Flexible photocell array based on bacteriorhodopsin film
Bibliographic record
Abstract
A bendable photocell array that exploits bioelectronic photoreceptors based on bacteriorhodopsin (bR) is described in this paper. Fabricating such a sensor array on a flexible plastic substrate introduces a new design approach that enables lightweight and durable non-planar sensing devices to be created with curved or spherical geometries. In this research, purple membrane patches obtained from wild-type bR are deposited onto a polyethylene terephthalate (PET) substrate coated with a patterned ITO layer using Electrophoretic Sedimentation (EPS) technique. The current prototype consists of a flexible 4x4 pixel array and an amplification circuit that magnifies the small electrical signal arising from the charge displacement and recombination within the dried bR film. Each individual pixel is a 2mm x 2mm square separated by a 1mm distance between neighboring elements. The measured photoelectric response of an individual pixel is approximately linear over the light power range between 200μW and 12mW. These bR photocells respond primarily to visible light with a spectral peak response at 568nm. The response times of the photoelectric signals can reach up to the microsecond range. Preliminary tests have demonstrated that photoresponse characteristics are maintained while the flexible substrate is deformed up to a 10mm bending radius. Unfortunately, dried bR photocells are inherently susceptible to electrical noise because of their extremely high film resistance, necessitating the employment of a noise-filtering amplifier. The image processing capabilities of bR are demonstrated in a motion detection application. Specifically, Reichardt's delay-and-correlate algorithm is implemented and is used to detect both the speed and direction of a moving light spot.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".