Tracing multi-isotopically labelled CdSe/ZnS quantum dots in biological media

The use and detection of quantum dots as nanotracers in environmental fate studies of engineered nanoparticles

Investigations of the behavior and effects of engineered nanoparticles (ENPs) on human health and the environment need detailed knowledge of their fate and transport in environmental compartments. Such studies are highly challenging due to low environmental concentrations, varying size distribution of the particles and the interference with the natural background. A strategy to overcome these limits is to use mimics of ENPs with unique detectable properties that match the properties of the ENPs as nanotracers. A special class of ENPs that can be tracked are quantum dots (QDs). QDs are composed of different metals, metalloids, or more recently also carbon (e.g., graphene), that result in unique optical properties. This allows the tracking of such particles by fluorescence microscopic and photometric techniques. Many types of QDs consist of heavy elements, allowing to track and visualize these particles also by electron microscopy and to quantitate the particles indirectly based on these elements. QDs can also be surface modified in various ways which enable them to be used as a label or as traceable mimics for ENPs. This review reflects a broad range of methods to synthesize and modify QDs based on metals, metalloids, and graphene for studying the environmental fate of nanoparticles and discusses and compares analytical methods that can be used for tracking and quantifying QDs. In addition, we review applications of QDs as ENP mimics in environmental studies of surface waters, soils, microorganisms, and plants with respect to the applied analytical techniques.

Aquatic Mesocosm Strategies for the Environmental Fate and Risk Assessment of Engineered Nanomaterials

In the past decade, mesocosms have emerged as a useful tool for the environmental study of engineered nanomaterials (ENMs) as they can mimic the relevant exposure scenario of contamination. Herein, we analyzed the scientific outcomes of aquatic mesocosm experiments, with regard to their designs, the ENMs tested, and the end points investigated. Several mesocosm designs were consistently applied in the past decade to virtually mimic various contamination scenarios with regard to ecosystem setting as well as ENMs class, dose, and dosing. Statistical analyses were carried out with the literature data to identify the main parameters driving ENM distribution in the mesocosms and the potential risk posed to benthic and planktonic communities as well as global ecosystem responses. These analyses showed that at the end of the exposure, mesocosm size (water volume), experiment duration, and location indoor/outdoor had major roles in defining the ENMs/metal partitioning. Moreover, a higher exposure of the benthic communities is often observed but did not necessarily translate to a higher risk due to the lower hazard posed by transformed ENMs in the sediments (e.g., aggregated, sulfidized). However, planktonic organisms were generally exposed to lower concentrations of potentially more reactive and toxic ENM species. Hence, mesocosms can be complementary tools to existing standard operational procedures for regulatory purposes and environmental fate and risk assessment of ENMs. To date, the research was markedly unbalanced toward the investigation of metal-based ENMs compared to metalloid- and carbon-based ENMs but also nanoenabled products. Future studies are expected to fill this gap, with special regard to high production volume and potentially hazardous ENMs. Finally, to take full advantage of mesocosms, future studies must be carefully planned to incorporate interdisciplinary approaches and ensure that the large data sets produced are fully exploited.

Selective detection of Cu2+ using nitrogen-doped carbon dots derived from humic acid and urea based on specific inner filter effect

A new kind of nitrogen-doped carbon dots (N-CDs) was synthesized via a simple hydrothermal strategy using humic acid as the carbon source and urea as the nitrogen source. The fluorescence intensity of as-prepared N-CDs was quenched significantly in presence of Cu²⁺ based on a specific inner filter effect, which could be utilized to construct a selective sensor for monitoring Cu²⁺ in aqueous samples. The sensor exhibited good linearity over the range of 0.1-2 μM, and high sensitivity with a detection limit of 25 nM. Under the optimal conditions, there was no significant interference by other metal ions such as Cd²⁺, Al³⁺, Cr³⁺, Fe³⁺, Pb²⁺, Na⁺, Ni⁺, Fe²⁺, Ba²⁺, Ca²⁺, Co²⁺, Mg²⁺, As³⁺, K⁺, Zn²⁺ for Cu²⁺ detecting except Hg²⁺.The interference of Hg²⁺ can be masked by addition of sodium chloride. The experimental results demonstrated that the prepared N-CDs owned strong fluorescence, high monodispersity, good stability and good water solubility, and the constructed sensor had many advances and great application prospect in environmental field.

Mobility and transformation of CdSe/ZnS quantum dots in soil: Role of the capping ligands and ageing effect

The increasing application of Quantum Dots (QDs) is cause of concern for the potential negative effects for the ecosystem, especially in soils that may act as a sink. In this study, soil leaching experiments were performed in quartz sand packed columns to investigate the behavior of core-shell CdSe/ZnS QDs coated with either small ligands (TGA-QDs) or more complex polymers (POAMA-QDs). Fluorescence emission was compared to mass spectrometric measurements to assess the nanoparticles (NPs) state in both the leachate (transported species) and porous media (deposited amounts). Although both QDs were strongly retained in the column, large differences were observed depending on their capping ligand stability. Specifically, for TGA-QDs elution was negligible and the retained fraction accumulated in the top-columns. Furthermore, 74% of the NPs were degraded and 38% of the Se was found in the leachate in non-NPs state. Conversely, POAMA-QDs were recovered to a larger extent (78.1%), and displayed a higher transport along the soil profile. Further experiments with altered NPs showed that homo-aggregation of the QDs prior injection determined a reduced mobility but no significant changes in their stability. Eventually, ageing of the NPs in the column (15 days) caused the disruption of up to 92% of the original QDs and the immobilization of NPs and metals. These results indicate that QDs will accumulate in top-soils, where transformations phenomena will determine the overall transport, persistency and degradation of these chemicals. Once accumulated, they may act as a source for potentially toxic Cd and Se metal species displaying enhanced mobility.

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87-0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service "ParticlesNN" based on the trained neural network, which can be used by any researcher in the world.