Electrochemical and Thermal Characterization of a Graphene-Based Electrochemical Double-Layer Capacitor (EDLC)
Bibliographic record
Abstract
Graphene-based electrodes are of great interest for developing high performance electrochemical double-layer capacitors (EDLCs) due to their excellent electrochemical properties, low electric resistance and unique structures. Meanwhile, EDLCs have become more attractive not only to offer high power and energy densities but also to withstand a harsh temperature ranges [1]. Since temperature effects can be crucial to estimate the rational evaluation of degradation, energy efficiency and lifetime, it requires better understanding of temperature-dependent electrochemical properties especially at electrode/electrolyte interfaces. In this study, the performance of an EDLC, assembled with two identical graphene electrodes and the 1M Et 4 NBF 4 /PC electrolyte in a coin cell, is systemically characterized under various operating temperature conditions, ranged from -30 °C to 60 °C. Graphene deposition is carried out by using the vacuum filtration method. This method yields an electrically strong conductive and thermally stable nano-structured graphene paper [2]. Electrochemical characterization techniques including cyclic voltammetry (CV), constant charging/discharging (CCD) and electrochemical impedance spectroscopy (EIS) are performed to evaluate capacitance retention, energy and power densities, internal resistance variation and interfacial processes at the double-layer region. In addition, EIS data is simulated with the proposed electric equivalent circuit in order to investigate the temperature dependency on interfacial reactions with respects to mass transfer (diffusion) and electrode kinetics (charge transfer) [3]. Each reaction is represented by electrical resistive elements in the equivalent circuit, and its values are correlated with the activation energy to quantitatively rationalize the observed behaviour of diffusion and the charge transfer kinetics with temperature. References: [1] R. Kotz, M. Hahn, R. Gallay, Journal of Power Sources, 154 (2006) 550-555. [2] H. Chen, M.B. Mueller, K.J. Gilmore, G.G. Wallace, D. Li, Advanced Materials, 20 (2008) 3557. [3] J.W. Jinhee Kang, Shesha H. Jayaram, Aiping Yu, Xiaohui Wang, Electrochimica Acta, 115 (2014) 587-598.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".