Engineering topics

Tuckerman, Mark

Publications and source records attributed to Tuckerman, Mark.

5 records

DOE OSTI2024

Breakthrough Conductivity Enhancement in Deep Eutectic Solvents via Grotthuss–Type Proton Transport

There is an increasing demand for the development of ion-conducting electrolytes for energy storage systems. Much attention is directed toward deep eutectic solvents as potential candidates. In the search for highly conductive systems, the possibility of designing deep eutectic solvents with Grotthuss-type proton transport is widely overlooked. Herein, ethaline, a mixture of choline chloride and ethylene glycol is used in a 1:2 molar ratio, to induce a significant conductivity increase with the addition of water and sulfuric acid (H 2 SO 4 ). The achieved breakthrough conductivity is analyzed experimentally and simulated with ab initio molecular dynamics (AIMD). At sufficient water content, an H-bonding network is formed that leads to a significant breakthrough conductivity based on H 2 SO 4 -derived proton transfer following the long-established Grotthuss proton transport mechanism. This result is substantiated by the positive deviation from the ideal KCl line in the Walden plot. Specifically, the data series positioned above the reference line indicates a Grotthuss mechanism in action. The AIMD simulations demonstrate proton transfer between water and ethylene glycol, supported by simulation frames captured at various times.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Tailoring Electrochemical CO 2 Reduction on Copper by Reactive Ionic Liquid and Native Hydrogen Bond Donors

Abstract Electrochemical CO 2 reduction (CO 2 RR) on copper (Cu) shows promise for higher‐value products beyond CO. However, challenges such as the limited CO 2 solubility, high overpotentials, and the competing hydrogen evolution reaction (HER) in aqueous electrolytes hinder the practical realization. We propose a functionalized ionic liquid (IL) which generates ion‐CO 2 adducts and a hydrogen bond donor (HBD) upon CO 2 absorption to modulate CO 2 RR on Cu in a non‐aqueous electrolyte. As revealed by transient voltammetry, electrochemical impedance spectroscopy (EIS), and in situ surface‐enhanced Raman spectroscopy (SERS) complemented with image charge augmented quantum‐mechanical/molecular mechanics (IC‐QM/MM) computations, a unique microenvironment is constructed. In this microenvironment, the catalytic activity is primarily governed by the IL and HBD concentrations; former controlling the double layer thickness and the latter modulating the local proton availability. This translates to ample CO 2 availability, reduced overpotential, and suppressed HER where C 4 products are obtained. This study deepens the understanding of electrolyte effects in CO 2 RR and the role of IL ions towards electrocatalytic microenvironment design.

Coskun, Oguz Kagan↗

DOE OSTI2023

Tailoring Electrochemical CO 2 Reduction on Copper by Reactive Ionic Liquid and Native Hydrogen Bond Donors

Electrochemical CO 2 reduction (CO 2 RR) on copper (Cu) shows promise for higher-value products beyond CO. However, challenges such as the limited CO 2 solubility, high overpotentials, and the competing hydrogen evolution reaction (HER) in aqueous electrolytes hinder the practical realization. We propose a functionalized ionic liquid (IL) which generates ion-CO 2 adducts and a hydrogen bond donor (HBD) upon CO 2 absorption to modulate CO 2 RR on Cu in a non-aqueous electrolyte. As revealed by transient voltammetry, electrochemical impedance spectroscopy (EIS), and in situ surface-enhanced Raman spectroscopy (SERS) complemented with image charge augmented quantum-mechanical/molecular mechanics (IC-QM/MM) computations, a unique microenvironment is constructed. In this microenvironment, the catalytic activity is primarily governed by the IL and HBD concentrations; former controlling the double layer thickness and the latter modulating the local proton availability. This translates to ample CO 2 availability, reduced overpotential, and suppressed HER where C 4 products are obtained. This study deepens the understanding of electrolyte effects in CO 2 RR and the role of IL ions towards electrocatalytic microenvironment design.

25 ENERGY STORAGE↗

DOE OSTI2022

Evolution of microscopic heterogeneity and dynamics in choline chloride-based deep eutectic solvents

Deep eutectic solvents (DESs) are an emerging class of non-aqueous solvents that are potentially scalable, easy to prepare and functionalize for many applications ranging from biomass processing to energy storage technologies. Predictive understanding of the fundamental correlations between local structure and macroscopic properties is needed to exploit the large design space and tunability of DESs for specific applications. Here, we employ a range of computational and experimental techniques that span length-scales from molecular to macroscopic and timescales from picoseconds to seconds to study the evolution of structure and dynamics in model DESs, namely Glyceline and Ethaline, starting from the parent compounds. We show that systematic addition of choline chloride leads to microscopic heterogeneities that alter the primary structural relaxation in glycerol and ethylene glycol and result in new dynamic modes that are strongly correlated to the macroscopic properties of the DES formed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Energy-Efficient Self-Organization and Swarm Behavior in Active Matter

Living systems have the unique ability to form hierarchical assemblies, in which individual constituents can perform tasks cooperatively and emergently. Harnessing such properties is a long-standing challenge for the rational design of dynamic materials, that can respond to their environment, communicate with one another, and undergo a rapid, reversible, assembly through the transduction of energy. Recent developments in the design of smart and active colloidal building blocks have led to tremendous breakthroughs, with, for instance, the onset of synthetic photoactivated active assemblies. In this project, we develop a combined experimental, computational, theoretical and Machine Learning framework to shed light on the physical underpinnings of such assembly processes and program the assembly of smart active materials.

36 MATERIALS SCIENCE↗