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Register a new userTension enhancement in branched macromolecules upon adhesion on a solid substrate
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The effect of self-generated tension in the backbone of a bottle-brush (BB) macromolecule, adsorbed on an attractive surface, is studied by means of Molecular Dynamics simulations of a coarse-grained bead-spring model in the good solvent regime. The BB-molecule is modeled as a backbone chain of $L$ beads, connected by breakable bonds and with side chains, tethered pairwise to each monomer of the backbone. Our investigation is focused on several key questions that determine the bond scission mechanism and the ensuing degradation kinetics: how are frequency of bond scission and self-induced tension distributed along the BB-backbone at different grafting density $sigma_g$ of the side chains? How does tension $f$ depend on the length of the side chains $N$, and on the strength of surface adhesion $epsilon_s$? We examine the monomer density distribution profiles across the BB-backbone at different $epsilon_s$ and relate it to adsorption-induced morphological changes of the macromolecule whereby side chains partially desorb while the remaining chains spread better on the surface. Our simulation data are found to be in qualitative agreement with experimental results and recent theoretical predictions. Yet we demonstrate that the interval of parameter values where these predictions hold is limited in $N$. Thus, at high values of $epsilon_s$, too long side chains mutually block each other and freeze effectively the bottle-brush molecule.
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A range of technologies require the directed motion of nanoscale droplets on solid substrates. A way of realizing this effect is durotaxis, whereby a stiffness gradient of a substrate can induce directional motion without requiring an energy source. Here, we report on the results of extensive molecular dynamics investigations of droplets on a surface with varying stiffness. We find that durotaxis is enhanced by increasing the stiffness gradient and, also, by increased wettability of the substrate, in particular, when droplet size decreases. We anticipate that our study will provide further insights into the mechanisms of nanoscale directional motion.
The absorption of free linear chains in a polymer brush was studied with respect to chain size $L$ and compatibility $chi$ with the brush by means of Monte Carlo (MC) simulations and Density Functional Theory (DFT) / Self-Consistent Field Theory (SCFT) at both moderate, $sigma_g = 0.25$, and high, $sigma_g = 1.00$, grafting densities using a bead-spring model. Different concentrations of the free chains $0.0625 le phi_o le 0.375$ are examined. Contrary to the case of $chi = 0$ when all species are almost completely ejected by the polymer brush irrespective of their length $L$, for $chi < 0$ we find that the degree of absorption (absorbed amount) $Gamma(L)$ undergoes a sharp crossover from weak to strong ($approx 100%$) absorption, discriminating between oligomers, $1le Lle 8$, and longer chains. For a moderately dense brush, $sigma_g = 0.25$, the longer species, $L > 8$, populate predominantly the deep inner part of the brush whereas in a dense brush $sigma_g = 1.00$ they penetrate into the fluffy tail of the dense brush only. Gyration radius $R_g$ and end-to-end distance $R_e$ of absorbed chains thereby scale with length $L$ as free polymers in the bulk. Using both MC and DFT/SCFT methods for brushes of different chain length $32 le N le 256$, we demonstrate the existence of unique {em critical} value of compatibility $chi = chi^{c}<0$. For $chi^{c}(phi_o)$ the energy of free chains attains the {em same} value, irrespective of length $L$ whereas the entropy of free chain displays a pronounced minimum. At $chi^{c}$ all density profiles of absorbing chains with different $L$ intersect at the same distance from the grafting plane. The penetration/expulsion kinetics of free chains into the polymer brush after an instantaneous change in their compatibility $chi$ displays a rather rich behavior. We find three distinct regimes of penetration kinetics of free chains regarding the length $L$: I ($1le Lle 8$), II ($8 le L le N$), and III ($L > N$), in which the time of absorption $tau$ grows with $L$ at a different rate. During the initial stages of penetration into the brush one observes a power-law increase of $Gamma propto t^alpha$ with power $alpha propto -ln phi_o$ whereby penetration of the free chains into the brush gets {em slower} as their concentration rises.
Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model generalizability to noisy conditions: (1) mismatched noisy condition during testing, i.e., the performance is generally sub-optimal when models are tested with unseen noise types that are not involved in the training data; (2) local focus on specific noisy conditions, i.e., models trained using multiple types of noises cannot optimally remove a specific noise type even though the noise type has been involved in the training data. These problems are common in real applications. In this paper, we propose a novel denoising autoencoder with a multi-branched encoder (termed DAEME) model to deal with these two problems. In the DAEME model, two stages are involved: training and testing. In the training stage, we build multiple component models to form a multi-branched encoder based on a decision tree (DSDT). The DSDT is built based on prior knowledge of speech and noisy conditions (the speaker, environment, and signal factors are considered in this paper), where each component of the multi-branched encoder performs a particular mapping from noisy to clean speech along the branch in the DSDT. Finally, a decoder is trained on top of the multi-branched encoder. In the testing stage, noisy speech is first processed by each component model. The multiple outputs from these models are then integrated into the decoder to determine the final enhanced speech. Experimental results show that DAEME is superior to several baseline models in terms of objective evaluation metrics, automatic speech recognition results, and quality in subjective human listening tests.
We introduce an atomistic approach to the dissipative quantum dynamics of charged or neutral excitations propagating through macromolecular systems. Using the Feynman-Vernon path integral formalism, we analytically trace out from the density matrix the atomic coordinates and the heat bath degrees of freedom. This way we obtain an effective field theory which describes the real-time evolution of the quantum excitation and is fully consistent with the fluctuation-dissipation relation. The main advantage of the field-theoretic approach is that it allows to avoid using the Keldysh contour formulation. This simplification makes it straightforward to derive Feynman diagrams to analytically compute the effects of the interaction of the propagating quantum excitation with the heat bath and with the molecular atomic vibrations. For illustration purposes, we apply this formalism to investigate the loss of quantum coherence of holes propagating through a poly(3-alkylthiophene) polymer
Macromolecular diffusion in dense colloidal suspensions is an intriguing topic of interdisciplinary relevance in Science and Engineering. While significant efforts have been undertaken to establish the impact of crowding on the dynamics of macromolecules, less clear is the role played by long-range ordering. In this work, we perform Dynamic Monte Carlo simulations to assess the importance of ordered crowding on the diffusion of globular macromolecules, here modelled as spherical tracers, in suspensions of colloidal cuboids. We first investigate the diffusion of such guest tracers in very weakly ordered host phases of cuboids and, by increasing density above the isotropic-to-nematic phase boundary, study the influence of long-range orientational ordering imposed by the occurrence of liquid-crystalline phases. To this end, we analyse a spectrum of dynamical properties that clarify the existence of slow and fast tracers and the extent of deviations from Gaussian behaviour. Our results unveil the existence of randomly oriented clusters of cuboids that display a relatively large size in dense isotropic phases, but are basically absent in the nematic phase. We believe that these clusters are responsible for a pronounced non-Gaussian dynamics that is much weaker in the nematic phase, where orientational ordering smooths out such structural heterogeneities.
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