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The development of energy-efficient and low-cost desalination techniques is crucial. Among these techniques, reverse osmosis (RO) is considered one of the most promising solutions for addressing the global water crisis. It has been widely implemented for both large-scale and distributed water desalination. One such promising desalination membrane is MXene with nanopores, thanks to its unique properties. However, accurately predicting the performance of the desalination process or designing new materials is challenging due to the complexity of the process and the various tunable properties of the membranes and nanopores themselves. The combination of machine learning (ML) and global optimization algorithms offers a superior approach to material design from multiple perspectives. Our study demonstrates that Particle Swarm Optimization (PSO) exhibits faster convergence speed and stability compared to Genetic Algorithms. Ti 3 C 2 O 2 with a specific charge on the nanopore mouth is an excellent candidate, as determined by the particle swarm optimization algorithm. By analyzing water density and ion density along the nanopore, we gain a deep understanding of how pore charge and functionalized groups affect salt rejection and water permeation. The integration of ML and global optimization algorithms can facilitate the design of materials with outstanding desalination performance. Design of MXene desalination membrane based on combined machine learning and optimization algorithm. [Display omitted] • Combination of high-through computation and machine learning (ML) for desalination materials design. • Global optimization algorithms used to optimize the desalination performance of membrane. • An excellent desalination candidate membrane made from Ti 3 C 2 O 2 with a specific charge on the nanopore mouth was uncovered. • Physical chemical analysis explorer the effect of charge on the MXene nanopore mouth on the desalination performance. [ABSTRACT FROM AUTHOR]

• Molecular dynamic of phenolic organic pollutants (POPs) adsorption on UiO-66. • The adsorption behavior of POPs molecules occurred on the surface of UiO-66. • UiO-66 has highest adsorption capacity for nitrophenol due to the larger interaction of host–guest. • The interaction energy and π-π stacking are quantificationally calculated. • Van der Waals and π-π stacking dominate adsorption between POPs and UiO-66. Metal organic frameworks (MOFs) as an excellent adsorbent have been widely utilized in wastewater treatment due to high specific surface area, high porosity, adjustable structure and surface functionability. To design MOFs materials based on the application for removal of pollutant, understand the adsorption mechanism between pollutant and MOFs from the microscopic scale is necessary but rare. Herein, UiO-66 is selected as the adsorbent to investigate the adsorption mechanisms with phenolic organic pollutants (POPs) on the microscopic scale via MD simulation. Equilibrium configuration and density distribution for three different POPs (phenol, nitrophenol and bisphenol) prove the adsorption behavior occurred on the surface of UiO-66. Obviously, the difference of adsorption capacity proves UiO-66 tends to adsorb the nitrophenol compared to other two molecules and has the lower adsorption capacity for bisphenol due to larger molecule size, and in a neutral environment, it is more conducive to be adsorbed than in an alkaline environment. Mean square displacement (MSD) and radial distribution functions (RDFs) further support this conclusion, which exhibit the introduction of –NO 2 groups can enhance the interaction of host–guest. The H-bonds, π-π stacking and interaction energy between UiO-66 and POPs was quantificationally calculated, the results show Van der Waals and π-π stacking dominate adsorption between POPs and UiO-66. [ABSTRACT FROM AUTHOR]

Diversified ion-selective separation applications have dramatically incentivized the exploitation and performance modulation of highly ion-selective nanofiltration (NF) membranes. However, the ion selectivity of NF membranes is synergistically governed by multi-scale factors of membrane structural parameters and operational parameters, with the intrinsic ion-selective mechanism still ambiguous. Herein, we proposed an ensemble machine learning (ML) method to decouple key factors affecting the ionic selectivity of polyamide NF membranes. Membrane structural parameters and operational parameters were typically extracted as input variables and linked to mono−/divalent ion selectivity by model training based on Random Forest and XGBoost algorithms. The feature importance assessment indicated the critical role of membrane structure parameters on ion selectivity, wherein pore radius dominated the mono−/divalent anionic selectivity while zeta potential for mono−/divalent cationic selectivity. Partial dependence analyses further depicted intensive insights regarding the influence of membrane structural parameters on ion selectivity. Moreover, stochastic dataset splitting measurements demonstrated the accurate predictive capability of the model simultaneously possessing excellent stability and reliability. We anticipated that the implementation of ensemble ML in explicating the intricate ion-selective mechanism created platforms for understanding the structure-membrane performance correlation and orientally manufacturing highly ion-selective NF membranes. [Display omitted] • Ensemble machine learning was implemented to decouple the key features affecting ion selectivities of polyamide membranes. • RF and XGBoost algorithms were adopted to learn from relevant variables and evaluate permutation features importance. • Mono-/divalent anionic selectivity was dominated by pore size while zeta potential for mono-/divalent cationic selectivity. [ABSTRACT FROM AUTHOR]

Nanofiltration (NF) plays an increasingly central role in water/salts separation, which puts forward tailored requirements on NF membranes in a variety of application scenarios. However, the ambiguous separation mechanisms of NF including membrane structure parameters and operating conditions hinder the rational design of versatile NF membranes. Herein, machine learning was used to explore the correlation between membrane structure parameters and operating conditions with water/salts selectivity, and reveal the importance of the diverse features based on literature data. Two structural features of polyamide NF membrane (pore radius and zeta potential) and two operating parameters (pressure and feed concentration) were typically extracted and associated with water/salts selectivity. Random Forest and XGBoost models were employed to learn from relevant variables and assess their importance. The results showed that membrane structure parameters attached more importance to water/salts selectivity than that of operating conditions accompanying the variable influence for different typed salts, where symmetrical salts were mainly governed by size sieving while Donnan exclusion for asymmetrical salts. Structure-performance relationships between pore radius, zeta potential and diverse water/salts selectivity were established using partial dependence plot analysis. It is anticipated that the constructed comprehensive insights can be further leveraged to tackle performance modulation and oriented design of multi-scenarios NF membranes. [Display omitted] • Machine learning was used to understand NF separation mechanisms. • Random Forest and XGBoost models were employed to learn from relevant variables and assess features importance. • Symmetrical salts were mainly governed by size sieving while Donnan exclusion for asymmetrical salts. • The integrated management of pore size and zeta potential for the oriented design of versatile NF membranes was highlighted. [ABSTRACT FROM AUTHOR]

Various order parameters have been suggested to characterize the local structure of liquid water in bulk. Characterization the local structure of water molecules in the confined systems by various order parameters is important but still unclear. In this work, a series of popular order parameters are used to analyze the local structure of water molecules in carbon nanotubes (CNTs) by molecular dynamics simulation. Based on the value of order parameters, the different structures of water molecules in CNTs and in bulk were discussed. The orientational tetrahedral order (q) and translational tetrahedral order (S k) are much better than local structure index (LSI) to describe the local structure of water in CNTs. We also demonstrated that the local structure of water molecules in the CNT(40,40) is almost the same with that in bulk. The findings could give us a guideline to investigate the water molecules in confined system. [ABSTRACT FROM AUTHOR]

The development of two-dimensional (2D) materials rise new options on the promising application of biosensors and DNA sequencing. Recently, 2D molybdenum disulfide (MoSe 2) material has been successfully fabricated and shows the unique excellent properties. An in-depth understanding of the interfacial interaction between materials and biomolecules is critical in biomedical field. Therefore, the directional selection of double-stranded DNA (ds-DNA) on the MoSe 2 surface was studied by molecular dynamics (MD) simulation in this work. The simulation results showed that the vertical adsorption of double-strand DNA (ds-DNA) on the MoSe 2 surface is favored through terminal bases. With the analysis of interaction energy, the main driving forces to absorb DNA on MoSe 2 is attributed to van der Waals force. In addition, our results indicate that the length and base sequence of ds-DNA are independent of its selection of vertically adsorbed orientation, and the ds-DNA structure is still stable after adsorption on the surface of MoSe 2. These simulation results greatly enhanced our understanding on the interaction between ds-DNA and MoSe 2 , which may be beneficial for improving the development of fast DNA sequencing technology through MoSe2 nanopore in the future. [ABSTRACT FROM AUTHOR]