Quantifying surface properties of silica particles by combining Hansen Parameters and Reichardt's Dye indicator data

To obtain quantitative understanding of the effects of a chemisorbed organic modification on the surface of particles, the use of Reichardt's dye (RD) and Hansen solubility parameter (HSP) is discussed, whereby the S should be understood in terms of “similarity” rather than solubility as dispersibility is in focus. Silica nanoparticles modified to different extents with a medium chain silane including completely hydrophilic and hydrophobic particles are chosen. During spray‐drying such particles form fully redispersible micro‐raspberry superstructures. After qualitative estimations of the particles' polarity based on measuring both immersion time and ability of modified particles to stabilize oil–water emulsions, surface properties are quantified by HSP and RD. With increasing hydrophobicity, i.e., increasing amount of silane at the surface, all three contributions to HSP change. At the same time, RD analysis reveals that the normalized solvent polarity parameter decreases progressively. HSP and RD analysis are in good agreement, giving strong confidence on each method applied individually. This work demonstrates that after noticeable attempts for combined solubility parameters in case of molecules, carbon allotropes, and gelators, such studies can be extended toward functional (nano)particles and that a full picture of particle surface properties is possible via the combination of different, quantitative techniques

Exhibitive Nano-to-Micron Scale Sedimentation Dynamics of Colloidal Formulations Through Direct Visualization

The study of sedimentation behavior of nanoparticle dispersions is important for revealing particle size and colloidal stability characteristics. Quantitative appraisal of real-world colloidal systems in their native state, is key for replacing prevailing empiricism in formulation science by knowledge-based design. Herein, we choose fuel cell inks as one case-example amongst many other possibilities to present a new visualization technique, called Transmittogram. This technique readily depicts the time-resolved settling behavior of solid-liquid dispersions, measured by analytical centrifugation (AC). Although AC enables the causal examination of agglomeration, settling, and creaming behavior of dispersions, along with its consequent effect on structure formation and product properties, the understanding of the main transmission readout is often non-intuitive and complex. Transmittograms are, therefore, the missing link for straightforward data interpretation. First, we illustrate the utility of transmittogram analysis using model silica nanoparticle systems and further validate it against known characteristics of the system. Then, we demonstrate the application of transmittograms to characterize fuel cell inks, showing the strength of the approach in deconvoluting and distilling information to the reader. Finally, we discuss the potential of the technique for routine analysis using analytical centrifugation.<br /

From In Situ Characterization to Process Control of Quantum Dot Systems

AbstractQuantum confined semiconductor nanoparticles (quantum dots, QDs) are promising candidates for various applications in emerging fields like electronics, solar cells, sensors and diagnostics. However, a larger scale production of QDs at high product quality is still missing. One of the key requirements to address this issue in the near future was identified to be a fast and in situ applicable characterization method. Suitable characterization requires knowledge on the full shape of the particle size distributions (PSDs) under investigation. Thus, determination of a mean particle size together with the width of the PSD is not sufficient. In the following, a method will be presented that allows the derivation of arbitrary shaped PSDs for QDs with direct band gap based on their optical absorbance spectra. After validation of the technique by means of ZnO nanoparticles the transfer of the concept to other QD materials like PbS and PbSe will be proven. Therefore we will extend our methodology and show how our approach can be used to derive spectral properties like the size dependent band gap energy. This is realized by proper calibration of the calculation results against PSDs determined by an independent analysis technique like transmission electron microscopy (TEM)

Accelerating CO2 electrochemical conversion towards industrial implementation

The electrochemical conversion of carbon dioxide by means of renewable electricity holds great promise. However, despite significant progress in current literature, there remains a significant gap between fundamental research and the industrial demands to establish new disruptive technologies in real world applications. This gap primarily arises from a mismatch between performance parameters and requirements in both areas, leading to significant challenges in technology transfer. We herein suggest pathways to bridge this gap and outline current limitations in the field, proposing key parameters and procedures towards accelerated and streamlined technology development

Towards a Framework for Evaluating and Reporting Hansen Solubility Parameters: Applications to Nano and Micron Scale Particle Dispersions

A thorough understanding of complex interactions within particulate systems is a key for knowledge-based formulations. Hansen solubility parameters (HSP) are widely used to assess the compatibility of the dispersed phase with the continuous phase. At present, the determination of HSP is often based on a liquid ranking list obtained by evaluating a pertinent dispersion parameter using only one pre-selected characterization method. Furthermore, one cannot rule out the possibility of subjective judgment especially for liquids for which it is difficult to decipher the compatibility or underlying interactions. As a result, the end value of HSP might be of little or no information. To overcome these issues, we introduce a generalized technology-agnostic combinatorics-based approach. We discuss the principles of the approach and the implications of evaluating and reporting particle HSP values. We demonstrate the approach by using SiNx particles synthesized in the gas phase. We leverage the analytical centrifugation data to evaluate stability trajectories of SiNx dispersions in various liquids to deduce particle-liquid compatibility. </p

The effect of mixing on silver particle morphology in flow synthesis

Silver particles, prepared in a T-mixer under different flow rates, were selected to study the influences of mixing on particle shape evolution. Mixing effects on particle growth were visualized by scanning electron microscopy (SEM) and analysed quantitatively by sedimentation coefficient distributions derived from analytical centrifugation (AC). The mixing time under different flow rates was determined by the Villermaux-Dushman method to quantify the mixing quality. Based on the finding of a mixing-induced shape transformation from plates to dendrites, an extended growth mechanism involving mixing effects was proposed. Slow mixing leads to a non-uniform distributed reactant mixture and low effective supersaturation. This causes preferential growth of high-energy facets resulting in plate-like particles with broad, multimodal sedimentation distributions. In contrast, fast mixing, corresponding to uniform reactant mixture and thus high effective supersaturation and nucleation rate, leads to dendritic products, and narrow but bimodal sedimentation distributions. (C) 2018 Elsevier Ltd. All rights reserved.</p

New possibilities of accurate particle characterisation by applying direct boundary models to analytical centrifugation

Analytical centrifugation (AC) is a powerful technique for the characterisation of nanoparticles in colloidal systems. As a direct and absolute technique it requires no calibration or measurements of standards. Moreover, it offers simple experimental design and handling, high sample throughput as well as moderate investment costs. However, the full potential of AC for nanoparticle size analysis requires the development of powerful data analysis techniques. In this study we show how the application of direct boundary models to AC data opens up new possibilities in particle characterisation. An accurate analysis method, successfully applied to sedimentation data obtained by analytical ultracentrifugation (AUC) in the past, was used for the first time in analysing AC data. Unlike traditional data evaluation routines for AC using a designated number of radial positions or scans, direct boundary models consider the complete sedimentation boundary, which results in significantly better statistics. We demonstrate that meniscus fitting, as well as the correction of radius and time invariant noise significantly improves the signal-to-noise ratio and prevents the occurrence of false positives due to optical artefacts. Moreover, hydrodynamic non-ideality can be assessed by the residuals obtained from the analysis. The sedimentation coefficient distributions obtained by AC are in excellent agreement with the results from AUC. Brownian dynamics simulations were used to generate numerical sedimentation data to study the influence of diffusion on the obtained distributions. Our approach is further validated using polystyrene and silica nanoparticles. In particular, we demonstrate the strength of AC for analysing multimodal distributions by means of gold nanoparticles

On the State and Stability of Fuel Cell Catalyst Inks

Catalyst layers (CL), as an active component of the catalyst coated membrane (CCM), form the heart of the proton electrolyte membrane fuel cell (PEMFC). For optimum performance of the fuel cell, obtaining suitable structural and functional characteristics for the CL is crucial. Direct tuning of the microstructure and morphology of the CL is non-trivial; hence catalyst inks as CL precursors need to be modulated, which are then applied onto a membrane to form the CCM. Obtaining favorable dispersion characteristics forms an important prerequisite in engineering catalyst inks for large scale manufacturing. In order to facilitate a knowledge-based approach for developing fuel cell inks, this work introduces new tools and methods to study both the dispersion state and stability characteristics, simultaneously. Catalyst inks were prepared using different processing methods, which include stirring and ultrasonication. The proposed tools are used to characterize and elucidate the effects of the processing method. Structural characterization of the dispersed particles and their assemblages was carried out by means of transmission electron microscopy. Analytical centrifugation (AC) was used to study the state and stability of the inks. Herein, we introduce new concepts, S score, and stability trajectory, for a time-resolved assessment of inks in their native state using AC. The findings were validated and rationalized using transmittograms as a direct visualization technique. The flowability of inks was investigated by rheological measurements. It was found that probe sonication only up to an optimum amplitude leads to a highly stable colloidal ink.</p

2D analysis of polydisperse core–shell nanoparticles using analytical ultracentrifugation

Accepted author manuscriptAccurate knowledge of the size, density and composition of nanoparticles (NPs) is of major importance for their applications. In this work the hydrodynamic characterization of polydisperse core–shell NPs by means of analytical ultracentrifugation (AUC) is addressed. AUC is one of the most accurate techniques for the characterization of NPs in the liquid phase because it can resolve particle size distributions (PSDs) with unrivaled resolution and detail. Small NPs have to be considered as core–shell systems when dispersed in a liquid since a solvation layer and a stabilizer shell will significantly contribute to the particle's hydrodynamic diameter and effective density. AUC measures the sedimentation and diffusion transport of the analytes, which are affected by the core–shell compositional properties. This work demonstrates that polydisperse and thus widely distributed NPs pose significant challenges for current state-of-the-art data evaluation methods. The existing methods either have insufficient resolution or do not correctly reproduce the core–shell properties. First, we investigate the performance of different data evaluation models by means of simulated data. Then, we propose a new methodology to address the core–shell properties of NPs. This method is based on the parametrically constrained spectrum analysis and offers complete access to the size and effective density of polydisperse NPs. Our study is complemented using experimental data derived for ZnO and CuInS2 NPs, which do not have a monodisperse PSD. For the first time, the size and effective density of such structures could be resolved with high resolution by means of a two-dimensional AUC analysis approach.Ye