A critical review of ultra-violet light emitting diodes as a one water disinfection technology
Bibliographic record
Abstract
UV light emitting diode (LED) disinfection technologies have advanced over the last decade and expanded the design space for applications in point of use, industrial, and now full-scale water treatment. This literature review examines the progression of UV LED technologies from 2007 to 2023 using key features such as total optical power, price, and wall-plug efficiency. The review found that optical power is increasing while the price per Watt is decreasing; however, the wall plug energy (WPE) is slowly improving over the last decade. These factors govern the feasibility of many UV LEDs applications and establish the current state of the art for these technologies. An analysis of inactivation rate constants for low-pressure, medium-pressure, and UV LED sources was undertaken and provides a comprehensive view of how current UV LED technologies compare to traditional technologies. This comparison found that UV LEDs perform comparably vs conventional UV technologies when disinfecting bacteria and viruses. Furthermore, comparison of reported reduction equivalent fluences for UV LED flow-through reactors at the bench-, pilot-, and full-scale were explored in this review, and it was found that LED treatment is becoming more effective at handling increased flowrates and has been proven to work at full-scale. UV LEDs do however require additional research into the impacts of water matrices at different wavelengths and the impact that each available LED wavelength has on disinfection. Overall, this work provides a broad assessment of UV disinfection technologies and serves as a state-of-the-art reference document for those who are interested in understanding this rapidly developing technology.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".