Carbon dots (CDs) have wide application potentials in optoelectronic devices, biology, medicine, chemical sensors, and quantum techniques due to their excellent fluorescent properties. However, synthesis of CDs with controllable spectrum is challenging because of the diversity of the CD components and structures. In this report, machine learning (ML) algorithms were applied to help the synthesis of CDs with predictable photoluminescence (PL) under the excitation wavelengths of 365 and 532 nm. The combination of precursors was used as the variable. The PL peaks of the strongest intensity ( λ s ) and the longest wavelength ( λ l ) were used as target functions. Among six investigated ML models, the random forest (RF) model showed outstanding performance in the prediction of the PL peaks.
Phosphorus nanoribbons combine the tunable bandgap and high mobility with the inherent anisotropy of one-dimensional systems, offering promise for functional electronics, but their intrinsic low stability hinders practical applications. Here, we report phosphorus-lithium double-helix nanoribbons with a well-ordered helical architecture and high structural stability under harsh conditions such as in air up to 225°C, water, and even acidic solutions. Comprehensive experimental characterizations and theoretical analyses show that the stability arises from a synergistic combination of Zintl phase formation between phosphorus and lithium atoms, noncovalent interhelical interactions, and geometric protection offered by the distinctive helical architecture. The nanoribbons show tunable optical properties dependent on temperature, thickness, and polarization state. It is demonstrated that these properties enabled nanoribbon-based hydrogels with self-healability and highly efficient photothermal conversion, showing a general approach for stabilizing active low-dimensional materials and paving the way for applying phosphorus-based nanostructures in biomedical engineering and quantum technologies.
危险品化学品经营许可证（带存储）