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Silica materials are popular in biomedical applications as composites and drug delivery platforms due to their low toxicity and biocompatibility. Mesoporous silica nanoparticles are attractive drug delivery systems based on their porous silica framework with high surface area. In the preparation of mesoporous silica frameworks, most commonly, MCM-41, the efficient removal of the template responsible for introducing porous networks, cetyltrimethyl ammonium bromide (CTAB), is a critical step due to the template’s high toxicity in the environment and human health. In this work, we present a new one-pot approach of introducing challenging antibiotics within a silica framework without the need of toxic templates, but instead using micelle formation by an antibacterial agent. We demonstrate that micelles formed by cetylpyridinium chloride (CPC), a known antibacterial agent, entrap antibiotics such as rifampicin and ciprofloxacin. Extensive NMR studies elucidate the precise localisation of the antibiotic within the CPC micelle. Ciprofloxacin is placed between the outer and palisade region while rifampicin is located further into the hydrophobic CPC micelle core. In both cases, the formation of the silica framework can be built around the CPC-antibiotic loaded micelles. The resulting silica nanoparticles show loading of both CPC and antibiotic agents, porosity and dual antibacterial release upon disruption of the micelle within the silica framework. The design not only provides a strategy of a therapeutic design to form porous frameworks but also highlights the potential of precise antibiotic dose and release in nanoparticle systems.
The cowpea chlorotic mottle virus (CCMV) is a plant virus explored as a nanotechnological platform. The robust self-assembly mechanism of its capsid protein allows for drug encapsulation and targeted delivery. Additionally, the capsid nanoparticle can be used as a programmable platform to display different molecular moieties. In view of future applications, efficient production and purification of plant viruses are key steps. In established protocols, the need for ultracentrifugation is a significant limitation due to cost, difficult scalability, and safety issues. In addition, the purity of the final virus isolate often remains unclear. Here, an advanced protocol for the purification of the CCMV from infected plant tissue was developed, focusing on efficiency, economy, and final purity. The protocol involves precipitation with PEG 8000, followed by affinity extraction using a novel peptide aptamer. The efficiency of the protocol was validated using size exclusion chromatography, MALDI-TOF mass spectrometry, reversed-phase HPLC, and sandwich immunoassay. Furthermore, it was demonstrated that the final eluate of the affinity column is of exceptional purity (98.4%) determined by HPLC and detection at 220 nm. The scale-up of our proposed method seems to be straightforward, which opens the way to the large-scale production of such nanomaterials. This highly improved protocol may facilitate the use and implementation of plant viruses as nanotechnological platforms for in vitro and in vivo applications.
The core−shell NaYF4:Yb3+/Tm3+@NaYF4:Yb3+ upconversion nanoparticles were successfully prepared by a solvothermal method, and a layer of mesoporous silica (mSiO2) was successfully coated on the periphery of the core−shell nanoparticles to transform their surface from lipophilic to hydrophilic, further expanding their applications in biological tissues. The physical phase, morphology, structure, and fluorescence properties were characterized by X-ray diffraction (XRD), field emission transmission electron microscopy (TEM), Fourier infrared spectroscopy (FT-IR), ζ potential analysis, and fluorescence spectroscopy. It was found that the material has a hexagonal structure with good hydrophilicity and emits intense fluorescence under 980 nm pump laser excitation. The non-contact temperature sensing performance of nanoparticles was evaluated by analyzing the upconversion fluorescence of Tm3+ (1G4 → 3F4 and 3F3 → 3H6) in the temperature range of 284−344 K. The absolute and relative sensitivities were found to be 0.0067 K−1 and 1.08 % K−1, respectively, with high-temperature measurement reliability and good temperature cycling performance. More importantly, its temperature measurement in phosphate-buffered saline (PBS) solution is accurate. In addition, the temperature of the cells can be increased by adjusting the laser power density and laser irradiation time. Therefore, an optical temperature sensing platform was built to realize the application of real-time monitoring of cancer cell temperature and the dual function of photothermal therapy.
A solvothermal method was used to prepare a series of Yb3+/Tm3+/Ca2+ co-doped NaYF4 nanoparticles with different Ca2+ contents. Strong upconversion blue fluorescence could be observed under 980 nm laser excitation of the samples. The effect of different Ca2+ contents on the luminescence intensity was investigated, and it was found that the UV-vis upconversion luminescence increased and then decreased with an increasing Ca2+ concentration during the increase of the Ca2+ content from 0 mol% to 25 mol%, reaching the strongest fluorescence at 15 mol%, which was up to about 28 times stronger than that without Ca2+ doping. Furthermore, the mechanism was investigated, and it was found that the doping of Ca2+ disrupted the symmetry of the crystal field, resulting in a significant enhancement of the overall fluorescence. Applied to fluorescence intensity ratio thermometry, the absolute and relative sensitivities are as high as 0.0418 K−1 and 2.31% K−1, respectively, with a minimum temperature resolution of 0.0129 K.
Core–shell nanoparticles have attracted much attention in recent years due to their unique properties and their increasing importance in many technological and consumer products. However, the chemistry of nanoparticles is still rarely investigated in comparison to their size and morphology. In this review, the possibilities, limits, and challenges of X-ray photoelectron spectroscopy (XPS) for obtaining more insights into the composition, thickness, and homogeneity of nanoparticle coatings are discussed with four examples: CdSe/CdS quantum dots with a thick coating and a small core; NaYF4-based upconverting nanoparticles with a large Yb-doped core and a thin Er-doped coating; and two types of polymer nanoparticles with a poly(tetrafluoroethylene) core with either a poly(methyl methacrylate) or polystyrene coating. Different approaches for calculating the thickness of the coating are presented, like a simple numerical modelling or a more complex simulation of the photoelectron peaks. Additionally, modelling of the XPS background for the investigation of coating is discussed. Furthermore, the new possibilities to measure with varying excitation energies or with hard-energy X-ray sources (hard-energy X-ray photoelectron spectroscopy) are described. A discussion about the sources of uncertainty for the determination of the thickness of the coating completes this review.
The roughness as a property of core–shell (CS) microparticles plays a key role in their functionality. Quantitative evaluation of the roughness of CS microparticles is, however, a challenging task with approaches using electron microscopy images being scarce and showing pronounced differences in terms of methodology and results. This work presents a generalized method for the reliable roughness determination of nonplanar specimens such as CS particles from electron microscopic images, the method being robust and reproducible with a high accuracy. It involves a self-written software package (Python) that analyzes the recorded images, extracts corresponding data, and calculates the roughness based on the deviation of the identified contour. Images of single particles are taken by a dual mode scanning electron microscopy (SEM) setup which permits imaging of the same field-of-view of the sample with high resolution and surface sensitive in SE InLens mode as well as in transmission mode (TSEM). Herein, a new type of polystyrene core–iron oxide shell–silica shell particles is developed to serve as a set of lower micrometer-sized study objects with different surface roughness; the analysis of their images by the semiautomatic workflow is demonstrating that the particles’ profile roughness can be quantitatively obtained.
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.
Large-scale nanoimprinted metasurfaces based on silicon photonic crystal slabs were produced and coated with a NaYF4:Yb3+/Er3+ upconversion nanoparticle (UCNP) layer. UCNPs on these metasurfaces yield a more than 500-fold enhanced upconversion emission compared to UCNPs on planar surfaces. It is also demonstrated how the optical response of the UCNPs can be used to estimate the local field energy in the coating layer. Optical simulations using the finite element method validate the experimental results and the calculated spatial three-dimensional field Energy distribution helps us to understand the emission enhancement mechanism of the UCNPs closely attached to the metasurface. In addition, we analyzed the spectral shifts of the resonances for uncoated and coated metasurfaces and metasurfaces submerged in water to enable a prediction of the optimum layer thicknesses for different excitation wavelengths, paving the way to applications such as electromagnetic field sensors or bioassays.
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.