Electrospun Molecularly Imprinted Polymers for Environmental Remediation: A Mini Review
Bibliographic record
Abstract
This review critically examines the recent advancements in the development and application of electrospun molecularly imprinted polymer (MIP) nanofiber membranes for environmental remediation. Emphasizing the significance of these materials, the discussion highlights the mechanisms by which electrospun MIPs achieve high selectivity and efficiency in removing various pollutants, including dyes, heavy metals, and pharmaceutical residues such as NSAIDs and antiretroviral drugs. The synthesis methodologies are explored in detail, focusing on the choice of monomers, templates, and polymerization conditions that influence the structural and functional properties of the membranes. Characterization techniques used to assess morphology, surface area, porosity, and imprinting efficacy are also examined, providing insights into how these parameters affect adsorption performance. Furthermore, the review evaluates the performance metrics of electrospun MIPs, including adsorption capacities, selectivity, reusability, and stability in complex environmental matrices. Practical considerations, such as scalability, regeneration, and long-term operational stability, are discussed to assess their potential for real-world applications. The article concludes with an outline of future research directions, emphasizing the need for multi-template imprinting, integration with existing treatment technologies, and field-scale validation to address current limitations.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".