Iron (Fe) minerals play a crucial role in biogeochemical cycles due to their ubiquity in nature, high adsorption capacity and redox activity towards many other elements. Mixed-valent Fe minerals are unique since they contain Fe(II) and Fe(III). For example, magnetite (Fe(II)Fe(III)2O4) nanoparticles (MNPs) can affect the availability and mobility of nutrients and contaminants. This is due to the high surface area to volume ratio and the presence of Fe(II) and Fe(III), allowing redox transformation of (in‑)organic contaminants. Recent studies have shown that magnetite can serve as an electron source and sink for Fe(II)-oxidizing and Fe(III)-reducing microorganisms, storing and releasing electrons; thus, it functions as a biogeobattery. However, the ability of MNPs to act as biogeobatteries over consecutive redox cycles and the consequences for mineral integrity and identity remain unknown. Here, we show MNPs working as biogeobatteries in two consecutive redox cycles over 41 days. MNPs were first oxidized by the autotrophic nitrate-reducing Fe(II)-oxidizing culture KS and subsequently reduced by the Fe(III)-reducing Geobacter sulfurreducens. In addition to reduced magnetite, we identified the Fe(II) mineral vivianite after reductions, suggesting partial reductive dissolution of MNPs and re-crystallization of Fe2+ with phosphate from the growth medium. Measurements of the Fe(II)/Fe(III) ratio revealed microbial oxidation and reduction for both the first redox cycle (oxidation: 0.29±0.014, reduction: 0.75±0.023) and the second redox cycle (oxidation: 0.30±0.015, reduction: 1.64±0.10). Relative changes in magnetic susceptibility (∆κ in %) revealed greater changes for the second oxidation (–8.7±1.99%) than the first (–3.9±0.19%) but more minor changes for the second reduction (+14.29±0.39%) compared to the first (+25.42±1.31%). Our results suggest that MNPs served as biogeobatteries but became less stable over time, which has significant consequences for associated contaminants, nutrients and bioavailability for Fe-metabolizing microorganisms.

Soil aggregates consist of sand, silt, and clay size particles. Many of the clay size particles in soils are clay minerals, which actively influence soil behavior. The properties of clay minerals may change significantly as soil particle size decreases to the nanoscale; however, little information is available about these properties for the Ultisols in China. In the present study, the clay mineral components and structural characteristics of four particle-size fractions (i.e., <2000, 450–2000, 100–450, and 25–100 nm) of two Ultisol samples (Ult-1 and Ult-2) were investigated using elemental analysis, X-ray diffraction, Fouriertransform infrared spectroscopy, and thermal analysis. The molar SiO2 to Al2O3 ratios were lower in the nanoscale particle-size fraction (25–100 nm) than in the 450–2000 and <2000 nm fractions. This indicates greater desilicification and allitization of the smaller Ultisol particles. Furthermore, the Fe oxide and Al oxide contents increased and reached a maximum level in the 25–100 nm fraction of the two Ultisols. Goethite was mainly found in the 100–450 nm and 25–100 nm fractions. The dominant clay minerals in the Ultisol 25–100 nm fraction were kaolinite and illite with a small amount of a hydroxy-interlayered mineral in Ult-1 and gibbsite in Ult-2. The kaolinite crystallinity decreased as particle size decreased. The low crystallinity of the kaolinite in the A horizon 25–100 nm fraction was attributed to a reduction in the thickness of coherent scattering domains, as well as to decreases in OH groups and the dimensions of octahedral AlO6 sheets. A determination of the chemical and mineralogic properties of the different size fractions of the Ultisols is important to understand the desilicification and Al and Fe oxide enrichment mechanisms during soil formation. The significance of these results can help to reveal the nanoscale transformations of clay minerals. Analysis of clay mineral compositions in nanoparticles can provide the additional data needed to understand the adsorption and mobility of nutrients and pollutants.

One of the prominent peculiarities of nanoparticles (NPs) is their ability to cross biological barriers. Therefore, the development of NPs with different properties has great therapeutic potential in the area of reproduction because the association of drugs, hormones and other compounds with NPs represents an alternative for delivering substances directly at a specific site and for treatment of reproductive problems. Additionally, lipid-based NPs can be taken up by the tissues of patients with ovarian failure, deep endometriosis, testicular dysfunctions, etc., opening up new perspectives for the treatment of these diseases. The development of nanomaterials with specific size, shape, ligand density and charge certainly will contribute to the next generation of therapies to solve fertility problems in humans. Therefore, this review discusses the potential of NPs to treat reproductive disorders, as well as to regulate the levels of the associated hormones. The possible limitations of the clinical use of NPs are also highlighted.

The current study aimed to investigate biofortification of maize grown under different irrigation intervals, i.e. 15, 20 and 25 days (hereinafter referred to as IR15, IR20 and IR25, respectively), using foliar application treatments (silicon (Si), zinc (Zn), silver nanoparticles (AgNPs), Si + Zn, Si + AgNPs, Zn + AgNPs and Si + Zn + AgNPs) in two growing seasons, 2020 and 2021. A split-plot design with four replications was used, where irrigation intervals and foliar treatments were assigned in main plots and subplots, respectively. IR15 received a total of 7925 m3/ha irrigation water divided over seven irrigations, while IR20 received 5690 m3/ha divided over five irrigations and IR25 received 4564 m3/ha divided over four irrigations. The highest yield and grain quality were observed in plants irrigated at 15-day intervals. Spraying the canopy with Si, Zn and AgNPs, either individually or in combination, reduced the negative impact of water stress caused by longer irrigation intervals on plant growth, yield, yield components and grain protein content. In IR15 + AgNPs + Zn, most of the studied parameters, except for proline content, showed a high positive impact, especially on 100-kernel weight (KW). In contrast, IR25 + Si + AgNPs + Zn showed the highest positive effects on proline and protein contents but a negative impact on the harvest index. Collectively, IR15 + Si + AgNPs + Zn resulted in the highest values of all studied parameters, followed by IR15 + Si + AgNPs and IR15 + Si + Zn. In conclusion, our results suggest that an irrigation interval of 15 days combined with application of Si, Zn and AgNPs has the potential to improve yield and quality of maize under water deficit stress.

In the absence of government safety regulation in the field of nanotechnology, ISO standards are being used as the basis for establishing technical and management guidelines at an international level. There are more than 50 current ISO standards on nanotechnology. Some of these relate to the working environment and occupational risk management. In Latin America, entities that are members of ISO are enunciating national versions of the international standards. In this article, this context is analysed critically, starting from the Mexican standard on occupational risk management in the working environment. Even though risk management standards may guarantee better and safer working conditions, in the field of nanotechnology, they simultaneously unlock detrimental implications for workers and society. Reliance on such private and voluntary forms of industry self-regulation is identified as a by-product of global neoliberalism.

Electron tomography (ET) has gained increasing attention for the 3D characterization of nanoparticles. However, the missing wedge problem due to a limited tilt angle range is still the main challenge for accurate reconstruction in most experimental TEM setups. Advanced algorithms could in-paint or compensate to some extent the missing wedge artifacts, but cannot recover the missing structural information completely. 360° ET provides an option to solve this problem by tilting a needle-shaped specimen over the full tilt range and thus filling the missing information. However, sample preparation especially for fine powders to perform full-range ET is still challenging, thus limiting its application. In this work, we propose a new universal sample preparation method that enables the transfer of selected individual nanoparticle or a few separated nanoparticles by cutting a piece of carbon film supporting the specimen particles and mounting them onto the full-range tomography holder tip with the help of an easily prepared sharp tungsten tip. This method is demonstrated by 360° ET of Pt@TiO2 hollow cage catalyst showing high quality reconstruction without missing wedge.

Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

Analytical studies of nanoparticles (NPs) are frequently based on huge datasets derived from hyperspectral images acquired using scanning transmission electron microscopy. These large datasets require machine learning computational tools to reduce dimensionality and extract relevant information. Principal component analysis (PCA) is a commonly used procedure to reconstruct information and generate a denoised dataset; however, several open questions remain regarding the accuracy and precision of reconstructions. Here, we use experiments and simulations to test the effect of PCA processing on data obtained from AuAg alloy NPs a few nanometers wide with different compositions. This study aims to address the reliability of chemical quantification after PCA processing. Our results show that the PCA treatment mitigates the contribution of Poisson noise and leads to better quantification, indicating that denoised results may be reliable from the point of view of both uncertainty and accuracy for properly planned experiments. However, the initial data need to be of sufficient quality: these results can only be obtained if the signal-to-noise ratio of input data exceeds a minimal value to avoid the occurrence of random noise bias in the PCA reconstructions.

Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior requires processing short time exposure images which usually have severely degraded signal-to-noise ratios. The poor signal-to-noise associated with high temporal resolution makes it challenging to determine the position and intensity of atomic columns in materials undergoing structural dynamics. To address this challenge, we propose a noise-robust, processing approach based on blob detection, which has been previously established for identifying objects in images in the community of computer vision. In particular, a blob detection algorithm has been tailored to deal with noisy TEM image series from nanoparticle systems. In the presence of high noise content, our blob detection approach is demonstrated to outperform the results of other algorithms, enabling the determination of atomic column position and its intensity with a higher degree of precision.

Scanning transmission electron microscopy is a crucial tool for nanoscience, achieving sub-nanometric spatial resolution in both image and spectroscopic studies. This generates large datasets that cannot be analyzed without computational assistance. The so-called machine learning procedures can exploit redundancies and find hidden correlations. Principal component analysis (PCA) is the most popular approach to denoise data by reducing data dimensionality and extracting meaningful information; however, there are many open questions on the accuracy of reconstructions. We have used experiments and simulations to analyze the effect of PCA on quantitative chemical analysis of binary alloy (AuAg) nanoparticles using energy-dispersive X-ray spectroscopy. Our results demonstrate that it is possible to obtain very good fidelity of chemical composition distribution when the signal-to-noise ratio exceeds a certain minimal level. Accurate denoising derives from a complex interplay between redundancy (data matrix size), counting noise, and noiseless data intensity variance (associated with sample chemical composition dispersion). We have suggested several quantitative bias estimators and noise evaluation procedures to help in the analysis and design of experiments. This work demonstrates the high potential of PCA denoising, but it also highlights the limitations and pitfalls that need to be avoided to minimize artifacts and perform reliable quantification.

The oral delivery of compounds associated with diet or medication have an impact on the gut microbiota balance, which in turn, influences the physiologic process. Several reports have shown significant advances in clarifying the impact, interactions and outcomes of oral intake of nanoparticles and the human gut. These interactions may affect the bioavailability of the delivered compounds. In addition, there is a considerable breakthrough in the development of antimicrobial nanoparticles for intestinal pathogenic bacteria. Several in vitro fermentation and in vivo models have been developed throughout the years and were used to test these systems. The methodologies and studies carried out so far on the modulation of human and animal gut microbiome by oral delivery nanosized materials were reviewed. Overall, the available in vitro studies mimic the real physiological events enabling to select the best production conditions of nanoparticulate systems in a preliminary stage of research. On the other hand, animal studies can be used to access the dosage effect, safety and correlation between haematological, biochemical and symptoms, with gut microbiota groups and metabolites.

Low electron dose observation is indispensable for observing various samples using a transmission electron microscope; consequently, image processing has been used to improve transmission electron microscopy (TEM) images. To apply such image processing to in situ observations, we here apply a convolutional neural network to TEM imaging. Using a dataset that includes short-exposure images and long-exposure images, we develop a pipeline for processed short-exposure images, based on end-to-end training. The quality of images acquired with a total dose of approximately $5$ $e^{-}$ per pixel becomes comparable to that of images acquired with a total dose of approximately $1{,}000$ $e^{-}$ per pixel. Because the conversion time is approximately 8 ms, in situ observation at 125 fps is possible. This imaging technique enables in situ observation of electron-beam-sensitive specimens.

A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.

The ability to analyze nanoparticles in the atom probe has often been limited by the complexity of the sample preparation. In this work, we present a method to lift–out single nanoparticles in the scanning electron microscope. First, nanoparticles are dispersed on a lacey carbon grid, then positioned on a sharp substrate tip and coated on all sides with a metallic matrix by physical vapor deposition. Compositional and structural insights are provided for spherical gold nanoparticles and a segregation of silver and copper in silver copper oxide nanorods is shown in 3D atom maps. Using the standard atom probe reconstruction algorithm, data quality is limited by typical standard reconstruction artifacts for heterogeneous specimens (trajectory aberrations) and the choice of suitable coatings for the particles. This approach can be applied to various unsupported free-standing nanoparticles, enables preselection of particles via correlative techniques, and reliably produces well-defined structured samples. The only prerequisite is that the nanoparticles must be large enough to be manipulated, which was done for sizes down to ~50 nm.

Quantitative structural characterization of nanomaterials is important to tailor their functional properties. Corrosion of AgAu-alloy nanoparticles (NPs) results in porous structures, making them interesting for applications especially in the fields of catalysis and surface-enhanced Raman spectroscopy. For the present report, structures of dealloyed NPs were reconstructed three-dimensionally using scanning transmission electron microscopy tomography. These reconstructions were evaluated quantitatively, revealing structural information such as pore size, porosity, specific surface area, and tortuosity. Results show significant differences compared to the structure of dealloyed bulk samples and can be used as input for simulations of diffusion or mass transport processes, for example, in catalytic applications.

The quantification of the particle size and the number concentration (PNC) of nanoparticles (NPs) is key for the characterization of nanomaterials. Transmission electron microscopy (TEM) is often considered as the gold standard for assessing the size of NPs; however, the TEM sample preparation suitable for estimating the PNC based on deposited NPs is challenging. Here, we use an ultrasonic nebulizer (USN) to transfer NPs from aqueous suspensions into dried aerosols which are deposited on TEM grids in an electrostatic precipitator of an aerosol monitor. The deposition efficiency of the electrostatic precipitator was ≈2%, and the transport efficiency of the USN was ≈7%. Experiments using SiO2 NPs (50–200 nm) confirmed an even deposition of the nebulized particles in the center of the TEM grids. PNCs of the SiO2 NPs derived from TEM images underestimated the expected PNCs of the suspensions by a factor of up to three, most likely resulting from droplet coagulation and NP aggregation in the USN. Nevertheless, single particles still dominated the PNC. Our approach results in reproducible and even deposition of particles on TEM grids suitable for morphological analysis and allows an estimation of the PNC in the suspensions based on the number of particles detected by TEM.