No abstract

Carbon dots (CDs) have been studied for years as one of the most promising fluorescent nanomaterials. However, CDs with red or solid-state fluorescence are rarely reported. Herein, through a one-pot solvothermal treatment, hydrophobic CDs (H-CDs) with blue dispersed emission and red aggregation-induced emission are obtained. When water is introduced, the hydrophobic interaction leads to aggregation of the H-CDs. The formation of H-CD clusters induces the turning off of the blue emission, as the carbonized cores suffer from π-π stacking interactions, and the turning on of the red fluorescence, due to restriction of the surfaces’ intramolecular rotation around disulfide bonds, which conforms to the aggregation-induced-emission phenomenon. This on-off fluorescence of the H-CDs is reversible when the H-CD powder is completely dissolved. Moreover, the H-CD solution dispersed in filter paper is nearly colorless. Finally, we develop a reversible two switch-mode luminescence ink for advanced anti-counterfeiting and dual-encryption.

A multifunctional nanoplatform based on black phosphorus quantum dots (BPQDs) was developed for cancer bioimaging and combined photothermal therapy (PTT) and photodynamic therapy (PDT). BPQDs were functionalized with PEG chains to achieve improved biocompatibility and physiological stability. The as-prepared nanoparticles exhibite prominent near-infrared (NIR) photothermal and red-light-triggered photodynamic properties. The combined therapeutic application of PEGylated BPQDs were then performed in vitro and in vivo. The results demonstrate that the combined phototherapy significantly promote the therapeutic efficacy of cancer treatment in comparison with PTT or PDT alone. BPQDs could also serve as the loading platform for fluorescent molecules, allowing reliable imaging of cancer cells. In addition, the low cytotoxicity and negligible side effects to main organs were observed in toxicity experiments. The theranostic characteristics of PEGylated BPQDs provide an uplifting potential for the future clinical applications.

Graphene quantum dots (GQDs) have been developed as promising optical probes for bioimaging due to their excellent photoluminescent properties. Additionally, the fluorescence spectrum and quantum yield of GQDs are highly dependent on the surface functional groups on the carbon sheets. However, the distribution and cytotoxicity of GQDs functionalized with different chemical groups have not been specifically investigated. Herein, the cytotoxicity of three kinds of GQDs with different modified groups (NH2, COOH, and CO-N (CH3)2, respectively) in human A549 lung carcinoma cells and human neural glioma C6 cells was investigated using thiazoyl blue colorimetric (MTT) assay and trypan blue assay. The cellular apoptosis or necrosis was then evaluated by flow cytometry analysis. It was demonstrated that the three modified GQDs showed good biocompatibility even when the concentration reached 200 μg/mL. The Raman spectra of cells treated with GQDs with different functional groups also showed no distinct changes, affording molecular level evidence for the biocompatibility of the three kinds of GQDs. The cellular distribution of the three modified GQDs was observed using a fluorescence microscope. The data revealed that GQDs randomly dispersed in the cytoplasm but not diffused into nucleus. Therefore, GQDs with different functional groups have low cytotoxicity and excellent biocompatibility regardless of chemical modification, offering good prospects for bioimaging and other biomedical applications.

This study aims to present a noninvasive prostate cancer screening methods using serum surface-enhanced Raman scattering (SERS) and support vector machine (SVM) techniques through peripheral blood sample. SERS measurements are performed using serum samples from 93 prostate cancer patients and 68 healthy volunteers by silver nanoparticles. Three types of kernel functions including linear, polynomial, and Gaussian radial basis function (RBF) are employed to build SVM diagnostic models for classifying measured SERS spectra. For comparably evaluating the performance of SVM classification models, the standard multivariate statistic analysis method of principal component analysis (PCA) is also applied to classify the same datasets. The study results show that for the RBF kernel SVM diagnostic model, the diagnostic accuracy of 98.1% is acquired, which is superior to the results of 91.3% obtained from PCA methods. The receiver operating characteristic curve of diagnostic models further confirm above research results. This study demonstrates that label-free serum SERS analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive prostate cancer screening.