ABSTRACT
Reducing the economic and environmental impact of industrial process may be achieved by the smartisation of different components. In this work, tube smartisation is presented via direct fabrication of a copper (Cu)-based resistive temperature detector (RTD) on their outer surfaces. The testing was carried out between room temperature and 250 °C. For this purpose, copper depositions were studied using mid-frequency (MF) and high-power impulse magnetron sputtering (HiPIMS). Stainless steel tubes with an outside inert ceramic coating were used after giving them a shot blasting treatment. The Cu deposition was performed at around 425 °C to improve adhesion as well as the electrical properties of the sensor. To generate the pattern of the Cu RTD, a photolithography process was carried out. The RTD was then protected from external degradation by a silicon oxide film deposited over it by means of two different techniques: sol-gel dipping technique and reactive magnetron sputtering. For the electrical characterisation of the sensor, an ad hoc test bench was used, based on the internal heating and the external temperature measurement with a thermographic camera. The results confirm the linearity (R2 > 0.999) and repeatability in the electrical properties of the copper RTD (confidence interval < 0.0005).
Subject(s)
Copper , Stainless Steel , Surface Properties , CeramicsABSTRACT
There is an increasing focus on the part of academic institutions, funding agencies, and publishers, if not researchers themselves, on preservation and sharing of research data. Motivations for sharing include research integrity, replicability, and reuse. One of the barriers to publishing data is the extra work involved in preparing data for publication once a journal article and its supporting information have been completed. In this work, a method is described to generate both human and machine-readable supporting information directly from the primary instrumental data files and to generate the metadata to ensure it is published in accordance with findable, accessible, interoperable, and reusable (FAIR) guidelines. Using this approach, both the human readable supporting information and the primary (raw) data can be submitted simultaneously with little extra effort. Although traditionally the data package would be sent to a journal publisher for publication alongside the article, the data package could also be published independently in an institutional FAIR data repository. Workflows are described that store the data packages and generate metadata appropriate for such a repository. The methods both to generate and to publish the data packages have been implemented for NMR data, but the concept is extensible to other types of spectroscopic data as well.
ABSTRACT
NMR binding assays are routinely applied in hit finding and validation during early stages of drug discovery, particularly for fragment-based lead generation. To this end, compound libraries are screened by ligand-observed NMR experiments such as STD, T1ρ, and CPMG to identify molecules interacting with a target. The analysis of a high number of complex spectra is performed largely manually and therefore represents a limiting step in hit generation campaigns. Here we report a novel integrated computational procedure that processes and analyzes ligand-observed proton and fluorine NMR binding data in a fully automated fashion. A performance evaluation comparing automated and manual analysis results on (19)F- and (1)H-detected data sets shows that the program delivers robust, high-confidence hit lists in a fraction of the time needed for manual analysis and greatly facilitates visual inspection of the associated NMR spectra. These features enable considerably higher throughput, the assessment of larger libraries, and shorter turn-around times.