Design and Development of Automated Ultraviolet (UV-C) Surface Sterilizer and Disinfection Device Using a User-Centered Design Approach
Bibliographic record
Abstract
Background The global outbreak of the COVID-19 pandemic in 2019 highlighted the urgent need for innovative technologies for sterilization and disinfection of various healthcare utilities. While steam sterilization is widely used in both developing and industrialized countries, it has limitations in disinfecting several critical healthcare items such as hospital rooms, beds, N-95 masks, ambulance beds, medical clothing and devices. During the pandemic, Ethiopia and other developing countries faced significant challenges in sterilization and disinfection system for these healthcare utilities in COVID-19 treatment centers. Methodology The development of the UV-C dry surface disinfection and sterilization device followed a structured engineering design approach, with a focus on functionality, efficiency, and safety. The methodology included key stages such as problem identification and needs analysis, concept design and circuit development, material selection with specification, prototype fabrication, product testing and validation. Result The results of the sterilization and disinfection efficiency test demonstrated that the UV-C device achieved over 90% effectiveness, confirming its viability as an efficient solution for sterilization in healthcare settings and other industries. Conclusion The development of these automated UV-C sterilizers addresses a critical gap in disinfection technology, particularly in resource-limited settings, and enhances the capability to manage infectious diseases like COVID-19 and related pandemics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".