A Self-Sustained Smart Monitoring Platform for Capacitive De-Ionization Cell in Wireless Sensor Network
Bibliographic record
Abstract
Water treatment is the most concerned research area as it is closely related to the quality of human life. Capacitive de-ionization (CDI) has become a popular desalination technique for water treatment in recent years; however, transferring CDI technology to practice industrial applications face many problems due to the lack of experience and ability of monitoring on its operation status. Thus, in this article, a novel self-powered smart monitoring platform (SMP) is codesigned with a laboratory-scale CDI cell by providing regulated polarized voltage to prevent faradaic reactions and to evaluate CDI's performance using desalination metrics. The proof-of-concept SMP can acquire sensory data and wirelessly transmit information to the reader by radio-frequency identification (RFID) technique. A laboratory-scale CDI cell is fabricated in house by the cost-effective carbon electrodes with high electrochemical stability. Experiments are conducted to evaluate the function of the system on real-time monitoring of the CDI cell for their conductivity, salt absorption, and charge efficiency. The measurement results demonstrate that the proposed prototype is effective in terms of supplying, monitoring, and diagnosing the operation condition of the CDI cell. Furthermore, the proof-of-concept SMP developed for the CDI reactor can achieve up to 8.8-m communication distance, while consuming 402.6 μW active power during operation. Therefore, it is a suitable choice for low-power and low-cost wireless sensor network.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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 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".