Liuxuan Ren, Tinghao Jia, Mengen Zhang, Xiaoqiang Fan, Congjing Ren, Jingdai Wang, Yongrong Yang, Yao Yang
In high‐pressure free‐radical polymerization, peroxide initiators are routinely blended to improve process performance, yet their synergistic interactions remain insufficiently elucidated. Here, these effects are probed using reaction‐network analysis. Reaction networks of blended initiators were generated with reaction mechanism generator (RMG) and refined through experiments. Network analysis revealed diverse interaction patterns, motivating three complementary metrics to quantify synergy: rate enhancement ( k ‐fold increase), radical generation capacity (GI), and radical persistence. The combined behavior of these metrics distinguishes cooperative synergy from largely independent decomposition. Synergistic enhancement requires meaningful decomposition overlap and the formation of a complementary, stable radical pool. Incorporating the extracted parameters and GI‐corrected radical release into a reactor model enabled formulation optimization. Application to an industrial tubular reactor reduced initiator consumption by over 30% while maintaining conversion and molecular‐weight targets. These findings offer mechanistic insight and practical guidance for initiator design in industrial high‐pressure polymerization.
{"title":"Quantifying synergistic initiator interactions in high‐pressure polymerization through reaction‐network analysis","authors":"Liuxuan Ren, Tinghao Jia, Mengen Zhang, Xiaoqiang Fan, Congjing Ren, Jingdai Wang, Yongrong Yang, Yao Yang","doi":"10.1002/aic.70308","DOIUrl":"https://doi.org/10.1002/aic.70308","url":null,"abstract":"In high‐pressure free‐radical polymerization, peroxide initiators are routinely blended to improve process performance, yet their synergistic interactions remain insufficiently elucidated. Here, these effects are probed using reaction‐network analysis. Reaction networks of blended initiators were generated with reaction mechanism generator (RMG) and refined through experiments. Network analysis revealed diverse interaction patterns, motivating three complementary metrics to quantify synergy: rate enhancement ( <jats:italic>k</jats:italic> ‐fold increase), radical generation capacity (GI), and radical persistence. The combined behavior of these metrics distinguishes cooperative synergy from largely independent decomposition. Synergistic enhancement requires meaningful decomposition overlap and the formation of a complementary, stable radical pool. Incorporating the extracted parameters and GI‐corrected radical release into a reactor model enabled formulation optimization. Application to an industrial tubular reactor reduced initiator consumption by over 30% while maintaining conversion and molecular‐weight targets. These findings offer mechanistic insight and practical guidance for initiator design in industrial high‐pressure polymerization.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":"12 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Galen Wang, Umesh Dhumal, Monica E. A. Zakhari, Roseanna N. Zia
Monodisperse, purely repulsive hard spheres (MPRHS) are a canonical model for fluid–solid phase behavior in atomic and colloidal systems. Liquid-state theory, free-energy calculations, simulations, and experiments establish a first-order fluid–solid transition in this model, a thermodynamic picture we take as given. Following Alder and Wainwright, we treat explicit fluid–crystal phase separation—coexistence of fluid-and-crystal domains—as an important benchmark for confirming first-order behavior, complementary to demonstrating phase transition between single phases. Against this backdrop, we highlight a specific gap. Decades of simulations have mapped equations of state, coexistence properties, and nucleation rates, forming a foundational body of results, yet spontaneous, long-lived fluid–crystal coexistence has not been reported in unbiased MPRHS simulations. Instead, coexistence appears either when physical/model-level bias is introduced (e.g., seeding, gravity) or under algorithmic bias designed to accelerate barrier crossing. Studies that avoid bias typically observe transient mixed states ultimately overtaken by a single metastable phase, consistent with Frenkel's estimate that a spontaneous coexistence state in even large simulations would require