A Self-Sustained Smart Monitoring Platform for Capacitive De-Ionization Cell in Wireless Sensor Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it