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Results for “molecular properties”
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Molecular Modeling of Adsorption and Hydration Properties of Layered Minerals
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Molecular beam epitaxy growth and optoelectronic properties of droplet-free lattice-matched GaInAsSbBi on GaSb with photoluminescence wavelength exceeding 5 µm
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Molecular Regulatory Mechanisms Drive Emergent Pathogenetic Properties of Neisseria gonorrhoeae
Neisseria gonorrhoeae is the causative agent of the sexually transmitted infection (STI) gonorrhea, with an estimated 87 million annual cases worldwide. N. gonorrhoeae predominantly colonizes the male and female genital tract (FGT). In the FGT, N. gonorrhoeae confronts fluctuating levels of nutrients and oxidative and non-oxidative antimicrobial defenses of the immune system, as well as the resident microbiome. One mechanism utilized by N. gonorrhoeae to adapt to this dynamic FGT niche is to modulate gene expression primarily through DNA-binding transcriptional regulators. Here, we describe the major N. gonorrhoeae transcriptional regulators, genes under their control, and how these regulatory processes lead to pathogenic properties of N. gonorrhoeae during natural infection. We also discuss the current knowledge of the structure, function, and diversity of the FGT microbiome and its influence on gonococcal survival and transcriptional responses orchestrated by its DNA-binding regulators. We conclude with recent multi-omics data and modeling tools and their application to FGT microbiome dynamics. Understanding the strategies utilized by N. gonorrhoeae to regulate gene expression and their impact on the emergent characteristics of this pathogen during infection has the potential to identify new effective strategies to both treat and prevent gonorrhea.
Ion dynamics in hexagonal boron nitride ionogel electrolytes
Ionogel electrolytes incorporating exfoliated hexagonal boron nitride (hBN) nanoplatelets are promising materials for next-generation energy storage systems. However, detailed understanding of their ion transport properties at the molecular level remains limited. This study employs diffusion and relaxation nuclear magnetic resonance (NMR) techniques, including fast-field cycling (FFC) NMR, to investigate the dynamics of ionic species in hBN-ionogels. By spanning a broad frequency range from 30 kHz using FFC NMR to high-field NMR (500–800 MHz), we reveal distinct relaxation mechanisms governing ion dynamics in ionogels with and without lithium salts. Our results highlight the role of hBN in modulating molecular rotation and translational motion, significantly affecting 1 H and 19 F relaxation profiles. The presence of Li + alters the dynamic behavior in ionogels, enhancing anion mobility at the interface. Notably, 7 Li relaxation reveals strong interactions with the hBN surface that cannot be detected by diffusion NMR. Furthermore, these findings underscore the importance of spanning a broad frequency range in NMR studies of ionogels and provide critical insights into optimizing their design as novel electrolytes.
Deuteration Effects on the Physical and Optoelectronic Properties of Donor–Acceptor Conjugated Polymers
The significant differences in scattering cross sections between deuterium and protium are unique to neutron scattering techniques and have been a long-standing area of interest within the neutron scattering community. Researchers have explored selective deuteration to manipulate scattering contrast in soft matter systems, leading to the widespread use of deuterium labeling in materials development. As deuteration changes the atomic mass, it alters physical properties such as molecular volume, polarizability, and polarity, which in turn may affect noncovalent interactions and crystal ordering. Despite previous studies, there remains a limited understanding of how deuteration impacts donor–acceptor (DA) conjugated polymers. To address this, we synthesized deuterated DPP polymers and systematically investigated the effects of side-chain deuteration on their thermal stability, crystal packing, morphology, and optoelectronic properties. We found that deuteration increased the melting and crystallization temperatures of DPP polymers, although it did not significantly alter their morphology, molecular packing, or charge mobility. These properties were assessed by using atomic force microscopy (AFM), X-ray scattering, and thin-film transistor device measurements, respectively, for DPP polymers. Our work shows that deuterium labeling could be a powerful method for controlling scattering length density, enabling neutrons to study the structure and dynamics of conjugated polymers without impacting their electronic performance.
Molecular packing modulation enabling optimized blend morphology and efficient all small molecule organic solar cells
The molecular planarity of a small molecule donor is one of the key factors affecting its molecular packing behavior, the morphology of the related active blend and thus the photovoltaic properties of all small molecule organic solar cells (ASM-OSCs). Here, a series of new benzodithiophene (BDT)-based A-π-D-π-A type small molecules were developed, namely SD1, SD2 and SD3, by combining the chemical modification of side chain (sulfuration), π-bridge (alkoxy groups introducing) and end group (different type or different alkyl chain). The sulfuration of the BDT side chains and introducing alkoxy group to the π-bridge could induce intermolecular S … S and intramolecular S … O non-covalent interactions for higher planarity of these small molecules. The rhodanine and cyanoacetate end groups showed less effect on the optical and electronic properties but significantly affected the molecular packing behavior and blend morphology. From SD1:Y6-T to SD2:Y6-T and SD3:Y6-T, the face-to-face packing was gradually reduced, while the face-to-edge packing was increased, which resulted in lowered exciton dissociation efficiency, further decreased the device performance. SD1:Y6-T blend exhibited more favorable face-to-face packing and ideal morphology for efficient charge dissociation and transport, contributing to a champion PCE of 10.12%. This work provided a deep insight of the donor and acceptor packing behavior effect on the photovoltaic property by molecular design.
Tetracene Functionalized Si(111) Achieves Enhanced Solar-to-Chemical Energy Conversion via Molecular Acceptor States
The properties of semiconductor|liquid interfaces play a critical role in determining the efficiency of solar-to-hydrogen (STH) conversion. Here, we investigate how molecular functionalization of Si(111) and Si(111)|TiO 2 surfaces impacts photoelectrochemical (PEC) hydrogen production efficiency. We find that functionalization of ∼3% of the atop sites of Si(111) with either 9-anthracene (Anth) or 5-tetracene (Tet), with the remaining sites passivated by methyl groups, provides substrates with high electronic quality and low surface oxide densities, as determined by X-ray photoelectron spectroscopy (XPS) measurements. Surface photovoltage (SPV) spectroscopy shows that surfaces modified with Anth or Tet exhibit an increased photovoltage, with Tet-functionalized surfaces yielding an additional 192 meV relative to methyl-terminated Si(111), indicating improved charge separation for Si-Tet. Further improvement in onset potential was achieved by replacing a nitrogen-containing TiO 2 atomic layer deposition (ALD) precursor (TDMAT) with a precursor lacking nitrogen (TTIP), which eliminates the parasitic defect band in the TiO 2 overlayer (p-Si(111)-Tet|TTIP-TiO 2 |Pt: V OC = +0.283 ± 0.041 V vs RHE). Density functional theory (DFT) analysis demonstrates that compared with Anth-modified Si(111), the Tet-modified surface exhibits more hybridized Si(111)-Tet states closer to the silicon band edges. Mercury contact current–voltage (I–V, dark) measurements quantified the relative interfacial density of states of Si-Tet, Si-Anth and Si-Me surfaces─revealing that the interfacial state density was highest for Si-Tet. This suggests that such hybridized interfaces serve to capture better photoexcited charge, which enables facile electron transfer to molecular acceptors in solution. Altogether, the data indicate that beneficial hybrid molecular LUMO surface states interacting with the Si conduction band edge results in improved hydrogen evolution (HER) performance for p-Si devices.
Many‐body response of benzene at monolayer MoS 2 : Van der Waals interactions and spectral broadening
Abstract Models of surface enhancement of molecular electronic response properties are challenging for two reasons: (a) molecule‐surface interactions require a simultaneous solution of the molecular and the surface dynamic response (a daunting task), and (b) when solving for the electronic structure of the combined molecule + surface system, it is not trivial to single out the particular physical effects responsible for enhancement. To tackle this problem, in this work, we apply a formally exact decomposition of the system's response function into subsystem contributions by using subsystem density functional theory (DFT), which grants access to dynamic polarizabilities and optical spectra. In order to access information about the interactions between the subsystems, we extend a previously developed subsystem‐based adiabatic connection fluctuation‐dissipation theorem of DFT to separate the additive from the nonadditive correlation energy and identify the nonadditive correlation as the van der Waals interactions. As an example, we choose benzene adsorbed on monolayer MoS 2 . We isolate the contributions to benzene's dynamic response arising from the interaction with the surface, and for the first time, we evaluate the enhancements to the effectiveness of C 6 coefficients as a function of benzene‐MoS 2 distance and adsorption site. We also quantify the spectral broadening of the benzene's electronic excited states due to their interaction with the surface. We find that the broadening has a similar decay law with the molecule‐surface distance as the leading van der Waals interactions (ie, R −6 ) and that the surface enhancement of dispersion interactions between benzene molecules is less than 5% but is still large enough (0.5 kcal/mol) to likely play a role in the prediction of interface morphologies.
Revealing the relationship between molecular weight of lignin and its color, UV-protecting property
Lignin has great potential as a natural, green, and sustainable broad-spectrum sunscreen active ingredient. However, the coexistence of dark color and sunscreen properties hinders its application in cosmetics. In this study, we focus on the effects of the molecular weight of lignin on tis UV-protecting property and color in order to prepare lignin-based sunscreen with high performance. We report a prepared sunscreen containing low molecular weight lignin (F5, <1000 g/mol) exhibits good UV-protecting property (sun protection factor (SPF) = 7.14) and light color advantages (ΔE = 46.2). Moreover, a strong synergistic effect on UV-protecting property exists between low molecular weight lignin and ethylhexyl methoxycinnamate (EHMC), resulting in high SPF of F5@EHMC-based sunscreen (55.56). Additionally, added TiO 2 can efficiently mitigate the dark color of lignin-based sunscreens due to prominent covering power of TiO 2 . Moreover, lignin-based sunscreens have good biocompatibility with HaCaT cells. This work is useful for understanding the mechanism of the UV-protecting property and dark color of lignin, and for designing an efficient and safe lignin-based sunscreen.
Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluid
Predicting the functional properties of many molecular systems relies on understanding how atomistic interactions give rise to macroscale observables. However, current attempts to develop predictive models for the structural and thermodynamic properties of condensed-phase systems often rely on extensive parameter fitting to empirically selected functional forms whose effectiveness is limited to a narrow range of physical conditions. Here, we illustrate how these traditional fitting paradigms can be superseded using machine learning. Specifically, we use the results of molecular dynamics simulations to train machine learning protocols that are able to produce the radial distribution function, pressure, and internal energy of a Lennard-Jones fluid with increased accuracy in comparison to previous theoretical methods. The radial distribution function is determined using a variant of the segmented linear regression with the multivariate function decomposition approach developed by Craven et al. [J. Phys. Chem. Lett. 11, 4372 (2020)]. The pressure and internal energy are determined using expressions containing the learned radial distribution function and also a kernel ridge regression process that is trained directly on thermodynamic properties measured in simulation. The presented results suggest that the structural and thermodynamic properties of fluids may be determined more accurately through machine learning than through human-guided functional forms.
Geometry-complete diffusion for 3D molecule generation and optimization
Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .
The synergistic effects of central core size and end group engineering on performance of narrow bandgap nonfullerene acceptors
Understanding the relationship between the molecular structure and photoelectric properties of fused-ring non-fullerene acceptors (NFAs) is of far-reaching significance to the development of organic solar cells (OSCs). Herein, six NFAs based on multiple thiophenes (4 T, 6 T and 8 T) are employed to systematically probe the synergistic effects of extending central core size and terminal fluorination. The absorption results manifest that the molecular absorption is comprehensively affected by molecular crystallinity, planarity, conjugation length and intramolecular electron push–pull effect, simultaneously. The intensity of electron push–pull effect is not only related to the electron-donating ability of central core and the electron-withdrawing ability of end group, but also may be related to the distance between the positive and negative centers. The extension of central core leads to the more planar backbone and stronger crystallinity of NFAs, and less energy loss (E loss ) in its based OSC. Compared with the extension of central core, terminal fluorination has a greater impact on molecular photoelectric properties. The terminal fluorination significantly enhances the push–pull effect, lowers the energy levels, and slightly increases the vibrational relaxation. As a result, the strongest crystallinity and coplanarity of 8TIC-4F lead to a low vibrational relaxation of 0.18 eV, which makes PTB7-Th:8TIC-4F device exhibit a small E loss of 0.51 eV and a high efficiency of 10.4%. In addition, the fluorinated 6TIC-4F with suitable core size exhibits suitable energy level, absorption, crystallization, and phase separation morphology, making its as-cast device up to 11.61% efficiency. Finally, to the best of the authors’ knowledge, the PCE of 11.61% for PTB7-Th:6TIC-4F based device without any treatment is one of the highest values reported for the NAFs with over 1000 nm absorption.
Improving the Recyclability of Polymer Composites With Cellulose Nanofibrils
Cellulose nanofibers (CNFs) have been widely studied for their reinforcing potential in high-performance composites. While there are numerous publications on CNF-reinforced composites in a variety of polymer matrices, few have considered the recyclability of such thermoplastic composites and whether the incorporation of CNFs deteriorates or improves their performance upon reprocessing. In this study, two thermoplastic resins, poly(lactic acid) (PLA), and glycol-modified polyethylene terephthalate (PETg), were prepared with CNF reinforcement and thermomechanically recycled to investigate the effect of CNF inclusion on the composite properties after reprocessing as well as their effect on the composites’ number of useful life cycles. Additionally, changes in mechanical, thermal, rheological, molecular, and microstructural properties of the composites and/or base resins were monitored as a function of cycle numbers. As is typical, the polymers’ molecular weight and mechanical performance deteriorated with continued processing. However, the addition of spray dried CNF was found to better maintain the mechanical performance of both polymers throughout multiple recycling steps as compared to neat samples. For example, the tensile strength of PETg with 20 wt% CNF after 6 processing cycles was found to exceed that of virgin neat PETg, and higher loadings of CNF were found to preserve a higher yield strength during multiple rounds of reprocessing compared to PETg composites with lower CNF loadings. Ultimately this study indicates that the addition of CNF to some thermoplastic materials can increase both their sustainability by offsetting the use of high-embodied energy resins and their circularity by enabling performance retention over more use cycles.
Characterizing the magnetic fields of nearby molecular clouds using submillimeter polarization observations
Of all the factors that influence star formation, magnetic fields are perhaps the least well understood. The goal of this paper is to characterize the 3D magnetic field properties of nearby molecular clouds through various methods of statistically analysing maps of polarized dust emission. Our study focuses on nine clouds, with data taken from the Planck Sky Survey as well as data from the Balloon-borne Large Aperture Submillimeter Telescope for Polarimetry observations of Vela C. We compare the distributions of polarization fraction (p), dispersion in polarization angles ($\mathcal {S}$), and hydrogen column density (N H ) for each of our targeted clouds. To broaden the scope of our analysis, we compare the distributions of our clouds’ polarization observables with measurements from synthetic polarization maps generated from numerical simulations. We also use the distribution of polarization fraction measurements to estimate the inclination angle of each cloud’s cloud-scale magnetic field. We obtain a range of inclination angles associated with our clouds, varying from 16° to 69°. We establish inverse correlations between p and both $\mathcal {S}$ and NH in almost every cloud, but we are unable to establish a statistically robust $\mathcal {S}$ versus NH trend. By comparing the results of these different statistical analysis techniques, we are able to propose a more comprehensive view of each cloud’s 3D magnetic field properties. These detailed cloud analyses will be useful in the continued studies of cloud-scale magnetic fields and the ways in which they affect star formation within these molecular clouds.
Are we modeling the properties of unconventional shales correctly?
Technological advancements have led to impressive growth in hydrocarbon (HC) production from unconventional shale reservoirs. However, major limiting factors in shale gas development are i) low recovery of gas in place (<20%), (ii) a rapid decline in well productivity, iii) release of contaminants, despite the use of advanced hydraulic fracturing fluids and multiple fracturing stages. This is mainly due to a lack of understanding of the nanoscale physicochemical properties of shales, especially that of kerogen, the macromolecule that is not only the source of the majority of the HC’s in shales but also holds most of the HC’s in adsorbed state. Over the last few years, a large number of studies have been published on simulating the physicochemical properties of shale utilizing molecular models of kerogen. However, the molecular models of kerogen input into these simulations are based on the kerogen “type” derived from a very limited number of shale samples. In this paper, we examine the variations that can exist in kerogen structure within a particular kerogen “type” across different shale basins and single shale basin at similar thermal maturity levels. We propose that using kerogen “type” based structural models for molecular simulations could lead to inaccurate estimation of HC reserves, HC recovery, HC production, frackability, and quantity and quality of produced water. Our study highlights the need for developing a better classification of kerogen based on its “molecular structure” instead of “type” for a more accurate prediction of physicochemical properties of shales.
Steric and Electronic Influence of Excited-State Decay in Cu(I) MLCT Chromophores
For the past eleven years, a dedicated effort in our research group focused on fundamentally advancing the photophysical properties of cuprous bis-phenanthroline-based metal-to-ligand charge transfer (MLCT) excited states. We rationalized that by gaining control over the numerous factors limiting the more widespread use of Cu I MLCT photosensitizers, they would be readily adopted in numerous light-activated applications given the earth-abundance of copper and the extensive library of 1,10-phenanthrolines developed over the last century. Significant progress has been achieved by recognizing valuable structure-property concepts developed by other researchers in tandem with detailed ultrafast and conventional time-scale investigations, in-silico-inspired molecular designs to predict spectroscopic properties, and applying novel synthetic methodologies. Ultimately, we achieved a plateau in exerting cooperative steric influence to control Cu I MLCT excited state decay. This led to combining sterics with π-conjugation and/or inductive electronic effects to further exert control over molecular photophysical properties. The lessons gleaned from our studies of homoleptic complexes were recently extended to heteroleptic bis(phenanthrolines) featuring enhanced visible light absorption properties and long-lived room-temperature photoluminescence. Furthermore, this Account navigates the reader through our intellectual journey of decision-making, molecular and experimental design, and data interpretation in parallel with appropriate background information related to the quantitative characterization of molecular photophysics using Cu I MLCT chromophores as prototypical examples.