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Abstract The production of elemental sulfur from petroleum refining has created a technological opportunity to increase the valorization of elemental sulfur by the creation of high‐performance sulfur based plastics with improved thermomechanical properties, elasticity and flame retardancy. We report on a synthetic polymerization methodology to prepare the first example of sulfur based segmented multi‐block polyurethanes (SPUs) and thermoplastic elastomers that incorporate an appreciable amount of sulfur into the final target material. This approach applied both the inverse vulcanization of S8with olefinic alcohols and dynamic covalent polymerizations with dienes to prepare sulfur polyols and terpolyols that were used in polymerizations with aromatic diisocyanates and short chain diols. Using these methods, a new class of high molecular weight, soluble block copolymer polyurethanes were prepared as confirmed by Size Exclusion Chromatography, NMR spectroscopy, thermal analysis, and microscopic imaging. These sulfur‐based polyurethanes were readily solution processed into large area free standing films where both the tensile strength and elasticity of these materials were controlled by variation of the sulfur polyol composition. SPUs with both high tensile strength (13–24 MPa) and ductility (348 % strain at break) were prepared, along with SPU thermoplastic elastomers (578 % strain at break) which are comparable values to classical thermoplastic polyurethanes (TPUs). The incorporation of sulfur into these polyurethanes enhanced flame retardancy in comparison to classical TPUs, which points to the opportunity to impart new properties to polymeric materials as a consequence of using elemental sulfur.
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This content will become publicly available on July 13, 2026
Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning
Abstract The development of high‐performance elastomers for additive manufacturing requires overcoming complex property trade‐offs that challenge conventional material discovery pipelines. Here, a human‐in‐the‐loop reinforcement learning (RL) approach is used to discover polyurethane elastomers that overcome pervasive stress–strain property tradeoffs. Starting with a diverse training set of 92 formulations, a coupled multi‐component reward system was identified that guides RL agents toward materials with both high strength and extensibility. Through three rounds of iterative optimization combining RL predictions with human chemical intuition, we identified elastomers with more than double the average toughness compared to the initial training set. The final exploitation round, aided by solubility prescreening, predicted twelve materials exhibiting both high strength (>10 MPa) and high strain at break (>200%). Analysis of the high‐performing materials revealed structure‐property insights, including the benefits of high molar mass urethane oligomers, a high density of urethane functional groups, and incorporation of rigid low molecular weight diols and unsymmetric diisocyanates. These findings demonstrate that machine‐guided, human‐augmented design is a powerful strategy for accelerating polymer discovery in applications where data is scarce and expensive to acquire, with broad applicability to multi‐objective materials optimization.
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- PAR ID:
- 10618095
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Angewandte Chemie International Edition
- Volume:
- 64
- Issue:
- 36
- ISSN:
- 1433-7851
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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