ArticleJournal of photochemistry and photobiology2022
Characterization of a robot-assisted UV-C disinfection for the inactivation of surface-associated microorganisms and viruses.
Article in Journal of photochemistry and photobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Post-Harvest UV-C Treatment of Microgreens for Inactivation ofFoods (Basel, Switzerland) · 2026Article
- Kinetics of inactivation of bacteria responsible for infections in hospitals using UV-LED.Heliyon · 2024Article
- Improved RRT* Algorithm for Disinfecting Robot Path Planning.Sensors (Basel, Switzerland) · 2024Article
- Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm.Advances in engineering software (Barking, London, England : 1992) · 2023Article
- Ultraviolet disinfection (UV-D) robots: bridging the gaps in dentistry.Frontiers in oral health · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microorganisms pose a serious threat for us humans, which is exemplified by the recent emergence of pathogens such as SARS-CoV-2 or the increasing number of multi-resistant pathogens such as MRSA. To control surface microorganisms and viruses, we investigated the disinfection properties of an AI-controlled robot, HERO21, equipped with eight 130-W low pressure UV-C mercury vapor discharge lamps emitting at a wavelength of 254 nm, which is strongly absorbed by DNA and RNA, thus inactivating illuminated microorganisms. Emissivity and spatial irradiance distribution of a single UV-C lamp unit was determined using a calibrated spectrometer and numerical simulation, respectively. The disinfection efficiency of single lamps is determined by microbiological tests using
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.