A Miniature Sensor Integrating Multiple Detection Technologies for Evaluating Water Pollution
Bibliographic record
Abstract
A portable system to monitor water quality in both developed and developing countries is highly required. Currently, there is no commercial test available to satisfy this demand, as only accredited laboratories can perform such evaluations. Miniaturization of analytical instrumentation is one of the dominant trends within the chemical and biological sciences. Miniaturization of diagnostic devices not only affords portability and significant cost reductions but also performance gains in terms of speed, analytical efficiency, automation and reproducibility. To address this issue, a phytoplankton (alga and cyanobacteria) based detection system implemented in a micro-fluidic platform was developed. As change of the environment of algae induced by toxic compound can affect their metabolism, this makes phytoplankton ideal natural biosensors. Their response can be measured in real time by various detection mechanisms i.e., optical, chemical or electrical. The innovation of the system presented in this study, is to integrate both electrochemical and fluorescence detection in a single chamber. The system is shown in Fig.1. In general, photosynthesis process of algae is inhibited in presence of toxic compound, in our case, Diuron. Electrochemical detection is based on the measurement of oxygen produced by algae under such conditions. It was carried out using transparent silver nanowires electrode. The size distribution of the Ag nanowires is ~ 80 nm in length and the transparency of the electrode is 80%. Fluorescence detection is based on the activity of photosystem II in the presence and absence of Diuron. The concentration of Diuron can be detected by electrochemical detection between 50 and 1000 nM. (Fig. 2A) and 0.50 to 1000 nM using fluoresecense detection (Fig. 2B). The two detection systems were integrated into a single chamber in a way which enables for more reliable and sensitive detection of water pollutant. Figure 1 Edge view of the opto-electrochemical sensing device: a transparent oxygen sensor is integrated with a fluorescence sensor into the same chamber. Figure 2 response curves of electrochemical and fluorescense detections of algae to the herbicide, Diuron Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".