Electrochemical Production of >1 M Acid and Base from Neutral Salt at High Current Density and Low Energy Demand
Bibliographic record
Abstract
The use of acid and base to drive chemical transformations underpins industrial hydrometallurgical processes and many proposed carbon management technologies. The production of the acid and base are key drivers of the energy demand, emissions, and waste generation of these processes. Generating acid and base electrochemically from salt solutions enables the use of low-carbon power and avoids stoichiometric salt waste. However, conventional electrochemical approaches that use ion exchange membranes (IEMs) have excessive energy demand, low productive current densities, and poor impurity tolerance. These shortcomings can be addressed by using a diaphragm flow cell (DFC), which has lower resistance than IEM-based systems and improved impurity tolerance. Here we report an improved design for the DFC that incorporates mesh spacers in the electrolyte compartments to reduce the residence time of the electrolyte and rigidify the compartments. This cell produces concentrated (1.1 - 1.5 M) acid and base from neutral salt at the lowest energy demand (0.051 - 0.067 kWh mol-1) and highest productive current densities (275 - 367 mA cm-2) reported to date while operating at 70 °C, a realistic temperature for a scaled system. Using a continuum model, we show that the electrolyte residence time has a significant impact on current efficiency by controlling diffusive losses.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".