Substrate Selection for Fundamental Studies of Electrocatalysts and Photoelectrodes: Inert Potential Windows in Acidic, Neutral, and Basic Electrolyte
Bibliographic record
Abstract
The selection of an appropriate substrate is an important initial step for many studies of electrochemically active materials. In order to help researchers with the substrate selection process, we employ a consistent experimental methodology to evaluate the electrochemical reactivity and stability of seven potential substrate materials for electrocatalyst and photoelectrode evaluation. Using cyclic voltammetry with a progressively increased scan range, we characterize three transparent conducting oxides (indium tin oxide, fluorine-doped tin oxide, and aluminum-doped zinc oxide) and four opaque conductors (gold, stainless steel 304, glassy carbon, and highly oriented pyrolytic graphite) in three different electrolytes (sulfuric acid, sodium acetate, and sodium hydroxide). We determine the inert potential window for each substrate/electrolyte combination and make recommendations about which materials may be most suitable for application under different experimental conditions. Furthermore, the testing methodology provides a framework for other researchers to evaluate and report the baseline activity of other substrates of interest to the broader community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".