Iron-catalyzed depolymerizations of end-of-life silicones with fatty alcohols

During the last decades, polymers became one of the major materials in our society and a future without polymers is hardly imaginable. However, as negative issue of this success enormous amount of end-of-life materials are accumulated, which are mainly treated by landfill storage, thermal recycling or down-cycling. On the other hand, feedstock recycling can be an interesting option to convert end-of-life polymers to high quality polymers, via depolymerization reactions to low-molecular weight building blocks and subsequent transformation via polymerization reactions. In this regard, we present herein the depolymerization of polysiloxanes (silicones) applying fatty alcohols as depolymerization reagents. In more detail, in the presence of catalytic amounts of simple iron salts, low-molecular weight products with the motif R(OSiMe2)mOR (R = alkyl, m = 1-2) were attained. Remarkably, the reaction of R(OSiMe2)mOR with water showed the formation of new cyclic siloxanes, which are useful starting materials for long-chain silicones, and the corresponding fatty alcohol as side product, which can be directly reused in subsequent depolymerization reactions. Importantly, a recycling of the silicones and a straightforward recycling of the depolymerization reagent are feasible. © 2015 Tomsk Polytechnic University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer review under responsibility of Tomsk Polytechnic University

SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data

Training sophisticated machine learning (ML) models requires large datasets that are difficult or expensive to collect for many applications. If prior knowledge about system dynamics is available, mechanistic representations can be used to supplement real-world data. We present SimbaML (Simulation-Based ML), an open-source tool that unifies realistic synthetic dataset generation from ordinary differential equation-based models and the direct analysis and inclusion in ML pipelines. SimbaML conveniently enables investigating transfer learning from synthetic to real-world data, data augmentation, identifying needs for data collection, and benchmarking physics-informed ML approaches. SimbaML is available from https://pypi.org/project/simba-ml/.Comment: 6 pages, 1 figur