Reconfigurable microfluidics: technologies for spatio-temporal studies of heterogenous biological systems
Bibliographic record
Abstract
I will present our work on scanning, non-contact microfluidic technologies that can dynamically shape liquids over surfaces without needing physical walls unlike standard microfluidics, which are typically closed. These technologies form ‘hydrodyamic flow confinements’ on a surface by the flow of a shaping liquid around a processing liquid. In this talk, I will discuss how these open-space embodiments enable chemical and biochemical reactions to be performed locally on immersed surfaces completely eliminating gas–liquid interfaces thereby providing new opportunities for handling, analyzing and interacting with biological samples. I will then describe two key application themes that my team has been pursuing in recent years. First is the precise patterning of surfaces with proteins and other biomolecules in an additive and subtractive manner, forming complex gradients on surfaces, and their interactions with cells on surfaces for use in biopatterning and measuring reaction kinetics. Second, I will present new concepts around tissue microprocessing for spatial multi-omic analysis of biopsy samples for possible use in pathology.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".