Quantitative Analysis of Nanorod Aggregation and Morphology from Scanning Electron Micrographs Using SEMseg

d-Band Holes React at the Tips of Gold Nanorods

Reactive hot spots on plasmonic nanoparticles have attracted attention for photocatalysis as they allow for efficient catalyst design. While sharp tips have been identified as optimal features for field enhancement and hot electron generation, the locations of catalytically promising d-band holes are less clear. Here we exploit d-band hole-enhanced dissolution of gold nanorods as a model reaction to locate reactive hot spots produced from direct interband transitions, while the role of the plasmon is to follow the reaction optically in real time. Using a combination of single-particle electrochemistry and single-particle spectroscopy, we determine that d-band holes increase the rate of gold nanorod electrodissolution at their tips. While nanorods dissolve isotropically in the dark, the same nanoparticles switch to tip-enhanced dissolution upon illimitation with 488 nm light. Electron microscopy confirms that dissolution enhancement is exclusively at the tips of the nanorods, consistent with previous theoretical work that predicts the location of d-band holes. We, therefore, conclude that d-band holes drive reactions selectively at the nanorod tips.

Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy

The automated analysis of nanoparticles, imaged by scanning electron microscopy, was implemented by a deep-learning (artificial intelligence) procedure based on convolutional neural networks (CNNs). It is possible to extract quantitative information on particle size distributions and particle shapes from pseudo-three-dimensional secondary electron micrographs (SE) as well as from two-dimensional scanning transmission electron micrographs (STEM). After separation of particles from the background (segmentation), the particles were cut out from the image to be classified by their shape (e.g. sphere or cube). The segmentation ability of STEM images was considerably enhanced by introducing distance- and intensity-based pixel weight loss maps. This forced the neural network to put emphasis on areas which separate adjacent particles. Partially covered particles were recognized by training and excluded from the analysis. The separation of overlapping particles, quality control procedures to exclude agglomerates, and the computation of quantitative particle size distribution data (equivalent particle diameter, Feret diameter, circularity) were included into the routine.

Thermodynamic investigation of nanoplastic aggregation in aquatic environments

In this study, the aggregation behavior of polystyrene nanoplastics (PS NPs) in the absence or presence of oppositely charged particulate matters is systematically investigated for a wide range of electrolyte conditions. Herein, we used isothermal titration calorimetry combined with time-resolved dynamic light scattering to provide kinetic and thermodynamic insights into the NP aggregation. The thermodynamic profiles of homoaggregation and heteroaggregation were fit using an independent site and two independent sites models, respectively, demonstrating different interaction modes of both aggregation processes. We found that the contribution of solvation entropy was significant and variable in most cases, and this thermodynamic parameter was a large determinant of the thermodynamics of NP aggregation. Furthermore, the stability of PS NPs in natural water matrices was found to be correlated with ionic strength and the content of natural colloids (e.g., metal oxides and clay particles). These results point to the importance of considering the role of thermodynamic variables when studying the fate of NPs within various environmental conditions.

Measuring the order parameter of vertically aligned nanorod assemblies

Vertically aligned nanorod assemblies are of great interest both for fundamental studies of anisotropic physical properties arising from the structures and for the development of functional devices utilizing such anisotropic characteristics. Simultaneous measurement of the homeotropic order parameter (Shomeo) of assemblies in dynamic states can allow further optimization of the assembly process and the device performance. Although many techniques (e.g. birefringence measurement, SAXS analysis, and high-resolution microscopy) have been proposed to characterise Shomeo, these do not yet meet the essential criteria such as for rapid, in situ and non-destructive analyses. Here, we propose a novel approach employing a unique photoluminescence behaviour of lanthanide-doped crystalline nanorods, of which the emission spectrum contains the detailed information on the structure of the assembly. We demonstrate a rapid in situ determination of Shomeo of Eu3+-doped NaYF4 nanorods of which the orientation is controlled under an external electric field. The method does not require the consideration of polarization and can be performed using a conventional fluorescence microscopy setup. This new methodology would provide a more in-depth examination of various assembled nanostructures and the collective dynamics of their building blocks.

Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy

The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and slows down the automation of the processing chain. The Mask-RCNN is adapted here to the case study and we present results obtained on titanium dioxide samples (non-spherical particles) with a level of performance evaluated by different metrics such as the DICE coefficient, which reaches an average value of 0.95 on the test images.

Quantifying patterns in optical micrographs of one- and two-dimensional ellipsoidal particle assemblies

Current developments in colloidal science include the assembly of anisotropic colloids with broad geometric diversity. As the complexity of particle assemblies increases, the need for ubiquitous algorithms that quantitatively analyze images of the assemblies to deliver key information such as quantification of crystal structures becomes more urgent. This contribution describes algorithms capable of image analysis for classifying colloidal structures based on abstracted interparticle relationship information and quantitatively analyzing the abundance of each structure in mixed pattern assemblies. The algorithm parameters can be adjusted, allowing for the algorithms to be adapted for different image analyses. Three different ellipsoidal particle assembly images are presented to demonstrate the effectiveness of the algorithms: a one-dimensional (1D) particle chain assembly and two two-dimensional (2D) polymorphic crystals each consisting of assemblies of two distinct plane symmetry groups. Angle relationships between neighbouring particles are calculated and neighbour counts of each particle are determined. Combining these two parameters as rules for classification criteria allows for the labeling and quantification of each particle into a defined symmetry class within an assembly. The algorithms provide a labelled image comprising classification results and particle counts of each defined class. For multiple images or individual frames from a video, the script can be looped to achieve automatic processing. The yielded classification data allow for more in-depth image analysis of mixed pattern particle assemblies. We envision that these algorithms will have utility in quantitative analysis of images comprising ellipsoidal colloidal materials, nanoparticles, or biological matter.