New insights into phenazine-based organic redox flow batteries by using high-throughput DFT modelling

Cruz, Carlos de la; Molina, Antonio Manuel Pozo; Patil, Nagaraj; Ventosa, Edgar; Marcilla, Rebeca; Mavrandonakis, Andreas

doi:10.1039/d0se00687d

Cited by 52 publications

(79 citation statements)

References 65 publications

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“…The main goal of this contribution is the establishment and validation of a standard procedure for the computational prediction of redox potentials of organic molecules undergoing a proton-coupled electron transfer. One of the main motivations for benchmarking such predictions is the computational screening of the vast chemical space of organic molecules to identify electrolytes for redox flow batteries (RFBs) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. At some point in any computational workflow for materials discovery, one needs a reliable method to predict with reasonable accuracy and moderate computational cost the properties of an already pre-selected candidate pool.…”

Section: Introductionmentioning

confidence: 99%

A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials

Fornari

Silva

2021

Molecules

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Discovering new materials for energy storage requires reliable and efficient protocols for predicting key properties of unknown compounds. In the context of the search for new organic electrolytes for redox flow batteries, we present and validate a robust procedure to calculate the redox potentials of organic molecules at any pH value, using widely available quantum chemistry and cheminformatics methods. Using a consistent experimental data set for validation, we explore and compare a few different methods for calculating reaction free energies, the treatment of solvation, and the effect of pH on redox potentials. We find that the B3LYP hybrid functional with the COSMO solvation method, in conjunction with thermal contributions evaluated from BLYP gas-phase harmonic frequencies, yields a good prediction of pH = 0 redox potentials at a moderate computational cost. To predict how the potentials are affected by pH, we propose an improved version of the Alberty-Legendre transform that allows the construction of a more realistic Pourbaix diagram by taking into account how the protonation state changes with pH.

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Section: Introductionmentioning

confidence: 99%

A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials

Fornari

Silva

2021

Molecules

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“…ONPZ is considered a promising anode redox-active candidate for RBFs containing phenazine derivatives. 18 Additionally, most models were able to predict the correct relative order of compounds with respect to the redox potential, clustering potential cathode redox-active candidates in the negative region, and potential anode redox-active candidates in the region close to zero. It can be seen that the promising cathode redox-active species contain electron-donating groups (i.e., NH2), whereas promising anode redox-active species contain electron-withdrawing groups (i.e., CN, NO2).…”

Section: Identification Of the Promising Redox-active Candidatesmentioning

confidence: 99%

“…Data used in this study was obtained from the work reported by Mavrandonakis and coworkers. 18 The redox potentials of 189 phenazine derivatives in DME were provided in that work. These potentials were computed using DFT.…”

Section: Data Setmentioning

confidence: 99%

“…13,17 So far, transition metal-based redox flow batteries (such as vanadium, iron, and chromium) have found some commercial success. However, their widespread adoption has been limited mainly due to high production cost, toxicity, and cell component corrosion associated with the use of transition metal salts 18,19 . Therefore, redox flow batteries containing organic redox-active species are being heavily investigated due to their low production cost, access to a massive space of electroactive compounds, and low environmental impact.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning the Redox Potentials of Phenazine Derivatives: A Comparative Study on Molecular Features

Ghule

Bagchi²,

Vanka³

2021

Preprint

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<div>Electricity generation is a major contributing factor for greenhouse gas emissions. Energy storage systems available today have a combined capacity to store less than 1% of the electricity being consumed worldwide. Redox Flow Batteries (RFBs) are promising candidates for green and efficient energy storage systems. RFBs are being used in renewable energy systems, but their widespread adoption is limited due to high production costs and toxicity associated with the transition-metal-based redox-active species. Therefore, cheaper and greener alternative organic redox-active species are being investigated. Recent reports have shown organic molecules based on phenazine are promising candidates for redox-active species in RFBs. However, the large number of available organic compounds makes the conventional experimental and DFT methods impractical to screen thousands of molecules in a reasonable amount of time. In contrast, machine-learning models have low development time, short prediction time, and high accuracy; thus, are being heavily investigated for virtual screening applications. In this work, we developed machine-learning models to predict the redox potential of phenazine derivatives in DME solvent using a small dataset of 185 molecules. 2D, 3D, and Molecular Fingerprint features were computed using readily available and easy-to-use python libraries, making our approach easily adaptable to similar work. Twenty linear and non-linear machine-learning models were investigated in this work. These models achieved excellent performance on the unseen data (i.e., R<sup>2</sup> > 0.98, MSE < 0.008 V2 and MAE < 0.07 V). Model performance was assessed in a consistent manner using the training and evaluation pipeline developed in this work. We showed that 2D molecular features are most informative and achieve the best prediction accuracy among four feature sets. We also showed that often less preferred but relatively faster linear models could perform better than non-linear models when the feature set contains different types of features (i.e., 2D, 3D, and Molecular Fingerprints). Further investigations revealed that it is possible to reduce the training and inference time without sacrificing prediction accuracy by using a small subset of features. Moreover, models were able to predict the previously reported promising redox-active compounds with high accuracy. Also, significantly low prediction errors were observed for the functional groups. Although some functional groups had only one compound in the training set, best-performing models could achieve errors (MAPE) less than 10%. The major source of error was a lack of data near-zero and in the positive region. Therefore, this work shows that it is possible to develop accurate machine-learning models that could potentially screen millions of compounds in a short amount of time with a small training set and limited number of easy to compute features. Thus, results obtained in this report would help in the adoption of green energy by accelerating the field of materials discovery for energy storage applications.</div>

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“…[8] Different families of organic molecules have been reported, including quinones, [8,9] viologens, [10] aza-aromatics, [11] ferrocene, [10c, 12] nitroxide radical species, [13] and azobenzene. [14] Phenazine and its derivatives, [15] which have been widely used as broad-spectrum antibiotics, [16] are good candidates for negolytes in AORFBs,owing to their low redox potentials and capability to accommodate two-electron transfers.However,the reported research of aqueous soluble redox-active phenazines for AORFBs is rather limited. The pioneering work reported by Schubert et al investigated the combined phenazine-TEMPO molecule through triethylene linker that exhibited reversible redox property but poor solubility in water.…”

Section: Introductionmentioning

confidence: 99%

Biomimetic Amino Acid Functionalized Phenazine Flow Batteries with Long Lifetime at Near‐Neutral pH

et al. 2021

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Aqueous organic redox flowb atteries (AORFBs) are ap romising electrochemical technology for large-scale energy storage.W er eport ab iomimetic,u ltra-stable AORFB utilizing an amino acid functionalizedp henazine (AFP). A series of AFPs with various commercial amino acids at different substituted positions were synthesized and studied. 1,6-AFPs displaym uchh igher stability during cycling when compared to 2,7-and 1,8-AFPs.M echanism investigations reveal that the reduced 2,7-and 1,8-AFPs tend to tautomerize and lose their reversible redox activities,w hile 1,6-AFPs possess ultra-high stability both in their oxidized and reduced states.B yp airing 3,3'-(phenazine-1,6-diylbis(azanediyl))dipropionic acid (1,6-DPAP)w ith ferrocyanide at pH 8w ith 1.0 Melectron concentration, this flowbattery exhibits an OCV of 1.15 Vand an extremely lowcapacity fade rate of 0.5 %per year.These results show the importance of molecular engineering of redox-active organics for robust redox-flowb atteries.

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New insights into phenazine-based organic redox flow batteries by using high-throughput DFT modelling

Abstract: DFT calculations reveal interesting structure–property relationships of the redox potentials of phenazines in non-aqueous media.

Cited by 52 publications

References 65 publications

A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials

A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials

Machine Learning the Redox Potentials of Phenazine Derivatives: A Comparative Study on Molecular Features

Biomimetic Amino Acid Functionalized Phenazine Flow Batteries with Long Lifetime at Near‐Neutral pH

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