In this work, we outlined an experimental workflow enabling the rapid assessment of primary and secondary nucleation and crystal growth kinetics. We used small-scale experiments in agitated vials with in situ imaging for crystal counting and sizing to quantify nucleation and growth kinetics of α-glycine in aqueous solutions as a function of supersaturation at isothermal conditions. Seeded experiments were required to assess crystallization kinetics when primary nucleation is too slow, especially at lower supersaturations often encountered in continuous crystallization processes. At higher supersaturations, we compared results from seeded and unseeded experiments and carefully analyzed interdependencies of primary and secondary nucleation and growth kinetics. This approach allows for the rapid estimation of absolute values of primary and secondary nucleation and growth rates without relying on any specific assumptions about functional forms of corresponding rate expressions used for estimation approaches based on fitting population balance models. Quantitative relationships between nucleation and growth rates at given conditions provide useful insights into crystallization behavior and can be explored to rationally manipulate crystallization conditions for achieving desirable outcomes in batch or continuous crystallization processes.

This paper presents a comprehensive exploration of data quality terminology, revealing a significant lack of standardisation in the field. The goal of this work was to conduct a comparative analysis of data quality terminology across different domains and structure it into a hierarchical data model. We propose a novel approach for aggregating disparate data quality terms used to describe the multiple facets of data quality under common umbrella terms with a focus on the ISO 25012 standard. We introduce four additional data quality dimensions: governance, usefulness, quantity, and semantics. These dimensions enhance specificity, complementing the framework established by the ISO 25012 standard, as well as contribute to a broad understanding of data quality aspects. The ISO 25012 standard, a general standard for managing the data quality in information systems, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of digital systems across a multitude of domains. In contrast, frameworks such as ALCOA+, which were originally developed for specific regulated industries, can be applied more broadly but may not always be generalisable. Ultimately, the model we propose aggregates and classifies data quality terminology, facilitating seamless communication of the data quality between different domains when collaboration is required to tackle cross-domain projects or challenges. By establishing this hierarchical model, we aim to improve understanding and implementation of data quality practices, thereby addressing critical issues in various domains.

This study reviews various data quality frameworks that have some form of regulatory backing. The aim is to identify how these frameworks define, measure, and apply data quality dimensions. This review identified generalisable frameworks, such as TDQM, ISO 8000, and ISO 25012, and specialised frameworks, such as IMF’s DQAF, BCBS 239, WHO’s DQA, and ALCOA+. A standardised data quality model was employed to map the dimensions of the data from each framework to a common vocabulary. This mapping enabled a gap analysis that highlights the presence or absence of specific data quality dimensions across the examined frameworks. The analysis revealed that core data quality dimensions such as “accuracy”, “completeness”, “consistency”, and “timeliness” are equally and well represented across all frameworks. In contrast, dimensions such as “semantics” and “quantity” were found to be overlooked by most frameworks, despite their growing impact for data practitioners as tools such as knowledge graphs become more common. Frameworks tailored to specific domains were also found to include fewer overall data quality dimensions but contained dimensions that were absent from more general frameworks, highlighting the need for a standardised approach that incorporates both established and emerging data quality dimensions. This work condenses information on commonly used and regulation-backed data quality frameworks, allowing practitioners to develop tools and applications to apply these frameworks that are compliant with standards and regulations. The bibliometric analysis from this review emphasises the importance of adopting a comprehensive quality framework to enhance governance, ensure regulatory compliance, and improve decision-making processes in data-rich environments.

Diffusion controls local concentration profiles at interfaces between segregated fluid elements during mixing processes. This is important for antisolvent crystallization, where it is intuitively argued that local concentration profiles at interfaces between solution and antisolvent fluid elements can result in significant supersaturation overshoots over and above that at the final mixture composition, leading to poorly controlled nucleation. Previous work on modeling diffusive mixing in antisolvent crystallization has relied on Fickian diffusion, where concentration gradients are the driving force for diffusion. This predicts large overshoots in the supersaturation at interfaces between solution and antisolvent, as is often intuitively expected. However, chemical potential gradients provide a more physically realistic driving force for diffusion, and in highly nonideal solutions, such as those in antisolvent crystallization, this leads to nonintuitive behavior. In particular, as solute diffusion toward antisolvent is severely hindered, it can diffuse against its concentration gradient away from antisolvent. We apply thermodynamically consistent diffusion model based on the multicomponent Maxwell–Stefan formulation to examine diffusive mixing in a nonideal antisolvent crystallization system. Large supersaturation overshoots above that at the final mixture composition are not found when a thermodynamically consistent approach is used, demonstrating that these overshoots are modeling artifacts and are not expected to be present in physical systems. In addition, for certain conditions, localized liquid–liquid spinodal demixing is predicted to occur during the diffusive mixing process, even when the final mixture composition is outside the liquid–liquid phase separation region. Intermittent spinodal demixing driven by diffusive mixing may provide a novel explanation for differences of nucleation behaviors among various antisolvents.

A better understanding of mixing times for mixed solvent systems in laboratory-scale vessels is crucial for improving mixing-sensitive processes such as antisolvent crystallisation. Whilst mixing in agitated vessels has been extensively studied using solutions of additives in the same solvent, there is very limited literature on the mixing of different miscible solvents and none which would be relevant to antisolvent crystallisation processes. In this work, the mixing times of water–ethanol systems in a 1 litre vessel, agitated by a pitched blade impeller with probes used as baffles, were investigated in the transitional flow regime using both experimental and computational fluid dynamics (CFD) approaches. We studied two scenarios: adding sodium chloride tracer to premixed water–ethanol solutions and adding ethanol containing a tracer to water. Mixing was investigated experimentally through conductivity measurements and computationally using large eddy simulations with the M-Star CFD software package. Empirical correlations from the Dynochem engineering toolbox were also used for comparison. The results showed significant run-to-run variability in the mixing times from both experiments and CFD simulations, with experimental ranges being notably wider than CFD ones under the given conditions. While the CFD simulations showed consistent mixing times across different solvent compositions, the experimental mixing times decreased with increasing ethanol content. The mixing times were approximately inversely proportional to the impeller speed. The CFD simulations indicated that 25–40 impeller rotations were required for homogenisation, while the experiments needed 25–100 rotations. The Dynochem correlations predicted 40 rotations, independent of speed, but could not capture the inherent variability of the mixing times.

This paper presents a comprehensive exploration of Data Quality terminology, revealing a significant lack of standardisation in the field. We propose a novel approach to aggregating disparate Data Quality terms used to describe the multiple facets of Data Quality, under common umbrella terms, with a focus on the ISO 25012 standard. Our aim is to design a Data Quality Data Model that serves as a universally applicable framework for Data Quality assessment. We introduce four additional Data Quality dimensions: Governance, Usefulness, Quantity, and Semantics, enhancing specificity, complementing the framework established by the ISO 25012 standard, and understanding of Data Quality aspects. The ISO 25012 standard, while tailored for software development, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of software development across a multitude of domains. In contrast, frameworks like ALCOA+ that are specific to certain domains lack the ability to be generalised. The model we propose can be seen as a "Rosetta Stone" for Data Quality terminology, facilitating a seamless communication of Data Quality between different domains when collaboration is required to tackle cross-domain projects or challenges.