Hydrophobicity Tuned Polymeric Redox Materials with Solution-Specific Electroactive Properties for Selective Electrochemical Metal Ion Recovery in Aqueous Environments
Bibliographic record
Abstract
Adaptable redox-active materials hold great potential for electrochemically mediated separation processes via targeted molecular recognition and reduced energy requirements. This work presents molecularly tunable vinylferrocene metallopolymers (P(VFc-co-X)) with modifiable operating potentials, charge storage capacities, capacity retentions, and analyte affinities in various electrolyte environments based on the hydrophobicity of X. The styrene (St) co-monomer impedes hydrophobic anions from ferrocene access, providing P(VFc-co-St) with specific response capabilities for and greatly improved cyclabilities in hydrophilic anions. This adjustable electrochemical stability enables preferential chromium and rhenium oxyanion separation from both hydrophobic and hydrophilic electrolytes that significantly surpasses capacitive removal by an order of magnitude, with a robust perrhenate uptake capacity of 329 mg/g VFc competitive with established metal-organic framework physisorbents and 17-fold selectivity over 20-fold excess nitrate. Pairing P(VFc-co-X) with other solution-specific electroactive macromolecules such as the pH-dependent poly(hydroquinone) (PHQ) and the cesium-selective nickel hexacyanoferrate (NiHCF) generates dual-functionalized electrosorption cells. P(VFc-co-X)//PHQ offers optimizable energetics based on X and pH for a substantial 4.6-fold reduction from 0.21 to 0.04 kWh/mol rhenium in acidic versus near-neutral media, and P(VFc-co-St)//NiHCF facilitates simultaneous extraction of rhenium, chromium, and cesium ions. Proof-of-concept reversible perrhenate separation in flow further highlights such frameworks as promising approaches for next-generation water purification technologies.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".