Towards an advanced neurotechnological system: colorimetric sensing with a novel grism-based spectrometer, functionalized gold nanoparticles and a heterogeneous embedded system
Bibliographic record
Abstract
Neurotransmitter sensing in the brain is crucial for the understanding of neuro-degenerative diseases. Most modern methods for the purpose rely on bulky instruments or are disruptive to the neurotransmitter medium. In this work, we describe and evaluate the design of a novel, compact and non-invasive instrument for neurotransmitter detection based on the colorimetric sensing method. The instrument includes a grism-based spectrometer that measures the wavelength shift of gold nanoparticles that are functionalized with aptamers to act as neurotransmitter-specific markers. It also includes microfluidic and electronic subsystems for sample preparation and control, and processing of the obtained signal. The instrument is tested with gold nanoparticles and its performance is compared to that of a commercial instrument, showing that the designed prototype matches the commercial instrument in performance while being much smaller, and it can surpass it with further improvements. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".