New insights into the application of microbial desalination cells for desalination and bioelectricity generation
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".