Enhancing gas-liquid mass transfer is key to promote gas hydrate formation kinetics. Encapsulation of CO₂ hydrate is expected to dramatically increase gas-liquid contact to enhance mass transfer. However, gas hydrate encapsulation has never been proposed as the technical issues of gas permeation through capsule shells have never been addressed. In this work, based on the principles of biomimetics, we proposed a novel red blood cell (RBC) inspired carbon capture capsule to promote CO₂ hydrate formation kinetics. An experimentally validated model is established to compare the carbon capture performance in an RBC-shaped and a spherical capsule. It is revealed that the gas uptake efficiency of the RBC-shaped capsule is 143% higher than that of the spherical one. The effect of initial pressure and capsule size on CO₂ hydrate formation kinetics is also investigated. Furthermore, the structure of RBC is optimised and it is found the average amount of hydrate formation per surface area achieves a peak when the ratio of the height at the centre to the width of the ring is between 0.128 and 0.160, which is close to that of real RBCs in human bodies. This work enables the informed design of hydrate-based carbon capture units with high gas uptake efficiency.

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.