3% hydrogen peroxide to disinfect urine-contaminated surfaces
Bibliographic record
Abstract
Purpose Disinfecting urine-contaminated floors, surfaces and objects is a persistent challenge in healthcare. While chlorine-based compounds such as bleach are often used to decontaminate surfaces, they are known to degrade plastics and may leave harmful residues and release potentially irritant vapors making them unsuitable disinfectants for materials that come in direct contact with humans. The objective of this study was to evaluate an alternative urine disinfection procedure. Treating urine-contaminated surfaces with 3% hydrogen peroxide (H 2 O 2 ) was hypothesized to remove bacteria. Furthermore, when applicable, the efficacy of the same H 2 O 2 stock solution for its repeated use over time was assessed further increasing simplicity and accessibility. Materials and methods The effectiveness of disinfecting two materials, a flat plastic surface and a long lumen representing a more challenging surface to clean, was evaluated with a commonly used method of water and soap versus using a 3% H 2 O 2 solution. Results Contamination persisted when washing with soap and water but was effectively removed after one hour of H 2 O 2 storage for flat plastic surfaces and after 3 hours for lumen surfaces. The same stock of H 2 O 2 solution could be reused for up to three weeks with no colony formation. Conclusions The results show that bacteria can be removed from a urine-contaminated surface by being soaked in 3% H 2 O 2 for one to three hours based on the surface type without the need for scrubbing or rinsing. The same stock solution can be used for repeated washes for up to three weeks to expand its sustainability and accessibility.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".