PhD Student Queen Mary University of London LONDON, England, United Kingdom
Abstract: Perovskite materials exhibit unique and desirable properties such as high dielectric tuning which can be engineered to have specific responses to biological stimuli, making them suitable for biosensors. Improved synthesis methods can lead to biosensors with higher sensitivity, selectivity, and stability, which are crucial for medical diagnostics and environmental monitoring. Conventional approaches to discovering and refining materials, are laborious and resource-intensive. To overcome these limitations, the development of self-driving laboratories (SDL) are vital. These encompass a comprehensive cycle, starting from ML-guided compositional screening, robot-assisted synthesis, and automated characterization. Here, we introduce an automated rapid sintering and dielectric analysis platform to streamline the dielectric characterization process and the discovery of new disordered layered materials. The platform's successful validation demonstrates its efficiency, accomplishing processing speeds within minutes per material, a significant improvement from conventional methods that often span hours or days. We further demonstrate the validation of novel perovskite solid solutions with materials screened from a combinatorial chemical space using ML. Looking ahead, we anticipate a growing trend towards user-friendly automation, open sourcing, and the use of collaborative robots in laboratories.
