AI 中文总结
本研究开发PC-MG-MoE框架，将塑料升级再造的碎片化实验数据转化为可操作指导，在严格验证下误差最低，可支持跨实验室异质性下的工程筛选与实验规划。
AI 中文摘要
塑料废弃物的热化学升级是关键的升级再造途径，但实验文献因条件异质性和报告不完整而碎片化。完整案例学习仅保留10.99%的精选实验，而目标插补会引入有偏监督。本文开发了Physics-Calibrated, Missingness-Gated, and Load-Balanced Mixture-of-Experts（PC-MG-MoE）框架，将结构化缺失转化为信息性学习信号。PC-MG-MoE无需目标插补，直接从部分观测的实验中学习，重构物理一致的产物分布，适配跨实验室异质性，且提供可解释的模型行为而非仅黑箱预测。在严格的源分组验证下，它在所有评估模型中实现了最低的总绝对误差，支持跨实验室异质性下的工程筛选。湿实验室实验作为外部对比，显示了关键的组成依赖趋势。PC-MG-MoE被实现为交互式基于网页的工作流，支持正向筛选、基于物理的约束逆设计、减少实验工作量和试错的靶向实验规划，以及基于新平台特定数据的实验室特定适配。本研究建立了可迁移的框架，用于将碎片化文献数据转化为模型引导的塑料升级再造及更广泛热化学系统的实验可操作指导。
英文摘要
Thermochemical upgrading of plastic waste is a key upcycling pathway, yet the experimental literature is fragmented by heterogeneous conditions and incomplete reporting. Complete-case learning would retain only 10.99% of the curated experiments, while target imputation can introduce biased supervision. Here we develop a Physics-Calibrated, Missingness-Gated, and Load-Balanced Mixture-of-Experts (PC-MG-MoE) framework that converts structured missingness into an informative learning signal. PC-MG-MoE learns directly from partially observed experiments without target imputation, reconstructs physically consistent product distributions, accommodates cross-laboratory heterogeneity, and provides interpretable model behaviour rather than black-box prediction alone. Under stringent source-grouped validation, it achieved the lowest aggregate absolute error among the evaluated models, supporting engineering screening under cross-laboratory heterogeneity. Wet-lab experiments provide an external comparison, showing key composition-dependent trends. Implemented as an interactive web-based workflow, PC-MG-MoE enables forward screening, physics-grounded constrained inverse design, targeted experimental planning that supports reduced experimental workload and trial-and-error, and laboratory-specific adaptation with new platform-specific data. This work establishes a transferable framework for converting fragmented literature data into experimentally actionable guidance for model-guided plastic upcycling and broader thermochemical systems.
