Characterization of a robot-assisted UV-C disinfection for the inactivation of surface-associated microorganisms and viruses

Kinetics of inactivation of bacteria responsible for infections in hospitals using UV-LED

Controlling the microbial load in the environment is crucial to prevent the spread of organisms. The continuous spread of nosocomial infections in hospital facilities and the emergence of the coronavirus (COVID-19) highlighted the importance of disinfection processes in health safety. This work aimed to evaluate the effectiveness of LED-based disinfection lamps on bacteria from the ESKAPEE group and virus phage in vitro inactivation to be applied in hospital environments and health facilities disinfection. This study evaluated the effect of different UV wavelengths (275 nm, 280 nm (UVC), 310 nm (UVB) and 340 nm (UVA)) on the disinfection process of various microbial indicators including E. coli, S. aureus, P. aeruginosa, B. subtilis and Bacteriophage lambda DSM 4499. Exposure time (5 min-30 min), exposure distance (0.25 m and 0.5 m) and surface materials (glass, steel, and polished wood) were evaluated on the disinfection efficiency. Furthermore, the study determined the recovery capacity of each species after UV damage. UVC-LED lamps could inactivate 99.99 % of microbial indicators after 20 min exposures at a 0.5 m distance. The exposure time needed to completely inactivate E. coli, S. aureus, P. aeruginosa, B. subtilis and Bacteriophage lambda DSM 4499 can be decreased by reducing the exposure distance. UVB-LED and UVA-LED lamps were not able to promote a log reduction of 4 and were not effective on B. subtilis or bacteriophage lambda DSM 4499 inactivation. Thus, only UVC-LED lamps were tested on the decontamination of different surface materials, which was successful. P. aeruginosa showed the ability to recover from UV damage, but its inactivation rate remains 99.99 %, and spores from B. subtilis were not completely inactivated. Nevertheless, the inactivation rate of these indicators remained at 99.99 % with 24 h incubation after UVC irradiation. UVC-LED lamps emitting 280 nm were the most indicated to disinfect surfaces from microorganisms usually found in hospital environments.

Enhancing disinfection and microcontaminant removal by coupling LED driven UVC and UVA/photo-Fenton processes in continuous flow reactors

For the first time, the sequential combination of UVC-LED (276 nm) and photo-Fenton/UVA-LED (376 nm) process has been assessed in continuous flow mode for wastewater reclamation according to the new European Regulation for reuse in agricultural irrigation (EU 2020/741). The results show that it is possible to obtain water quality class B (Escherichia coli ≤ 100 CFU/100 mL) by UVC-LED irradiation alone, operating the system with a hydraulic residence time (HRT) of 6.5 min and liquid depth of 5 cm in the case of secondary effluents with low Escherichia coli load (8.102-3.1.103 CFU/100 mL). As for high bacteria concentrations (1.2-4.2.104 CFU/100 mL), HRTs longer than 30 min are required. The bacterial load has not influenced decontamination, removing 18 ± 4 % of microcontaminants. Coupling the UVC (30-min HRT and 5.0 cm liquid depth) and the UVA/photo-Fenton (60-min and 15-cm liquid depth) systems allows 58 ± 4 % of real organic microcontaminants to be removed, in addition to achieving water quality class B.

Improved RRT* Algorithm for Disinfecting Robot Path Planning

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Ultraviolet disinfection (UV-D) robots: bridging the gaps in dentistry

Maintaining a microbe-free environment in healthcare facilities has become increasingly crucial for minimizing virus transmission, especially in the wake of recent epidemics like COVID-19. To meet the urgent need for ongoing sterilization, autonomous ultraviolet disinfection (UV-D) robots have emerged as vital tools. These robots are gaining popularity due to their automated nature, cost advantages, and ability to instantly disinfect rooms and workspaces without relying on human labor. Integrating disinfection robots into medical facilities reduces infection risk, lowers conventional cleaning costs, and instills greater confidence in patient safety. However, UV-D robots should complement rather than replace routine manual cleaning. To optimize the functionality of UV-D robots in medical settings, additional hospital and device design modifications are necessary to address visibility challenges. Achieving seamless integration requires more technical advancements and clinical investigations across various institutions. This mini-review presents an overview of advanced applications that demand disinfection, highlighting their limitations and challenges. Despite their potential, little comprehensive research has been conducted on the sterilizing impact of disinfection robots in the dental industry. By serving as a starting point for future research, this review aims to bridge the gaps in knowledge and identify unresolved issues. Our objective is to provide an extensive guide to UV-D robots, encompassing design requirements, technological breakthroughs, and in-depth use in healthcare and dentistry facilities. Understanding the capabilities and limitations of UV-D robots will aid in harnessing their potential to revolutionize infection control practices in the medical and dental fields.

Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm

The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C light to automate the disinfection processes. But performing this process in an optimum way introduces some challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the environment. In this work, both challenges are addressed with the design of a complete coverage path planning (CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN), a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed. One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on the performance of the robot navigation.