This review reports on recent advances in our knowledge about the stability of binary colloids. We focus not only on experimental results but also discuss theoretical and simulation studies regarding kinetic and structural aspects of heteroaggregation processes arising in such systems. In the first part of this work, heteroaggregation of oppositely charged particles is reviewed. When the interactions are short ranged, binary diffusion-limited cluster–cluster aggregation takes place. In this case, the short time behavior of the system follows the Hogg, Healy and Fuerstenau (HHF) theory. At long times, however, stable aggregates may form and remain in the system. Furthermore, cluster discrimination is observed, i.e. clusters that differ only by one constituent particle were found to behave quite differently. When the range of the interactions is increased, the latter effects become more pronounced. The fractal dimension of heteroaggregates is, in general, smaller than the values reported for fast and slow homoaggregation processes. In some cases, even values close to unity were obtained. This means that heteroaggregates have an open branched structure that may approach a chain-like morphology. In the second part of this work, we briefly discuss similar effects arising in heteroaggregation phenomena due to differences in particle size and chemical composition. The third part of this review tackles recent developments in the field of equilibrium phase diagrams of binary colloids. In the last section, the relatively small number of papers about heteroaggregation processes in two-dimensional systems is also discussed.

Rising financial fraud impacts industries, economies, and consumers, creating a need for advanced technological solutions. Compliance frameworks help detect and prevent illicit activities like money laundering, market manipulation, etc. However, with the rise of cryptocurrencies and blockchain, traditional detection methods are ineffective. As a result, Artificial Intelligence (AI) has emerged as a vital tool for combating fraud in the cryptocurrency sector. This systematic review examines the integration of AI in compliance for cryptocurrency fraud detection between 2014 and 2025, analyzing its evolution, methodologies, and emerging trends. Using RStudio (Biblioshiny) and VOSviewer, 353 peer-reviewed studies from leading databases including SciSpace, Elicit, Google Scholar, ScienceDirect, Scopus, and Web of Science were analyzed following the PRISMA methodology. Key trends include the adoption of machine learning, deep learning, natural language processing, and generative AI technologies to improve efficiency and innovation in fraud detection. However, challenges persist, including limited transparency in AI models, regulatory fragmentation, and limited access to quality data, all of which hinder effective fraud detection. The long-term real-world effectiveness of AI tools remains underexplored. This review highlights the trajectory of AI in compliance, identifies areas for further research, and emphasizes bridging theory and practice to strengthen fraud detection in cryptocurrency transactions.

Electrostatic heteroaggregation processes arising in 1:1 mixtures of oppositely charged microspheres at low and very low electrolyte concentrations were investigated by means of single-cluster light scattering. Cluster discrimination, i.e., the fact that clusters differing by only one constituent particle behave quite differently, was found for monomers and dimers. This effect was recently predicted by Brownian dynamic simulations but, to the best of our knowledge, not yet confirmed by experiments. The experimental data were confronted with the simulations and a good qualitative and quantitative agreement was obtained. The origin of the cluster discrimination phenomenon could be related to the range of the attractive electrostatic interactions.

Binary diffusion-limited cluster-cluster aggregation processes are studied as a function of the relative concentration of the two species. Both, short and long time behaviors are investigated by means of three-dimensional off-lattice Brownian Dynamics simulations. At short aggregation times, the validity of the Hogg-Healy-Fuerstenau approximation is shown. At long times, a single large cluster containing all initial particles is found to be formed when the relative concentration of the minority particles lies above a critical value. Below that value, stable aggregates remain in the system. These stable aggregates are composed by a few minority particles that are highly covered by majority ones. Our off-lattice simulations reveal a value of approximately 0.15 for the critical relative concentration. A qualitative explanation scheme for the formation and growth of the stable aggregates is developed. The simulations also explain the phenomenon of monomer discrimination that was observed recently in single cluster light scattering experiments.

Experimental evidence of a fully reversible aggregation process due to ionic specificity is presented for the first time. The results indicate the existence of repulsive structural forces between particles coming from the specific accumulation of poorly hydrated ions around hydrophobic surfaces. This result emphasizes the key role that ion-water structure near interfaces plays in Hofmeister effects, as recently suggested by molecular dynamics simulations.