Molecularly imprinted polymer membranes and thin films for the separation and sensing of biomacromolecules
Bibliographic record
Abstract
This review describes recent advances associated with the development of surface imprinting methods for the synthesis of polymeric membranes and thin films, which possess the capability to selectively and specifically recognize biomacromolecules, such as proteins and single- and double-stranded DNA, employing "epitope" or "whole molecule" approaches. Synthetic procedures to create different molecularly imprinted polymer membranes or thin films are discussed, including grafting/in situ polymerization, drop-, dip-, or spin-coating procedures, electropolymerization as well as micro-contact or stamp lithography imprinting methods. Highly sensitive techniques for surface characterization and analyte detection are described, encompassing luminescence and fluorescence spectroscopy, X-ray photoelectron spectroscopy, FTIR spectroscopy, surface-enhanced Raman spectroscopy, atomic force microscopy, quartz crystal microbalance analysis, cyclic voltammetry, and surface plasmon resonance. These developments are providing new avenues to produce bioelectronic sensors and new ways to explore through advanced separation science procedures complex phenomena associated with the origins of biorecognition in nature.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".