DEVELOPING HIERARCHICALLY STRUCTURED SUPER-REPELLENT COATINGS FOR THE REDUCED ADHESION OF BIOLOGICAL CONTAMINATES
Bibliographic record
Abstract
Recently, the rise and appearance of antibiotic resistant pathogenic bacteria, and pathogenic viruses have resulted in significant economic and societal repercussions. The spread of pathogens has led to increases in the prevalence of infections acquired from contaminated surface, especially within healthcare environments. In response to these issues, many new technologies have been developed to address the spread of these pathogens. Repellent and antipathogenic materials have been developed to reduce the levels of contamination seen on surfaces and the ability for these surfaces to transmit pathogens. In this thesis, we developed pathogen-repellent surfaces, which significantly reduce biofouling on their surface by reducing bacterial adhesion due to their omniphobic properties and deactivating the adhered pathogens via the production or Radical Oxygen Specie (ROS). Superhydrophobic wetting states have been shown to reduce the adhesion of biological contaminants and prevent biofouling at the surface. However, the performance of these repellent properties is dependent on the stability of the wetting states. Hierarchical structured surfaces with topography in both the micro- and nano-scale increase the stability of these wetting states when compared to structures at each individual length scale, and thus show potential in further increasing the repellency of surfaces in response to biological contaminants. To create a superhydrophobic and repellent surface with a hierarchically structured surface, we developed an all solution-based technique for depositing nanoparticle (NP) films. This method utilized self-assembled monolayers of ((3-Aminopropyl)triethoxysilane (APTES). The positive charge of the uniform amine monolayer was able to then ionically bond to negatively charged gold nanoparticles (AuNP) and silica nanoparticles (SiNP). This was combined with pre-strained polymer substrates which allowed for the formation of a wrinkled microstructure when shrunk, resulting in nanotextured microscale wrinkles. These Structures were then paired with a self-assembled coating of Fluorosilane (FS), which greatly reduced the surface energy of the surface and formed robust superhydrophobic states. To characterize the resistance of the surfaces to biofouling, we explored the interaction of the surface to blood staining and thrombosis under both static and dynamic conditions and found a greater than 90% reduction in contamination for all cases. To quantify the adhesion of pathogens to the repellant surfaces, a touch transfer assay was designed, which simulated the transmission from contaminated human hands to sterile surfaces. Two viral pathogens, Herpes Simplex Virus 2 (HSV2) and Human Coronavirus 229E (HuCoV), were tested under single and multiple contamination events, and the surfaces were imaged using SEM to confirm the levels of viral contamination, and showed a reduction greater than 4-log to the adhesion of both viruses. Bacterial contamination was tested for multiple different bacterial pathogens: Escherichia coli (E. coli), Bacillus subtilis (B. subtilis), Pseudomonas aeruginosa (P. aeruginosa) and Methicillin-Resistance Staphylococcus aureus (MRSA)). The omniphobic surface here showed a reduction of all bacterial pathogen species of roughly 2.77-log, both individually and in combination. To further reduce the contamination of the surface with pathogens, the repellant surface structures were modified with photoactive TiO2 nanoparticles, to introduce antipathogenic properties into the films. These surfaces were able to reduce the initial adhesion of bacteria pathogens by 2.77-log while also being able to decrease surface contamination by another 2.5-log after exposure to ultra-violet (UV) light.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".