Experimental Validation of a Computational Screening Approach to Predict Redox Potentials for a Diverse Variety of Redox-Active Organic Molecules

2020 ◽  
Vol 124 (44) ◽  
pp. 24105-24114
Author(s):  
Alexandra R. McNeill ◽  
Samantha E. Bodman ◽  
Amy M. Burney ◽  
Chris D. Hughes ◽  
Deborah L. Crittenden
Keyword(s):  
Experimental Validation ◽  
Organic Molecules ◽  
Redox Potentials ◽  
Computational Screening ◽  
Screening Approach ◽  
Redox Active

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

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3978
Author(s):  
Rocco Peter Fornari ◽  
Piotr de Silva
Keyword(s):  
Organic Molecules ◽  
Ph Value ◽  
Computational Cost ◽  
Legendre Transform ◽  
Good Prediction ◽  
Redox Potentials ◽  
Free Energies ◽  
Hybrid Functional ◽  
Data Set ◽  
State Changes

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.

scholarly journals A quantitative evaluation of computational methods to accelerate the study of alloxazine-derived electroactive compounds for energy storage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qi Zhang ◽  
Abhishek Khetan ◽  
Süleyman Er
Keyword(s):  
Energy Storage ◽  
Density Functional ◽  
Tight Binding ◽  
Computational Cost ◽  
Redox Potentials ◽  
Storage Compounds ◽  
Computational Screening ◽  
Chemical Descriptor ◽  
Lowest Unoccupied Molecular Orbital ◽  
Semi Empirical

AbstractAlloxazines are a promising class of organic electroactive compounds for application in aqueous redox flow batteries (ARFBs), whose redox properties need to be tuned further for higher performance. High-throughput computational screening (HTCS) enables rational and time-efficient study of energy storage compounds. We compared the performance of computational chemistry methods, including the force field based molecular mechanics, semi-empirical quantum mechanics, density functional tight binding, and density functional theory, on the basis of their accuracy and computational cost in predicting the redox potentials of alloxazines. Various energy-based descriptors, including the redox reaction energies and the frontier orbital energies of the reactant and product molecules, were considered. We found that the lowest unoccupied molecular orbital (LUMO) energy of the reactant molecules is the best performing chemical descriptor for alloxazines, which is in contrast to other classes of energy storage compounds, such as quinones that we reported earlier. Notably, we present a flexible in silico approach to accelerate both the singly and the HTCS studies, therewithal considering the level of accuracy versus measured electrochemical data, which is readily applicable for the discovery of alloxazine-derived organic compounds for energy storage in ARFBs.

scholarly journals Insight to Microbial Fe(III) Reduction Mediated by Redox-Active Humic Acids with Varied Redox Potentials

2021 ◽  
Vol 18 (13) ◽  
pp. 6807
Author(s):  
Jingtao Duan ◽  
Zhiyuan Xu ◽  
Zhen Yang ◽  
Jie Jiang
Keyword(s):  
Molecular Weight ◽  
Electron Transfer ◽  
Humic Acids ◽  
Weight Fraction ◽  
Electrochemical Analysis ◽  
Microbial Reduction ◽  
Redox Potentials ◽  
Groundwater Environment ◽  
Redox Active ◽  
Different Molecular Weight

Redox-active humic acids (HA) are ubiquitous in terrestrial and aquatic systems and are involved in numerous electron transfer reactions affecting biogeochemical processes and fates of pollutants in soil environments. Redox-active contaminants are trapped in soil micropores (<2 nm) that have limited access to microbes and HA. Therefore, the contaminants whose molecular structure and properties are not damaged accumulate in the soil micropores and become potential pollution sources. Electron transfer capacities (ETC) of HA reflecting redox activities of low molecular weight fraction (LMWF, <2.5) HA can be detected by an electrochemical method, which is related to redox potentials (Eh) in soil and aquatic environments. Nevertheless, electron accepting capacities (EAC) and electron donating capacities (EDC) of these LMWF HA at different Eh are still unknown. EDC and EAC of different molecular weight HA at different Eh were analyzed using electrochemical methods. EAC of LMWF at −0.59 V was 12 times higher than that at −0.49 V, while EAC increased to 2.6 times when the Eh decreased from −0.59 V to −0.69 V. Afterward, LMWF can act as a shuttle to stimulate microbial Fe(III) reduction processes in microbial reduction experiments. Additionally, EAC by electrochemical analysis at a range of −0.49–−0.59 V was comparable to total calculated ETC of different molecular weight fractions of HA by microbial reduction. Therefore, it is indicated that redox-active functional groups that can be reduced at Eh range of −0.49–−0.59 are available to microbial reduction. This finding contributes to a novel perspective in the protection and remediation of the groundwater environment in the biogeochemistry process.

Hybrid Screening Approach for Very Small Fragments: X-ray and Computational Screening on FKBP51

2020 ◽  
Vol 63 (11) ◽  
pp. 5856-5864
Author(s):  
Sebastian W. Draxler ◽  
Margit Bauer ◽  
Christian Eickmeier ◽  
Simon Nadal ◽  
Herbert Nar ◽  
...  
Keyword(s):  
X Ray ◽  
Computational Screening ◽  
Screening Approach ◽  
Hybrid Screening

Computational Screening of Porous Organic Molecules for Xenon/Krypton Separation

2017 ◽  
Vol 121 (28) ◽  
pp. 15211-15222 ◽  
Author(s):  
Marcin Miklitz ◽  
Shan Jiang ◽  
Rob Clowes ◽  
Michael E. Briggs ◽  
Andrew I. Cooper ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Computational Screening

scholarly journals Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages

2021 ◽  
Author(s):  
Steven Bennett ◽  
Filip Szczypiński ◽  
Lukas Turcani ◽  
Michael Briggs ◽  
Rebecca L. Greenaway ◽  
...  
Keyword(s):  
Organic Molecules ◽  
Class Imbalance ◽  
Computational Prediction ◽  
Material Development ◽  
Computational Screening ◽  
Machine Learning Model ◽  
Synthetic Accessibility ◽  
Internal Cavities ◽  
Computational Discovery ◽  
Novel Structures

<div>Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties. </div>

3D periodic polyimide nano-networks for ultrahigh-rate and sustainable energy storage

2021 ◽  
Author(s):  
Youngjin Ham ◽  
Nathan J. Fritz ◽  
Gayea Hyun ◽  
Young Bum Lee ◽  
Jong Seok Nam ◽  
...  
Keyword(s):  
Energy Storage ◽  
Organic Molecules ◽  
Sustainable Energy ◽  
Next Generation ◽  
Significant Progress ◽  
Redox Active

Organic molecules with redox-active motifs are of great interest for next-generation electrodes for sustainable energy storage. While there has been significant progress in designing redox-active molecules, the practical requirements of...

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