Can UVC-LEDs mitigate biofouling in community-scale photovoltaic-powered reverse osmosis systems?
Bibliographic record
Abstract
ABSTRACT The lack of safe drinking water infrastructure in off-grid communities is a significant risk to sustainable development, especially for low- and middle-income countries (LMICs). Photovoltaic-powered reverse osmosis (PVRO) has emerged as a promising method due to its performance, scalability, consistency, and the robust global supply chain of its components. However, reliability issues like biofouling can quickly reduce its performance, shorten membrane lifetime, and hinder adoption in off-grid settings. Ultraviolet light emitting diodes (UV-LEDs) ranging from 200–285 nm wavelengths reduce the number of microorganisms in water but there are mixed results about their use on mitigating biofouling in reverse osmosis systems. Herein, we aim to provide a preliminary assessment on whether UV-LED pre-treatment can mitigate biofouling in PVRO systems. Analysis of the E. coli concentration pre- and post-UV treatment for batch and flow cell experiments demonstrated reduced bacterial concentration after treatment but suggest that remaining bacteria after UV treatment can grow back over time, i.e., UV-LEDs do delay, but not completely eliminate, biofouling. These results encourage further investigation into how UV-LEDs can be optimally integrated in PVRO systems and also set the premise for controlled biofouling mitigation studies for intermittently operated, small-scale PVRO.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".