In vitro assessment of antibacterial and antiviral activity of three copper products after 200 rounds of simulated use
Bibliographic record
Abstract
Abstract Copper has well-documented antibacterial effects but few have evaluated it after prolonged use and against bacteria and viruses. Coupons from three copper formulations (solid, thermal coating, and decal applications) and carbon steel controls were subjected to 200 rounds simulated cleaning using a Wiperator™ and either an accelerated hydrogen peroxide, quaternary ammonium, or artificial sweat products. Antibacterial activity against S. aureus and P. aeruginosa was then evaluated using a modified Environmental Protection Agency protocol. Antiviral activity against coronavirus (229E) and norovirus (MNV-1) surrogates was assessed using the TCID 50 method. Results were compared to untreated control coupons. One hour after inoculation, S. aureus exhibited a difference in log kill of 1.16 to 4.87 and P. aeruginosa a log kill difference of 3.39–5.23 (dependent upon copper product and disinfectant) compared to carbon steel. MNV-1 demonstrated an 87–99% reduction on each copper surfaces at 1 h and 99% reduction at 2 h compared to carbon steel. Similarly, coronavirus 229E exhibited a 97–99% reduction after 1 h and 90–99% after 2 h. Simulated use with artificial sweat did not hinder the antiviral nor the antibacterial activity of Cu surfaces. Self-sanitizing copper surfaces maintained antibacterial and antiviral activity after 200 rounds of simulated cleaning. Graphical abstract
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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) | 0.001 | 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".