magnetic skyrmions near the skyrmion cores, boundaries, and dislocations in an FeGe single crystal. Our study establishes a quantitative, high-resolution magnetic microscopy technique that can reveal the nanoscale spin textures, especially magnetization discontinuities and topological defects in nanomagnets. In conclusion, the technique’s high dose efficiency should also make it well-suited for the exploration of magnetic textures in electron radiation-sensitive materials such as organic or molecular magnets.
The coupling of excitons in π-conjugated molecules to high-frequency vibrational modes, particularly carbon–carbon stretch modes (1,000–1,600 cm -1 ) has been thought to be unavoidable. These high-frequency modes accelerate non-radiative losses and limit the performance of light-emitting diodes, fluorescent biomarkers and photovoltaic devices. Here, by combining broadband impulsive vibrational spectroscopy, first-principles modelling and synthetic chemistry, we explore exciton–vibration coupling in a range of π-conjugated molecules. We uncover two design rules that decouple excitons from high-frequency vibrations. First, when the exciton wavefunction has a substantial charge-transfer character with spatially disjoint electron and hole densities, we find that high-frequency modes can be localized to either the donor or acceptor moiety, so that they do not significantly perturb the exciton energy or its spatial distribution. Second, it is possible to select materials such that the participating molecular orbitals have a symmetry-imposed non-bonding character and are, thus, decoupled from the high-frequency vibrational modes that modulate the π-bond order. We exemplify both these design rules by creating a series of spin radical systems that have very efficient near-infrared emission (680–800 nm) from charge-transfer excitons. We show that these systems have substantial coupling to vibrational modes only below 250 cm -1 , frequencies that are too low to allow fast non-radiative decay. This enables non-radiative decay rates to be suppressed by nearly two orders of magnitude in comparison to π-conjugated molecules with similar bandgaps. Our results show that losses due to coupling to high-frequency modes need not be a fundamental property of these systems.
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.
We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. The approach encodes the information contained in STEM image sequences using a small number of latent variables, allowing the exploration of chemical transformation pathways by tracing the evolution of atoms in the latent space of the system. The results suggest that atomically resolved STEM data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods in the physical sciences and will stimulate the development of more sophisticated ways to encode physical constraints in the encoder–decoder architectures and generative physical laws and causal relationships in the latent space of VAEs.
The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.
Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.
The interplay between coherence and system-environment interactions is at the basis of a wide range of phenomena, from quantum information processing to charge and energy transfer in molecular systems, biomolecules, and photochemical materials. In this work, we use a Frenkel exciton model with long-range interacting qubits coupled to a damped collective bosonic mode to investigate vibrationally assisted transfer processes in donor-acceptor systems featuring internal substructures analogous to light-harvesting complexes. We find that certain delocalized excitonic states maximize the transfer rate and that the entanglement is preserved during the dissipative transfer over a wide range of parameters. We investigate the reduction in transfer caused by static disorder, white noise, and finite temperature and study how transfer efficiency scales as a function of the number of dimerized monomers and the component number of each monomer, finding which excitonic states lead to optimal transfer. Finally, we provide a realistic experimental setting to realize this model in analog trapped-ion quantum simulators. Analog quantum simulation of systems comprising many and increasingly complex monomers could offer valuable insights into the design of light-harvesting materials, particularly in the nonperturbative intermediate parameter regime examined in this study, where classical simulation methods are resource intensive.
Nanoporous carbons and carbon nanostructures can store hydrogen at cryogenic temperatures but lack the volumetric and gravimetric capacity to be industrially significant. Recent inelastic neutron scattering experiments suggest a highly dense phase of hydrogen at temperatures well above the melting point of solid hydrogen. However, it remains unclear how pore geometry and intermolecular interactions enable these dense phases to exist, with dispersion (van der Waals) or electrostatic/induction suggested to be the key effects in slit and curved pores but their relative contributions have yet to be quantified. In this paper, we perform benchmark electronic structure calculations allowing the interactions between planar and curved aromatic molecules with hydrogen to be accurately determined. Dispersion was found to dominate over electrostatic and inductive effects with some many-body charge transfer (Dobson type-A) effects needed to capture the most highly curved structures. Density functional methods that include type-A many-body effects were found to accurately describe the intermolecular interactions at a fraction of the cost of coupled-cluster simulations and these approaches were used to calculate the energies inside large carbon bowl and slit pores. The interaction energies inside the bowl pores were found to depend on the orientation of the hydrogen molecule. This rotational barrier, modeled as a quantum hindered rotor, could reproduce the peak splitting observed in inelastic neutron scattering experiments, with weak splitting arising from bowl-like fullerene pores and strong splitting from highly confining nanotubelike pores. Increasing the fraction of such curved pores in nanoporous carbons may therefore offer a pathway to enhance their hydrogen-storage capacity. Moreover, the preferential adsorption of ortho hydrogen on nanotubelike pores could enable the storage of high-density hydrogen without the need to remove heat produced during the ortho-para hydrogen conversion.
Obesity can increase the risk of bone fragility, even when bone mass is intact. This fragility stems from poor bone quality, potentially caused by deficiencies in bone matrix material properties. However, cellular and molecular mechanisms leading to obesity-related bone fragility are not fully understood. Using male mouse models of obesity, we discovered TGF-β signaling plays a critical role in mediating the effects of obesity on bone. High-carbohydrate and high-fat diets increase TGF-β signaling in osteocytes, which impairs their mitochondrial function, increases cellular senescence, and compromises perilacunar/canalicular remodeling and bone quality. By specifically inhibiting TGF-β signaling in mouse osteocytes, some of the negative effects of high-fat and high-carbohydrate diets on bones, including the lacunocanalicular network, perilacunar/canalicular remodeling, senescence, and mechanical properties such as yield stress, were mitigated. DMP1-Cre–mediated deletion of TGF-β receptor II also blunted adverse effects of high-fat and high-carbohydrate diets on energy balance and metabolism. These findings suggest osteocytes are key in controlling bone quality in response to high-fat and high-carbohydrate diets. Calibrating osteocyte function could mitigate bone fragility associated with metabolic diseases while reestablishing energy balance.
Time-resolved spectroscopies using high-energy photons in the vacuum ultraviolet (VUV) to the X-ray region of the electromagnetic spectrum, have proven to be powerful probes of chemical dynamics. These high-energy photons can access valence and core orbitals of molecules and materials, providing key information on molecular and electronic structure and their time evolution. This report details the development of table-top sources of extreme ultraviolet (XUV) and VUV pulses at Sandia National Laboratories for use in studies of gas phase chemical dynamics. Femtosecond duration XUV pulses are produced using laser-driven high harmonic generation and their detected range span ~40-140 eV photon energies. These pulses are used in conjunction with ultraviolet pulses in a pump-probe scheme to study excited state dynamics of gas phase molecules. VUV pulses at 7.75 eV are generated using a four-wave-mixing scheme driven by 800 nm and 266 nm pulses in an argon-filled hollow-core fiber.