Beyond the Pipeline: Assessing the Efficiency Limits of Advanced Technologies for Solar Water Disinfection
Bibliographic record
Abstract
Abstract This critical review analyzes and compares the efficiency of select technologies that harness solar energy for point-of-use water disinfection, including photocatalysts, photosensitizing chromophores, UVC light-emitting diodes, and visible-to-UVC upconversion phosphors. The volume rate of water that each material can treat to achieve 99% inactivation of model microorganisms, given the same sunlight exposure, was estimated on the basis of literature data and theoretical predictions, in the context of both currently reported efficiencies and theoretical thermodynamic maximum efficiencies. Each material is further critiqued in terms of the spectral match with sunlight, quantum efficiency, and the relative strength of the resulting disinfecting agent such as hydroxyl radicals, singlet oxygen, and UVC radiation. This review emphasizes critical needs for disinfection strategies that can efficiently inactivate more than one type of microorganism. In addition, the approach described herein can guide future research in efforts to identify more efficient materials and technologies for capturing sunlight for water disinfection.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".