Design of an Automated UV-C Disinfection Capsule Prototype
Bibliographic record
Abstract
Rapid and safe disinfection of frequently used objects is a critical requirement in hospitals, offices, and public transport, where chemical methods generate toxic residues, entail high costs, and require manual application.To overcome these limitations, this study presents the design and implementation of an automated UV-C disinfection capsule equipped with safety interlocks and microbiological validation.The system integrates an ESP32 microcontroller, proximity sensors, and a servo-based locking mechanism to prevent accidental exposure, ensuring a reliable and autonomous operation.Microbiological assays using Escherichia coli and Staphylococcus aureus on agar plates demonstrated a 90% reduction in bacterial load after only 10 s of exposure at 254 nm, and more than 99% inactivation after 60 s.Compared with commercial devices and chemical disinfection, the prototype achieved shorter disinfection times, lower fabrication cost (USD 120), and complete elimination of toxic residues.These findings confirm that the UV-C capsule is a viable and efficient biosecurity alternative for high-traffic environments, with potential for further improvement through optimized light distribution and intelligent monitoring in future iterations.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".