scholarly journals Dilute Solution Structure of Bottlebrush Polymers

Author(s):  
Sarit Dutta ◽  
Matthew A. Wade ◽  
Dylan J. Walsh ◽  
Damien Guironnet ◽  
Simon A. Rogers ◽  
...  

<div>Bottlebrush polymers are a class of macromolecules that has recently found use</div><div>in a wide variety of materials, ranging from lubricating brushes and</div><div>nanostructured coatings to elastomeric gels that exhibit structural color. These</div><div>polymers are characterized by dense branches extending from a central backbone,</div><div>and thus have properties distinct from linear polymers. It remains a challenge</div><div>to specifically understand conformational properties of these molecules, due to</div><div>the wide range of architectural parameters that can be present in a system, and</div><div>thus there is a need to accurately characterize and model these molecules. In</div><div>this paper, we use a combination of viscometry, light scattering, and computer</div><div>simulations to gain insight into the conformational properties of dilute</div><div>bottlebrush polymers. We focus on a series of model bottlebrushes consisting of</div><div>a poly(norbornene) (PNB) backbone with poly(lactic acid) (PLA) side chains. We</div><div>demonstrate that intrinsic viscosity and hydrodynamic radius are experimental</div><div>observations \emph{sensitive} to molecular architecture, exhibiting distinct</div><div>differences with different choices of branch and backbone lengths. Informed by</div><div>the atomistic structure of this PNB-PLA system, we rationalize a coarse-grained</div><div>simulation model that we evaluate using a combination of Brownian Dynamics and</div><div>Monte Carlo simulations. We show that this exhibits quantitative matching to</div><div>experimental results, enabling us to characterize the overall shape of the</div><div>bottlebrush via a number of metrics that can be extended to more general</div><div>bottlebrush architectures.</div>

2019 ◽  
Author(s):  
Sarit Dutta ◽  
Matthew A. Wade ◽  
Dylan J. Walsh ◽  
Damien Guironnet ◽  
Simon A. Rogers ◽  
...  

<div>Bottlebrush polymers are a class of macromolecules that has recently found use</div><div>in a wide variety of materials, ranging from lubricating brushes and</div><div>nanostructured coatings to elastomeric gels that exhibit structural color. These</div><div>polymers are characterized by dense branches extending from a central backbone,</div><div>and thus have properties distinct from linear polymers. It remains a challenge</div><div>to specifically understand conformational properties of these molecules, due to</div><div>the wide range of architectural parameters that can be present in a system, and</div><div>thus there is a need to accurately characterize and model these molecules. In</div><div>this paper, we use a combination of viscometry, light scattering, and computer</div><div>simulations to gain insight into the conformational properties of dilute</div><div>bottlebrush polymers. We focus on a series of model bottlebrushes consisting of</div><div>a poly(norbornene) (PNB) backbone with poly(lactic acid) (PLA) side chains. We</div><div>demonstrate that intrinsic viscosity and hydrodynamic radius are experimental</div><div>observations \emph{sensitive} to molecular architecture, exhibiting distinct</div><div>differences with different choices of branch and backbone lengths. Informed by</div><div>the atomistic structure of this PNB-PLA system, we rationalize a coarse-grained</div><div>simulation model that we evaluate using a combination of Brownian Dynamics and</div><div>Monte Carlo simulations. We show that this exhibits quantitative matching to</div><div>experimental results, enabling us to characterize the overall shape of the</div><div>bottlebrush via a number of metrics that can be extended to more general</div><div>bottlebrush architectures.</div>


2020 ◽  
Vol 29 (3S) ◽  
pp. 631-637
Author(s):  
Katja Lund ◽  
Rodrigo Ordoñez ◽  
Jens Bo Nielsen ◽  
Dorte Hammershøi

Purpose The aim of this study was to develop a tool to gain insight into the daily experiences of new hearing aid users and to shed light on aspects of aided performance that may not be unveiled through standard questionnaires. Method The tool is developed based on clinical observations, patient experiences, expert involvement, and existing validated hearing rehabilitation questionnaires. Results An online tool for collecting data related to hearing aid use was developed. The tool is based on 453 prefabricated sentences representing experiences within 13 categories related to hearing aid use. Conclusions The tool has the potential to reflect a wide range of individual experiences with hearing aid use, including auditory and nonauditory aspects. These experiences may hold important knowledge for both the patient and the professional in the hearing rehabilitation process.


2020 ◽  
Vol 648 ◽  
pp. 19-38
Author(s):  
AI Azovsky ◽  
YA Mazei ◽  
MA Saburova ◽  
PV Sapozhnikov

Diversity and composition of benthic diatom algae and ciliates were studied at several beaches along the White and Barents seas: from highly exposed, reflective beaches with coarse-grained sands to sheltered, dissipative silty-sandy flats. For diatoms, the epipelic to epipsammic species abundance ratio was significantly correlated with the beach index and mean particle size, while neither α-diversity measures nor mean cell length were related to beach properties. In contrast, most of the characteristics of ciliate assemblages (diversity, total abundance and biomass, mean individual weight and percentage of karyorelictids) demonstrated a strong correlation to beach properties, remaining low at exposed beaches but increasing sharply in more sheltered conditions. β-diversity did not correlate with beach properties for either diatoms or ciliates. We suggest that wave action and sediment properties are the main drivers controlling the diversity and composition of the intertidal microbenthos. Diatoms and ciliates, however, demonstrated divergent response to these factors. Epipelic and epipsammic diatoms exhibited 2 different strategies to adapt to their environments and therefore were complementarily distributed along the environmental gradient and compensated for each other in diversity. Most ciliates demonstrated a similar mode of habitat selection but differed in their degree of tolerance. Euryporal (including mesoporal) species were relatively tolerant to wave action and therefore occurred under a wide range of beach conditions, though their abundance and diversity were highest in fine, relatively stable sediments on sheltered beaches, whereas the specific interstitial (i.e. genuine microporal) species were mostly restricted to only these habitats.


Open Biology ◽  
2013 ◽  
Vol 3 (11) ◽  
pp. 130100 ◽  
Author(s):  
Zhisheng Lu ◽  
Julien R. C. Bergeron ◽  
R. Andrew Atkinson ◽  
Torsten Schaller ◽  
Dennis A. Veselkov ◽  
...  

The HIV-1 viral infectivity factor (Vif) neutralizes cell-encoded antiviral APOBEC3 proteins by recruiting a cellular ElonginB (EloB)/ElonginC (EloC)/Cullin5-containing ubiquitin ligase complex, resulting in APOBEC3 ubiquitination and proteolysis. The suppressors-of-cytokine-signalling-like domain (SOCS-box) of HIV-1 Vif is essential for E3 ligase engagement, and contains a BC box as well as an unusual proline-rich motif. Here, we report the NMR solution structure of the Vif SOCS–ElonginBC (EloBC) complex. In contrast to SOCS-boxes described in other proteins, the HIV-1 Vif SOCS-box contains only one α-helical domain followed by a β-sheet fold. The SOCS-box of Vif binds primarily to EloC by hydrophobic interactions. The functionally essential proline-rich motif mediates a direct but weak interaction with residues 101–104 of EloB, inducing a conformational change from an unstructured state to a structured state. The structure of the complex and biophysical studies provide detailed insight into the function of Vif's proline-rich motif and reveal novel dynamic information on the Vif–EloBC interaction.


2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.


Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1001
Author(s):  
Rui Huang ◽  
David C. Luther ◽  
Xianzhi Zhang ◽  
Aarohi Gupta ◽  
Samantha A. Tufts ◽  
...  

Nanoparticles (NPs) provide multipurpose platforms for a wide range of biological applications. These applications are enabled through molecular design of surface coverages, modulating NP interactions with biosystems. In this review, we highlight approaches to functionalize nanoparticles with ”small” organic ligands (Mw < 1000), providing insight into how organic synthesis can be used to engineer NPs for nanobiology and nanomedicine.


2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Brandon S. DiNunno ◽  
Niko Jokela ◽  
Juan F. Pedraza ◽  
Arttu Pönni

Abstract We study in detail various information theoretic quantities with the intent of distinguishing between different charged sectors in fractionalized states of large-N gauge theories. For concreteness, we focus on a simple holographic (2 + 1)-dimensional strongly coupled electron fluid whose charged states organize themselves into fractionalized and coherent patterns at sufficiently low temperatures. However, we expect that our results are quite generic and applicable to a wide range of systems, including non-holographic. The probes we consider include the entanglement entropy, mutual information, entanglement of purification and the butterfly velocity. The latter turns out to be particularly useful, given the universal connection between momentum and charge diffusion in the vicinity of a black hole horizon. The RT surfaces used to compute the above quantities, though, are largely insensitive to the electric flux in the bulk. To address this deficiency, we propose a generalized entanglement functional that is motivated through the Iyer-Wald formalism, applied to a gravity theory coupled to a U(1) gauge field. We argue that this functional gives rise to a coarse grained measure of entanglement in the boundary theory which is obtained by tracing over (part) of the fractionalized and cohesive charge degrees of freedom. Based on the above, we construct a candidate for an entropic c-function that accounts for the existence of bulk charges. We explore some of its general properties and their significance, and discuss how it can be used to efficiently account for charged degrees of freedom across different energy scales.


Marine Drugs ◽  
2021 ◽  
Vol 19 (2) ◽  
pp. 60
Author(s):  
David A. Armstrong ◽  
Ai-Hua Jin ◽  
Nayara Braga Emidio ◽  
Richard J. Lewis ◽  
Paul F. Alewood ◽  
...  

Conotoxins are disulfide-rich peptides found in the venom of cone snails. Due to their exquisite potency and high selectivity for a wide range of voltage and ligand gated ion channels they are attractive drug leads in neuropharmacology. Recently, cone snails were found to have the capability to rapidly switch between venom types with different proteome profiles in response to predatory or defensive stimuli. A novel conotoxin, GXIA (original name G117), belonging to the I3-subfamily was identified as the major component of the predatory venom of piscivorous Conus geographus. Using 2D solution NMR spectroscopy techniques, we resolved the 3D structure for GXIA, the first structure reported for the I3-subfamily and framework XI family. The 32 amino acid peptide is comprised of eight cysteine residues with the resultant disulfide connectivity forming an ICK+1 motif. With a triple stranded β-sheet, the GXIA backbone shows striking similarity to several tarantula toxins targeting the voltage sensor of voltage gated potassium and sodium channels. Supported by an amphipathic surface, the structural evidence suggests that GXIA is able to embed in the membrane and bind to the voltage sensor domain of a putative ion channel target.


Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2566
Author(s):  
Boris A. Boom ◽  
Alessandro Bertolini ◽  
Eric Hennes ◽  
Johannes F. J. van den Brand

We present a novel analysis of gas damping in capacitive MEMS transducers that is based on a simple analytical model, assisted by Monte-Carlo simulations performed in Molflow+ to obtain an estimate for the geometry dependent gas diffusion time. This combination provides results with minimal computational expense and through freely available software, as well as insight into how the gas damping depends on the transducer geometry in the molecular flow regime. The results can be used to predict damping for arbitrary gas mixtures. The analysis was verified by experimental results for both air and helium atmospheres and matches these data to within 15% over a wide range of pressures.


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.


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