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Singh SK, Sose AT, Wang F, Bejagam KK, Deshmukh SA. Data Driven Discovery of MOFs for Hydrogen Gas Adsorption. J Chem Theory Comput 2023; 19:6686-6703. [PMID: 37756641 DOI: 10.1021/acs.jctc.3c00081] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.
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Affiliation(s)
- Samrendra K Singh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Abhishek T Sose
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Fangxi Wang
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Karteek K Bejagam
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Sanket A Deshmukh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
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Aguila-Rosas J, Ramos D, Quirino-Barreda CT, Flores-Aguilar JA, Obeso JL, Guzmán-Vargas A, Ibarra IA, Lima E. Copper(II)-MOFs for bio-applications. Chem Commun (Camb) 2023; 59:11753-11766. [PMID: 37703047 DOI: 10.1039/d3cc03146b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
The recent development and implementation of copper-based metal-organic frameworks in biological applications are reviewed. The advantages of the presence of copper in MOFs for relevant applications such as drug delivery, cancer treatment, sensing, and antimicrobial are highlighted. Advanced composites such as MOF-polymers are playing critical roles in developing materials for specific applications.
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Affiliation(s)
- Javier Aguila-Rosas
- Laboratorio de Fisicoquímica y Reactividad de Superficies (LaFReS), Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito Exterior s/n, CU, Del. Coyoacán, 04510, Ciudad de México, Mexico.
- Laboratorio de Farmacia Molecular y Liberación Controlada, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, Col. Villa Quietud, C.P. 04960, CDMX, Mexico
| | - Dalia Ramos
- Laboratorio de Farmacia Molecular y Liberación Controlada, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, Col. Villa Quietud, C.P. 04960, CDMX, Mexico
| | - Carlos T Quirino-Barreda
- Laboratorio de Farmacia Molecular y Liberación Controlada, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, Col. Villa Quietud, C.P. 04960, CDMX, Mexico
| | - Juan Andrés Flores-Aguilar
- Laboratorio de Fisicoquímica y Reactividad de Superficies (LaFReS), Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito Exterior s/n, CU, Del. Coyoacán, 04510, Ciudad de México, Mexico.
| | - Juan L Obeso
- Laboratorio de Fisicoquímica y Reactividad de Superficies (LaFReS), Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito Exterior s/n, CU, Del. Coyoacán, 04510, Ciudad de México, Mexico.
- Instituto Politécnico Nacional, CICATA U. Legaria, Laboratorio Nacional de Ciencia, Tecnología y Gestión Integrada del Agua (LNAgua), Legaria 694, Irrigación 11500, Miguel Hidalgo, CDMX, Mexico
| | - Ariel Guzmán-Vargas
- ESIQIE - Instituto Politécnico Nacional, Avenida IPN UPALM Edificio 7, Zacatenco, 07738 México D.F, Mexico.
| | - Ilich A Ibarra
- Laboratorio de Fisicoquímica y Reactividad de Superficies (LaFReS), Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito Exterior s/n, CU, Del. Coyoacán, 04510, Ciudad de México, Mexico.
| | - Enrique Lima
- Laboratorio de Fisicoquímica y Reactividad de Superficies (LaFReS), Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito Exterior s/n, CU, Del. Coyoacán, 04510, Ciudad de México, Mexico.
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Mohammadi E, Joshi SY, Deshmukh SA. Development, Validation, and Applications of Nonbonded Interaction Parameters between Coarse-Grained Amino Acid and Water Models. Biomacromolecules 2023; 24:4078-4092. [PMID: 37603467 DOI: 10.1021/acs.biomac.3c00441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
Interactions between amino acids and water play an important role in determining the stability and folding/unfolding, in aqueous solution, of many biological macromolecules, which affects their function. Thus, understanding the molecular-level interactions between water and amino acids is crucial to tune their function in aqueous solutions. Herein, we have developed nonbonded interaction parameters between the coarse-grained (CG) models of 20 amino acids and the one-site CG water model. The nonbonded parameters, represented using the 12-6 Lennard Jones (LJ) potential form, have been optimized using an artificial neural network (ANN)-assisted particle swarm optimization (PSO) (ANN-assisted PSO) method. All-atom (AA) molecular dynamics (MD) simulations of dipeptides in TIP3P water molecules were performed to calculate the Gibbs hydration free energies. The nonbonded force-field (FF) parameters between CG amino acids and the one-site CG water model were developed to accurately reproduce these energies. Furthermore, to test the transferability of these newly developed parameters, we calculated the hydration free energies of the analogues of the amino acid side chains, which showed good agreement with reported experimental data. Additionally, we show the applicability of these models by performing self-assembly simulations of peptide amphiphiles. Overall, these models are transferable and can be used to study the self-assembly of various biomaterials and biomolecules to develop a mechanistic understanding of these processes.
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Affiliation(s)
- Esmat Mohammadi
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Soumil Y Joshi
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Sanket A Deshmukh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
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Sose AT, Joshi SY, Kunche LK, Wang F, Deshmukh SA. A review of recent advances and applications of machine learning in tribology. Phys Chem Chem Phys 2023; 25:4408-4443. [PMID: 36722861 DOI: 10.1039/d2cp03692d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.
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Affiliation(s)
- Abhishek T Sose
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
| | - Soumil Y Joshi
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
| | | | - Fangxi Wang
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
| | - Sanket A Deshmukh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
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Nosakhare Amenaghawon A, Lewis Anyalewechi C, Uyi Osazuwa O, Agbovhimen Elimian E, Oshiokhai Eshiemogie S, Kayode Oyefolu P, Septya Kusuma H. A Comprehensive Review of Recent Advances in the Synthesis and Application of Metal-Organic Frameworks (MOFs) for the Adsorptive Sequestration of Pollutants from Wastewater. Sep Purif Technol 2023. [DOI: 10.1016/j.seppur.2023.123246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Role of Bimetallic Solutions in the Growth and Functionality of Cu-BTC Metal-Organic Framework. MATERIALS 2022; 15:ma15082804. [PMID: 35454498 PMCID: PMC9033043 DOI: 10.3390/ma15082804] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 03/28/2022] [Accepted: 04/05/2022] [Indexed: 11/29/2022]
Abstract
Bimetallic solutions play a vital role in the growth and functionality of copper trimesate (Cu-BTC) metal–organic frameworks (MOFs). The effect of Ag+, Ca2+, Mn2+, Co2+, and Zn2+ on the growth of Cu-BTC was studied by fabricating M-Cu-BTC MOFs at room temperature using bimetallic M-Cu solutions. While Ag+ in the MOF had a rod-like morphology and surface properties, divalent cations deteriorated it. Moreover, unconventional Cu+ presence in the MOF formed a new building unit, which was confirmed in all the MOFs. Apart from Ag and Mn, no other MOF showed any presence of secondary cations in the structure. While Ag-Cu-BTC showed an improved H2S uptake capacity, other M-Cu-BTC MOFs had superior organic pollutant adsorption behavior. Thus, we have demonstrated that the physicochemical properties of Cu-BTC could be modified by growing it in bimetallic solutions.
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