A novel portable oxidation-reduction potential and microbial fuel cell-based sensor to monitor microbial growth
Bibliographic record
Abstract
Bioremediation, the most environment-friendly soil remediation method, should receive adequate attention. However, its efficiency has often been criticized, reflecting the dearth of information about microbial activity in the soil. Biosensors can use the signals sent by microorganisms to quantify and analyze microbial activity. Therefore, combining biosensors with bioremediation can enhance the application of bioremediation technology.\nThis thesis focused on designing and fabricating of portable microbial fuel cell (MFC) and oxidation-reduction potential (ORP) based sensor to achieve in situ soil bioremediation application in the future. This is because conventional biosensors cannot reflect the detailed microbial growth characteristics during soil bioremediation.\nDuring the experiment, two portable sensors were designed. First, two cylindrical polypropylene bottles were compressed tightly to form a preliminary sensor containing a proton exchange membrane (PEM), an O-ring, and a cathode electrode. After successfully testing the preliminary sensor’s workability, a smaller, easier-to-assemble 3D-printed sensor was designed based on the same concept.\nThe extracellular electrogenic bacterial Bacillus subtilis was used to test both sensors’ workability. MFC and ORP sensors provide voltage and redox potential outputs. By integrating real-time redox potential and voltage outputs, a typical microbial growth (potential parameter) curve can be created. The derivative optical density (OD) value (OD per hour) was found to correspond to the potential parameter. The preliminary sensor could acquire detailed microbial growth characteristics at 6.5 and 18 hours, and the 3D-printed sensor at 10 and 21 hours. The accompanying derivative OD values supported these conclusions. This novel sensor can monitor real-time microbial growth, report detailed growth characteristics in soil, and help select better bioremediation solutions.\nFuture work is required to improve the responsive of the 3D-printed sensor to achieve higher-resolution result.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".