Development of ultrathin molecularly imprinted \npolymer films for polyaromatic hydrocarbon \nsensing
Bibliographic record
Abstract
Produced water is one of the pollutants produced at a large scale during offshore oil \nand gas activities. Wastewater which has been separated from oil and gas during production \ncan be defined as produced water. Although produced water contains a wide \nvariety of toxic chemicals, polycyclic aromatic hydrocarbons (PAHs) within produced \nwater have received considerably greater attention due to their capability of causing \nlong term toxic effects at the individual level in the marine environment. Due to the \nadverse effect of PAHs (organic pollutants) on the environment and lives, accurate \ndetection and monitoring of PAHs is required before discharging the produced water \ninto the sea. Molecularly imprinted polymers (MIPs) can capture analytes such as \nPAHs, and when coupled with a detection mechanism, can act as sensors for those \npollutants. These films are suitable for remote sensing because they can effectively \nconcentrate the analyte in situ. To combine MIPs with an optical sensing element, it \ncan be useful to create them in an ultrathin film format, which is also important for \nportability to remote areas. One way to achieve a uniform film with nanoscale thickness \nis through spin coating. In this study, ultrathin film MIPs have been prepared \nfollowing a new procedure. Various processing parameters including spin time and \nspeed have been explored to determine their effects on MIP film structure as well as \ntheir removal and uptake of template molecules. Raman spectroscopy was used for \nthe detection of the analyte, and atomic force microscopy was used to characterize the films’ morphology as well as to measure the thickness of the films, which ranged from \n300 nm down to 4 nm. Our findings have determined that different spin speeds produce \ndifferent film morphology. Furthermore, thinner films showed more homogeneity \nand reproducibility.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".