Solar Photothermal Disinfection using Broadband-Light Absorbing Gold Nanoparticles and Carbon Black
Bibliographic record
Abstract
A simple heat treatment, perhaps the most globally recognized point-of-use water sterilization method, is seemingly effective against all major pathogens of concern, but bulk water boiling is not energy efficient or sustainable. Herein, we present the first application of solar-to-thermal converting nanomaterials for the direct inactivation of bacteria and viruses in drinking water through the application of Au nanorods, carbon black, and Au nanorod-carbon black composite materials as light absorbers. With broad absorption bands spanning the visible and near-infrared wavelengths, at sufficient concentrations, these nanoparticles induce multiple scattering events, increasing photon absorption probability and concentrating the light within a small spatial domain, leading to localized, intense heating that inactivates microorganisms in close proximity. Moving toward practical device design, we have developed a facile silane immobilization approach to fabricate films with densely packed layers of photothermal nanomaterials. Our results suggest that upon irraditaion with simulated solar light, these films can thermally inactivate bacteria and viruses, as demonstrated through the inactivation of surrogate organisms Escherichia coli K-12, and bacteriophages MS2 and PR772.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".