Capacitance Determination for the Evaluation of Electrochemically Active Surface Area in a Catalyst Layer of NiFe-Layered Double Hydroxides for Anion Exchange Membrane Water Electrolyser
Bibliographic record
Abstract
The determination of the electrochemically active surface area (ECSA) of a catalyst layer (CL) of a non-precious metal catalyst is of fundamental importance in optimizing the design of a durable CL for anion exchange membrane (AEM) water electrolysis, but has yet to be developed. Traditional double layer capacitance (Cdl), measured by cyclic voltammetry (CV), is not suitable for the estimation of the ECSA due to the nonconductive nature of Ni-based oxides and hydroxides in the non-Faradaic region. This paper analyses the applicability of electrochemical impedance spectroscopy (EIS) compared to CV in determining capacitances for the estimation of the ECSA of AEM-based CLs in an aqueous KOH electrolyte solution. A porous electrode transmission line (TML) model was employed to obtain the capacitance–voltage dependence from 1.0 V to 1.5 V at 20 mV intervals, covering both non-Faradic and Faradic regions. This allows for the identification of the contribution of a NiFe-layered double hydroxide (LDH) catalyst and supports in a CL, to capacitances in both non-Faradic and Faradic regions. A nearly constant double layer capacitance (Qdl) observed in the non-Faradic region represents the interfaces between catalyst supports and electrolytes. The capacitance determined in the Faradic region by EIS experiences a peak capacitance (QF), which represents the maximum achievable ECSA in an AEMCL during reactions. The EIS method was additionally validated in durability testing. An approximate 30% loss of QF was noted while Qdl remained unchanged following an eight-week test at 1 A/cm2 constant current density, implying that QF, determined by EIS, is sensitive to and therefore suitable for assessing the loss of ECSA. This universal method can provide a reasonable estimate of catalyst utilization and enable the monitoring of catalyst degradation in CLs, in particular in liquid alkaline electrolyte water electrolysis systems.
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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 | 0.002 | 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".