Overcoming Diffusion Mass Transfer Barriers by Surface Electro-Precipitation (SEP)
Bibliographic record
Abstract
The economically and environmentally sustainable recovery of dilute (<100 mg L –1 ) and ultradilute (<1 mg L –1 ) metal ions from complex aqueous matrices is one of the grand challenges in inorganic separation science and technology. Existing separation methods fail in this task due to their inherently slow recovery rates, stemming from either the slow diffusion of dilute elements toward the separating surface or the slow agglomeration of colloidal precipitates. This work reports a novel electrochemical–chemical phenomenon observed during surface electro-precipitation (SEP) that can surmount these mass transport limitations in an essentially green manner, without using reagents and at low energy and low material costs. Using a porous carbon electrode, we demonstrate that SEP can reversibly uptake dilute and ultradilute Pb 2+ at least 2 orders of magnitude faster than diffusion-controlled electrodeposition/electrowinning and adsorption. Paradoxically, the rate of SEP increases with a decrease in adsorbent dosage. We put forward a semiquantitative model that explains these extraordinary results by the rapid precipitation of basic lead carbonates at the cathode–solution interface. This discovery suggests a shift in the existing paradigm of accelerating mass transport at low concentrations, opening the door to recovering, rather than just removing, valuable and toxic elements from abundant depleted resources and contaminated water.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".