Immobilization of Gold Nanoparticles for Colourimetric Detection of Biofilms on Surfaces
Bibliographic record
Abstract
Biofilms in contact lens cases amplify the risk of microbial and infiltrative keratitis, which can lead to severe eye damage and vision loss. A method warning users of biofilm contamination on the contact lens case surface is needed so they can discontinue use of the case to prevent related eye infections. Biosensors based on gold nanoparticles in solution are being explored as they can provide a simple colourimetric sensor response to bacteria. However, for consumer-level applications, gold nanoparticle-based biosensors need to be immobilized onto a surface to reduce potential health risks associated with nanomaterial exposure. \nThis thesis focuses on the development of an immobilized gold nanoparticle biosensor for the colourimetric detection of biofilms on surfaces. Development of the biosensor begins with controlling the deposition of gold nanoparticles onto the surface, as their immobilization state dictates the optical properties critical to the sensor performance. A literature review of the current methods to immobilize colloidal gold nanoparticles demonstrates that there are a variety of strategies to control the immobilization state. Building on current strategies, a new method to immobilize charged gold nanoparticles is explored through modification of the surface with weak polyelectrolytes. By varying the deposition pH of weak polyelectrolytes, the electrostatic immobilization of gold nanoparticles can be tuned from dispersed particles to large three-dimensional particle aggregates, producing a broad range of optical properties. The ability to modulate the immobilization state is dependent on the polyelectrolyte used as well as the particle size. \nUsing the developed method, an optimal immobilization state of the gold nanoparticles is used to create the colourimetric biosensor. Having populations of both single and small clusters of gold nanoparticles on the surface, a visible colour change from red to blue is produced with an increase in refractive index. This biosensor surface is capable of detecting biofilms from Gram-positive Staphylococcus aureus and Gram-negative Achromobacter xylosoxidans visually and through simple image analysis. Finally, the colourimetric biosensor was successfully integrated onto and capable of detecting the presence of biofilm on plastic substrates, including a commercial contact lens case. \nThis work demonstrates the capabilities of this immobilized gold nanoparticle biosensor as a new platform for the detection of biofilms on surfaces. In addition to biofilm detection in contact lens cases, this technology can be exploited for biofilm detection in healthcare, food services and water treatment industries.
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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 | 0.001 | 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".