Report on potential toxicity of analyzed materials

Grant Agreement number: 101058371 Project acronym: ESTELLA Project title: Design of biobased thermoset polymer with recycling capability by dynamic bonds for bio-composite manufacturing Grant Agreement number: 101058371 Project acronym: ESTELLA Project title: Design of biobased thermoset polymer with recycling capability by dynamic bonds for biocomposite manufacturing REPORT ON POTENTIAL TOXICITY OF ANALYZED MATERIALS DELIVERABLE 5.3 Contractual Date of Delivery: 01/10/2025 Actual Date of Delivery: 02/10/2025 Lead contractor for this deliverable: IDENER Author(s): IDENER Participants(s): IDENER (Task 5.2 leader), CIDAUT (Coordination) WP contributing to the deliverable: WP 5 Nature: SEN - Sensitive Version V. 1.0 Grant Agreement 101058371 Project ESTELLA Deliverable5.3_v1.docx ©ESTELLA - This is the property of ESTELLA Parties: shall not be distributed/reproduced without formal approval of ESTELLA General Assembly. therein. Executive Summary Deliverable 5.3 describes the potential toxicity of developed materials within ESTELLA project. A holistic approach for Risk Assessment has been carried out along the project. This deliverable compiles the hazard dimension of SSbD assessment (meanwhile Deliverable 5.4 will do the same for exposure dimension). Following the principles of SSbD Framework, a toxicity assessment has been performed for all the compounds used in the development of new products, ensuring the level of safety not only for workers but also for consumers and even the environment. In addition, to assess the impact of these new products developed within the projects, new predictive models have been created. These models are mathematical models that correlate the structure of the materials (specially, the by-products) with their potential toxicological profile in a quantitative way. Therefore, the following report contains a detailed description of the main principles of the SSbD Framework, the assessment of the intrinsic properties of chemical used in the project, and the development of machine learning models for predicting toxicity of the new materials.