Tribo-electrification of pharmaceutical powder blends

Tribocharging of granular materials and influence on their flow

Once granular materials flow, particles charge because of the triboelectric effect. When particles touch each other, charges are exchanged during contact whether they are made of the same material or not. Surprisingly, when different sizes of particles are mixed together, large particles tend to charge positively while small particles charge negatively. If the particles are relatively small (typically smaller than a millimeter), the electrostatic interaction between the particles becomes significant and leads to aggregation or sticking on the surface of the container holding them. Studying those effects is challenging as the mechanisms that govern the triboelectric effect are not fully understood yet. We show that the patch model (or mosaic model) is suitable to reproduce numerically the flow of triboelectrically charged granular materials as the specific charging of bi-disperse granular materials can be retrieved. We investigate the influence of charging on the cohesion of granular materials and highlight the relevant parameters related to the patch model that influence cohesion. Our results shed new light on the mechanisms of the triboelectric effect as well as on how the charging of granular materials influences cohesion using numerical simulations.

Assessment of Tribo-charging and Continuous Feeding Performance of Direct Compression Grades of Isomalt and Mannitol Powders

Tribo-charging is often a root cause of mass flow deviations and powder adhesion during continuous feeding. Thus, it may critically impact product quality. In this study, we characterized the volumetric (split- and pre-blend) feeding behavior and process-induced charge of two direct compression grades of polyols, galenIQ™ 721 (G721) for isomalt and PEARLITOL^® 200SD (P200SD) for mannitol, under different processing conditions. The feeding mass flow range and variability, hopper end fill level, and powder adhesion were profiled. The feeding-induced tribo-charging was measured using a Faraday cup. Both materials were comprehensively characterized for relevant powder properties, and their tribo-charging was investigated for its dependence on particle size and relative humidity. During split-feeding experiments, G721 showed a comparable feeding performance to P200SD with lower tribo-charging and adhesion to the screw outlet of the feeder. Depending on the processing condition, the charge density of G721 ranged from -0.01 up to -0.39 nC/g, and for P200SD from -3.19 up to -5.99 nC/g. Rather than differences in the particle size distribution of the two materials, their distinct surface and structural characteristics were found as the main factors affecting their tribo-charging. The good feeding performance of both polyol grades was also maintained during pre-blend feeding, where reduced tribo-charging and adhesion propensity was observed for P200SD (decreasing from -5.27 to -0.17 nC/g under the same feeding settings). Here, it is proposed that the mitigation of tribo-charging occurs due to a particle size-driven mechanism.

Single-Collision Statistics Reveal a Global Mechanism Driven by Sample History for Contact Electrification in Granular Media

Models for same-material contact electrification in granular media often rely on a local charge-driving parameter whose spatial variations lead to a stochastic origin for charge exchange. Measuring the charge transfer from individual granular spheres after contacts with substrates of the same material, we find instead a "global" charging behavior, coherent over the sample's whole surface. Cleaning and baking samples fully resets charging magnitude and direction, which indicates the underlying global parameter is not intrinsic to the material, but acquired from its history. Charging behavior is randomly and irreversibly affected by changes in relative humidity, hinting at a mechanism where adsorbates, in particular, water, are fundamental to the charge-transfer process.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

A Simplex Centroid Design to Quantify Triboelectric Charging in Pharmaceutical Mixtures

The present study focuses on the implementation of a modified simplex centroid statistical design to predict the triboelectrification phenomenon in pharmaceutical mixtures. Two drugs (Ibuprofen and Theophylline), 2 excipients (lactose monohydrate and microcrystalline cellulose/MCC), and 2 blender wall materials (aluminum and poly-methyl methacrylate) were studied to identify the trends in charge transfer in pharmaceutical blends. The statistical model confirmed the excipient-drug interactions, irrespective of the blender wall materials, as the most significant factor leading to reduced charging. Also, lactose monohydrate was able to explain the charge variability more consistently compared with MCC powders when used as secondary material. The ratio of the individual components in the blends explained almost 80% of the bulk charging for Ibuprofen mixtures and 70% for Theophylline mixtures. The study also explored the potential lack of efficacy of lactose-MCC as a combination in ternary systems when compared with binary mixtures, for impacts on charge variability in pharmaceutical blends.