Dynamical properties of nematic liquid crystals subjected to shear flow and magnetic fields: tumbling instability and nonequilibrium fluctuations

Prediction of Glass Transition Temperature of Polymers Using Simple Machine Learning

Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (Tg) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring Tg, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict Tg efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R2 value of 0.79; however, the XGB model, with an R2 value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for Tg prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model's robustness, with an average prediction deviation of 9.76 from actual Tg values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials.

Electrically-driven modulation of flow patterns in liquid crystal microfludics

The flow of liquid crystals in the presence of electric fields is investigated as a possible means of flow control. The Beris-Edwards model is coupled to a free energy incorporating electric field effects. Simulations are conducted in straight channels and in junctions. Our findings reveal that local flow mediation can be achieved by the application of spatially varying electric fields. In rectangular straight channels, we report a two-stream velocity profile arising in response to the imposed electric field. Furthermore, we observe that the flow rate in each stream scales inversely with the Miesowicz viscosities, leading to the confinement of 70% of the throughput to one half of the channel. Similar flow partitioning is also demonstrated in channel junction geometries, where we show that using external fields provides a novel avenue for flow modulation in microfluidic circuits.

Three-dimensional observation of Brownian particles under steady shear flow by stereo microscopy

Three-dimensional observation of Brownian particles under shear flow is performed with a stereo microscope to examine the nature of the Brownian motion that occurs in the presence of shear flow. From the three-dimensional trajectories of the particles, we clearly demonstrate the occurrence of anomalous diffusion in the flow direction and the coupling of the displacements in the flow and velocity gradient directions. Furthermore, we experimentally obtain the probability distribution function and current density, which also exhibit characteristic features, and compare the obtained results with theoretical results derived using the Fokker-Planck equation.

Two-shape-tensor model for tumbling in nematic polymers and liquid crystals

Most, but not all, liquid crystals tend to align when subject to shear flow, while most nematic polymeric liquid crystals undergo a tumbling instability, where the director rotates with the flow. The reasons of this instability remain elusive, as it is possible to find similar molecules exhibiting opposite behaviors. We propose a continuum theory suitable for describing a wide range of material behaviors, ranging form nematic elastomers to nematic polymers and nematic liquid crystals, where the material parameters have meaningful physical interpretations and the conditions for tumbling emerge clearly. There are two possible ways to relax the internal stress in a nematic material. The first is the reorganization of the polymer network, the second is the alignment of the network natural axis with respect to the principal direction of the effective strain. We show that tumbling occurs whenever the second mechanism is less efficient than the first. Furthermore, we provide a justification of the experimental fact that at high temperatures, in an isotropic phase, only flow alignment is observed and no tumbling is possible, even in polymers.

Liquid relaxation: A new Parodi-like relation for nematic liquid crystals

We put forward a hydrodynamic theory of nematic liquid crystals that includes both anisotropic elasticity and dynamic relaxation. Liquid remodeling is encompassed through a continuous update of the shear-stress free configuration. The low-frequency limit of the dynamical theory reproduces the classical Ericksen-Leslie theory, but it predicts two independent identities between the six Leslie viscosity coefficients. One replicates Parodi's relation, while the other-which involves five Leslie viscosities in a nonlinear way-is new. We discuss its significance, and we test its validity against evidence from physical experiments, independent theoretical predictions, and molecular-dynamics simulations.

Electric-field-induced flow-aligning state in a nematic liquid crystal

The response of shear stress to a weak ac electric field as a probe is measured in a nematic liquid crystal under shear flow and dc electric fields. Two states with different responses are clearly observed when the dc electric field is changed at a constant shear rate: the flow aligning and non-flow aligning states. The director lies in the shear plane in the flow aligning state and out of the plane in the non-flow aligning state. Through application of dc electric field, the non-flow aligning state can be changed to the flow aligning state. In the transition from the flow aligning state to the non-flow aligning state, it is found that the response increases and the relaxation time becomes longer. Here, the experimental results in the flow aligning state are discussed on the basis of the Ericksen-Leslie theory.

Nonequilibrium steady-state response of a nematic liquid crystal under simple shear flow and electric fields

The effect of a dc electric field on the response of a nematic liquid crystal under shear flow has been investigated by measuring the shear stress response to an ac electric field used as a probe. It was found that both the first- and second-order responses do not vanish at high frequencies, but have constant nonzero values. The experimental results are in good agreement with calculations based on the Ericksen-Leslie theory. The role of the Parodi relation (which is derived from the Onsager reciprocal relation) in the stress response is discussed.