Quantitative Measurement of Thermal Conductivity by SThM Technique: Measurements, Calibration Protocols and Uncertainty Evaluation.

Thermal management is a key issue for the downsizing of electronic components in order to optimise their performance. These devices incorporate more and more nanostructured materials, such as thin films or nanowires, requiring measurement techniques suitable to characterise thermal properties at the nanoscale, such as Scanning Thermal Microscopy (SThM). In active mode, a hot thermoresistive probe scans the sample surface, and its electrical resistance R changes as a function of heat transfers between the probe and sample. This paper presents the measurement and calibration protocols developed to perform quantitative and traceable measurements of thermal conductivity k using the SThM technique, provided that the heat transfer conditions between calibration and measurement are identical, i.e., diffusive thermal regime for this study. Calibration samples with a known k measured at the macroscale are used to establish the calibration curve linking the variation of R to k. A complete assessment of uncertainty (influencing factors and computational techniques) is detailed for both the calibration parameters and the estimated k value. Outcome analysis shows that quantitative measurements of thermal conductivity with SThM (with an uncertainty value of 10%) are limited to materials with low thermal conductivity (k<10Wm-1K-1).

New approaches for interlaboratory comparisons analysis using dark uncertainty applied to radioactive materials.

In order to further improve the management of contaminated materials in nuclear facilities subject to a decommissioning programme, as well as during post-accidental site remediation and clearance, the definition and selection of the most appropriate intervention scenarios producing well-characterized radioactive waste for which storage and disposal routes are clearly identified is needed. As a step towards this accomplishment, we propose a methodology for the organization and analysis of coordinated interlaboratory comparisons (ILC) for the performance assessment and the uncertainty evaluation of available measurement techniques (methods and tools) of radioactive materials. This methodology is new for this type of comparison and demonstrated on the BR3 (Belgian Reactor 3, Belgian Nuclear Research Centre, Mol) case study from the H2020 INSIDER project (2017-2021), for which barium 133, cobalt 60 and europium 152 are analysed with gamma spectroscopy in ILC, based either on irradiated concrete from the BR3 bioshield or from spiked concrete certified reference material (CRM). On one hand, we show the advantage of organizing ILC on CRM for a more reliable uncertainty evaluation taking bias into account following ISO 21748:2017. But using CRM may be impossible due to their scarcity or too costly for performance assessment thus limiting the use of CRM in ILC in practice. On the other hand, we show that for performance evaluation and monitoring, ILC can be alternately performed on reference materials provided that laboratories' uncertainties are reported and the most appropriate analysis of data is performed using dark uncertainty (excess variance) in the presence of inconsistent data.

A Bayesian framework to update scaling factors for radioactive waste characterization.

Nuclear power plants and research facilities commonly employ the so-called scaling factor (SF) method to quantify the activity of difficult-to-measure (DTM) radionuclides within their radioactive waste packages. The method relies on the establishment of a relationship between an easy-to-measure (ETM) radionuclide, called key nuclide (KN), and difficult-to-measure radionuclides, after the collection of a representative sample from the waste population. The distribution of the scaling factors, as well as the parameters defining the distribution, can change over time. Therefore, the accuracy of the calculated activity of the DTM radionuclides depends on the capacity of the scaling factor method to follow the time evolution of the waste population. In practice, waste producers collect periodically new samples from the waste population and check the variation and the validity of the scaling factors. In this article, we present a simple Bayesian framework to update scaling factors when a new data set becomes available. The method is tested and validated for radioactive waste produced at CERN (European Organization for Nuclear Research) and can be easily implemented for waste of different origin.