New insights into the application of microbial desalination cells for desalination and bioelectricity generation
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
Microbial desalination cell (MDC) is considered as a cost-effective substitution to the present energy-intensive desalination methods. Transfer of salt ions through ion exchange membranes towards the counter electrodes takes place through the utilization of self-generated bioelectricity and the concentration gradient. Ions transportation is one of the main challenges faced in MDCs to which less attention has been paid during the course of development. Therefore, new insights into the application of MDCs for efficient utilization of the generated bioelectricity for desalination are of high demand. In light of this, the present research thoroughly investigated the behavior of ions transportation and bioelectricity generation in three MDCs using three different salt solutions; NaCl, synthetic and artificial seawater. The findings obtained suggested that the efficiency of ions transportation and fouling behavior were influenced by salt compositions and concentration of the salt solution. Multivalent ions (i.e. Mg2+, Ca2+, and PO43-) were found more prone to precipitation on the CEM forming a scaling layer, whereas, inorganic deposition and biofouling development were more likely to happen on the AEM. This study also confirmed the occurrence of a significant back diffusion of K+ from catholyte into desalination chamber. Such back diffusion could limit the use of potassium buffer in catholyte in real-scale applications. Moreover, the coefficients of salt transfer and ion diffusion were calculated using mathematical model and Excel solver in three running MDCs. Low salt transfer and ion diffusion coefficients values obtained for all three MDCs could explain the general low performance of MDCs. Further studies are required to optimize the salt transfer and ion diffusion coefficients to boost MDC performance in general; affecting their real-scale implementation.
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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 | 0.002 | 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 | 0.000 |
| 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 it