In‐Operando Mapping of pH Distribution in Electrochemical Processes
Bibliographic record
Abstract
In aqueous electrochemical processes, the pH evolves spatially and temporally, and often dictates the process performance. Herein, a new method for the in-operando monitoring of pH distribution in an electrochemical cell is demonstrated. A combination of pH-sensitive fluorescent dyes, encompassing a wide pH range from ≈1.5 to 8.5, and rapid electrochemically coupled laser scanning confocal microscopy is used to observe pH changes in the cell. Using electrocoagulation as an example process, we show that the method provides new insights into the reaction mechanisms. The pH close to the aluminium electrode surface is influenced by the applied current density, hydrolysis of aluminium cations, and gas evolution. Through quantification of the pH at the anode, along with gas analysis, we find that hydrogen is evolved at the anode due to a non-Faradaic chemical reaction. This leads to increased production of coagulant, which may open new routes to enhance the process performance. This method for in-operando dynamic visualization of pH paves the way for studies of electrochemical processes, including other water treatment, electrosynthesis, and batteries.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".