Voltammetric Methods Augmented with Physical Models and Statistical Inference
Increasing adoption of low-cost renewable energy technologies can enable global sustainability goals. However, the intermittency of variable resources inhibits broad deployment, necessitating a range of energy management systems including rechargeable batteries (e.g., redox flow batteries (RFBs), li...
| Autor principal: | |
|---|---|
| Outros Autores: | |
| Formato: | Tese |
| Publicado em: |
Massachusetts Institute of Technology
2023
|
| Acesso em linha: | https://hdl.handle.net/1721.1/147421 https://orcid.org/0000-0003-2195-9408 |
| _version_ | 1826191970082488320 |
|---|---|
| author | Fenton Jr., Alexis M. |
| author2 | Brushett, Fikile R. |
| author_facet | Brushett, Fikile R. Fenton Jr., Alexis M. |
| author_sort | Fenton Jr., Alexis M. |
| collection | MIT |
| description | Increasing adoption of low-cost renewable energy technologies can enable global sustainability goals. However, the intermittency of variable resources inhibits broad deployment, necessitating a range of energy management systems including rechargeable batteries (e.g., redox flow batteries (RFBs), lithium-ion batteries). RFBs are particularly attractive because their system architecture enables decoupled energy capacity and power output—along with long service life and simplified maintenance—but their present-day costs remain prohibitively high. A promising pathway to economically competitive RFBs is the use of redox-active organic compounds (RAOs), which can be functionalized to improve battery performance (e.g., cell voltage, energy density). However, state-of-the-art RAOs often decompose during operation, shortening battery lifetime. Understanding and mitigating this decay is thus crucial; corresponding efforts typically rely on ex situ and post mortem analyses to elucidate the decay pathway(s) which, while often successful, may be time-consuming and expensive. These processes may be streamlined by incorporating more real-time studies using in situ or operando electrochemical methods such as voltammetry, a powerful technique able to accurately estimate the composition of an electrolyte solution in an automated fashion. However, proposed voltammetric routines usually do not leverage physical models, meaning they may perform poorly when confronting conditions not included in training data.
In this thesis, I seek to advance voltammetric analyses to evaluate the behavior of degrading RAOs by developing physics-informed protocols that leverage statistical inference. I first construct an algorithm that utilizes physical models and Bayesian inference to correctly identify RAOs using multiple techniques in several independently prepared multicomponent solutions in near-real-time (< 5 min). I subsequently estimate the degree to which an electrolyte is charged, as well as the total RAO concentration, with high accuracy (< 4 % average error), in real-time (< 1 min), and in an automated fashion. Finally, I present a protocol that jointly evaluates dissimilar voltammetry techniques to improve the initial compound identification protocol. Through these developments, I lay a foundation for advanced in situ or operando voltammetric methods to aid in understanding, and consequently mitigating, RAO decay; this, in turn, may accelerate the development of new electrochemical technologies for a sustainable energy economy. |
| first_indexed | 2024-09-23T09:03:52Z |
| format | Thesis |
| id | mit-1721.1/147421 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T09:03:52Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1474212023-01-20T03:56:50Z Voltammetric Methods Augmented with Physical Models and Statistical Inference Fenton Jr., Alexis M. Brushett, Fikile R. Massachusetts Institute of Technology. Department of Chemical Engineering Increasing adoption of low-cost renewable energy technologies can enable global sustainability goals. However, the intermittency of variable resources inhibits broad deployment, necessitating a range of energy management systems including rechargeable batteries (e.g., redox flow batteries (RFBs), lithium-ion batteries). RFBs are particularly attractive because their system architecture enables decoupled energy capacity and power output—along with long service life and simplified maintenance—but their present-day costs remain prohibitively high. A promising pathway to economically competitive RFBs is the use of redox-active organic compounds (RAOs), which can be functionalized to improve battery performance (e.g., cell voltage, energy density). However, state-of-the-art RAOs often decompose during operation, shortening battery lifetime. Understanding and mitigating this decay is thus crucial; corresponding efforts typically rely on ex situ and post mortem analyses to elucidate the decay pathway(s) which, while often successful, may be time-consuming and expensive. These processes may be streamlined by incorporating more real-time studies using in situ or operando electrochemical methods such as voltammetry, a powerful technique able to accurately estimate the composition of an electrolyte solution in an automated fashion. However, proposed voltammetric routines usually do not leverage physical models, meaning they may perform poorly when confronting conditions not included in training data. In this thesis, I seek to advance voltammetric analyses to evaluate the behavior of degrading RAOs by developing physics-informed protocols that leverage statistical inference. I first construct an algorithm that utilizes physical models and Bayesian inference to correctly identify RAOs using multiple techniques in several independently prepared multicomponent solutions in near-real-time (< 5 min). I subsequently estimate the degree to which an electrolyte is charged, as well as the total RAO concentration, with high accuracy (< 4 % average error), in real-time (< 1 min), and in an automated fashion. Finally, I present a protocol that jointly evaluates dissimilar voltammetry techniques to improve the initial compound identification protocol. Through these developments, I lay a foundation for advanced in situ or operando voltammetric methods to aid in understanding, and consequently mitigating, RAO decay; this, in turn, may accelerate the development of new electrochemical technologies for a sustainable energy economy. Ph.D. 2023-01-19T19:49:11Z 2023-01-19T19:49:11Z 2022-09 2022-08-12T14:20:08.149Z Thesis https://hdl.handle.net/1721.1/147421 https://orcid.org/0000-0003-2195-9408 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Fenton Jr., Alexis M. Voltammetric Methods Augmented with Physical Models and Statistical Inference |
| title | Voltammetric Methods Augmented with Physical Models and Statistical Inference |
| title_full | Voltammetric Methods Augmented with Physical Models and Statistical Inference |
| title_fullStr | Voltammetric Methods Augmented with Physical Models and Statistical Inference |
| title_full_unstemmed | Voltammetric Methods Augmented with Physical Models and Statistical Inference |
| title_short | Voltammetric Methods Augmented with Physical Models and Statistical Inference |
| title_sort | voltammetric methods augmented with physical models and statistical inference |
| url | https://hdl.handle.net/1721.1/147421 https://orcid.org/0000-0003-2195-9408 |
| work_keys_str_mv | AT fentonjralexism voltammetricmethodsaugmentedwithphysicalmodelsandstatisticalinference |