Variations In Molecular Structure Research Articles

Charge transfer behavior of triboelectric polymers and triboelectric sensors operated under ultrahigh pressure

AbstractThe energy generation performance of triboelectric materials under ultrahigh pressure remains to be investigated. Here, the variations in molecular structure and built‐in electric field of triboelectric polymers under ultrahigh pressure have been thoroughly studied. The attenuation of built‐in electric field and the escaping of triboelectric charges under ultrahigh pressure are observed in different triboelectric polymers, whereas the existence of deep traps allows the built‐in electric field to be recoverable with the release of pressure. Moreover, the macromolecular conformational changes, including twisting molecular chains and crystal structure changes, can also induce the redistribution of deep traps, leading to a sudden increase in built‐in electric field under specific pressure. Finally, a triboelectric sensor for ultrahigh pressure condition is fabricated with excellent cycle repeatability and a total thickness of 2 mm, which has a sensitivity of 0.07 V MPa−1 within a linear region of 1–100 MPa. This study offers in‐depth insight into the physical understanding of charge behavior both on interface and in bulk of triboelectric materials, whereas the proposed ultrahigh pressure sensors can promote various potential applications of triboelectric sensor in extreme environments.

Extracting the Heterogeneous 3D Structure of Molecular Films Using Higher Dimensional SFG Microscopy.

Ultrathin molecular films are widespread in both natural and industrial settings, where details of the molecular structure such as density, out-of-plane tilt angles, and in-plane directionality determine their physicochemical properties. Many of these films possess important molecular-to-macroscopic heterogeneity in these structural parameters, which have traditionally been difficult to characterize. Here, we show how extending sum-frequency generation (SFG) microscopy measurements to higher dimensionality by azimuthal-scanning can extract the spatial variation in the three-dimensional molecular structure at an interface. We extend the commonly applied theoretical assumptions used to analyze SFG signals to the study of systems possessing in-plane anisotropy. This theoretical framework is then applied to a phase-separated mixed lipid monolayer to investigate the variation in molecular density and 3D orientation across the chirally packed lipid domains. The results show little variation in out-of-plane structure but a distinct micron-scale region at the domain boundaries with a reduction in both density and in-plane ordering.

Phenomenology of a Rydberg impurity in an ideal Bose-Einstein condensate

We investigate the absorption spectrum of a Rydberg impurity immersed in and interacting with an ideal Bose-Einstein condensate. Here, the impurity-bath interaction range can greatly exceed the mean interparticle distance; this discrepancy in length scales challenges the assumptions underlying the universal aspects of impurity atoms in dilute bosonic environments. Our analysis finds three distinct parameter regimes, each characterized by a unique spectral response. In the low-density regime, we find that the Rydberg impurity is dressed by the surrounding bath similarly to the known Bose polaron. Transitioning to intermediate densities, the impurity response, given by sharp quasiparticle peaks, fragments into an intricate pattern bearing the hallmarks of a diverse molecular structure. Finally, at high density, a universal Gaussian response emerges as the statistical nature of the bath dominates its quantum dynamics. We complement this analysis with a study of an ionic impurity, which behaves equivalently. Our exploration offers insights into the interplay between interaction range, density, and many-body behavior in impurity systems. Published by the American Physical Society 2024

Crystal structure-mechanical property relationship in succinic acid and L- alanine probed by nanoindentation

Establishing structure–mechanical property relationships is crucial for understanding and engineering the performance of pharmaceutical molecular crystals. In this study, we employed nanoindentation, a powerful technique that can probe mechanical properties at the nanoscale, to investigate the hardness and elastic modulus of single crystals of succinic acid and L-alanine. Nanoindentation results reveal distinct mechanical behaviors between the two compounds, with L-alanine exhibiting significantly higher hardness and elastic modulus compared to succinic acid. These differences are attributed to the underlying variations in molecular crystal structures − the three-dimensional bonding network and high intermolecular interaction energies of L-alanine molecules leads to its stiffness compared to the layered and weakly bonded crystal structure of succinic acid. Furthermore, the anisotropic nature of succinic acid is reflected in the directional dependence of the mechanical responses where it has been found that the (111) plane is more resistant to indentation than (100). By directly correlating the nanomechanical properties obtained from nanoindentation with the detailed crystal structures, this study provides important insights into how differences in molecular arrangements can translate into different macroscopic mechanical performance. These findings have implications on the selection of molecular crystals for optimized drug manufacturability.

Chemical Diversity, Pharmacology, Synthesis and Detection of Naturally Occurring Peroxides.

Natural occurring peroxides are interesting bioprospecting targets due to their molecular structural diversity and the wide range of pharmacological activities. In this systematic review, a total of 123 peroxide compounds were analysed from 99 published papers with the compounds distributed in 31 plants, 18 animals and 41 microorganisms living in land and water ecosystems. The peroxide moiety exists as both cyclic and acyclic entities and can include 1,2-dioxolanes, 1,2-dioxane rings and common secondary metabolites with a peroxo group. These peroxides possessed diverse bioactivities including anticancer, antimalarial, antimicrobial, anti-inflammatory, neuroprotective, adipogenic suppressor, antituberculosis, anti-melanogenic and anti-coagulant agents. Biosynthetic pathways and mechanisms of most endoperoxides have not been well established. Method development in peroxide detection has been a challenging task requiring multidisciplinary investigation and exploration on peroxy-containing secondary metabolites are necessary.

Graph neural network coarse-grain force field for the molecular crystal RDX

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

Base-Mediated Allylic Defluorinative Functionalizations of β-CF2H-1,3-enynes Enables the Construction of Terminal Monofluoroenynes.

The base-mediated allylic defluorinative functionalization of β-CF2H-1,3-enynes with nucleophiles is described, affording terminal monofluoroalkenes bearing an alkynyl group in synthetically useful yields and Z/E selectivities. Importantly, the resultant Z/E mixture could be separated by flash chromatography in all cases; thus, stereoisomerically pure monofluoroenynes were obtained. Postsynthetic modifications of the synthesized monofluoroenynes were also accomplished to access diverse molecular structures. Computational studies disclosed the origin of the diastereoselectivity.

Virus-Based Separation of Rare Earth Elements.

The utilization of biomaterials for the separation of rare earth elements (REEs) has attracted considerable interest due to their inherent advantages, including diverse molecular structures for selective binding and the use of eco-friendly materials for sustainable systems. We present a pioneering methodology for developing a safe virus to selectively bind REEs and facilitate their release through pH modulation. We engineered the major coat protein of M13 bacteriophage (phage) to incorporate a lanthanide-binding peptide. The engineered lanthanide-binding phage (LBPh), presenting ∼3300 copies of the peptide, serves as an effective biological template for REE separation. Our findings demonstrate the LBPh's preferential binding for heavy REEs over light REEs. Moreover, the LBPh exhibits remarkable robustness with excellent recyclability and stability across multiple cycles of separations. This study underscores the potential of genetically integrating virus templates with selective binding motifs for REE separation, offering a promising avenue for environmentally friendly and energy-efficient separation processes.

Light harvesting in purple bacteria does not rely on resonance fine-tuning in peripheral antenna complexes

The ring-like peripheral light-harvesting complex 2 (LH2) expressed by many phototrophic purple bacteria is a popular model system in biological light-harvesting research due to its robustness, small size, and known crystal structure. Furthermore, the availability of structural variants with distinct electronic structures and optical properties has made this group of light harvesters an attractive testing ground for studies of structure–function relationships in biological systems. LH2 is one of several pigment-protein complexes for which a link between functionality and effects such as excitonic coherence and vibronic coupling has been proposed. While a direct connection has not yet been demonstrated, many such interactions are highly sensitive to resonance conditions, and a dependence of intra-complex dynamics on detailed electronic structure might be expected. To gauge the sensitivity of energy-level structure and relaxation dynamics to naturally occurring structural changes, we compare the photo-induced dynamics in two structurally distinct LH2 variants. Using polarization-controlled 2D electronic spectroscopy at cryogenic temperatures, we directly access information on dynamic and static disorder in the complexes. The simultaneous optimal spectral and temporal resolution of these experiments further allows us to characterize the ultrafast energy relaxation, including exciton transport within the complexes. Despite the variations in PPC molecular structure manifesting as clear differences in electronic structure and disorder, the energy-transport and—relaxation dynamics remain remarkably similar. This indicates that the light-harvesting functionality of purple bacteria within a single LH2 complex is highly robust to structural perturbations and likely does not rely on finely tuned electronic- or electron-vibrational resonance conditions.

Hydrophilic Fraction of Dissolved Organic Matter Largely Facilitated Microplastics Photoaging: Insights from Redox Properties and Reactive Oxygen Species.

Dissolved organic matter (DOM) exists widely in natural water, which inevitably influences microplastic (MP) photoaging. Nevertheless, the impacts of DOM fractions with diverse molecular structures on MP photoaging remain to be elucidated. This study explored the photoaging mechanisms of polylactic acid (PLA)-MPs and polystyrene (PS)-MPs in the presence of DOM and its subfractions (hydrophobic acid (HPOA), hydrophobic neutral (HPON), and hydrophilic (HPI)). Across DOM fractions, HPI exhibited the highest electron accepting capacity (23 μmol e- (mg C)-1) due to its abundant tannin-like species (36.8%) with carboxylic groups, which facilitated more reactive oxygen species generation (particularly hydroxyl radical), leading to the strongest photoaging rate of two MPs by HPI. However, the sequences of bond cleavage during photoaging of each MPs were not clearly shifted as revealed by two-dimensional infrared correlation spectra. Inconspicuous effects on the extent of PS- and PLA-MPs photoaging were observed for HPOA and HPON, respectively. This was mainly ascribed to the occurrence of inhibitory mechanisms (e.g., light-shielding and quenching effect) counteracting the reactive oxygen species-promoting effects. The findings identified the HPI fraction of DOM for promoting PS- and PLA-MPs photoaging rate and first constructed a link among DOM molecular structures, redox properties, and effects on MP photoaging.

Programmable Computational RNA Droplets Assembled via Kissing-Loop Interaction.

DNA droplets, artificial liquid-like condensates of well-engineered DNA sequences, allow the critical aspects of phase-separated biological condensates to be harnessed programmably, such as molecular sensing and phase-state regulation. In contrast, their RNA-based counterparts remain less explored despite more diverse molecular structures and functions ranging from DNA-like to protein-like features. Here, we design and demonstrate computational RNA droplets capable of two-input AND logic operations. We use a multibranched RNA nanostructure as a building block comprising multiple single-stranded RNAs. Its branches engaged in RNA-specific kissing-loop (KL) interaction enables the self-assembly into a network-like microstructure. Upon two inputs of target miRNAs, the nanostructure is programmed to break up into lower-valency structures that are interconnected in a chain-like manner. We optimize KL sequences adapted from viral sequences by numerically and experimentally studying the base-wise adjustability of the interaction strength. Only upon receiving cognate microRNAs, RNA droplets selectively show a drastic phase-state change from liquid to dispersed states due to dismantling of the network-like microstructure. This demonstration strongly suggests that the multistranded motif design offers a flexible means to bottom-up programming of condensate phase behavior. Unlike submicroscopic RNA-based logic operators, the macroscopic phase change provides a naked-eye-distinguishable readout of molecular sensing. Our computational RNA droplets can be applied to in situ programmable assembly of computational biomolecular devices and artificial cells from transcriptionally derived RNA within biological/artificial cells.

Regression Study of Odorant Chemical Space, Molecular Structural Diversity, and Natural Language Description.

Odor is analyzed on the human olfactometry systems in various steps. The mapping from chemical structures to olfactory perceptions of smell is an extremely challenging task. Scientists have been unable to find a measure to distinguish the perceptual similarity between odorants. In this study, we report regression analysis and visualization based on the odorant chemical space. We discuss the relation between the odor descriptors and their structural diversity for odorants groups associated with each odor descriptor. We studied the influence of structural diversity on the odor descriptor predictability. The results suggest that the diversity of molecular structures, which is associated with the same odor descriptor, is related to the resolutional confusion with the odor descriptor.

Computer-aided pattern scoring – A multitarget dataset-driven workflow to predict ligands of orphan targets

The identification of lead molecules and the exploration of novel pharmacological drug targets are major challenges of medical life sciences today. Genome‐wide association studies, multi-omics, and systems pharmacology steadily reveal new protein networks, extending the known and relevant disease-modifying proteome. Unfortunately, the vast majority of the disease-modifying proteome consists of ‘orphan targets’ of which intrinsic ligands/substrates, (patho)physiological roles, and/or modulators are unknown. Undruggability is a major challenge in drug development today, and medicinal chemistry efforts cannot keep up with hit identification and hit-to-lead optimization studies. New ‘thinking-outside-the-box’ approaches are necessary to identify structurally novel and functionally distinctive ligands for orphan targets. Here we present a unique dataset that includes critical information on the orphan target ABCA1, from which a novel cheminformatic workflow – computer-aided pattern scoring (C@PS) – for the identification of novel ligands was developed. Providing a hit rate of 95.5% and molecules with high potency and molecular-structural diversity, this dataset represents a suitable template for general deorphanization studies.

Coarse-grained modeling and dynamics tracking of nanoparticles diffusion in human gut mucus

The gastrointestinal (GI) tract's mucus layer serves as a critical barrier and a mediator in drug nanoparticle delivery. The mucus layer's diverse molecular structures and spatial complexity complicates the mechanistic study of the diffusion dynamics of particulate materials. In response, we developed a bi-component coarse-grained mucus model, specifically tailored for the colorectal cancer environment, that contained the two most abundant glycoproteins in GI mucus: Muc2 and Muc5AC. This model demonstrated the effects of molecular composition and concentration on mucus pore size, a key determinant in the permeability of nanoparticles. Using this computational model, we investigated the diffusion rate of polyethylene glycol (PEG) coated nanoparticles, a widely used muco-penetrating nanoparticle. We validated our model with experimentally characterized mucus pore sizes and the diffusional coefficients of PEG-coated nanoparticles in the mucus collected from cultured human colorectal goblet cells. Machine learning fingerprints were then employed to provide a mechanistic understanding of nanoparticle diffusional behavior. We found that larger nanoparticles tended to be trapped in mucus over longer durations but exhibited more ballistic diffusion over shorter time spans. Through these discoveries, our model provides a promising platform to study pharmacokinetics in the GI mucus layer.

Research Progress in Structure Synthesis, Properties, and Applications of Small-Molecule Silicone Surfactants.

Silicone surfactants have garnered significant research attention owing to their superior properties, such as wettability, ductility, and permeability. Small-molecular silicone surfactants with simple molecular structures outperform polymeric silicone surfactants in terms of surface activity, emulsification, wetting, foaming, and other areas. Moreover, silicone surfactants with small molecules exhibit a diverse and rich molecular structure. This review discusses various synthetic routes for the synthesis of different classes of surfactants, including single-chain, "umbrella" structure, double chain, bolaform, Gemini, and stimulus-responsive surfactants. The fundamental surface/interface properties of the synthesized surfactants are also highlighted. Additionally, these surfactants have demonstrated enormous potential in agricultural synergism, drug delivery, mineral flotation, enhanced oil recovery, separation, and extraction, and foam fire-fighting.

Mix-Key: graph mixup with key structures for molecular property prediction.

Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets.

Valency based novel quantitative structure property relationship (QSPR) approach for predicting physical properties of polycyclic chemical compounds

In this study, we introduce a novel valency-based index, the neighborhood face index (NFI), designed to characterize the structural attributes of benzenoid hydrocarbons. To assess the practical applicability of NFI, we conducted a linear regression analysis utilizing numerous physiochemical properties associated with benzenoid hydrocarbons. Remarkably, the results revealed an extraordinary correlation exceeding 0.9991 between NFI and these properties, underscoring the robust predictive capability of the index. The NFI, identified as the best-performing descriptor, is subsequently investigated within certain infinite families of carbon nanotubes. This analysis demonstrates the index’s exceptional predictive accuracy, suggesting its potential as a versatile tool for characterizing and predicting properties across diverse molecular structures, particularly in the context of carbon nanotubes.

Molecular and Supramolecular Materials: From Light-Harvesting to Quantum Information Science and Technology.

The past two decades have witnessed immense advances in quantum information technology (QIT), benefited by advances in physics, chemistry, biology, and materials science and engineering. It is intriguing to consider whether these diverse molecular and supramolecular structures and materials, partially inspired by quantum effects as observed in sophisticated biological systems such as light-harvesting complexes in photosynthesis and the magnetic compass of migratory birds, might play a role in future QIT. If so, how? Herein, we review materials and specify the relationship between structures and quantum properties, and we identify the challenges and limitations that have restricted the intersection of QIT and chemical materials. Examples are broken down into two categories: materials for quantum sensing where nonclassical function is observed on the molecular scale and systems where nonclassical phenomena are present due to intermolecular interactions. We discuss challenges for materials chemistry and make comparisons to related systems found in nature. We conclude that if chemical materials become relevant for QIT, they will enable quite new kinds of properties and functions.

A smartphone-integrated aptasensor for pesticide detection using gold-decorated microparticles.

The increasing incidence of environmental concerns related to excessive use of pesticides, such as imidacloprid and carbendazim, poses risks to pollinators, water bodies, and human health, prompting regulatory scrutiny and bans in developed countries. In this study, we propose a portable smartphone-based biosensor for rapid and label-free colorimetric detection by using the gold-decorated polystyrene microparticles (Ps-AuNP) functionalized with specific aptamers to imidacloprid and carbendazim on amicrofluidic paper-based analytical device (μ-PAD). Four aptamers were selected for the detection of these pesticides and their sensitivity and selectivity performancewas evaluated. The sensitivity results show a detection limit for imidacloprid of 3.12ppm and 1.56ppm for carbendazim. The aptamers also exhibited high selectivity performance against other pesticides, such as thiamethoxam, fenamiphos, isoproturon, and atrazine. However, the platform presented cross-selectivity when detecting imidacloprid, carbendazim, and linuron, which is discussed herein. Overall, we present a promising platform for simple, on-site, and rapid colorimetric screening of specific pesticides, while highlighting the challenges of aptasensors in achieving selectivity amidst diverse molecular structures.

Uncovering the toxicity mechanisms of a series of carboxylic acids in liver cells through computational and experimental approaches

Hepatotoxicity poses a significant concern in drug design due to the potential liver damage that can be caused by new drugs. Among common manifestations of hepatotoxic damage is lipid accumulation in hepatic tissue, resulting in liver steatosis or phospholipidosis. Carboxylic derivatives are prone to interfere with fatty acid metabolism and cause lipid accumulation in hepatocytes. This study investigates the toxic behaviour of 24 structurally related carboxylic acids in hepatocytes, specifically their ability to cause accumulation of fatty acids and phospholipids. Using high-content screening (HCS) assays, we identified two distinct lipid accumulation patterns. Subsequently, we developed structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models to determine relevant molecular substructures and descriptors contributing to these adverse effects. Additionally, we calculated physicochemical properties associated with lipid accumulation in hepatocytes and examined their correlation with our chemical structure characteristics. To assess the applicability of our findings to a wide range of chemical compounds, we employed two external datasets to evaluate the distribution of our QSAR descriptors. Our study highlights the significance of subtle molecular structural variations in triggering hepatotoxicity, such as the presence of nitrogen or the specific arrangement of substitutions within the carbon chain. By employing our comprehensive approach, we pinpointed specific molecules and elucidated their mechanisms of toxicity, thus offering valuable insights to guide future toxicology investigations.