Accelerating Metal–Organic Framework Selection for Type III Porous Liquids by Synergizing Machine Learning and Molecular Simulation

Sheng, Lisha; Wang, Yi; Mou, Xinzhu; Xu, Bo; Chen, Zhenqian

doi:10.1021/acsami.3c12507

Cited by 5 publications

(2 citation statements)

References 63 publications

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“…For example, the presence of defects can have profound effects on gas diffusion into and within microporous particles, and although more defects might be expected in the smaller particles, the concentration and types of defects which may be present are not known in the current work. With regard to layer diffusion, a further complication is that we currently have no knowledge of the structure, dynamics, and depth of the boundary layer at the PDMS-AF interface or how it might vary with particle size (although we note with interest the work of Sheng et al on type 3 porous ionic liquids in which TEM analysis revealed a substantial ∼100–200 nm adsorbed layer of IL at the MOF-IL interface and it is possible that a similar boundary layer exists in PL1–3). Overall, therefore, while we note the clear systematic trend in rate of gas uptake, a mechanistic interpretation of this trend is not possible at this time.…”

Section: Resultsmentioning

confidence: 99%

Effects of Particle Size on the Gas Uptake Kinetics and Physical Properties of Type III Porous Liquids

Liu,

Lai,

James

2024

ACS Appl. Mater. Interfaces

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Type III porous liquids (PLs) consist of porous solid particles dispersed in a size-excluded liquid phase and are attracting much attention as novel media for applications such as gas separation. However, the effects of fundamental variables such as particle size on their physical properties are currently largely unknown. Here we study the effects of particle size in a series of porous liquids based on solid Al(OH)(fumarate) (a microporous metal−organic framework, MOF) with particle sizes of 60 nm, 200−600 nm, or 800−1000 dispersed in liquid polydimethylsiloxane (PDMS). Properties examined include physical stability of the dispersion, viscosity, total CO 2 uptake, and kinetics of CO 2 uptake. As expected, both physical stability and viscosity decreased with increasing particle size. Unexpectedly, total gravimetric gas uptake also varied with particle size, being greatest for the largest particles, which we ascribe to larger particles having a lower relative content of surface-bound FMA ligands. Various models for the gas uptake kinetic data were considered, specifically adsorption reaction models such as pseudo-first-order, pseudo-second-order, and Elovich models. In contrast to pure PDMS, which showed first-order kinetics, all PLs fit best to the Elovich model confirming that their uptake mechanism is more complex than for a simple liquid. Adsorption diffusion models, specifically Weber and Morris' intraparticle model and Boyd's model, were also applied which revealed a three-step process in which a combination of diffusion through a surface layer and intraparticle diffusion were ratelimiting. The rate of gas uptake follows the order PDMS < PL1 < PL2 < PL3, showing that the porous liquids take up gas more rapidly than does PDMS and that this rate increases with particle size. Overall, the study suggests that for high gas uptake and fast uptake kinetics, large particles may be preferred. Also, the fact that large particles resulted in low viscosity may be advantageous in reducing the pumping energy needed in flow separation systems. Therefore, the work suggests that finding ways to stabilize PLs with large particles against phase separation could be advantageous for optimizing the properties of PLs toward applications.

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Section: Resultsmentioning

confidence: 99%

Effects of Particle Size on the Gas Uptake Kinetics and Physical Properties of Type III Porous Liquids

Liu,

Lai,

James

2024

ACS Appl. Mater. Interfaces

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“…Employing big data analysis and constructing predictive models through the application of machine learning (ML) 20,21 techniques is certainly an option worth exploring for this purpose. ML techniques have attracted much attention in screening conventional 3D MOFs for various purposes, including gas adsorption and storage, [22][23][24][25][26][27][28] however, their application for predicting the electronic properties of conductive MOFs is fewer and far between. In 2018, He et al 29 applied ML techniques for identifying metallic MOF crystal structures for the first time.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of Thermodynamic Stability and Electronic Properties of 2D Layered Conductive Metal-Organic Frameworks

Zhang,

Shi,

A. Shakib

2024

Preprint

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2D layered metal-organic frameworks (MOFs) are a new class of multifunctional materials that can provide electrical conductivity on top of the conventional structural characteristics of MOFs, offering potential applications in electronics and optics. Here, for the first time, we employ Machine Learning (ML) techniques to predict the thermodynamic stability and electronic properties of layered electrically conductive (EC) MOFs, bypassing expensive ab initio calculations for the design and discovery of new materials. Proper feature engineering is a very important factor in utilizing ML models for such purposes. Here, we show that a combination of elemental features, using generic statistical reduction methods and crystal structure information curated from the recently introduced EC-MOF database, leads to a reasonable prediction of the thermodynamic and electronic properties of EC MOFs. We utilize these features in training a diverse range of ML classifiers and regressors. Evaluating the performance of these different models, we show that an ensemble learning approach in the form of stacking ML models can lead to higher accuracy and more reliability on the predictive power of ML to be employed in future MOF research.

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Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks

Du,

Xin,

Wang

et al. 2024

Chemical Engineering Journal

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Accelerating Metal–Organic Framework Selection for Type III Porous Liquids by Synergizing Machine Learning and Molecular Simulation

Cited by 5 publications

References 63 publications

Effects of Particle Size on the Gas Uptake Kinetics and Physical Properties of Type III Porous Liquids

Effects of Particle Size on the Gas Uptake Kinetics and Physical Properties of Type III Porous Liquids

Machine Learning Prediction of Thermodynamic Stability and Electronic Properties of 2D Layered Conductive Metal-Organic Frameworks

Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks

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