Metal manufacturing is responsible for 8% of global CO2 emissions and if carbon neutrality is to be achieved by 2050, we critically need to transition to more sustainable processes. In this project we address the underlying science and understanding to allow a higher utilisation of low embedded-carbon, higher impurity recycled metal as a feedstock for metal manufacturing. Current manufacturing approaches are highly dependent on energy-intensive primary metal as they rely on tightly controlled compositions with very low impurity contents to provide the required materials properties. We believe that the new understanding needed to provide transformative and efficient methods to manufacture high grade metal alloys using a much higher fraction of lower embedded-carbon recycled material as a feedstock can be delivered by leveraging the combined power of multi-modal X-ray imaging and in-line artificial intelligence. We will develop a new wholistic characterisation system comprising both newly developed hardware and AI algorithms named Artificial Intelligence X-ray Imaging (AIXI) as an intelligent tool to investigate the solidification of impurity-rich alloys in experimental conditions comparable to those found in industrial processes such as continuous casting, direct chill casting, shape casting and additive manufacturing for a wide range of aluminium and steel alloy compositions. AIXI will provide a significant advantage over existing approaches as AI will be embedded in the data acquisition system and used to interpret raw data in real-time, drastically reducing the complexity and time required for data analysis and significantly increasing the analytical power of the system. The new knowledge will allow us to finally understand the role that impurities and minor alloy additions play in the developing solidification microstructure, and to develop methodologies to mitigate their deleterious effects. It will also promote a shift to a more holistic approach for alloy design in which the solidification microstructure is engineered to both provide enhanced properties and to facilitate subsequent downstream processes with minimised environmental impact. The newly acquired knowledge will foster the development of science for `sustainable' alloys, which will: enhance metal recyclability by reducing the need for dilution of recycled scrap with energy intensive primary metal; encourage greater use of lower-grade scrap, widely available in the UK but currently exported; decrease the number of downstream processing steps (process intensification), especially heat treatment practices; simplify component recoverability by reducing the reliance on tight compositions specifications; and enhance materials properties by improving control over the final microstructure. We will uncover and apply the missing science to control phase transformations to create more benign and impurity tolerant microstructures and allow more efficient use of expensive and potentially scarce alloy additions, which will substantially cut resource use in the CO2-intensive metal industries. Furthermore, we envisage that the application of the developed hardware/AI analysis could potentially facilitate rapid scientific development in many fields of materials science and beyond where efficient, rapid collection and analysis of complex and large multi-modal datasets is critical to unlock the necessary understanding