Expediting carbon dots synthesis by the active adaptive method with machine learning and applications in dental diagnosis and treatment

Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co^(2+) and Fe^(3+), with a concentration range of 0–120 and 0–150 µM, and their detection limits are 1.17 and 0.06 µM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction.

Bi-doped carbon quantum dots functionalized liposomes with fluorescence visualization imaging for tumor diagnosis and treatment

Liposomes are one of the significant classes of antitumor nanomaterials and the most successful nanomedicine drugs in clinical translation. However, it is difficult to accurately reveal liposome delivery modes and drug release rates at different p H values to assess the biodistribution and drug delivery pathways in vivo. Here, we established a strategy to integrate Bi-doped carbon quantum dots(CQDs)with liposomes to produce fluorescence visualization and therapeutic effects, namely lipo/Bi-doped CQDs.Lipo/Bi-doped CQDs show good water solubility and physicochemical properties, which can be used for in vitro labeling of colon cancer(CT26) cells and in vivo imaging localization tracking tumors for monitoring. Simultaneously, thanks to the excellent p H sensitivity and ion doping characteristic of Bi-doped CQDs, lipo/Bi-doped CQDs can be used to reveal the drug release rate of liposomes at different p H values and exhibit potential effects in vivo antitumor therapy.

Recent progress of quantum dots for energy storage applications

The environmental problems of global warming and fossil fuel depletion are increasingly severe,and the demand for energy conversion and storage is increasing.Ecological issues such as global warming and fossil fuel depletion are increasingly stringent,increasing energy conversion and storage needs.The rapid development of clean energy,such as solar energy,wind energy and hydrogen energy,is expected to be the key to solve the energy problem.Several excellent literature works have highlighted quantum dots in supercapacitors,lithium-sulfur batteries,and photocatalytic hydrogen production.Here,we outline the latest achievements of quantum dots and their composites materials in those energy storage applications.Moreover,we rationally analyze the shortcomings of quantum dots in energy storage and conversion,and predict the future development trend,challenges,and opportunities of quantum dots research.