Machine Learning Approaches for Further Developing the Understanding of the Property Trends Observed in Protic Ionic Liquid Containing Solvents

Recent Progress in Folic Acid Detection Based on Fluorescent Carbon Dots as Sensors: A Review

Folic acid (FA) is a water-soluble vitamin found in diverse natural sources and is crucial for preserving human health. The risk of health issues due to FA deficiency underscores the need for a straightforward and sensitive FA detection methodology. Carbon dots (CDs) have gained significant attention owing to their exceptional fluorescence performance, biocompatibility, and easy accessibility. Consequently, numerous research studies have concentrated on developing advanced CD fluorescent probes to enable swift and precise FA detection. Despite these efforts, there is still a requirement for a thorough overview of the efficient synthesis of CDs and their practical applications in FA detection to further promote the widespread use of CDs. This review paper focuses on the practical applications of CD sensors for FA detection. It begins with an in-depth introduction to FA and CDs. Following that, based on various synthetic approaches, the prepared CDs are classified into diverse detection methods, such as single sensing, visual detection, and electrochemical methods. Furthermore, persistent challenges and potential avenues are highlighted for future research to provide valuable insights into crafting effective CDs and detecting FA.

Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures

Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.

Protein aggregation and crystallization with ionic liquids: Insights into the influence of solvent properties

Ionic liquids (ILs) have been used in solvents for proteins in many applications, including biotechnology, pharmaceutics, and medicine due to their tunable physicochemical and biological properties. Protein aggregation is often undesirable, and predominantly occurs during bioprocesses, while the aggregation process can be reversible or irreversible and the aggregates formed can be native/non-native and soluble/insoluble. Recent studies have clearly identified key properties of ILs and IL-water mixtures related to protein performance, suggesting the use of the tailorable properties of ILs to inhibit protein aggregation, to promote protein crystallization, and to control protein aggregation pathways. This review discusses the critical properties of IL and IL-water mixtures and presents the latest understanding of the protein aggregation pathways and the development of IL systems that affect or control the protein aggregation process. Through this feature article, we hope to inspire further advances in understanding and new approaches to controlling protein behavior to optimize bioprocesses.

Theoretical Understanding of Structure-Property Relationships in Luminescence of Carbon Dots

Carbon dots (CDs) have excellent luminescence characteristics, such as good light stability, high quantum yield (QY), long phosphorescence lifetime, and a wide emission wavelength range, resulting in CDs' great success in optical applications. Understanding the structure-property relationships in CDs is essential for their use in optoelectronic applications. However, because of the complex nature of CD structures and synthesis processes, understanding the luminescence mechanism and structure-property relationships of CDs is a big challenge. This Perspective reviews the theoretical efforts toward the understanding of structure-property relationships and discusses the challenges that need to be overcome in future development of CDs.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Machine learning prediction of cellulose-rich materials from biomass pretreatment with ionic liquid solvents

Ionic liquid solvents (ILSs) have been effectively utilized in biomass pretreatment to produce cellulose-rich materials (CRMs). Predicting CRM properties and evaluating multi-dimensional relationships in this system are necessary but complicated. In this work, machine learning algorithms were applied to predict CRM properties in terms of cellulose enrichment factor (CEF) and solid recovery (SR), using 23-feature datasets from biomass characteristics, operating conditions, ILSs identities, and catalyst. Random forest algorithm was found to have the highest prediction accuracy with RMSE and R² of 0.22 and 0.94 for CEF, as well as 0.07 and 0.84 for SR, respectively. Highly influential features on making predictions were mainly from biomass characteristics andILS treatment'soperating conditions, totally contributed 80% on CEF and 60% on SR. One- and two-way partial dependence plots were used to explain/interpret the multi-dimensional relationships of the most important features. Our findings could be applied in designing new ILSs and optimizing the process conditions.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.