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Hypothesis: Additives like Tetrahydrofuran (THF) and Sodium Dodecylsulfate (SDS) improve Carbon Dioxide (CO2) hydrates thermal stability and growth rate when used separately. It has been hypothesised that combining them could improve the kinetics of growth and the thermodynamic stability of CO2 hydrates. Simulations and Experiments: We exploit atomistic molecular dynamics simulations to investigate the combined impact of THF and SDS under different temperatures and concentrations. The simulation insights are verified experimentally using pendant drop tensiometry conducted at ambient pressures and high-pressure differential scanning calorimetry. Findings: Our simulations revealed that the combination of both additives is synergistic at low temperatures but antagonistic at temperatures above 274.1 K due to the aggregation of SDS molecules induced by THF molecules. These aggregates effectively remove THF and CO2 from the hydrate-liquid interface, thereby reducing the driving force for hydrates growth. Experiments revealed that the critical micelle concentration of SDS in water decreases by 20% upon the addition of THF. Further experiments in the presence of THF showed that only small amounts of SDS are sufficient to increase the CO2 storage efficiency by over 40% compared to results obtained without promoters. Overall, our results provide microscopic insights into the mechanisms of THF and SDS promoters on CO2 hydrates, useful for determining the optimal conditions for hydrate growth. 

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce “PLUMED Tutorials,” a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources. 

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.