The size of inorganic colloidal nanoparticles coated with organic layers of different thickness has been measured with different techniques, including transmission electron microscopy, gel electrophoresis, size exclusion chromatography, fluorescence correlation spectroscopy, and thermophoresis. The results are critically compared, and the advantages and disadvantages of the respective methods are discussed.

A new one-step method for the preparation of graphene oxide (GO) nanostructures has been developed by pulsed laser ablation in GO solution. The formation of different shapes of GO nanostructures, such as ribbons, nanoflakes (including nano-squares, nano-rectangles, nano-triangles, nano-hexagons, and nano-disks) and quantum dots, has been demonstrated by scanning electron microscopy and transmission electron microscopy. Photoreduction for the GO occurred during irradiation by the pulsed laser. The GO quantum dots exhibit a blue photoluminescence, originating from recombination of the localized carriers in the zigzag-edge states.

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.