Engineering topics
Doan, Hieu A.
Publications and source records attributed to Doan, Hieu A..
Acidity-Governed Rules in the Electrochemical Performance of Fluorinated Benzenes for High-Voltage Lithium Metal Batteries
Judicious selection of the optimal fluorobenzene (FB) as a nonsolvating cosolvent for lithium metal batteries (LMBs) is reported. For this work, we found the key correlation between FB structures and cycling stabilities of cells: increased fluorine substitution of FBs results in higher anodic stability but at the expense of reduced reductive stability, and FBs containing three or more fluorine atoms exhibit insufficient anodic stability in the electrolyte system comprised of fluoroethylene carbonate (FEC) and ethyl methyl carbonate (EMC). More importantly, FBs with higher acidity (lower pK a ) due to protons located between two adjacent fluorine atoms tend to be more susceptible to side reactions during cycling. Our results indicate that difluorobenzenes with no “acidic” proton (DFB2 and DFB4) have emerged as the optimal choice with the desired redox stability in high-voltage LMBs. Nuclear magnetic resonance and X-ray photoelectron spectroscopy confirmed these findings, providing guidance for selecting the most suitable FB variants as nonsolvating cosolvents for high-voltage LMBs.
An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations
Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.
Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries
Organic nonaqueous redox flow batteries (ONRFBs) are promising energy storage devices due to their scalability and reliance on sourceable materials. However, finding suitable redox-active organic molecules (redoxmers) for these batteries remains a challenge. Using plant-based compounds as precursors for these redoxmers can decrease their costs and environmental toxicity. In this computational study, flavonoid molecules have been examined as potential redoxmers for ONRFBs. Flavone and isoflavone derivatives were selected as catholyte (positive charge carrier) and anolyte (negative charge carrier) molecules, respectively. To drive their redox potentials to the opposite extremes, in silico derivatization was performed using a novel algorithm to generate a library of > 40000 candidate molecules that penalizes overly complex structures. A multiobjective Bayesian optimization based active learning algorithm was then used to identify best redoxmer candidates in these search spaces. Furthermore, our study provides methodologies for molecular design and optimization of natural scaffolds and highlights the need of incorporating expert chemistry awareness of the natural products and the basic rules of synthetic chemistry in machine learning.
Trimer Quinoxalines as Organic Cathode Materials for Lithium-Ion Batteries
Due to synthetic ease, high redox potentials, low solubility in polar electrolytes, and good electric conductivity of their semiconducting crystals, discotic quinoxaline trimers (3Q) have been considered as possible candidates for 4 V organic cathodes in lithium-ion batteries. To assess their feasibility as such materials, several 3Q derivatives have been synthesized and tested in half-cells. In voltage limited cycling tests at 1.2–3.9 V vs Li/Li + , the specific discharge capacities of 40–180 mAh g -1 were obtained at a rate of 1 C, and multiple lithiation of 3Q and its derivatives was observed during discharge. However, the obtained discharge capacity was only a fraction of the theoretical capacity expected for reversible six-electron reduction; there was also rapid capacity fade. Our spectroscopic studies indicate the reversible three-electron lithiation at 2 V vs Li/Li + , but suggest instability of more highly discharged states. Finally, our conclusion is that while the 3Q derivatives combine several traits that are desirable in an organic cathode material (including negligible solubility, capacity for multiple charging, and near-100% coulombic efficiency), these materials are still impractical to use.