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We studied the dissolution behavior of β NaYF4:Yb(20%), Er(2%) UCNP of two different sizes in biologically relevant media i.e., water (neutral pH), phosphate buffered saline (PBS), and Dulbecco’s modified Eagle medium (DMEM) at different temperatures and particle concentrations. Special emphasis was dedicated to assess the influence of different surface functionalizations, particularly the potential of mesoporous and microporous silica shells of different thicknesses for UCNP stabilization and protection. Dissolution was quantified electrochemically using a fluoride ion selective electrode (ISE) and by inductively coupled plasma optical emission spectrometry (ICP OES). In addition, dissolution was monitored fluorometrically. These experiments revealed that a thick microporous silica shell drastically decreased dissolution. Our results also underline the critical influence of the chemical composition of the aqueous environment on UCNP dissolution. In DMEM, we observed the formation of a layer of adsorbed molecules on the UCNP surface that protected the UCNP from dissolution and enhanced their fluorescence. Examination of this layer by X ray photoelectron spectroscopy (XPS) and mass spectrometry (MS) suggested that mainly phenylalanine, lysine, and glucose are adsorbed from DMEM. These findings should be considered in the future for cellular toxicity studies with UCNP and other nanoparticles and the design of new biocompatible surface coatings.
Photon upconversion nanomaterials have a wide range of applications, including biosensing and deep-tissue imaging. Their typically very weak and narrow absorption bands together with their size dependent luminescence efficiency can limit their application potential. This has been addressed by increasingly sophisticated core-shell particle architectures including the sensitization with organic dyes that strongly absorb in the near infrared (NIR). In this work, we present a simple water-dispersible micellar system that features energy transfer from the novel NIR excitable dye, 1859 SL with a high molar absorption coefficient and a moderate fluorescence quantum yield to oleate-capped NaYF4:20%Yb(III), 2%Er(III) upconversion nanoparticles (UCNP) upon 808 nm excitation. The micelles were formed using the surfactants Pluronic F-127 and Tween 80 to produce a hydrophilic dye-UCNP system. Successful energy transfer from the dye to the UCNP could be confirmed by emission measurements that revealed the occurrence of upconversion emission upon excitation at 808 nm and an enhancement of the green Er(III) emission compared to direct Er(III) excitation at 808 nm.
Gold-shell coated NaYF4:Er3+, Yb3+ nanoparticles for the enhancement of fluorescence emission
(2018)
In the present work, we aim to explore how far the UCNP emission intensity can be enhanced by the aid of plasmonic interactions using a gold shell. The distance between the UCNP core and the gold shell is varied by adding a silica spacer of different thicknesses.
The synthetic conditions for obtaining UCNP@SiO2@Au core-shell nanoparticles with precisely tuneable silica shell thicknesses were investigated. A gold shell on the UCNP@SiO2 nanoparticles is expected to give rise to a noticeable enhancement of particle brightness and fluorescence, given that the thicknesses of the silica shell and the gold coating can be controlled and fine-tuned. First single particle studies revealing shortening of the Er3+ lifetimes suggest that plasmonic enhancement occurs.
The synthesis and characterization of a chemiluminescent metal–organic framework with high porosity is reported. It consists of Zr6O6(OH)4 nodes connected by 4,4′‐(anthracene‐9,10‐diyl)dibenzoate as the linker and luminophore. It shows the topology known for UiO‐66 and is therefore denoted PAP‐UiO. The MOF was not only obtained as bulk material but also as a thin film. Exposure of PAP‐UiO as bulk or film to a mixture of bis‐(2,4,6‐trichlorophenyl) oxalate, hydrogen peroxide, and sodium salicylate in a mixture of dimethyl and dibutyl phthalate evoked strong and long lasting chemiluminescence of the PAP‐UiO crystals. Time dependent fluorescence spectroscopy on bulk PAP‐UiO and, for comparison, on dimethyl 4,4′‐(anthracene‐9,10‐diyl)dibenzoate provided evidence that the chemiluminescence originates from luminophores being part of the PAP‐UiO, including the luminophores inside the crystals.
Gold-shell coated NaYF4:Er3+, Yb3+ nanoparticles for the enhancement of fluorescence emission
(2018)
In the present work, we aim to explore how far the UCNP emission intensity can be enhanced by the aid of plasmonic interactions using a gold shell. The distance between the UCNP core and the gold shell is varied by adding a silica spacer of different thicknesses.
The synthetic conditions for obtaining UCNP@SiO2@Au core-shell nanoparticles with precisely tuneable silica shell thicknesses were investigated. A gold shell on the UCNP@SiO2 nanoparticles is expected to give rise to a noticeable enhancement of particle brightness and fluorescence, given that the thicknesses of the silica shell and the gold coating can be controlled and fine-tuned. First single particle studies revealing shortening of the Er3+ lifetimes suggest that plasmonic enhancement occurs.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
Organic and inorganic micro- and nanoparticles are increasingly used as drug carriers, fluorescent sensors, and multimodal labels in the life and material sciences. Typically, these applications require further functionalization of the particles with, e.g., antifouling ligands, targeting bioligands, stimuli-responjsive caps, or sensor molecules. Besides serving as an anchor point for subsequent functionalization, the surface chemistry of these particles also fundamentally influences their interaction with the surrounding medium and can have a significant effect on colloidal stability, particle uptake, biodistribution, and particle toxicity in biological systems. Moreover, functional groups enable size control and tuning of the surface during the synthesis of particle systems.
For these reasons, a precise knowledge of the chemical nature, the total number of surface groups, and the number of groups on the particle surface that are accessible for further functionalization is highly important. In this contribution, we will will discuss the advantages and limitiations of different approaches to quantify the amount of commonly used surface functional groups such as amino,[1,2] carboxy,[1,2] and aldehyde groups.[3] Preferably, the quantification is carried out using sensitive and fast photometric or fluorometric assays, which can be read out with simple, inexpensive instrumentation and can be validated by complimentary analytic techniques such as ICP-OES and quantitative NMR.