We studied the drying process of polymer solution drops placed on a substrate having a large contact angle with the drop. The drying process takes place in three stages. First, the droplet evaporates keeping the contact line fixed. Second, the droplet shrinks uniformly with receding contact line. Finally the contact line is pinned again, and the droplet starts to be deformed. The shape of the final polymer deposit changes from concave dot, to flat dot, and then to concave dot again with the increase of the initial polymer concentration. This shape change is caused by the gradual transition from the solute piling mechanism proposed by Deegan et al. to the crust buckling mechanism proposed by de Gennes and Pauchard.

The swelling dynamics of long cylindrical gel is analyzed by the stress diffusion coupling model [T. Yamaue and M. Doi, Phys. Rev. E 69, 41402 (2004)]. Two situations are analyzed: (i) stress induced swelling, where the swelling is caused by an elongational force applied on the gel, and (ii) free swelling, where the swelling is caused by thermodynamic force. The relaxation times characterizing these processes are calculated. It is shown that earlier calculations for the relaxation time, which are based on some physical or mathematical approximations, give results surprisingly close to the rigorous calculation, but the difference can be still seen experimentally.

We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

Recently, the swelling kinetics of thin-plate gels with rectangular surfaces under mechanical constraint was experimentally investigated [A. Suzuki and T. Hara, J. Chem. Phys. 114, 5012 (2001)]. In this system, the top and bottom surfaces of gels were chemically clamped on the glass plates, and the gels could swell and shrink only along the thickness direction when the osmotic pressure of the solvent is changed. Here, we analyze this process using the linearized stress-diffusion coupling model of gels based on the two fluids model. The result is somewhat unusual in that the time evolution of the thickness is described by a single exponential even though the swelling is governed by the diffusion of solvent. This result and that the characteristic relaxation time depends on the lengths of the rectangular surfaces and not on the thickness of gels agree well with the experiment.