TY - CONF A1 - Zaki, Mohammad T1 - Nano- and Advanced Materials Synthesis in a Self-Driving Lab N2 - Nano- and advanced materials are recognized as key enabling technologies of the 21st century, offering exceptional potential to drive innovation and tackle pressing challenges in materials science [1]. To fully realize this potential, it is essential to develop and improve tools that accelerate their design, development, and optimization. Recognizing this pressing need, we present a Self-Driving Lab (SDL) [2] that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials, with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating these three process steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. The system also exhibits modularity, impressive adaptability in terms of reactions scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence) of the nanomaterials, along with automating data analysis of at-line or off-line characterization techniques such as electron microscopy image analyses [3]. Automated characterization and data analysis is complemented by a machine learning–driven feedback loop employing active learning algorithms (e.g., Bayesian optimization, artificial neural networks, and downhill simplex methods) to iteratively suggest new experimental parameters toward desired material properties. Therefore, the excellent reproducibility for material syntheses when run on the SDL platform multiple times, the material agnostic behavior, the adaptability, and modularity, underscore the SDL’s reliability and potential as a transformative tool for advancing the development and applications of nano- and advanced materials, offering solutions for a sustainable future. T2 - Gordon Research Seminar and Conference CY - Ventura, CA, USA DA - 24.01.2026 KW - Self-Driving Laboratories KW - Materials Acceleration Platforms KW - Nanomaterials PY - 2026 AN - OPUS4-65596 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ciornii, Dmitri T1 - Knowledge Readiness Level (KaRL) approach for nanorisk governance and beyond N2 - Regulatory decisions require reliable data and knowledge derived from this. Among stakeholders in nanotechnology, however, there is often uncertainty about the quality of data for regulatory purposes. In addition, the general public often finds itself excluded from nanoregulation and policy decisions. This creates uncertainty in the nanotechnology field and also in other branches of technology and leads to concerns among the society. To address these issues, NANORIGO elaborates a framework to support decision making as well as data, information and knowledge sharing and use. We refer to “reliability” of data and knowledge as a degree of readiness or maturity. According to these criteria we worked out a 9-level scale in analogy to TRL (technology readiness level), the KaRL system (Knowledge, Data and Information Readiness Level). KaRL allows assessment of knowledge readiness for decision making by applying defined quality criteria for each level. It also provides guidance on how to enhance the readiness level by the help of available tools and procedures. KaRL addresses SEIN[1] principles, circular economy and thus involves the public concerns in regulation. A specialized nanorisk governance council (being under development in NANORIGO) is suggested to perform quality check of an actionable document, thus, aiding in consensus on the reliability (maturity) of knowledge for decision making. Moreover, KaRL facilitates traceability of knowledge before its use in decision making. This enables the transparency demanded by all stakeholders. T2 - EuroNanoForum 2021 CY - Online meeting DA - 05.05.2021 KW - Knowledge Readiness Level KW - Nanorisk KW - Nanomaterials KW - Data PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524725 AN - OPUS4-52472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -