Screening of Covalent–Organic Frameworks for Adsorption Heat Pumps

Li, Wei; Xia, Xiaoxiao; Song, Li

doi:10.1021/acsami.9b20837

Cited by 43 publications

(50 citation statements)

References 38 publications

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“…Therefore, computational material design combined with the ML algorithm is usually used to accelerate the design of COFs or other porous materials. 25 , 26 …”

Section: Introductionmentioning

confidence: 99%

In Silico Design of Covalent Organic Framework-Based Electrocatalysts

Zhou

Yang

Wang

et al. 2021

JACS Au

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Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M–N x O y (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M–N x O y were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni–N 2 O 2 unit is subsequently employed to synthesize the corresponding Ni–COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni–N 2 O 2 coordination environment. The studies of electrocatalytic activities confirm that Ni–COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm –2 at a low overpotential of 335 mV. Furthermore, Ni–COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts.

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“…Therefore, computational material design combined with the ML algorithm is usually used to accelerate the design of COFs or other porous materials. 25 , 26 …”

Section: Introductionmentioning

confidence: 99%

In Silico Design of Covalent Organic Framework-Based Electrocatalysts

Zhou

Yang

Wang

et al. 2021

JACS Au

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“…[394] It is also becoming more widespread to see machine learning impacting the identification of useful COFs. Li et al [395] reported a recent study to identify COFs as potential heat pumps as is discussed in greater detail in Section 5.4.4. Descriptors were identified to predict their performance as heating or cooling agents.…”

Section: Covalent Organic Frameworkmentioning

confidence: 99%

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

et al. 2020

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Notwithstanding these impediments, in many fields of materials science, solutions are being designed to mitigate hindrances to the efficient sampling of chemical space and improving the robustness of computational screening models through a combination of high throughput synthesis (HTS), characterization, and machine learning. In this review, we highlight key developments in high throughput approaches pertinent to porous materials. Specifically, we focus on developments in the field of zeolitic materials, metal-organic frameworks (MOFs), and touch upon covalent organic frameworks (COFs). Although superficially these classes of materials have little in common, there are structural links such as topology which allow for the consideration of how high throughput methods contrast with one another. By contrasting developments in different fields, we identify some underutilized approaches which could be leveraged in the future. We target structurally ordered materials because the exploration of chemical and topological space is more straightforward to enumerate, though similar approaches could be applied to disordered materials. The scale of the problem faced in materials science of targeted structure and function [1] is by no means unique; in drug discovery, HTS and chemoinformatic approaches have been used for more than 40 years in an attempt to accelerate the discovery of druggable molecules. [2,3] Unlike the drug discovery process, where Lipinski's "rule of 5" [4] provides a reliable top level sift for viable targets, identifying the most promising material for a target application requires very particular properties that may be intertwined in complex and contradictory ways. For example, thermoelectrics require high electrical conductivity and low thermal conductivity and yet these properties are typically correlated unless they can be separated. [5] Given the large scope of potential applications, the advent of the Materials Genome Initiative (MGI) [6,7] in the United States has undoubtedly helped to promote ways to solve the aforementioned obstacles and numerous others. Similarly there are major initiatives within the EU (e.g., NOMAD [8] and BIGmax [9]) and Switzerland (MARVEL [10]). In the MGI, nanoporous materials, including zeolites and MOFs, have been specifically targeted [11] and in this review, we seek to highlight particular challenges in the field of porous materials and how researchers have sought to overcome these challenges. We focus primarily on the methods used in HTS and rapid throughput/screening computational approaches. Aspects of high throughput computation applied to porous materials have been reviewed before, [12-14] as has high throughput experimentation (HTE) [15,16] but here we focus on their combination within an integrated workflow. Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre-and post-synthetic computer simulations are mostly carried out in a piecemeal a...

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“…In spite of the great interest in ZIFs research, the reports on the optimization of ZIF for heat storage and reallocation applications are scarce. The majority of studies for ZIFs (and MOFs) focus on water as a working fluid [6][7][8][9]. This is due to water being cost effective and environmentally friendly [9].…”

Section: Introductionmentioning

confidence: 99%

“…The majority of studies for ZIFs (and MOFs) focus on water as a working fluid [6][7][8][9]. This is due to water being cost effective and environmentally friendly [9]. However, most of the stable ZIFs are hydrophobic and, therefore, alternative working fluids have been considered.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of ZIF-8 and ZIF-90 as Heat Storage Materials by Using Water, Methanol and Ethanol as Working Fluids

et al. 2021

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The increasing demand for heating/cooling is of grave concern due to the ever-increasing population. One method that addresses this issue and uses renewable energy is Thermochemical Energy Storage (TCES), which is based on the reversible chemical reactions and/or sorption processes of gases in solids or liquids. Zeolitic imidazolate frameworks (ZIFs), composed of transition metal ions (Zn, Co, etc.) and imidazolate linkers, have gained significant interest recently as porous adsorbents in low temperature sorption-based TES (sun/waste heat). In this study, we examined two different sodalite-type ZIF structures (ZIF-8 and ZIF-90) for their potential heat storage applications, based on the adsorption of water, methanol and ethanol as adsorbates. Both ZIF structures were analysed using PXRD, TGA, SEM and N2 physisorption while the % adsorbate uptake and desorption enthalpy was evaluated using TGA and DSC analysis, respectively. Among the studied adsorbent–adsorbate pairs, ZIF-90-water showed the highest desorption enthalpy, the fastest sorption kinetics and, therefore, the best potential for use in heat storage/reallocation applications. This was due to its significantly smaller particle size and higher specific surface area, and the presence of mesoporosity as well as polar groups in ZIF-90 when compared to ZIF-8.

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Screening of Covalent–Organic Frameworks for Adsorption Heat Pumps

Cited by 43 publications

References 38 publications

In Silico Design of Covalent Organic Framework-Based Electrocatalysts

In Silico Design of Covalent Organic Framework-Based Electrocatalysts

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Evaluation of ZIF-8 and ZIF-90 as Heat Storage Materials by Using Water, Methanol and Ethanol as Working Fluids

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