(Invited) Engineering the Bio-Interface at the Nanoscale for Diagnostics and Therapeutics Applications
Bibliographic record
Abstract
Miniaturized platforms such as micro/nano patterned interfaces and microfluidic devices provide powerful tools to study biological phenomena at micro and nano scale and to develop novel technologies for a variety of biomedical applications such as point of care diagnostics, cell sorting and dug discovery. The biological/non-biological interface system is an important cornerstone for the fabrication of biomedical devices. Platforms as diverse as lab-on-chip and point-of-care diagnostics, 3D tissue culture scaffolds, organs-on-chips and implants all rely on the effective interaction of cells and/or bio-recognition elements (proteins/peptides, enzymes, antibodies, etc.) with non-biological surfaces. An overview of our research on micro/nano-scale design of novel biomedical coatings and their integration into medical devices such as biosensors, catheters, vascular grafts as well as flexible sensing interfaces will be presented. More specifically, I will discuss our recently developed technologies for the design and development of devices with omniphobic lubricant-infused coatings that provide simultaneous repellency and targeted binding of desired biological species where bio-fouling and coagulation is minimized.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.041 |
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".