Rapid, Selective, and Chemical-Free Removal of Dissolved Silica from Water via Electrosorption: Feasibility and Mechanisms
Why this work is in the frame
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Bibliographic record
Abstract
The unavoidable and detrimental formation of silica scale in engineered processes necessitates the urgent development of effective, economic, and sustainable strategies for dissolved silica removal from water. Herein, we demonstrate a rapid, chemical-free, and selective silica removal method using electrosorption. Specifically, we confirm the feasibility of exploiting local pH dynamics at the electrodes in flow-through electrosorption, achieved through a counterintuitive cell configuration design, to induce ionization and concomitant electrosorption of dissolved silica. In addition, to improve the feasibility of silica electrosorption under high-salinity solutions, we developed a silica-selective anode by functionalizing porous activated carbon cloths with aluminum hydroxide nanoparticles (Al(OH) 3 –p–ACC). The modification markedly enhances silica sorption capacity (2.8 vs 1.1 mg silica g anode –1 ) and reduces the specific energy consumption (13.3 vs 19.8 kWh kg silica –1 ). Notably, the modified electrode retains remarkable silica sorption capacity even in the presence of high concentrations of co-occurring ions (up to 100 mM NaCl). The mechanisms underlying the superior silica removal stability and selectivity with the Al(OH) 3 –p–ACC electrode are also elucidated, revealing a synergistic interaction involving outer-sphere and inner-sphere complexation between dissolved silica and Al(OH) 3 nanoparticles on the electrodes. Moreover, we find that effective regeneration of the electrodes may be achieved by applying a reverse potential during discharge, although complete regeneration of the modified electrodes may necessitate alternative materials or process optimization. We recommend the adoption of feedwater-specific designs for the development of future silica-selective electrodes in electrosorption capable of meeting silica removal demands across a wide range of engineered systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it