Photonic wire biosensor microarray chip and instrumentation with application to serotyping ofEscherichia coliisolates
Bibliographic record
Abstract
A complete photonic wire molecular biosensor microarray chip architecture and supporting instrumentation is described. Chip layouts with 16 and 128 independent sensors have been fabricated and tested, where each sensor can provide an independent molecular binding curve. Each sensor is 50 μm in diameter, and consists of a millimeter long silicon photonic wire waveguide folded into a spiral ring resonator. An array of 128 sensors occupies a 2 × 2 mm2 area on a 6 × 9 mm2 chip. Microfluidic sample delivery channels are fabricated monolithically on the chip. The size and layout of the sensor array is fully compatible with commercial spotting tools designed to independently functionalize fluorescence based biochips. The sensor chips are interrogated using an instrument that delivers sample fluid to the chip and is capable of acquiring up to 128 optical sensor outputs simultaneously and in real time. Coupling light from the sensor chip is accomplished through arrays of sub-wavelength surface grating couplers, and the signals are collected by a fixed two-dimensional detector array. The chip and instrument are designed so that connection of the fluid delivery system and optical alignment are automated, and can be completed in a few seconds with no active user input. This microarray system is used to demonstrate a multiplexed assay for serotyping E. coli bacteria using serospecific polyclonal antibody probe molecules.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".