Nano-Sized Cyclodextrin-Based Molecularly Imprinted Polymer Adsorbents for Perfluorinated Compounds—A Mini-Review
Bibliographic record
Abstract
Recent efforts have been directed towards the design of efficient and contaminant selective remediation technology for the removal of perfluorinated compounds (PFCs) from soils, sediments, and aquatic environments. While there is a general consensus on adsorption-based processes as the most suitable methodology for the removal of PFCs from aquatic environments, challenges exist regarding the optimal materials design of sorbents for selective uptake of PFCs. This article reviews the sorptive uptake of PFCs using cyclodextrin (CD)-based polymer adsorbents with nano- to micron-sized structural attributes. The relationship between synthesis of adsorbent materials and their structure relate to the overall sorption properties. Hence, the adsorptive uptake properties of CD-based molecularly imprinted polymers (CD-MIPs) are reviewed and compared with conventional MIPs. Further comparison is made with non-imprinted polymers (NIPs) that are based on cross-linking of pre-polymer units such as chitosan with epichlorohydrin in the absence of a molecular template. In general, MIPs offer the advantage of selectivity, chemical tunability, high stability and mechanical strength, ease of regeneration, and overall lower cost compared to NIPs. In particular, CD-MIPs offer the added advantage of possessing multiple binding sites with unique physicochemical properties such as tunable surface properties and morphology that may vary considerably. This mini-review provides a rationale for the design of unique polymer adsorbent materials that employ an intrinsic porogen via incorporation of a macrocyclic compound in the polymer framework to afford adsorbent materials with tunable physicochemical properties and unique nanostructure properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".