Hydrogels are able to exhibit optical transitions in the presence of external stimuli such as temperature, driven by the lower critical solution temperature (LCST) phenomena. However, they suffer from inherent...
Progress in data-driven self-driving laboratories for solving materials grand challenges has accelerated with the advent of machine learning, robotics and automation but usually designed with specific materials and processes in mind. To develop the next generation of Materials Acceleration Platforms (MAPs), we propose a unified framework to enable collaboration between MAPs, leveraging on object-oriented programming principles using which research groups around the world would be able to effectively evolve experimental workflows. We demonstrate the framework via three experimental case studies from disparate fields to illustrate the evolution of, and seamless integration between workflows, promoting efficient resource utilisation and collaboration. Moving forward, we project our framework on three other research areas that would benefit from such an evolving workflow. Through the wide adoption of our framework, we envision a collaborative, connected, global community of MAPs working together to solve scientific grand challenges.
Identification of spectroscopically silent heavy‐metal ions in environmental and bio‐analytical samples has gained immense importance. Lowering the limit of detection of these ions at low concentrations is crucial for biomedicine and food safety applications. Optical chemical sensors have shown great potential in improving the sensing solutions for heavy‐metal ions because of their fast and label‐free detection capabilities. However, achieving high sensitivity without compromising on scalability and simplicity is challenging. Here, we experimentally demonstrate the detection of heavy metal ions at ultralow concentrations using lithography‐free scalable thin film optical coatings that show singular behavior of phase of light at the point‐of‐darkness. Since the phase‐sensitive optical techniques have shown superior sensitivity over traditional sensing techniques based on spectroscopy, we use this extreme phase change to detect zinc ions concentration down to femtomolar. In particular, it allows us to monitor very small changes in refractive index due to the real‐time binding of zinc ions on a poly (lactic‐co‐glycolic acid) functionalized sensor surface. This cost‐effective extreme sensitivity sensing platform can be used for the label‐free detection of various heavy metal ions by properly functionalizing the sensor surface.
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