详细信息

Engineering of bioinspired hierarchical guanine crystals  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Engineering of bioinspired hierarchical guanine crystals

作者:Zhang, Chong[1];Li, Yuanbo[1];Cui, Congcong[1];Che, Shunai[1,3];Han, Lu[1];Cao, Yuanyuan[2]

机构:[1]Tongji Univ, Sch Chem Sci & Engn, Shanghai 200092, Peoples R China;[2]East China Univ Sci & Technol, Sch Mat Sci & Engn, Shanghai 200237, Peoples R China;[3]Shanghai Jiao Tong Univ, Shanghai Key Lab Mol Engn Chiral Drugs, State Key Lab Synergist Chem Bio Synth, Frontiers Sci Ctr Transformat Mol,Sch Chem & Chem, Shanghai 200240, Peoples R China

年份:2026

卷号:69

期号:4

起止页码:2030

外文期刊名:SCIENCE CHINA-CHEMISTRY

收录:;EI(收录号:20260219903293);WOS:【SCI-EXPANDED(收录号:WOS:001656607900001)】;

基金:This work was supported by the National Natural Science Foundation of China (22425303 to Lu Han, 22472058 to Yuanyuan Cao) and the Fundamental Research Funds for the Central Universities (Lu Han, Yuanyuan Cao).

语种:英文

外文关键词:guanine crystal; biomimetic synthesis; crystal growth; optical property; principal component analysis

摘要:Guanine crystals are renowned as one of the most appealing biogenic organic optical crystals in living organisms, widely known for their high refractive index, rivaling inorganic crystals. Since their optical excellence is governed by specific morphologies, it is imperative to comprehend the methods for controlling the growth and assembly of guanine crystals. However, the artificial control over their morphology, especially in repeating the biogenic ones, remains a formidable challenge. Herein, we present a biomimetic synthesis strategy to engineer hierarchical guanine crystal polymorphs by controlling the primary nuclei growth processes, followed by the structural modulation under the competition of hydrogen bonding and pi-pi interactions. Five typical crystal morphologies, ranging from dense spherical polycrystalline aggregates to thin flake crystals, were successfully synthesized. The crystallization mechanism was analyzed using principal component analysis (PCA) method in machine learning technology, where the crystal nucleation behaviors were found to be critical in determining the crystal hierarchies and a three-dimensional synthesis-field diagram mapping 30,000 possible parameter combinations has been predicted. Additionally, these morphologies significantly affect the birefringence and the reflection of the crystals. This work provides a way for tailoring biogenic crystals through precise crystal nucleation regulation and growth-directed assemblies.

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