Efficient chiral hydrogel template based on supramolecular self-assembly driven by chiral carbon dots for circularly polarized luminescence.

Since the chiral emission of excited states is observed on carbon dots (CDs), exploration towards the design and synthesis of chiral CDs nanomaterials with circularly polarized luminescence (CPL) properties has been at a brisk pace. In this regard, the "host and guest" co-assembly strategy based on the combination of CDs and chiral templates has been of unique interest recently for its convenient operation, multicolor tunable CPL, and wide application of prepared CDs-composited materials in optoelectronic devices and information encryption. However, the existing chiral templates that match perfectly with chiral CDs exhibiting optical activity both in ground and excited states are rather scarce. In this work, we synthesize the chiral CDs that could induce the spontaneous supramolecular self-assembly of N-(9-fluorenylmethox-ycarbonyl) (Fmoc)-protected glutamic acid to form chiral hydrogels with helical nanostructure. The co-assembled hydrogels show powerful chiral template function, which not only enable chiral CDs with a luminescence dissymmetry factor (glum) up to 10-2, but also have universal chiral transfer to inserted dye molecules, realizing full-color CPL and Förster resonance energy transfer (FRET) CPL as well as the distinction between left and right circularly polarized light. This CPL-active template based on chiral CDs enriches the design scenario of chiral functionalized nanomaterials.

Machine-Learning-Driven G-Quartet-Based Circularly Polarized Luminescence Materials.

Circularly polarized luminescence (CPL) materials have garnered significant interest due to their potential applications in chiral functional devices. Synthesizing CPL materials with a high dissymmetry factor (glum ) remains a significant challenge. Inspired by efficient machine learning (ML) applications in scientific research, this work demonstrates ML-based techniques for the first time to guide the synthesis of G-quartet-based CPL gels with high glum values and multiple chiral regulation strategies. Employing an "experiment-prediction-verification" approach, this work devises a ML classification and regression model for the solvothermal synthesis of G-quartet gels in deep eutectic solvents. This process illustrates the relationship between various synthesis parameters and the glum value. The decision tree algorithm demonstrates superior performance across six ML models, with model accuracy and determination coefficients amounting to 0.97 and 0.96, respectively. The screened CPL gels exhibiting a glum value up to 0.15 are obtained through combined ML guidance and experimental verification, among the highest ones reported till now for biomolecule-based CPL systems. These findings indicate that ML can streamline the rational design of chiral nanomaterials, thereby expediting their further development.