(Invited) Capacitive Deionization (CDI) – an Industrial Research Perspective
Bibliographic record
Abstract
As the demand for freshwater keeps increasing due to industrialization and population growth, the world is progressively interested in desalination for sourcing potable water. While membrane-based reverse osmosis (RO) and thermal-based evaporation/distillation are proven technologies for desalination, they are also expensive. Capacitive deionization (CDI) - introduced in the 1970s - where the positive and negative ions are separated by the application of an electric field across pairs of high surface area electrodes, has been considered as a potentially cheaper technology. In the mid-2000s, Corning carried out a research project on developing CDI. The workstreams involved developing novel materials for electrodes, new device architecture, predictive theoretical models at electrode and device scales, and eventually a plant-level cost analysis that connected the electrode and device-level parameters to the final cost of water desalination, in $/gallon, which is the ultimate arbiter for techno-economic feasibility. We made significant progress in: (a) developing thin all-carbon electrodes which were electrochemically inert, highly conductive, and possessed high specific capacitance (F/cc) (b) designing a flow-past device architecture based on stacked planar electrodes with a small footprint (c) developing user-friendly simplified theoretical models, and (d) formulating two high-level figures of merit (FOM), namely, volumetric efficiency (VE) and recovery ratio (RR) – both of which need to be maximized to compete against incumbent technologies such as RO, especially in the high-throughput brackish water market sector. Our prototype achieved an equivalent VE of ~40 kg/ft 3 /day of salt removal. However, the plant-level cost analysis suggested that there is not much room for improving the overall cost structure compared to RO even with such high device level performance for high throughput desalination markets. In this talk we will present our experience with the development of CDI technology. We will cover all aspects of the project - electrode materials development, device architecture, theoretical models, and cost analysis – and will also offer an industrial perspective on CDI technology development, particularly when it comes to competing against well-established and entrenched technologies. We will also highlight how systems-level thinking and analyses are extremely important for such technology assessments and point out that only focusing on lab-scale performance metrics can be misleading.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".