Faradaic Electrodes Open a New Era for Capacitive Deionization
Bibliographic record
Abstract
Capacitive deionization (CDI) is an emerging desalination technology for effective removal of ionic species from aqueous solutions. Compared to conventional CDI, which is based on carbon electrodes and struggles with high salinity streams due to a limited salt removal capacity by ion electrosorption and excessive co-ion expulsion, the emerging Faradaic electrodes provide unique opportunities to upgrade the CDI performance, i.e., achieving much higher salt removal capacities and energy-efficient desalination for high salinity streams, due to the Faradaic reaction for ion capture. This article presents a comprehensive overview on the current developments of Faradaic electrode materials for CDI. Here, the fundamentals of Faradaic electrode-based CDI are first introduced in detail, including novel CDI cell architectures, key CDI performance metrics, ion capture mechanisms, and the design principles of Faradaic electrode materials. Three main categories of Faradaic electrode materials are summarized and discussed regarding their crystal structure, physicochemical characteristics, and desalination performance. In particular, the ion capture mechanisms in Faradaic electrode materials are highlighted to obtain a better understanding of the CDI process. Moreover, novel tailored applications, including selective ion removal and contaminant removal, are specifically introduced. Finally, the remaining challenges and research directions are also outlined to provide guidelines for future research.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".