In today’s world, there is an ever-increasing demand for electrochemical energy storage like batteries and supercapacitors due to sustainable energy and technological advancements. Researchers have been looking for state-of-the-art materials and designs to deal with this need. While these efforts have significantly enhanced energy storage systems, the underlying mechanisms driving their effectiveness are not yet fully understood. One of the essential techniques is Cyclic Voltammetry (CV).
CV is a multifaceted and powerful tool in the realms of material science and electrochemistry, holistically providing insights into the redox behavior of materials. This test can be applied to a wide range of materials in diverse applications involving the electron-transfer process.
In CV, the working electrode's potential is varied stepwise between two predetermined values, and the response current is recorded. During the negative potential scanning, all species at the electrode interface undergo reduction, resulting in the recording of a cathodic current. Conversely, when the potential is positively swept, the oxidation of these species produces an anodic current. The reiteration of these cycles will be continued until reaching a cyclical steady state.
Since most systems are inherently complex and often include multiple redox reactions simultaneously, resulting in overlapped peaks, we need to accurately separate these from each other to examine the electron transfer-initiated chemical reactions in more detail.
The Dunn method is a widely used approach to differentiate and analyze the capacitive and diffusive contributions to the total current in electrode materials. This method is particularly used for studying energy storage devices like supercapacitors and batteries.
Capacitive current: i(v) ∝ v → i(v) = k₁v
Diffusion-controlled current:i(v) ∝ √v → i(v) = k₂√v
Total current: i(v) = k₁v + k₂√v
i(v)/√v = k₁√v + k₂
This repository includes a Python-based script designed to perform deconvolution and visualization of cyclic voltammetry (CV) data using the Dunn method. The script facilitates the separation of capacitive and diffusive contributions to the total current. After executing the code, it will generate an Excel file with the calculated k₁ and k₂ values.
- Perform CV experiments at least two scan rates (ensure the recorded potential interval remains consistent across all scan rates, resulting in fixed potentials).
- Format the data Excel as follows:
- First Column: Keep the potentials (fixed potentials) in this column.
- Subsequent Columns: Organize the corresponding currents for each scan rate in the following columns.
- Open the file src/CapDiffAnalyzer.ipynb in Jupyter Notebook, and load your formatted Excel data using the following code line (line 12): "data = pd.read_excel('example.xlsx')"
- Run the code!
**Evaluated k₁ and k₂ coefficients**
**CV curves with capacitive and diffusive contributions at the scan rate of 0,2 mV/s**
**CV curves with capacitive and diffusive contributions at the scan rate of 0,6 mV/s**
**CV curves with capacitive and diffusive contributions at the scan rate of 0,8 mV/s**