MERGING OMNIPHOBIC LUBRICANT-INFUSED COATINGS WITH DIFFERENT MICROFLUIDIC MODALITIES TO ENHANCE DEVICE FABRICATION AND FUNCTIONALITY
Bibliographic record
Abstract
Surface science is a multidisciplinary subject which affects us on a daily basis. Surfaces are of particular interest because the chemical bonding and atomic structure is different at the surface compared to the bulk properties of a material. This interface is of great significance because it is where charge exchange, or new chemical bonds occur. One essential aspect of surface science is surface wettability, which can be harnessed to produce self-cleaning surfaces. This very lucrative notion, where surfaces with low adhesion to liquids, can result in quick and autonomous shedding, has inspired a multitude of device fabrication and implementation. Over the past decade, several self-cleaning surfaces have been fabricated from superhydrophobic surfaces, which depends on a stable interface between solid, liquid and gas. These surfaces, however, are restricted in their applications and fail to operate upon mechanical damage or nonhomogeneous fabrication processes. Recent advances in wettability science have produced omniphobic lubricant-infused surfaces (OLIS). These surfaces are created by tethering a liquid to a surface, providing a stable liquid interface, which results in excellent aqueous and organic liquid repellency, and high robustness toward physical damage. This thesis will encompass an overview of the classical models for surface wettability, new models for liquid mobility, the criteria required to obtain OLIS, as well as some of the biomedical engineering applications fabricated from this technology. Herein, a novel manufacturing process was developed to produce smooth channeled polymeric microfluidic devices from rough 3D printed molds. Additionally, we integrated OLIS technology with electroconductive sensors to create high surface area electroactive material with self-cleaning properties, ideal to combat non-specific adhesion of biomolecules. Furthermore, our fabrication methods are inexpensive and have the potential to be easily integrated into manufacturing processes to create highly functional microfluidic devices, optimal for lab-on-chip diagnostic platforms.
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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) | 0.000 | 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) | 0.007 | 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".