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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.
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.
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.