Recent Advances in Cu-Based Metal–Organic Frameworks and Their Derivatives for Battery Applications

Chen, Yueying; Zhang, Yuze; Huang, Qianhong; Lin, Xiaoming; Zeb, Akif; Wu, Yi-nan; Xu, Ziqiang; Xu, Xuan

doi:10.1021/acsaem.2c01405

Cited by 21 publications

(7 citation statements)

References 228 publications

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“…As a biological/eco-friendly transition metal, Zn exists in a wide variety of MOFs including DMOF, CID and IRMOFs, as well as zeolitic imidazolate frameworks (ZIFs) . Cu-based MOFs usually comprise coordinated unsaturated sites provided by strong electrophilic copper atoms; particularly, many of them with a paddle wheel structure like HKUST-1 are commonly investigated . In addition, Ni-, Co-, and Mg-based MOF-74 exhibit the highest K H among all the metal types, owing to the large density of open metal sites.…”

Section: Resultsmentioning

confidence: 99%

“…9 Cu-based MOFs usually comprise coordinated unsaturated sites provided by strong electrophilic copper atoms; particularly, many of them with a paddle wheel structure like HKUST-1 are commonly investigated. 52 In addition, Ni-, Co-, and Mg-based MOF-74 exhibit the highest K H among all the metal types, owing to the large density of open metal sites. Crbased MOFs (Cr-soc-MOF-1, MIL-100, and MIL-101) have the highest q max , attributing to the strong Lewis acidity and low redox potential of Cr 3+ .…”

Section: Featurization Of Mofsmentioning

confidence: 99%

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Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Zhang,

Tang,

Wang

et al. 2023

ACS Sustainable Chem. Eng.

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Atmospheric water harvesting based on metal− organic frameworks (MOFs) is an emerging technology to potentially mitigate water scarcity. Because of the tremendously large number of existing MOFs, it is challenging to find suitable candidates. In this context, a data-driven approach to identify topperforming MOFs represents an important direction. Herein, we develop a machine learning (ML) method to predict water adsorption in MOFs and screen out top-performing MOFs for water harvesting. First, experimental water adsorption isotherms in MOFs are collected and water adsorption properties are extracted. Quantitative structure−property relationships are analyzed in terms of pore structure and framework chemistry, providing taskspecific design principles. Then, ML models are trained and interpreted to predict water adsorption properties by using structural and chemical features, as well as operating conditions as descriptors. The transferability of the ML models is validated by out-of-sample predictions in seven newly reported MOFs. Finally, the ML models are applied to screen ∼8000 "Computation-Ready, Experimental" (CoRE) MOFs. Top-performing candidates are identified including 149 MOFs with the maximum adsorption capacity ≥35 mmol/g, 39 MOFs with working capacity ≥10 mmol/g in a relative pressure window 0.1−0.3, and 139 MOFs with working capacity ≥8.7 mmol/g in a relative pressure window 0.6−0.9. The developed ML-based method would advance task-oriented design and rapid discovery of reticular materials for energy and environmental applications.

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Section: Resultsmentioning

confidence: 99%

Section: Featurization Of Mofsmentioning

confidence: 99%

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Zhang,

Tang,

Wang

et al. 2023

ACS Sustainable Chem. Eng.

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“…Cu-MOFs generally hold large specific surface areas and high porosities through simple and easily scaled-up preparation methods with low cost . In recent years, the exploration of the chemical composition, framework structure, synthesis approaches, and applications of Cu-MOFs has experienced continuous revolutions and developments, and many of them have gradually evolved into brilliant candidates for gas adsorption and separation. , Considering the numerous advantages mentioned above, the experimental Cu-MOF libraries along with 876 MOFs are delicately selected, which have a large pore size enough to accommodate CH 4 , covering an extensive range of structural and chemical characteristics. This library has also been successfully employed for large-scale screening of other systems, such as C 2 H 2 /CO 2 , H 2 /D 2 , and CO 2 /N 2 . − …”

Section: Resultsmentioning

confidence: 99%

Discovery of a Scalable Metal–Organic Framework with a Switchable Structure for Efficient CH₄/N₂ Separation

et al. 2023

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Discovering optimum materials to realize an efficient CH 4 /N 2 mixture is of vital industrial significance but scientifically challenging. To solve this key obstacle, an efficient strategy from theoretical screening to scalable synthesis was thus proposed. A large-scale computational screening was first executed to identify high-performance adsorbents for CH 4 /N 2 separation from the experimental copper metal−organic framework (Cu-MOF) libraries. A series of MOFs with kagome-type lattice characteristics were computationally defined as candidates with good CH 4 /N 2 selectivity. As a proof-of-concept example, one of the candidates (SIWPAS) was further prepared in an experiment. Intriguingly, the pore sizes can be precisely regulated by altering the degassing temperature to induce the dynamic switch of the initial two-dimensional (2D) framework, further enhancing the separation performance for CH 4 /N 2 . The obtained selectivity of 15.0 at 298 K and 1.0 bar is consistent with the simulation prediction value, which is also one of the highest values presently. Meanwhile, the CH 4 /N 2 uptake ratio of 7.5 (298 K and 1.0 bar) and comprehensive separation performance index sorbent selection parameter (Ssp) of 283.0 are higher than those of all materials as yet. Significantly, the scalable continuous synthesis of this MOF was finally realized with the highest space-time yield (STY) of 18982 kg m −3 day −1 for all 2D MOFs with the aid of the process intensification approach based on high gravity technology. As a foremost finding, this work not only greatly shortens the discovery period of high-performance MOF materials but also effectively promotes their practical applications.

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“…In addition, CD-MOFs were also used for sensing assays. Recently, Chen Y et al (2022) proposed a novel sensor for determinating luteolin (3′, 4′, 5, 7-tetrahydroxyflavonoids, LU) by preparing a glassy carbon electrode (MoO 3 -PEDOT/CD-MOF/GCE) using γ-CD-MOF, MoO 3 and a flexible conducting polymer poly (3,4-ethylenedioxythiophene) (PEDOT). The in situ growth of PEDOT on MoO 3 nanorods effectively improves the stability, dispersion, and electrical conduction efficiency of PEDOT.…”

Section: Application Of Mofs In Food Contamination Adsorption and Det...mentioning

confidence: 99%

Metal-organic frameworks for food contaminant adsorption and detection

Guo

Wang²,

Wang³

et al. 2023

Front. Chem.

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Metal-organic framework materials (MOFs) have been widely used in food contamination adsorption and detection due to their large specific surface area, specific pore structure and flexible post-modification. MOFs with specific pore size can be targeted for selective adsorption of some contaminants and can be used as pretreatment and pre-concentration steps to purify samples and enrich target analytes for food contamination detection to improve the detection efficiency. In addition, MOFs, as a new functional material, play an important role in developing new rapid detection methods that are simple, portable, inexpensive and with high sensitivity and accuracy. The aim of this paper is to summarize the latest and insightful research results on MOFs for the adsorption and detection of food contaminants. By summarizing Zn-based, Cu-based and Zr-based MOFs with low cost, easily available raw materials and convenient synthesis conditions, we describe their principles and discuss their applications in chemical and biological contaminant adsorption and sensing detection in terms of stability, adsorption capacity and sensitivity. Finally, we present the limitations and challenges of MOFs in food detection, hoping to provide some ideas for future development.

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Recent Advances in Cu-Based Metal–Organic Frameworks and Their Derivatives for Battery Applications

Cited by 21 publications

References 228 publications

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Discovery of a Scalable Metal–Organic Framework with a Switchable Structure for Efficient CH₄/N₂ Separation

Metal-organic frameworks for food contaminant adsorption and detection

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Recent Advances in Cu-Based Metal–Organic Frameworks and Their Derivatives for Battery Applications

Cited by 21 publications

References 228 publications

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Discovery of a Scalable Metal–Organic Framework with a Switchable Structure for Efficient CH4/N2 Separation

Metal-organic frameworks for food contaminant adsorption and detection

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Product

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Discovery of a Scalable Metal–Organic Framework with a Switchable Structure for Efficient CH₄/N₂ Separation