Molecular sensing system based on multi-technologies architecture
Bibliographic record
Abstract
Considering the difficulty of measuring neurotransmitters in the field of biomedical research, a portable autonomous sensing and analysis system of neurotransmitters is needed. This type of device would improve the diagnostics of neurodegenerative diseases such as Alzeimer, Parkinson, Huntington diseases. Thus, in this work, we present a synthesis research paper to describe our device capable of measuring neurotransmitters in a liquid sample using functionalized ultrastable gold nanoparticles. It uses a colorimetric sensor to measure neurotransmitter indirectly. Indeed, using the colorimetric sensing approach, the plasmonic resonance band of nanoparticles shifts when they interact with neurotransmitters. The functionalization of the nanoparticles with dopamine-specific aptamer increases the response and selectivity towards the neurotransmitter of interest. Also, using ultrastable gold nanoparticles5 provides the potential to expose them to harsh conditions without agglomeration. Those harsh conditions includes the presence of salts that would otherwise compromise the efficiency of the sensing as well as conditions that are used to wash and clean the solutions (freeze drying, heating, ultracentrifugation and autoclaving). By being able to resist to those types of conditions, it gives the potential to recycle the nanoparticles to be reused for several sensing cycles. This sensing system uses a grism-based spectrometer design for the colorimetric analysis of neurotransmitters covering a bandwidth of 420 to 620 nm. Moreover, the system includes a microfluidic module for the manipulation of samples as well as an electronic module for data acquisition and analysis. Altogether, the system showed that the absorption spectrum of a nanoparticles sample with a resolution of 0.7 nm can be extracted autonomously using this system.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".