Formation and Encapsulation of Biomolecular Arrays for Developing Arrays of Membrane-Based Artificial Hair Cell Sensors
Bibliographic record
Abstract
Recent research in our group has shown that artificial cell membranes formed at the base of a hair-like structure can be used to sense air flow in a manner similar to the mechanotransduction processes found in mammalian hair cells. Our previous work demonstrated that a single artificial hair cell can be formed in an open substrate. However, that study also motivated the need to develop fully-encapsulated devices that feature arrays of hair-cells. Since the transduction element in this concept is an artificial cell membrane, or lipid bilayer, this work investigates two parallel substrate designs for providing encapsulation and a method for forming arrays of bilayers. In one effort, a flexible substrate with internal compartments for hosting the biomolecules and mating cap are constructed and experimentally characterized. The regulated attachment method (RAM) is used to form interface bilayers within the sealed device. Capacitance measurements of the sealed interface bilayer show that the sealing cap slightly compresses the bottom insert and reduces the size of the enclosed bilayer. Single channel measurements of alamethicin peptides further verify that the sealed device, which is also leak-proof under water, can be used to detect the insertion and gating activity of transmembrane proteins in the membrane. The second effort pursued herein is the fabrication and initial testing of a method to form arrays of interface bilayers by using anchored hydrogel pads that support curved aqueous lenses in oil. In this fashion, the configuration of the array does not require manipulating droplets, but instead depends on the arrangement of the built-in gels used to support the aqueous lenses. As with RAM, mechanical force is used to promote contact of adjacent aqueous lenses held in the flexible substrate. Initial tests show that gel-supported lenses can be used for forming multiple lipid bilayers within the device and that these interfaces can be interrogated individually or collectively using an electrode switching circuit.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".