LIBS and Raman spectroscopy for stand-off material and contamination characterisation on a robot for the nuclear industry

Coffey, P. and West, A. and Smith, N. and Lennox, B. and Joyce, M. and Martin, P.A. (2026) LIBS and Raman spectroscopy for stand-off material and contamination characterisation on a robot for the nuclear industry. Journal of hazardous materials, 513: 142387. ISSN 1873-3336

Abstract

A mobile robot platform was developed with on-board, stand-off LIBS and Raman probes as part of a broader system for in situ ‘total characterisation’ in nuclear environments. This includes gamma spectrometry and 3D imaging via LIDAR and photogrammetry. All characterisation techniques were guided by a 3D point-cloud model generated from the robot’s imaging systems, enabling precise positioning near target zones. The LIBS probe operated at a stand-off distance of 10 cm and successfully detected lead in a single shot, with signal quality comparable to benchtop instruments. It also distinguished metals such as stainless steels, nickel, and Incoloy using principal component analysis. The Raman probe used a collimated laser beam and acquired spectra from several metres away. It identified organic materials common in decommissioning environments, including plastics and EDTA, and differentiated concentrations of dibutyl and tributyl phosphate in low-odour kerosene, organophosphates relevant to uranium and plutonium removal. Both probes were tested in a simulated hot cell environment, operating entirely on battery power. Contamination detection of non-radioactive analogues of radioactive decay products like strontium and caesium on stainless steel and cement was demonstrated, supporting clean-up and disposal operations and decision making. Spectral, spatial, and radiological data are integrated into a database that updates a digital twin, enabling layered visualisation in a virtual environment. The robot can be deployed for periodic surveys, such as annual inspections of disused hot cells, to monitor environmental degradation. This data-driven approach supports auditable decision-making for waste disposal and remediation priorities.