Automated quantitative image analysis of nanoparticle assembly

Cryogenic electron tomography to determine thermodynamic quantities for nanoparticle dispersions

Here we present a method to extract thermodynamic quantities for nanoparticle dispersions in solvents. The method is based on the study of tomograms obtained from cryogenic electron tomography (cryoET). The approach is demonstrated for gold nanoparticles (diameter < 5 nm). Tomograms are reconstructed from tilt-series 2D images. Once the three-dimensional (3D) coordinates for the centres of mass of all of the particles in the sample are determined, we calculate the pair distribution function g(r) and the potential of mean force U(r) without any assumption. Importantly, we show that further quantitative information from 3D tomograms is readily available as the spatial fluctuation in the particles' position can be efficiently determined. This in turn allows for the prompt derivation of the Kirkwood-Buff integrals with all their associated quantities such as the second virial coefficient. Finally, the structure factor and the agglomeration states of the particles are evaluated directly. These thermodynamic quantities provide key insights into the dispersion properties of the particles. The method works well both for dispersed systems containing isolated particles and for systems with varying degrees of agglomerations.

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.

Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.

Development, Characterization and Use of Liposomes as Amphipathic Transporters of Bioactive Compounds for Melanoma Treatment and Reduction of Skin Inflammation: A Review

The skin is the largest organ in the human body, providing a barrier to the external environment. It is composed of three layers: epidermis, dermis and hypodermis. The most external epidermis is exposed to stress factors that may lead to skin conditions such as photo-aging and skin cancer. Some treatments for skin disease utilize the incorporation of drugs or bioactive compounds into nanocarriers known as liposomes. Liposomes are membranes whose sizes range from nano to micrometers and are composed mostly of phospholipids and cholesterol, forming similar structures to cell membranes. Thus, skin treatments with liposomes have lower toxicity in comparison to traditional treatment routes such as parenteral and oral. Furthermore, addition of edge activators to the liposomes decreases the rigidity of the bilayer structure making it deformable, thereby improving skin permeability. Liposomes are composed of an aqueous core and a lipidic bilayer, which confers their amphiphilic property. Thus, they can carry hydrophobic and hydrophilic compounds, even simultaneously. Current applications of these nanocarriers are mainly in the cosmetic and pharmaceutic industries. Nevertheless, new research has revealed promising results regarding the effectiveness of liposomes for transporting bioactive compounds through the skin. Liposomes have been well studied; however, additional research is needed on the efficacy of liposomes loaded with bioactive peptides for skin delivery. The objective of this review is to provide an up-to-date description of existing techniques for the development of liposomes and their use as transporters of bioactive compounds in skin conditions such as melanoma and skin inflammation. Furthermore, to gain an understanding of the behavior of liposomes during the process of skin delivery of bioactive compounds into skin cells.

Kinetically assembled binary nanoparticle networks

Embedding percolating networks of nanoparticles (NPs) within polymers is a promising approach for mechanically reinforcing polymers and for introducing novel electronic, transport, and catalytic properties into otherwise inert polymers. While such networks may be obtained through kinetic assembly of unary system of NPs, the ensuing structures exhibit limited morphologies. Here, we investigate the possibility of increasing the diversity of NP networks through kinetic assembly of multiple species of NPs. Using lattice Monte Carlo simulations we show that networks obtained from co-assembly of two NP species of different sizes exhibit significantly more diverse morphology than those assembled from a single species. In particular, we achieved considerable variations in the particle spatial distribution, proportions of intra- and interspecies contacts, fractal dimension, and pore sizes of the networks by simply modulating the stoichiometry of the two species and their intra and inter-species affinities. We classified these distinct morphologies into "integrated", "coated", "leaved", and "blocked" phases, and provide relevant phase diagrams for achieving them. Our findings are relevant to controlled and predictable assembly of particle networks for creating multifunctional composites with improved properties.

Nanoparticle Characterization: What to Measure?

What to measure? is a key question in nanoscience, and it is not straightforward to address as different physicochemical properties define a nanoparticle sample. Most prominent among these properties are size, shape, surface charge, and porosity. Today researchers have an unprecedented variety of measurement techniques at their disposal to assign precise numerical values to those parameters. However, methods based on different physical principles probe different aspects, not only of the particles themselves, but also of their preparation history and their environment at the time of measurement. Understanding these connections can be of great value for interpreting characterization results and ultimately controlling the nanoparticle structure-function relationship. Here, the current techniques that enable the precise measurement of these fundamental nanoparticle properties are presented and their practical advantages and disadvantages are discussed. Some recommendations of how the physicochemical parameters of nanoparticles should be investigated and how to fully characterize these properties in different environments according to the intended nanoparticle use are proposed. The intention is to improve comparability of nanoparticle properties and performance to ensure the successful transfer of scientific knowledge to industrial real-world applications.

Interfacial Assembly of Tunable Anisotropic Nanoparticle Architectures

We propose a strategy for assembling spherical nanoparticles (NPs) into anisotropic architectures in a polymer matrix. The approach takes advantage of the interfacial tension between two mutually immiscible polymers forming a bilayer and differences in the compatibility of the two polymer layers with polymer grafts on particles to trap NPs within two-dimensional planes parallel to the interface. The ability to precisely tune the location of the entrapment planes via the NP grafting density, and to trap multiple interacting particles within distinct planes, can then be used to assemble NPs into unconventional arrangements near the interface. We carry out molecular dynamics simulations of polymer-grafted NPs in a polymer bilayer to demonstrate the viability of the proposed approach in both trapping NPs at tunable distances from the interface and assembling them into a variety of unusual nanostructures. We illustrate the assembly of NP clusters, such as dimers with tunable tilt relative to the interface and trimers with tunable bending angle, as well as anisotropic macroscopic phases, including serpentine and branched structures, ridged hexagonal monolayers, and square-ordered bilayers. We also develop a theoretical model to predict the preferred positions and free energies of NPs trapped at or near the interface that could help guide the design of polymer-grafted NPs for achieving target NP architectures. Overall, this work suggests that interfacial assembly of NPs could be a promising approach for fabricating next-generation polymer nanocomposites with potential applications in plasmonics, electronics, optics, and catalysis where precise arrangement of polymer-embedded NPs is required for function.

Nanoparticle self-assembly: from interactions in suspension to polymer nanocomposites

Recent experimental results using in particular small-angle scattering to characterize the self-assembly of mainly hard spherical nanoparticles into higher ordered structures ranging from fractal aggregates to ordered assemblies are reviewed. The crucial control of interparticle interactions is discussed, from chemical surface-modification, or the action of additives like depletion agents, to the generation of directional patches and the use of external fields. It is shown how the properties of interparticle interactions have been used to allow inducing and possibly controlling aggregation, opening the road to the generation of colloidal molecules or potentially metamaterials. In the last part, studies of the microstructure of polymer nanocomposites as an application of volume-spanning and stress-carrying aggregates are discussed.

Quenching of fully symmetric mixtures of oppositely charged microgels: the role of soft stiffness

Using molecular dynamics simulations, we investigate the self-assembly of a coarse-grained binary system of oppositely charged microgels, symmetric in size and concentration. The microgel pair interactions are described by an effective pair potential which implicitly accounts for the averaged ionic contributions, in addition to a short-range elastic repulsion that accounts for the overlapping of the polymer chains, the latter being described by the Hertzian interaction. Particular emphasis is placed on the role played by the strength of the soft repulsive interaction on the resulting particle aggregation. It is found that the possibility of particle inter-penetration in oppositely charged soft particles results in a much wider variety of cluster morphologies in comparison with their hard-spheres counterparts. Specifically, the softness of the steric interactions enhances the competition between repulsive and attractive electrostatic interactions, leading to the formation of aggregates that are comprised of strongly bounded charged particles displaying a low degree of charge ordering.

Engineered Nanomaterials: Their Physicochemical Characteristics and How to Measure Them

Numerous types of engineered nanomaterials (ENMs) are commercially available and developments move towards producing more advanced nanomaterials with tailored properties. Such advanced nanomaterials may include chemically doped or modified derivatives with specific surface chemistries; also called higher generation or multiconstituent nanomaterials. To fully enjoy the benefits of nanomaterials, appropriate characterisation of ENMs is necessary for many aspects of their production, use, testing and reporting to regulatory bodies. This chapter introduces both structural and textural properties of nanomaterials with a focus on demonstrating the information that can be achieved by analysis of primary physicochemical characteristics and how such information is critical to understand or assess the possible toxicity of engineered nanomaterials. Many of characterization methods are very specific to obtain particular characteristics and therefore the most widely used techniques are explained and demonstrated.

Automated quantification of one-dimensional nanostructure alignment on surfaces

A method for automated quantification of the alignment of one-dimensional (1D) nanostructures from microscopy imaging is presented. Nanostructure alignment metrics are formulated and shown to be able to rigorously quantify the orientational order of nanostructures within a two-dimensional domain (surface). A complementary image processing method is also presented which enables robust processing of microscopy images where overlapping nanostructures might be present. Scanning electron microscopy (SEM) images of nanowire-covered surfaces are analyzed using the presented methods and it is shown that past single parameter alignment metrics are insufficient for highly aligned domains. Through the use of multiple parameter alignment metrics, automated quantitative analysis of SEM images is shown to be possible and the alignment characteristics of different samples are able to be quantitatively compared using a similarity metric. The results of this work provide researchers in nanoscience and nanotechnology with a rigorous method for the determination of structure/property relationships, where alignment of 1D nanostructures is significant.

Dynamics of nanoparticle assembly from disjointed images of nanoparticle-polymer composites

Understanding how nanoparticles (NPs) diffuse, stick, and assemble into larger structures within polymers is key to the design and fabrication of NP-polymer composites. Here we describe an approach for inferring the dynamic parameters of NP assembly from spatially and temporally disjointed images of composites. The approach involves iterative adjustment of the parameters of a kinetic model of assembly until the computed size statistics of NP clusters match those obtained from high-throughput analysis of the experimental images. Application of this approach to the assembly of shaped, metal NPs in polymer films suggests that NP structures grow via a cluster-cluster aggregation mechanism, where NPs and their clusters diffuse with approximately Stokes-Einstein diffusivity and stick to other NPs or clusters with a probability that depends strongly on the size and shape of the NPs and the molecular weight of the polymer.

High-Throughput, Algorithmic Determination of Nanoparticle Structure from Electron Microscopy Images

Electron microscopy (EM) represents the most powerful tool to directly characterize the structure of individual nanoparticles. Accurate descriptions of nanoparticle populations with EM, however, are currently limited by the lack of tools to quantitatively analyze populations in a high-throughput manner. Herein, we report a computational method to algorithmically analyze EM images that allows for the first automated structural quantification of heterogeneous nanostructure populations, with species that differ in both size and shape. This allows one to accurately describe nanoscale structure at the bulk level, analogous to ensemble measurements with individual particle resolution. With our described EM protocol and our inclusion of freely available code for our algorithmic analysis, we aim to standardize EM characterization of nanostructure populations to increase reproducibility, objectivity, and throughput in measurements. We believe this work will have significant implications in diverse research areas involving nanomaterials, including, but not limited to, fundamental studies of structural control in nanoparticle synthesis, nanomaterial-based therapeutics and diagnostics, optoelectronics, and catalysis.

Computationally Guided Assembly of Oriented Nanocubes by Modulating Grafted Polymer-Surface Interactions

The bottom-up fabrication of ordered and oriented colloidal nanoparticle assemblies is critical for engineering functional nanomaterials beyond conventional polymer-particle composites. Here, we probe the influence of polymer surface ligands on the self-orientation of shaped metal nanoparticles for the formation of nanojunctions. We examine how polymer graft-surface interactions dictate Ag nanocube orientation into either edge-edge or face-face nanojunctions. Specifically, we investigate the effect of end-functionalized polymer grafts on nanocube assembly outcomes, such as interparticle angle and interparticle distance. Our assembly results can be directly mapped onto our theoretical phase diagrams for nanocube orientation, enabling correlation of experimental variables (such as graft length and metal binding strength) with computational parameters. These results represent an important step toward unifying modeling and experimental approaches to understanding nanoparticle-polymer self-assembly.