Hair cell inspired mechanotransduction with a gel-supported, artificial lipid membrane
Bibliographic record
Abstract
A gel-supported lipid bilayer formed at the base of an artificial hair is used as the transduction element in an artificial, membrane-based hair cell sensor inspired by the structure and function of mammalian hair cells. This paper describes the initial fabrication and characterization of a bioderived, soft-material alternative to previous artificial hair cells that used the transduction properties of synthetic materials for flow and touch sensing. Under an applied air flow, the artificial hair structure vibrates, triggering a picoamp-level electrical current across the lipid bilayer. Experimental analysis of this mechanoelectrical transduction process supports the hypothesis that the current is produced by a time-varying change in the capacitance of the membrane caused by the vibration of the hair. Specifically, frequency analysis of both the motion of the hair and the measured current show that both phenomena occur at similar frequencies (0.1–1.0 kHz), which suggests that changes in capacitance occur as a result of membrane bending during excitation. In this paper, the bilayer-based hair cell sensor is experimentally characterized to understand the effects of transmembrane potential, the applied air flow, and the dimensions of the hair.
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 | 0.000 | 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) | 0.000 | 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 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".