Collaborative Research: Combining Models and Experiment for Quantitative Characterization of Electrocatalytic Carbon Dioxide Reduction on Doped Ceria

合作研究：结合模型和实验定量表征掺杂二氧化铈电催化二氧化碳还原

• 批准号: 1705397
• 负责人: David Mebane
• 金额: $ 18.99万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1705397&HistoricalAwards=false
• 关键词: Collaborative; Research; Combining; Models; Experiment

The project investigates a high-temperature electrochemical approach for converting the carbon in carbon dioxide (CO2) gas to higher value carbon-containing products such as hydrocarbon fuels, thereby serving as a critical component in eventual closed carbon-cycle processes for renewable energy generation. Specifically, the project will investigate the reduction of carbon dioxide to carbon monoxide (CO) on high-temperature cerium oxide based electrodes utilizing a combination of experimental techniques, theoretical models, and statistical methods to distinguish between various reaction mechanisms that have been proposed but remain unverified. The resulting understanding will provide guidance for designing solid oxide electrodes with improved efficiency and durability.Thin-film test cells with ceria working electrodes on yttria-stabilized zirconia (YSZ) electrolytes will be fabricated, exposed to CO and CO2-containing gases and polarized at high temperature. The potential gradient in the film caused by the polarization yields a gradient in the surface potential as a function of distance from the current collector. These changes in surface potential - a quantity closely related to the concentrations of active intermediates on the ceria surface - will be measured, during cell operation, with sub-100 nm lateral resolution utilizing in-operando high-temperature scanning surface potential microscopy (HT-SSPM). These data, along with full-cell electrochemical impedance spectroscopy data (EIS), will be collected for a matrix of different experimental conditions including changes in gas composition, temperature, applied cell potential, dopant concentration, and thermal aging. Electrocatalytic phase-field modeling (EPFM), which couples mechanistic models of the catalytic reaction with the material behavior of the ceria electrode, will be quantitatively calibrated to the entire experimental dataset using Bayesian model-based analysis (BMA). Models generated and conclusions drawn during the course of this project will be useful for further development of high-temperature, mixed ionic-electronic conducting catalysts. The unique coupling of state-of-the-art in-operando HT-SSPM and EPFM methods is made possible by Bayesian calibration, which quantitatively links large experimental datasets and complex models, leading to estimates of model parameters (with uncertainty quantification) along with assessments of the model fitness in light of the data. BMA also affords the opportunity to incorporate existing measurements or calculations (including quantum calculations) of key parameters in the analysis. In addition to the technical elements, the project will include a lab activity for high school physics students that will be developed in the researchers' local communities and publicized nationally.

该项目研究了一种高温电化学方法，将二氧化碳（CO2）气体中的碳转化为更高价值的含碳产品，如碳氢化合物燃料，从而成为可再生能源发电最终封闭碳循环过程的关键组成部分。具体来说，该项目将利用实验技术、理论模型和统计方法的结合，研究在高温氧化铈电极上将二氧化碳还原为一氧化碳（CO），以区分各种已提出但尚未得到验证的反应机制。由此产生的理解将为设计具有更高效率和耐用性的固体氧化物电极提供指导。在氧化钇稳定的氧化锆（YSZ）电解质上制作带有氧化铈工作电极的薄膜测试电池，将其暴露于CO和含二氧化碳的气体中并在高温下极化。薄膜中由极化引起的电位梯度产生表面电位梯度，这是与集流器距离的函数。这些表面电位的变化——一个与氧化铈表面活性中间体浓度密切相关的量——将在细胞运行过程中被测量，使用在操作中高温扫描表面电位显微镜（HT-SSPM）以低于100纳米的横向分辨率测量。这些数据以及全电池电化学阻抗谱数据（EIS）将收集不同实验条件下的矩阵，包括气体成分、温度、应用电池电位、掺杂剂浓度和热老化的变化。电催化相场建模（EPFM）将催化反应的机理模型与铈电极的材料行为耦合起来，将使用基于贝叶斯模型的分析（BMA）对整个实验数据集进行定量校准。在此过程中建立的模型和得出的结论将对进一步开发高温混合离子-电子导电催化剂有用。贝叶斯校准使最先进的运行中的HT-SSPM和EPFM方法的独特耦合成为可能，贝叶斯校准定量地将大型实验数据集和复杂模型联系起来，导致模型参数的估计（不确定性量化）以及根据数据评估模型适应度。BMA还提供了在分析中纳入关键参数的现有测量或计算（包括量子计算）的机会。除了技术元素外，该项目还将包括一个针对高中物理学生的实验室活动，该活动将在研究人员所在的当地社区开展，并在全国范围内宣传。

项目成果

New Data-Driven Interacting-Defect Model Describing Nanoscopic Grain Boundary Compositions in Ceramics

新的数据驱动的相互作用缺陷模型描述陶瓷中的纳米晶界成分

• DOI: 10.1021/acs.jpcc.0c05713
• 发表时间: 2020
• 期刊: The Journal of Physical Chemistry C
• 作者: Tong, Xiaorui;Bowman, William J.;Mejia-Giraldo, Alejandro;Crozier, Peter A.;Mebane, David S.
• 通讯作者: Mebane, David S.

Analyzing the grain‐boundary resistance of oxide‐ion conducting electrolytes: Poisson‐Cahn vs Poisson‐Boltzmann theories

• DOI: 10.1111/jace.16716
• 发表时间: 2019-09
• 期刊: Journal of the American Ceramic Society
• 影响因子: 3.9
• 作者: X. Tong;D. Mebane;R. D. De Souza

A Space-Charge Treatment of the Increased Concentration of Reactive Species at the Surface of a Ceria Solid Solution

二氧化铈固溶体表面活性物质浓度增加的空间电荷处理

• DOI: 10.1002/ange.201708118
• 发表时间: 2017
• 期刊: Angewandte Chemie
• 作者: Zurhelle, Alexander F.;Tong, Xiaorui;Klein, Andreas;Mebane, David S.;De Souza, Roger A.
• 通讯作者: De Souza, Roger A.