Développement d'une plateforme de laboratoire sur puce intégrant microcapteurs électrochimiques pour la détection des contaminants de l'eau basée sur le suivi de la physiologie des algues
Bibliographic record
Abstract
The monitoring of water quality has been of great importance in recent decades in order to find solutions to control water contamination, largely driven by agricultural and industrial activities. Although conventional methods such as chromatography, are very accurate and sensitive tools, increasing interest has been placed on promising techniques that can be used on site, are low cost, and offer the ability to perform rapid analysis. The present work is devoted to the development of lab on a chip component to the analysis of the toxicity of water. It consists of a portable system for detecting on-site and offers the possibility of a double complementary detection: optical and electrochemical. As the part dedicated to the electrochemical sensor has been previously validated, this study focused on the implementation of an electrochemical biosensor based on the use of an alga, for the detection of pollutants in water. The detection basic principle consists in monitoring changes in the metabolic activity of algae induced by the presence of herbicides. The seaweed response is different for each concentration of herbicide in an examined sample. Two herbicides selected affect the photosynthetic activity of the algae and consequently, induce changes in the amount of electro-active species produced by the alga: O2, H2O2 and H3O + / OH-. Before the development of the final component type Lab on Chip, detection principles as well as the electrode materials that will be integrated, have been validated using a simpler type of component, which was achieved through the silicon manufacturing technologies and which was characterized by simpler procedures.A silicon chip containing an electrochemical micro incorporating three electrodes was established. Once validated detecting materials and previously selected settings were used in the manufacture of components on Lab Chip. The Lab-on-Chip components were then used for the biological tests to detect the herbicides of interest. Special attention has been placed on monitoring like O2 indicator of the presence of herbicide, because this element is the most representative of changes of metabolic activity. An inhibiting effect on photosynthesis, concentration dependent of the herbicide been demonstrated. Detection of herbicidal was performed with great sensitivity and a range of covering the boundary concentration Maximum acceptable taxed by the Canadian Government.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".