本研究以 Bayesian Optimization 作為核心策略，將層級孔 ZSM-5 的合成由傳統試誤流程轉化為可系統搜尋的最佳化問題。透過同時考量微孔與介孔作為雙重目標，並以 Gaussian Process Regression 建立可量化不確定度的替代模型，僅需 15 組初始實驗即可有效引導後續三輪 BO 迭代，快速鎖定最具代表性的合成條件。最終獲得的 HZ-R0.55T50t2 不僅維持與基準樣品相當的介孔發育程度，亦顯著提升微孔保留比例，顯示其在改善分子傳質的同時成功避免骨架過度破壞。結構與化學表徵進一步揭示，NaOH 主導矽的溶蝕行為，而 TPAOH 則扮演調控與保護角色；兩者協同作用使介孔得以均勻生成並同時維持結晶度與酸性骨架。敏感度分析亦明確指出 TPAOH 比例與反應溫度為影響層級孔結構的關鍵因子。整體而言，本研究證實 BO–GPR 架構能以最小實驗成本，高效率完成複雜合成空間的探索，並為層級孔沸石的理性設計提供一條可推廣的資料驅動路徑。

Hierarchical ZSM-5 zeolites that integrate micropores and mesopores are highly desirable for improving mass transport while preserving intrinsic shape selectivity and acidity; however, rational optimization of desilication conditions remains challenging due to strongly coupled synthesis parameters. In this study, a data-driven optimization framework based on Bayesian Optimization (BO) was developed to accelerate the synthesis of hierarchical ZSM-5 with a balanced micro–mesoporous architecture. Gaussian Process Regression (GPR) was employed as a probabilistic surrogate model to map desilication parameters—including TPAOH fraction, treatment temperature, and treatment time—to two structural descriptors: micropore ratio (Vmicro/Vtotal) and mesopore ratio (Sext/SBET). Starting from 15 initial experiments, BO iteratively proposed new synthesis conditions using an Expected Improvement acquisition function, enabling efficient exploration of the parameter space with minimal experimental cost.

The optimized sample (HZ-R0.55T50t2) achieved the highest hierarchy factor (0.17), exhibiting enhanced mesoporosity while retaining a higher fraction of microporosity compared with a benchmark sample. Comprehensive characterizations revealed that NaOH induces aggressive framework dissolution, whereas TPAOH moderates desilication by protecting microporous channels; their combined use synergistically generates uniform mesopores with preserved crystallinity. Solid-state 27Al and 29Si NMR confirmed realumination and silanol formation, while acidity measurements indicated redistribution of acid sites. Sensitivity analysis identified the TPAOH fraction and treatment temperature as the dominant variables governing hierarchical evolution.

Overall, this work demonstrates that BO-guided active learning provides an efficient and generalizable strategy for rational zeolite engineering, enabling rapid optimization of complex synthesis spaces while significantly reducing experimental effort.

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