Quantitative analysis of iron residue in potassium hydroxide: A time-dependent study
Bibliographic record
Abstract
Iron (Fe) contamination in potassium hydroxide (KOH) poses a significant challenge for the reliability of membrane based alkaline electrolysis systems (AEMWE) due to its influence on electrocatalytic behaviour and system stability. The dissolution of Fe from cell components or as an electrolyte impurity can affect membrane resistivity, produce short circuit pathways, and result in anomalous deposition within the system. The objective of this study is to compare various benchmark purification methods and develop a simple, effective, and accelerated protocol for Fe removal using a 3 electrode Pt-mesh setup. Fe residues in KOH were quantified using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) over a 24-h pre-electrolysis period. The results indicated that Fe levels dropped significantly within the first two hours of pre-electrolysis. By combining acid washing and holding at open-circuit potential (OCP) in Fe-free KOH, Pt mesh was shown to be capable of reducing Fe levels in contaminated KOH solutions to near-zero levels within 2 h. • Pre-electrolysis protocol reduces Fe in KOH to near zero levels in under 2 h. • Pt mesh is an effective and highly reusable electrode for purifying KOH. • Iron deposits onto Pt rapidly within the first 30 min of pre-electrolysis.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".