Anomalous Terminal Shear Viscosity Behavior of Polycarbonate Nanocomposites Containing Grafted Nanosilica Particles

Viscosity controls an important issue in polymer processing. This paper reports on the terminal viscosity behavior of a polymer melt containing grafted nanosilica particles. The melt viscosity behavior of the nanocomposites was found to depend on the interaction between the polymer matrix and the nanoparticle surface. In the case of polycarbonate (PC) nanocomposites, the viscosity decreases by approximately 25% at concentrations below 0.7 vol% of nanosilica, followed by an increase at higher concentrations. Chemical analysis shows that the decrease in viscosity can be attributed to in situ grafting of PC on the nanosilica surface, leading to a lower entanglement density around the nanoparticle. The thickness of the graft layer was found to be of the order of the tube diameter, with the disentangled zone being approximately equal to the radius of gyration (Rg) polymer chain. Furthermore, it is shown that the grafting has an effect on the motion of the PC chains at all timescales. Finally, the viscosity behavior in the PC nanocomposites was found to be independent of the molar mass of PC. The PC data are compared with polystyrene nanocomposites, for which the interaction between the polymer and nanoparticles is absent. The results outlined in this paper can be utilized for applications with low shear processing conditions, e.g., rotomolding, 3D printing, and multilayer co-extrusion.

Mixing and Flow-Induced Silk Fibroin Self-Assembly in Microfluidic and Semi-Batch Nanoprecipitation

<div><div><div><p>Here, we report the modulation of silk fibroin self-assembly by varying factors which control shear and mixing during nanoprecipitation in semi-batch and micro-mixers. For feeds processed at low shear in a semi-batch format, the properties of secondary assemblies (nanoparticles) were scalable by reducing the mixing time by stirring (0 < 400 rpm). For low mixing times, moving from low to high shear processing increased the extent of self-assembly (0.017 < 16.96 mL min-1) for 0.5, 2 and 3% w/v silk. In high shear regimes, the size and polydispersity index of assemblies decreased with mixing time, as stirring rate (800, 400 < 0 rpm) and feed addition height (3.5 < 0 cm) increased. Finally, in conditions of high shear and low mixing time, the feed concentration controlled the assembly shape, size, and polydispersity index in microfluidic (0.5, 3.0 < 2% w/v) and semi-batch format (3.0 < 0.5% w/v). This work provides new insight into the manufacture of low polydispersity, spherical and worm-like silk nanoparticles.</p></div></div></div>

Mixing and Flow-Induced Silk Fibroin Self-Assembly in Microfluidic and Semi-Batch Nanoprecipitation

<div><div><div><p>Here, we report the modulation of silk fibroin self-assembly by varying factors which control shear and mixing during nanoprecipitation in semi-batch and micro-mixers. For feeds processed at low shear in a semi-batch format, the properties of secondary assemblies (nanoparticles) were scalable by reducing the mixing time by stirring (0 < 400 rpm). For low mixing times, moving from low to high shear processing increased the extent of self-assembly (0.017 < 16.96 mL min-1) for 0.5, 2 and 3% w/v silk. In high shear regimes, the size and polydispersity index of assemblies decreased with mixing time, as stirring rate (800, 400 < 0 rpm) and feed addition height (3.5 < 0 cm) increased. Finally, in conditions of high shear and low mixing time, the feed concentration controlled the assembly shape, size, and polydispersity index in microfluidic (0.5, 3.0 < 2% w/v) and semi-batch format (3.0 < 0.5% w/v). This work provides new insight into the manufacture of low polydispersity, spherical and worm-like silk nanoparticles.</p></div></div></div>

Machine-Learning-Enabled Automatic Sonic Shear Processing

Flexural-dipole sonic logging has been widely used as the primary method to measure formation shear slowness because it can be applied in both fast and slow formations and can resolve azimuthal anisotropy. The flexural-dipole waveforms are dispersive borehole-guided waves that are sensitive to borehole geometry, mud, and formation properties, and therefore the processing techniques need to honor the physical dispersive signatures to obtain an accurate estimation of shear slowness. Traditional processing techniques are based on either a model-dependent method, in which an isotropic model is used as a reference to compensate for the dispersion effect, or a model-independent method, which optimizes nonphysical parameters to fit a simplified model to the field dispersion data extracted in the slowness-frequency domain. Many methods often require inputs, such as mud slowness, frequency bandpass filter, or an initial guess of formation shear. Consequently, these methods often fail to interpret the dispersion signature properly when those inputs are inaccurate or when the waveform quality is poor due to downhole logging noises. The users must manually tune the processing parameters and/or choose different methods as a workaround, which causes extra time and effort to obtain the result, hence imposes a significant challenge for automating sonic shear processing. We developed a physics-driven, machine-learning-based method for enhancing the interpretation of borehole sonic dipole data for wireline logging in an openhole scenario. A synthetic database is generated from an anisotropic root-finding, mode-search routine and used to train a neural network model as an accurate and efficient proxy. This neural network model can be used for real-time sensitivity analysis and performing inversion to the measured sonic dipole dispersion data to estimate relevant model parameters with associated uncertainties. We introduce how this trained model can be used to enhance the labeling and extraction of the dipole dispersion mode. We developed a new method that outperforms previous model-dependent and model-independent approaches because the new method introduces a mechanism to constrain the solution with physics that also has the capability to incorporate more complicated physical dispersion signatures. This new method does not rely on a good initial guess on mud slowness and formation shear slowness, nor any tuning parameter. This leads to significant progress toward fully automated sonic interpretation. The algorithm has been tested on field data for challenging borehole and geological conditions.