Covalent Control of Excitonic Interactions in Perylene Diimide Trimers: A Computational Study
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36 MATERIALS SCIENCE↗
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Results for “Molecular aggregates”
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Copper Reductase Activity and Free Radical Chemistry by Cataract-Associated Human Lens γ-Crystallins
Cataracts are caused by high-molecular weight aggregates of human eye lens proteins that scatter light, causing lens opacity. Metal ions have emerged as important potential players in the etiology of cataract disease, as human lens γ-crystallins are susceptible to metal-induced aggregation. Here, the interaction of Cu 2+ ions with γD-, γC-, and γS- crystallins, the three most abundant γ-crystallins in the lens, has been evaluated. Cu 2+ ions induced non-amyloid aggregation in all three proteins. Solution turbidimetry, SDS-PAGE, circular dichroism and differential scanning calorimetry showed that the mechanism for Cu-induced aggregation involves: i) loss of beta-sheet structure in the N-terminal domain; ii) decreased thermal and kinetic stability; iii) formation of metal-bridged species; and iv) formation of disulfide-bridged dimers. Electron paramagnetic resonance (EPR) revealed two distinct Cu 2+ binding sites in each protein. Spin quantitation demonstrated reduction of γ-crystallin-bound Cu 2+ ions to Cu + under aerobic conditions, while X-ray absorption spectroscopy (XAS) confirmed the presence of linear or trigonal Cu + binding sites in γ-crystallins. Our EPR and XAS studies revealed that γ-crystallins’ Cu 2+ reductase activity yields a protein-based free radical that is likely a Tyr-based species in human γD-crystallin. This unique free radical chemistry carried out by distinct redox-active Cu sites in human lens γ-crystallins likely contributes to the mechanism of copper-induced aggregation. Finally, in the context of an aging human lens, γ-crystallins could be acting, not only as structural proteins, but also as key players for metal and redox homeostasis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The effect of low temperature on poly(3-methyl- N -vinylcaprolactam)- b -poly( N -vinylpyrrolidone) diblock copolymer nanovesicles assembled from all-aqueous media
Nanosized polymeric vesicles (polymersomes) self-assembled from double hydrophilic copolymers of poly(3-methyl-N-vinylcaprolactam) n -b-poly(N-vinylpyrrolidone) m (PMVC n -b-PVPON m ) using all aqueous media are a promising platform for biomedical applications, because of their superior stability over liposomes in vivo and high loading capacity. Herein, we explored the temperature-sensitive behavior of PMVC 58 -b-PVPON 65 vesicles using transmission electron microscopy (TEM), dynamic light scattering (DLS), atomic force microscopy (AFM), and small-angle neutron scattering (SANS) in response to lowering the solution temperature from 37 to 25, 20, 14 and 4 °C. The copolymer vesicles with an average size of 350 nm at 37 °C were assembled from the diblock copolymer dissolved in aqueous solution at 4 °C. We show that while the polymersome's size gradually decreases upon the temperature decrease from 37 to 4 °C, the average shell thickness increases from 17 nm to 25 nm, respectively. SANS study revealed that the PMVC 58 -b-PVPON 65 vesicle undergoes a gradual structure evolution from a dense-shell vesicle at 37–25 °C to a highly-hydrated shell vesicle at 20–14 °C to molecular chain aggregates at 4 °C. From SANS contrast matching study, this vesicle behavior is found to be driven by the gradual rehydration of PMVC block at 37–14 °C. The shell hydration at 20–14 °C also correlated with the 4.4-fold decrease in the relative fluorescence intensity from vesicle-encapsulated fluorescent dye, indicating ~80% of the dye release within 12 hours after the vesicle exposure to 14 °C. No significant (<5%) dye release was observed for the vesicle solutions at 37–20 °C, indicating excellent cargo retention inside the vesicles. Our study provides new fundamental insights on temperature-sensitive polymer vesicles and demonstrates that the copolymer assembly into polymersomes can be achieved by decreasing a copolymer aqueous solution temperature below 14 °C followed by solution exposure to ≥20 °C. This type of all-aqueous assembly, instead of nanoprecipitation from organic solvents or solvent exchange, can be highly desirable for encapsulating a wide range of biological molecules, including proteins, peptides, and nucleic acids, into stable polymer vesicles without a need for organic solvents for dissolution of the copolymers that are amphiphilic at physiologically relevant temperatures of 20–37 °C.
36 MATERIALS SCIENCE↗
Vibronic Coupling and Exciton Chirality: Electronic and Structural Rearrangement between Helical to Zero Momentum Molecular Exciton States
Helical porphyrin aggregates are attractive macromolecular nanostructures for biomimetic light-harvesting and energy transfer in solar energy technology due to their large light absorbing cross sections and promising energy conservation benefits from chiral-induced spin selectivity. However, with these soft materials, it is imperative that we understand how the nuclear degrees of freedom impact the excitonic energy landscape and dynamics, particularly pertaining to how it influences the chiral angular momentum of the excitons. To this end, we have measured time-resolved depolarization ratios using femtosecond stimulated Raman spectroscopy to uncover the vibronic coupling guided by molecular vibrations between excitons of different angular momentum in helical tetra(sulfonatophenyl)porphyrin aggregates. We find that while transient absorption anisotropy reveals rapid (1 ps lifetime) exciton rotation from helical to achiral states in the Q-band, time-resolved vibrational depolarization ratios evolve on remarkably faster time scales (<500 fs lifetime) for the totally symmetric 1530 cm –1 vibration and the nontotally symmetric 1540 cm –1 vibration. The time-resolved depolarization ratios of the 1540 cm –1 vibration, in particular, show strong evidence of vibronic coupling and nonadiabatic exciton behavior that mediates the nonradiative transition between chiral and achiral excitons. Furthermore, our study shows that for rational design of helical molecular aggregates for exciton transport, the vibronic coupling between excitons of different angular momenta driven by molecular vibrations must be considered especially in the case where chiral-induced spin selectivity is desired.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Investigating Lignin Aggregation and Interactions with Solvents during γ-Valerolactone (GVL) Pretreatment: A Combined Small Angle Neutron Scattering and Molecular Simulations Study
The strong tendency of lignin to aggregate in solution, coupled with limited understanding of how its molecular structure governs this behavior, hinders its effective utilization in biorefineries. Here, in this study, we investigated the solution behavior of lignin extracted from poplar using γ-valerolactone/water (GVL/H2O, 9:1 wt/wt) through combined small-angle neutron scattering (SANS) and molecular dynamics (MD) simulations. Lignin samples obtained at 100 °C (L100) and 120 °C (L120) differed in β–O–4 content, hydroxyl distribution, and S/G ratio, enabling direct assessment of how molecular composition governs solvation and aggregation. SANS showed that L120 formed rigid and elongated cylindrical aggregates at 25 °C that transitioned to more flexible spheroidal structures by 50 °C and remained stable up to 80 °C, whereas L100 adopted globular aggregates that progressively collapsed with increasing temperature. MD simulations reinforced these observations by showing that S-rich (L120-like) oligomers had larger radii of gyration, stronger solvent coordination driven by methoxy groups, and fewer lignin–lignin contacts. In contrast, G-rich (L100-like) oligomers displayed persistent aggregation and lower solubility. Collectively, these results reveal that increased aromatic methoxylation enhances lignin–solvent interactions and suppresses self-association, whereas reduced methoxylation and higher β–O–4 content promote persistent aggregation into colloid-like structures with restricted solvent penetration into the aggregate interior.
biorefinery pretreatment↗
Molecular Dynamics Simulations of Percolated Ionic Aggregates in PreciseSulfophenylated Polyethylene Polymers.
Abstract not provided.
Thurston, Bryce Andrew↗
Effect of Molecular Conformation on Intermolecular Interactions and Photovoltaic Performances of Giant Molecule Acceptors
The molecular conformation of giant molecule acceptors (GMAs) plays a significant role in regulating the intermolecular interactions and their photovoltaic performances in organic solar cells (OSCs). For the linear GMA GT‐l, the stronger homo‐molecular interaction causes its aggregation being weakly affected by the donor, thus forming an ordered molecular stacking and proper phase separation in its blend film. The star‐shaped GMA GT‐s‐based blend film shows a dominant hetero‐molecular interaction that suppresses the aggregation of the donor and acceptor, resulting in smaller phase separation and more uniform vertical phase distribution. While for another star‐shaped GMA GTs, the weakest hetero‐molecular interaction causes its blend film to form larger phase separation. Therefore, the GT‐l based OSC with PM6 as donor shows the highest charge mobilities, the fastest charge transfer (CT) process, reduced energy loss and less charge recombination, contributing to a higher power conversion efficiency (PCE) of 19.03%. Comparatively, the PCEs of the OSCs based on GTs and GT‐s are 18.05% and 17.58% respectively. Notably, all the three GMAs based OSCs show excellent thermal stability and long‐term storage stability. This study provides a facile strategy by tuning the linking unit and its connecting mode for designing highly efficient and stable organic photovoltaic materials.
Chemistry↗
Molecular Dynamics Simulation of the Structures, Dynamics, and Aggregation of Dissolved Organic Matter
Dissolved organic matter (DOM) plays a significant role in the transport and transformation of pollutants in the aquatic environment. However, the experimental characterization of DOM has been limited mainly to bulk properties, and the molecular-level interactions among various components of DOM remain to be fully characterized. In this study, we use molecular dynamics (MD) simulations to probe the structural properties of model DOM systems at atomic detail. The 200 ns simulations, validated by available experimental data, reveal processes and mechanisms by which chemical species (cations, peptides, lipids, lignin, carbohydrates, and some low-molecular-weight aliphatic and aromatic compounds) aggregate to form complex DOM. The DOM aggregates are dynamic, consisting of a hydrophobic core and amphiphilic exterior. The lipid tails and other hydrophobic fragments form the core, with hydrophilic and amphiphilic groups exposed to water, making DOM accessible to both polar and nonpolar species. Thus, the lipid component acts as a nucleator, whereas cations (especially Ca 2+ ) connect the molecular fragments on the surface by coordinating with the O-containing functional groups of DOM. The structural details revealed here provide new insights including surface accessible atoms, overall assemblage, and interactions among the molecules of DOM for understanding the kinetics and mechanisms through which DOM interacts with metal and other contaminants.
54 ENVIRONMENTAL SCIENCES↗
Manuscript Workflows from and Processed Organic Matter Composition of Experimentally Burned Open Air and Muffle Furnace Vegetation Chars across Differing Burn Severity and Feedstock Types from Pacific Northwest, USA (v3)
This dataset includes processed organic matter chemistry data from an experimental study designed to compare how the chemical composition of organic matter changes across different burn conditions and vegetation materials representative of major land cover types of the Pacific Northwest, USA. Chars were created in a closed muffle furnace or on an open burn table from four different feedstock species representing vegetation commonly impacted by fire regimes across the Pacific Northwest, USA. Source data and associated metadata (including methods and geospatial information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022). This dataset provides processing scripts and processed data for both solid and dissolved phase organic matter characterization data from experimentally generated chars. These processed data can be used to compare how different burn conditions may influence resultant organic matter chemistry and help further our understanding of potential biogeochemical impacts on river corridors post-fire. The processed data were subsequently analyzed; and the results and ecological implications of the findings were published in peer-reviewed manuscripts. The scripts and workflows used to develop the manuscripts are also included in this data package.This data package was originally published June 2024. It was updated September 2024 (new and modified files) and in January 2025 (modified files). See the change history section in the readme for more details.This dataset is comprised of one data package readme, one data dictionary (dd), one file level metadata (flmd), and folders containing (A) processed data; (B) general processing scripts; and (C) additional folders with specific manuscript analysis scripts and processed data. Step-by-step instructions to assist the user in recreating the workflow used to generate the results in the manuscripts is also provided. The processed data folder includes (1) a folder of processed Parallel Factor Analysis (PARAFAC) and spectra indices outputs from excitation emissions matrix (EEM) fluorescence and absorbance data; (2) a folder of processed solid state carbon-13 (13-C NMR) integrals; (3) folder of high resolution characterization of organic matter via 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) processed data outputs from Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude), blank corrections and data aggregation, and calculated molecular indices. All files are .pdf, .csv, .html, .Rmd, .R, or .RData.
54 ENVIRONMENTAL SCIENCES↗
Mechanistic Insights into Molecular Copper Hydride Catalysis: the Kinetic Stability of CuH Monomers toward Aggregation is a Critical Parameter for Catalyst Performance
The activity of molecular copper hydride (CuH) complexes towards the selective insertion of unsaturated hydrocarbons under mild conditions has contributed significantly to versatile methodologies for upgrading these feedstocks. However, these catalysts are particularly susceptible to deleterious aggregation, leading to the depletion of active CuH species. Little is known about the mechanisms of CuH aggregation, how it influences overall catalyst performance, and how it can be controlled. We address these challenges with mechanistic studies on a model reaction of unactivated alkene hydroboration catalyzed by (IPr*CPh 3 )CuH (LCuH). Here, we report a comprehensive mechanistic investigation of this system, identifying an aggregation pathway that continuously depletes catalytically active LCuH to form inactive CuH clusters during turnover. Deactivation of LCuH is controlled primarily by the competition between the kinetics of the initial LCuH dimerization step and that of alkene insertion. We therefore propose that a more comprehensive understanding of CuH catalyst performance must account for the kinetics of the initial LCuH dimerization step, revising a previously explored thermodynamic understanding of CuH aggregation, where the concentration of active species is controlled by equilibria established between CuH dimers and monomers. With a series of (NHC)CuH congeners (NHC = N-heterocyclic carbene), we demonstrate that ostensibly minor structural modifications to the ligand peripheries can drastically affect the LCuH dimerization kinetics, while maintaining reactivity towards on–cycle alkene insertion. We employed a computational approach based on molecular dynamics simulations to provide an in-depth understanding of how specific structural ligand modifications can substantially increase the kinetic stability of monomeric CuH catalysts. Our combined experimental and computational studies suggest strategies for rational ligand design that can be broadly applied to molecular catalyst systems that are susceptible to deactivation via aggregation pathways.
Ryan, David E. [Pacific Northwest National Laborat↗
Tunable and Robust Optical and Structural Properties of a Cooperative Squaraine-Dye Aggregate-DNA DX-DAE Tile System
Molecular excitons, which are excitations delocalized over multiple dyes in a wavelike manner, are of interest for a wide range of applications, including quantum information science. Numerous studies have templated a variety of synthetic dyes via a DNA scaffold to induce dye aggregation to create molecular excitons upon photoexcitation. Dye aggregate optical properties are critically dependent on relative dye geometry and local environment; therefore, an understanding of dye-dye and DNA-dye interactions is critical for advancing toward more complex DNA-dye systems. The extensively studied DNA Holliday junction (HJ) and less-studied double-crossover (DX) tile motif are fundamental test beds for designing complex and ultimately modular DNA-dye architectures. Here, we report the first study of single-linked squaraine dye aggregation and exciton delocalization on a larger and more stable (compared with the HJ) DX tile motif. We first highlight a few DNA-dye constructs that support single dyes and aggregates with distinct optical properties that are both tunable—through sample design, buffer conditions, and heat treatment—and robust to environment changes, including transfer to solid phase. Next, we assess several experimental and design considerations that demonstrate directed dye-driven assembly of a novel double-tile DNA configuration. Our results demonstrate that single-linked squaraine dyes templated to DX tiles provide a viable research path to design and evaluate dye aggregate networks that support exciton delocalization. We include herein the first report of exciton delocalization in the solid phase in a DNA-dye construct. Additionally, our findings indicate that dye aggregation impacts the assembly of the DNA-dye construct, and, in some cases, thereby cooperates with the DNA to determine a final robust system configuration. Finally, we show that a controlled annealing schedule can be employed to promote the homogeneous assembly of DNA-dye constructs. The findings in this study contribute to the understanding of DNA-dye systems and the relevant factors involved in their directed assembly to achieve specific constructs with desirable properties.
36 MATERIALS SCIENCE↗
Interfacing DNA nanotechnology and biomimetic photonic complexes: advances and prospects in energy and biomedicine
Abstract Self-assembled photonic systems with well-organized spatial arrangement and engineered optical properties can be used as efficient energy materials and as effective biomedical agents. The lessons learned from natural light-harvesting antennas have inspired the design and synthesis of a series of biomimetic photonic complexes, including those containing strongly coupled dye aggregates with dense molecular packing and unique spectroscopic features. These photoactive components provide excellent features that could be coupled to multiple applications including light-harvesting, energy transfer, biosensing, bioimaging, and cancer therapy. Meanwhile, nanoscale DNA assemblies have been employed as programmable and addressable templates to guide the formation of DNA-directed multi-pigment complexes, which can be used to enhance the complexity and precision of artificial photonic systems and show the potential for energy and biomedical applications. This review focuses on the interface of DNA nanotechnology and biomimetic photonic systems. We summarized the recent progress in the design, synthesis, and applications of bioinspired photonic systems, highlighted the advantages of the utilization of DNA nanostructures, and discussed the challenges and opportunities they provide. Graphical Abstract
59 BASIC BIOLOGICAL SCIENCES↗
An Optimized Fibril Network Morphology Enables High‐Efficiency and Ambient‐Stable Polymer Solar Cells
Abstract Morphological stability is crucially important for the long‐term stability of polymer solar cells (PSCs). Many high‐efficiency PSCs suffer from metastable morphology, resulting in severe device degradation. Here, a series of copolymers is developed by manipulating the content of chlorinated benzodithiophene‐4,8‐dione (T1‐Cl) via a random copolymerization approach. It is found that all the copolymers can self‐assemble into a fibril nanostructure in films. By altering the T1‐Cl content, the polymer crystallinity and fibril width can be effectively controlled. When blended with several nonfullerene acceptors, such as TTPTT‐4F, O‐INIC3, EH‐INIC3, and Y6, the optimized fibril interpenetrating morphology can not only favor charge transport, but also inhibit the unfavorable molecular diffusion and aggregation in active layers, leading to excellent morphological stability. The work demonstrates the importance of optimization of fibril network morphology in realizing high‐efficiency and ambient‐stable PSCs, and also provides new insights into the effect of chemical structure on the fibril network morphology and photovoltaic performance of PSCs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data
In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.
59 BASIC BIOLOGICAL SCIENCES↗
Characterization and molecular simulation of lignin in Cyrene pretreatment of switchgrass
Biomass-derived solvents have been proposed as a novel pathway in biorefining for the realization of biofuels and bioproducts derived from lignocellulosic biomass. Cyrene derived from cellulose has recently been shown to have a high potential as a green organic solvent for pretreating poplar biomass. However, due to its high dynamic viscosity nature, high Cyrene concentration could cause negative effects on the sugar release of the pretreated biomass as well as driving up the operational cost of the lignin recovery. In this study, we combine experimental and computational approaches to examine the impact of Cyrene pretreatment with reduced Cyrene concentration under mild conditions on switchgrass lignin. Our experimental studies indicated correlation between pretreatment condition and recovery and structure modification of lignin. Switchgrass lignin extracted by Cyrene pretreatment possessed high preservation of β-O-4 ether inter-unit linkage, which could provide versatility in the integration of downstream lignin valorization into the modern biorefinery industries. Molecular modeling examining the solvation of switchgrass lignin polymer and the disaggregation of low-molecular weight lignin aggregates under pretreatment conditions indicated that a preferential interaction exists between Cyrene and lignin, which likely drives lignin release, and that the disruption of inter-lignin contacts can be modulated as a non-monotonic function of Cyrene : water ratio. Further, while Cyrene–lignin interactions permit the solubilization of lignin, simulations with proxy reactive-species reveal that changes to the diffusion of these reactive proxies and their localization near linkage sites under Cyrene conditions may inhibit chemical processes. In conclusion, the results indicated that loss of pretreatment efficacy caused by low Cyrene concentration could be compensated by prolonged pretreatment time and high catalyst dosage.
09 BIOMASS FUELS↗
Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network
Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024
Zhang, Hengrui (ORCID:0000000231831654)↗
Revealing aggregation of non-fullerene acceptors in intermixed phase by ultraviolet-visible absorption spectroscopy
Non-fullerene acceptor (NFA) aggregation is crucial in determining bulk-heterojunction (BHJ) organic photovoltaic (OPV) performance. However, it is still a big challenge to characterize the nanostructure of NFAs in the disordered donor-acceptor intermixed phase. Here, we demonstrate a method to characterize NFA aggregation and composition in the intermixed phase by measuring NFA concentration-dependent ultraviolet-visible (UV-vis) absorption spectroscopy of BHJ films. In various polymer:NFA films, an absorption shift as a function of increasing molecular concentration (ASIMC) phenomenon is observed, and different NFA aggregation behaviors can be distinguished. The ASIMC method was then applied to study the influence of processing conditions on the NFA concentration in the intermixed phase of devices to establish a correlation with device efficiency. The current work provides a feasible tool to study the nanostructure of NFAs in the complex polymer matrix and to understand the variations in the NFA concentration in the intermixed phase under non-equilibrium conditions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗