Targeted Disinfection of E. coli via Bioconjugation to Photoreactive TiO<sub>2</sub>
Bibliographic record
Abstract
The selective control of pathogenic bacteria is an ongoing challenge. A strategy is proposed that combines targeted binding of the bacterium, using antibodies, with their photoactivated oxidative destruction. Photoactive colloidal TiO2 was first derivatized with E. coli antibodies (EA-TiO2). When mixtures of the organisms E. coli and Pseudomonas putida ( P. putida ) were exposed to modified EA-TiO2, the particles preferentially selected E. coli for surface binding. Two consequences arose from surface bioconjugation: bacteria were found to flocculate upon mixing at appropriate ratios of EA-TiO2/ E. coli , and EA-TiO2-bound E. coli underwent cell death after exposure to UV light. In the former case, flocculation of the bacteria was optimal at ~50 EA-TiO2 particles per E. coli . Selective flocculation provides an alternative strategy for pathogen removal. With respect to UV disinfection, as few as 26 EA-TiO2 particles per E. coli gave a 10 000-fold decrease in viable bacteria. Thus, it is possible to selectively target and kill one type of bacteria in a mixture of pathogens. The results give support to the proposal that photocatalytic TiO2 most effectively delivers an oxidizing agent when the titania is bound to the bacterial surface.
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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) | 0.001 | 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 | 0.001 | 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".