A membrane-less electrolyzer with porous walls for high throughput and pure hydrogen production.

Membrane-less electrolyzers utilize fluidic forces instead of solid barriers for the separation of electrolysis gas products. These electrolyzers have low ionic resistance, a simple design, and the ability to work with electrolytes at different pH values. However, the interelectrode distance and the flow velocity should be large at high production rates to prevent gas cross over. This is not energetically favorable as the ionic resistance is higher at larger interelectrode distances and the required pumping power increases with the flow velocity. In this work, a new solution is introduced to increase the throughput of electrolyzers without the need for increasing these two parameters. The new microfluidic reactor has three channels separated by porous walls. The electrolyte enters the middle channel and flows into the outer channels through the wall pores. Gas products are being produced in the outer channels. Hydrogen cross over is 0.14% in this electrolyzer at flow rate = 80 mL h-1 and current density (j) = 300 mA cm-2. This cross over is 58 times lower than hydrogen cross over in an equivalent membrane-less electrolyzer with parallel electrodes under the same working conditions. Moreover, the addition of a surfactant to the electrolyte further reduces the hydrogen cross over by 21% and the overpotential by 1.9%. This is due to the positive effects of surfactants on the detachment and coalescence dynamics of bubbles. The addition of the passive additive and implementation of the porous walls result in twice the hydrogen production rate in the new reactor compared to parallel electrode electrolyzers with similar hydrogen cross over.

Learning from droplet flows in microfluidic channels using deep neural networks.

A non-intrusive method is presented for measuring different fluidic properties in a microfluidic chip by optically monitoring the flow of droplets. A neural network is used to extract the desired information from the images of the droplets. We demonstrate the method in two applications: measurement of the concentration of each component of a water/alcohol mixture, and measurement of the flow rate of the same mixture. A large number of droplet images are recorded and used to train deep neural networks (DNN) to predict the flow rate or the concentration. It is shown that this method can be used to quantify the concentrations of each component with a 0.5% accuracy and the flow rate with a resolution of 0.05 ml/h. The proposed method can in principle be used to measure other properties of the fluid such as surface tension and viscosity.

Inertial manipulation of bubbles in rectangular microfluidic channels.

Inertial microfluidics is an active field of research that deals with crossflow positioning of the suspended entities in microflows. Until now, the majority of the studies have focused on the behavior of rigid particles in order to provide guidelines for microfluidic applications such as sorting and filtering. Deformable entities such as bubbles and droplets are considered in fewer studies despite their importance in multiphase microflows. In this paper, we show that the trajectory of bubbles flowing in rectangular and square microchannels can be controlled by tuning the balance of forces acting on them. A T-junction geometry is employed to introduce bubbles into a microchannel and analyze their lateral equilibrium position in a range of Reynolds (1 < Re < 40) and capillary numbers (0.1 < Ca < 1). We find that the Reynolds number (Re), the capillary number (Ca), the diameter of the bubble (D[combining macron]), and the aspect ratio of the channel are the influential parameters in this phenomenon. For instance, at high Re, the flow pushes the bubble towards the wall while large Ca or D[combining macron] moves the bubble towards the center. Moreover, in the shallow channels, having aspect ratios higher than one, the bubble moves towards the narrower sidewalls. One important outcome of this study is that the equilibrium position of bubbles in rectangular channels is different from that of solid particles. The experimental observations are in good agreement with the performed numerical simulations and provide insights into the dynamics of bubbles in laminar flows which can be utilized in the design of flow based multiphase flow reactors.