Experimental studies of zinc oxide and silica peptide interactions
Bibliographic record
Abstract
In nature, mineral-forming organisms achieve outstanding control over the assembly and properties of minerals. Understanding interactions during biomolecule-mediated synthesis is key to addressing the challenges that arise when designing new materials and synthesizing superior nanostructures, especially under aqueous conditions. The studies of peptide-mineral interactions presented in this thesis aimed to identify the peptide-surface affinity and its binding mechanism(s) as well as the effect of peptides on mineral formation by in vitro studies. The minerals; crystalline zinc oxide (ZnO) and amorphous silica (SiO2) and their specific binding peptides identified by phage display were chosen for this investigation. Firstly, the growth of ZnO was investigated via a hydrothermal synthesis route. Product formation, precipitation processes and phase transformation was then compared in the presence of two peptides; EAHVMHKVAPRP (EM-12, a ZnO-binding peptide) and its mutant EAHVCHKVAPRP (EC-12). Both peptides affected the crystal formation process; however, their effect and mechanism of interaction was shown to follow different pathways. X-Ray Photoelectron Spectroscopy (XPS) revealed that the peptide EC-12 interacted with the Zn2+ species in the solid phase through the thiol group (from cysteine). This interaction caused a drastic change in the mineral morphology with sphere-like ZnO crystals being formed. In contrast, the delay and/or suppression of ZnO formation in the presence of the EM-12 peptide was shown to be not due to peptide-mineral interactions (proved by XPS) and, instead, interactions with Zn2+ species in solution were proposed. As ZnO properties and applications are directly related with its morphology, these outcomes can be applied for the design of advanced material. For silica, the effect of particle size and surface chemistry on peptide binding response was studied, with specific emphasis on the effect of level of functionalization on binding. Exhaustive characterisation of the silica surfaces, particularly by XPS, was crucial for knowledge of the chemistry and topography of the solid surface under study; and thus, to understand their impact on peptide adsorption. Peptide interactions at the aqueous interface were influenced by the surface chemistry and by the extent of functionalization where a ‘switch’ of peptide adsorption behaviour was observed. These new insights into silica-peptide interactions may facilitate the synthesis of novel organic/inorganic nanocomposite materials for biomedical applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".