SYNTHESIS AND CHARACTERIZATION OF MESOPOROUS POLYMER-SILICA HYBRID MONOLITH USING CONVENTIONAL SOL-GEL METHOD FOR ENZYME SUPPORT
Bibliographic record
Abstract
This research involved developing a novel solid support for an enzyme attachment, focusing on synthesizing a polymer-silica hybrid monolith via in-situ sol-gel polymerization method.The fabrication of a very large surface area of the monolith was done using a cold mixture of poly(ethylene-glycol) (PEG) with tetraethyl-orthosilicate (TEOS) and acetic acid with different ratios of PEG amount and molecular weights, namely PEG-0.1,PEG-0.2, and PEG-0.3.The experiments were conducted at a very low temperature of 0 °C, followed by overnight gelification and aging.The sol then underwent calcination at 200 °C forming a hybrid monolith.The characterizations of hybrid monoliths were performed by Attenuated-Total Reflection-Fourier Transformed Infrared Spectroscopy (ATR-FTIR), Scanning Electron Microscope (SEM), and Surface Area and Porosity Analyzer using both Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) methods to describe the developed monoliths.FTIR shows the presence of Si-O-Si stretching associated with the monolith network due to the polymerization process together with the presence of silanol functional group (Si-OH) that can be exploited further for covalent attachment with the enzyme.Results also showed that the optimum ratios for the hybrid polymer-silica synthesis were PEG-0.1with10,000 M n surface area of mesoporous network recorded for 494.121 m 2 /g and pore volume of 0.265 cm 3 /g.These findings showed that the synthesized hybrid monolith on fused silica capillary will provide a vast surface area with desirable functional groups; thus, very promising for lipase immobilization support that can be used in future small-scale lipid transformation.
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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) | 0.000 | 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 | 0.000 | 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".