Durable Carboxylated Porous Polymer Foam as a High-Performance Adsorbent for Dye and Heavy Metal Ion Removal
Bibliographic record
Abstract
PolyHIPEs are porous emulsion-templated polymers synthesized within high internal phase emulsions (HIPEs), which are generally used as adsorbents due to their excellent porosity and large specific surface area. However, most reported polyHIPEs-based adsorbents showed inferior adsorption performance due to their hydrophobic nature and limited stability. Herein, we prepared a novel carboxylated porous polymer foam (CPPF) by covalently grafting iminodiacetic acid onto glycidyl methacrylate (GMA)-based PolyHIPEs. CPPF has a large specific surface area of 25.07 m 2 /g and abundant surface hydrophilic carboxyl groups, which give it a lower potential than porous foam without carboxylation treatment (stable at −32 mV when the pH is higher than or equal to 5). The strong electronegativity on the CPPF surface gives it highly effective adsorption of electropositive contaminants. Notably, the CPPF can effectively adsorb various dyes (methylene blue, malachite green, and methyl violet) and heavy metals (Cu 2+ and Cr 3+ ) with adsorption capacities over 500 and 30 mg/g, respectively, which are superior to most reported adsorbents under similar conditions. Furthermore, the CPPF demonstrates great stability and recyclability with removal efficiency higher than 97% after five consecutive adsorption/desorption cycles, indicating its great potential for diverse environmental engineering applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".