Bibliographic record
Abstract
Incorporating proteins with nanomaterials is an effective way to enhance the stability and function of proteins. The protein-nanomaterials hybrid systems have been extensively applied in drug delivery and biocatalysis. This thesis focuses on the different interactions between proteins and nanomaterials. Three sub-projects have been studied as follows;\n1) Chemical interaction: Gold nanoparticles with a particle size of 15nm were applied to label bovine serum albumin (BSA), a globular protein, for realizing a colorimetric protein assay. The results of FTIR and Raman spectra indicate that gold nanoparticles bond to BSA via the amine bonds. The surface plasma resonance (SPR) of gold nanoparticle labelled BSA shows a linear relationship with the concentration of BSA.\n2) Electrostatic interaction: To make enzyme stable and recyclable in biocatalysis, the enzyme, carbonic anhydrase (CA), was immobilized on zinc oxide (ZnO) nanorods via electrostatic interaction. The highest immobilization ratio (60%) is achieved at pH 8.0. The CO2 capturing by the immobilized CA has been investigated. It is found that a 2.2×2.2cm ZnO nanorod chip with 0.75mg immobilized CA could capture up to 6mmol CO2 in 100mL water.\n3) Multiple interactions: Via hydrogen bond and electrostatic interaction, basic fibroblast growth factor (bFGF) was incorporated within chitosan and silica nanoparticles, respectively. In vitro release tests indicate that chitosan and silica nanoparticles could achieve a sustained bFGF release up to 230 and 180 hours, respectively. The bFGF-loaded nanoparticles were afterwards immobilized within HEMA hydrogel. The nanoparticle-HEMA nanocomposites can provide a localized sustained protein release without systematic absorption.\nThe incorporation of proteins with nanomaterials can enhance the stability and activity of proteins which are otherwise prone to denaturation.
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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".