Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining–synthesis–inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond.

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human–machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

In this research, ab initio calculations and experimental approach were adopted to reveal the mechanism of Hg⁰ adsorption on MoS₂ nanosheets that contain various types of defects. The ab initio calculation showed that, among different structural defects, S vacancies (Vs) in the MoS₂ nanosheets exhibited outstanding potential to strongly adsorb Hg⁰. The MoS₂ material was then prepared in a controlled manner under conditions, such as temperature, concentration of precursors, etc., that were determined by adopting the new method developed in this study. Characterisation confirmed that the MoS₂ material is of graphene-like layered structure with abundant structural defects. The integrated dynamic and steady state (IDSS) testing demonstrated that the Vs-rich nanosheets showed excellent Hg⁰ adsorption capability. In addition, ab initial calculation on charge density difference, PDOS, and adsorption pathways revealed that the adsorption of Hg⁰ on the Vs-rich MoS₂ surface is non-activated chemisorption.