Conductive inks based on graphite and carbon black are used in a host of applications including energy storage, energy harvesting, electrochemical sensors and printed heaters. This requires accurate control of electrical properties tailored to the application; ink formulation is a fundamental element of this. Data on how formulation relates to properties have tended to apply to only single types of conductor at any time, with data on mixed types of carbon only empirical thus far. Therefore, screen printable carbon inks with differing graphite, carbon black and vinyl polymer content were formulated and printed to establish the effect on rheology, deposition and conductivity. The study found that at a higher total carbon loading ink of 29.4% by mass, optimal conductivity (0.029 X cm) was achieved at a graphite to carbon black ratio of 2.6 to 1. For a lower total carbon loading (21.7 mass %), this ratio was reduced to 1.8 to 1. Formulation affected viscosity and hence ink transfer and also surface roughness due to retention of features from the screen printing mesh and the inherent roughness of the carbon components, as well as the ability of features to be reproduced consistently.

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Exhaled volatile organic compounds (VOCs) have shown promise in diagnosing chronic obstructive pulmonary disease (COPD) but studies have been limited by small sample size and potential confounders. An investigation was conducted in order to establish whether combinations of VOCs could identify COPD patients from age and BMI matched controls. Breath samples were collected from 119 stable COPD patients and 63 healthy controls. The samples were collected with a portable apparatus, and then assayed by gas chromatography and mass spectroscopy. Machine learning approaches were applied to the data and the automatically generated models were assessed using classification accuracy and receiver operating characteristic (ROC) curves. Cross-validation of the combinations correctly predicted the diagnosis in 79% of COPD patients and 64% of controls and an optimum area under the ROC curve of 0.82 was obtained. Comparison of current and ex smokers within the COPD group showed that smoking status was likely to affect the classification; with correct prediction of smoking status in 85% of COPD subjects. When current smokers were omitted from the analysis, prediction of COPD was similar at 78% but correct prediction of controls was increased to 74%. Applying different analytical methods to the largest group of subjects so far, suggests VOC analysis holds promise for diagnosing COPD but smoking status needs to be balanced.