Polydimethylsiloxane (PDMS) is widely used in dynamic biological microfluidic applications. As a highly hydrophobic material, native PDMS does not support cell attachment and culture, especially in dynamic conditions. Previous covalent coating methods use glutaraldehyde (GA) which, however, is cytotoxic. This paper introduces a novel and simple method for binding collagen type I covalently on PDMS using ascorbic acid (AA) as a cross-linker instead of GA. We compare the novel method against physisorption and GA cross-linker-based methods. The coatings are characterized by immunostaining, contact angle measurement, atomic force microscopy and infrared spectroscopy, and evaluated in static and stretched human adipose stem cell (hASC) cultures up to 13 days. We found that AA can replace GA as a cross-linker in the covalent coating method and that the coating is durable after sonication and after 6 days of stretching. Furthermore, we show that hASCs attach and proliferate better on AA cross-linked samples compared with physisorbed or GA-based methods. Thus, in this paper, we provide a new PDMS coating method for studying cells, such as hASCs, in static and dynamic conditions. The proposed method is an important step in the development of PDMS-based devices in cell and tissue engineering applications.

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The aim was to study if odors evaporated by an olfactory display prototype can be used to affect participants' cognitive and emotionrelated responses to audiovisual stimuli, and whether the display can benefit from objective measurement of the odors. The results showed that odors and videos had significant effects on participants' responses. For instance, odors increased pleasantness ratings especially when the odor was authentic and the video was congurent with odors. The objective measurement of the odors was shown to be useful. The measurement data was classified with 100 % accuracy removing the need to speculate whether the odor presentation apparatus is working properly.

Luminescence-based oxygen sensing is a widely used tool in cell culture applications. In a typical configuration, the luminescent oxygen indicators are embedded in a solid, oxygen-permeable matrix in contact with the culture medium. However, in sensitive cell cultures even minimal leaching of the potentially cytotoxic indicators can become an issue. One way to prevent the leaching is to immobilize the indicators covalently into the supporting matrix. In this paper, we report on a method where platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorphenyl)-porphyrin (PtTFPP) oxygen indicators are covalently immobilized into a polymer matrix consisting of polystyrene and poly(pentafluorostyrene). We study how the covalent immobilization influences the sensing material's cytotoxicity to human induced pluripotent stem cell-derived (hiPSC-derived) neurons and cardiomyocytes (CMs) through 7-13 days culturing experiments and various viability analyses. Furthermore, we study the effect of the covalent immobilization on the indicator leaching and the oxygen sensing properties of the material. In addition, we demonstrate the use of the covalently linked oxygen sensing material in real time oxygen tension monitoring in functional hypoxia studies of the hiPSCderived CMs. The results show that the covalently immobilized indicators substantially reduce indicator leaching and the cytotoxicity of the oxygen sensing material, while the influence on the oxygen sensing properties remains small or nonexistent. Keywords Luminescent-based oxygen sensing. PtTFPP cytotoxicity. Covalently immobilized indicators. Human induced pluripotent stem cell-derived cells. hiPSC-derived neurons and cardiomyocytes Electronic supplementary material The online version of this article (

For ion-mobility spectrometry (IMS)-based electronic noses (eNose) samples of scents are markedly time-dependent, with a transient phase and a highly volatile stable phase in certain conditions. At the same time, the samples depend on various environmental factors, such as temperature and humidity. This makes fast classification of scents challenging. The present aim was to develop and test an algorithm for online scent classification that mitigates these dependencies by using both baseline measurements and sequences of samples for classification. A classifier based on the K nearest neighbors approach was derived. The classifier is able to use measurements from both transient and stable phase, yields a label for the analyzed scent, and information on the trustworthiness of the returned label. In order to avoid the classifier being fooled by irrelevant features and to reduce the dimensionality of the feature space, principal component analysis was applied to the data. The classifier was tested with four food scents, each presented in two different ways to the IMS. By using baseline measurements, the misclassification rate was reduced from 20.0 to 13.3%. A second experiment showed that the used IMS type experiences device heterogeneity.