Integration of hydrogels into microfluidic devices with porous membranes as scaffolds enables their drying and reconstitution
Bibliographic record
Abstract
Hydrogels are a critical component of many microfluidic devices. They have been used in cell culture applications, biosensors, gradient generators, separation microdevices, micro-actuators, and microvalves. Various techniques have been utilized to integrate hydrogels into microfluidic devices such as flow confinement and gel photopolymerization. However, in these methods, hydrogels are typically introduced in post processing steps which add complexity, cost, and extensive fabrication steps to the integration process and can be prone to user induced variations. Here, we introduce an inexpensive method to locally integrate hydrogels into microfluidic devices during the fabrication process without the need for post-processing. In this method, porous and fibrous membranes such as electrospun membranes are used as scaffolds to hold gels and they are patterned using xurography. Hydrogels in various shapes as small as 200 μm can be patterned using this method in a scalable manner. The electrospun scaffold facilitates drying and reconstitution of these gels without loss of shape or leakage that is beneficial in a number of applications. Such reconstitution is not feasible using other hydrogel integration techniques. Therefore, this method is suitable for long time storage of hydrogels in devices which is useful in point-of-care (POC) devices. This hydrogel integration method was used to demonstrate gel electrophoretic concentration and quantification of short DNA (150 bp) with different concentrations in rehydrated agarose embedded in electrospun polycaprolactone (PCL) membrane. This can be developed further to create a POC device to quantify cell-free DNA, which is a prognostic biomarker for severe sepsis patients.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".