Thermodynamic modeling and rational design of ionic liquids for pre-combustion carbon capture

Integrative studies of ionic liquid interface layers: bridging experiments, theoretical models and simulations

Ionic liquids (ILs) are a class of salts existing in the liquid state below 100 °C, possessing low volatility, high thermal stability as well as many highly attractive solvent and electrochemical capabilities, etc., making them highly tunable for a great variety of applications, such as lubricants, electrolytes, and soft functional materials. In many applications, ILs are first either physi- or chemisorbed on a solid surface to successively create more functional materials. The functions of ILs at solid surfaces can differ considerably from those of bulk ILs, mainly due to distinct interfacial layers with tunable structures resulting in new ionic liquid interface layer properties and enhanced performance. Due to an almost infinite number of possible combinations among the cations and anions to form ILs, the diversity of various solid surfaces, as well as different external conditions and stimuli, a detailed molecular-level understanding of their structure-property relationship is of utmost significance for a judicious design of IL-solid interfaces with appropriate properties for task-specific applications. Many experimental techniques, such as atomic force microscopy, surface force apparatus, and so on, have been used for studying the ion structuring of the IL interface layer. Molecular Dynamics simulations have been widely used to investigate the microscopic behavior of the IL interface layer. To interpret and clarify the IL structure and dynamics as well as to predict their properties, it is always beneficial to combine both experiments and simulations as close as possible. In another theoretical model development to bridge the structure and properties of the IL interface layer with performance, thermodynamic prediction & property modeling has been demonstrated as an effective tool to add the properties and function of the studied nanomaterials. Herein, we present recent findings from applying the multiscale triangle "experiment-simulation-thermodynamic modeling" in the studies of ion structuring of ILs in the vicinity of solid surfaces, as well as how it qualitatively and quantitatively correlates to the overall ILs properties, performance, and function. We introduce the most common techniques behind "experiment-simulation-thermodynamic modeling" and how they are applied for studying the IL interface layer structuring, and we highlight the possibilities of the IL interface layer structuring in applications such as lubrication and energy storage.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Transformer-Based Representation of Organic Molecules for Potential Modeling of Physicochemical Properties

In this work, we study the use of three configurations of an autoencoder neural network to process organic substances with the aim of generating meaningful molecular descriptors that can be employed to develop property prediction models. A total of 18,322,500 compounds represented as SMILES strings were used to train the model, demonstrating that a latent space of 24 units is able to adequately reconstruct the data. After AE training, an analysis of the latent space properties in terms of compound similarity was carried out, indicating that this space possesses desired properties for the potential development of models for forecasting physical properties of organic compounds. As a final step, a QSPR model was developed to predict the boiling point of chemical substances based on the AE descriptors. 5276 substances were used for the regression task, and the predictive ability was compared with models available in the literature evaluated on the same database. The final AE model has an overall error of 1.40% (1.39% with augmented SMILES) in the prediction of the boiling temperature, while other models have errors between 2.0 and 3.2%. This shows that the SMILES representation is comparable and even outperforms the state-of-the-art representations widely used in the literature.

Prediction of carbon-dioxide activity coefficient for solubility in ionic liquids using multi-non-linear regression analysis

Activity coefficient values offer insight into the intermolecular interactions between the solute and the solvent and the deviation from the ideal behavior. CO2 capture from different industrial processes is a globally pertinent issue and the search for suitable chemicals is required. To address the issue, knowledge of activity coefficient values is crucial for CO2 separation-based process. In this regard, a correlation is developed that predicts the coefficient of CO2 activity in ionic liquids by multi-nonlinear regression analysis. The correlation is developed between the pressure range of 1-50 bar and the temperature range of 298.15-33.15 K for mole fractions of 0.3, 0.5, and 0.7. Outliers' analysis is performed using the boxplot method to determine the suitability of ranges of the selected input parameters. The preceding literature does not predict the activity coefficient in relatively lower to higher temperature and pressure ranges for CO2 solubility in ionic liquids. Initially, the activity coefficient values from COSMO-RS were obtained and compared with the correlation results. The COSMO-RS and the correlation predicted results were subsequently validated with the experimental data. The average absolute error (AAE%) of the predicted correlation values is 19.53% while the root mean square error (RMSE) value is 0.465. The correlation can be used in the future to predict the CO2 activity coefficient values in ionic liquids to facilitate qualitative analyses of their CO2 capture efficiency.

Reviewing and screening ionic liquids and deep eutectic solvents for effective CO2 capture

CO₂ capture is essential for both mitigating CO₂ emissions and purifying/conditioning gases for fuel and chemical production. To further improve the process performance with low environmental impacts, different strategies have been proposed, where developing liquid green absorbent for capturing CO₂ is one of the effective options. Ionic liquids (IL)/deep eutectic solvents (DES) have recently emerged as green absorbents with unique properties, especially DESs also benefit from facile synthesis, low toxicity, and high biodegradability. To promote their development, this work summarized the recent research progress on ILs/DESs developed for CO₂ capture from the aspects of those physical- and chemical-based, and COSMO-RS was combined to predict the properties that are unavailable from published articles in order to evaluate their performance based on the key properties for different IL/DES-based technologies. Finally, top 10 ILs/DESs were listed based on the corresponding criteria. The shared information will provide insight into screening and further developing IL/DES-based technologies for CO₂ capture.

Environmental management of industrial decarbonization with focus on chemical sectors: A review

A considerable portion of fossil CO₂ emissions comes from the energy sector for production of heat and electricity. The industrial sector has the second order in emission in which the main parts are released from energy-intensive industries, namely metallurgy, building materials, chemicals, and manufacturing. The decarbonization of industrial wastes contemplates the classic decarbonization through optimization of conventional processes as well as utilization of renewable energy and resources. The upgrading of existing processes and integration of the methodologies with a focus on efficiency improvement and reduction of energy consumption and the environment is the main focus of this review. The implementation of renewable energy and feedstocks, green electrification, energy conversion methodologies, carbon capture, and utilization, and storage are also covered. The main objectives of this review are towards chemical industries by introducing the potential technology enhancement at different subsectors. For this purpose, state-of-the-art roadmaps and pathways from the literature findings are presented. Both common and innovative renewable attempts are needed to reach out both short- and long-term deep decarbonization targets. Even though all of the innovative solutions are not economically viable at the industrial scale, they play a crucial role during and after the energy transition interval.

High-Throughput Computational Screening of Ionic Liquids for Butadiene and Butene Separation

The separation of 1,3-butadiene (1,3-C4H6) and 1-butene (n-C4H8) is quite challenging due to their close boiling points and similar molecular structures. Extractive distillation (ED) is widely regarded as a promising approach for such a separation task. For ED processes, the selection of suitable entrainer is of central importance. Traditional ED processes using organic solvents suffer from high energy consumption. To tackle this issue, the utilization of ionic liquids (ILs) can serve as a potential alternative. In this work, a high-throughput computational screening of ILs is performed to find proper entrainers, where 36,260 IL candidates comprising of 370 cations and 98 anions are involved. COSMO-RS is employed to calculate the infinite dilution extractive capacity and selectivity of the 36,260 ILs. In doing so, the ILs that satisfy the prespecified thermodynamic criteria and physical property constraints are identified. After the screening, the resulting IL candidates are sent for rigorous process simulation and design. 1,2,3,4,5-pentamethylimidazolium methylcarbonate is found to be the optimal IL solvent. Compared with the benchmark ED process where the organic solvent N-methyl-2-pyrrolidone is adopted, the energy consumption is reduced by 26%. As a result, this work offers a new IL-based ED process for efficient 1,3-C4H6 production.

Targeted modifications in ionic liquids - from understanding to design

Ionic liquids are extremely versatile and continue to find new applications in academia as well as industry. This versatility is rooted in the manifold of possible ion types, ion combinations, and ion variations. However, to fully exploit this versatility, it is imperative to understand how the properties of ionic liquids arise from their constituents. In this work, we discuss targeted modifications as a powerful tool to provide understanding and to enable design. A 'targeted modification' is a deliberate change in the structure of an ionic liquid. This includes chemical changes in an experiment as well as changes to the parameterisation in a computer simulation. In any case, such a change must be purposeful to isolate what is of interest, studying, as far as is possible, only one concept at a time. The concepts can then be used as design elements. However, it is often found that several design elements interact with each other - sometimes synergistically, and other times antagonistically. Targeted modifications are a systematic way of navigating these overlaps. We hope this paper shows that understanding ionic liquids requires experimentalists and theoreticians to join forces and provides a tool to tackle the difficult transition from understanding to design.

Machine Learning for Ionic Liquid Toxicity Prediction

In addition to proper physicochemical properties, low toxicity is also desirable when seeking suitable ionic liquids (ILs) for specific applications. In this context, machine learning (ML) models were developed to predict the IL toxicity in leukemia rat cell line (IPC-81) based on an extended experimental dataset. Following a systematic procedure including framework construction, hyper-parameter optimization, model training, and evaluation, the feedforward neural network (FNN) and support vector machine (SVM) algorithms were adopted to predict the toxicity of ILs directly from their molecular structures. Based on the ML structures optimized by the five-fold cross validation, two ML models were established and evaluated using IL structural descriptors as inputs. It was observed that both models exhibited high predictive accuracy, with the SVM model observed to be slightly better than the FNN model. For the SVM model, the determination coefficients were 0.9289 and 0.9202 for the training and test sets, respectively. The satisfactory predictive performance and generalization ability make our models useful for the computer-aided molecular design (CAMD) of environmentally friendly ILs.