Air pollution by nitrogen oxides represents a serious environmental problem in urban areas where numerous sources of these pollutants are concentrated. One approach to reduce the concentration of these air pollutants is their light-induced oxidation in the presence of molecular oxygen and a photocatalytically active building material which uses titanium dioxide as the photocatalyst. Herein, results of an investigation concerning the influence of the photon flux and the pollutant concentration on the rate of the photocatalytic oxidation of nitrogen(II) oxide in the presence of molecular oxygen and UV(A) irradiated titanium dioxide powder are presented. A Langmuir-Hinshelwood-type rate law for the photocatalytic NO oxidation inside the photoreactor comprising four kinetic parameters is derived being suitable to describe the influence of the pollutant concentration and the photon flux on the rate of the photocatalytic oxidation of nitrogen(II) oxide.

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The introduction of smart virtual assistants (VAs) and corresponding smart devices brought a new degree of freedom to our everyday lives. Voice-controlled and Internet-connected devices allow intuitive device controlling and monitoring from all around the globe and define a new era of human-machine interaction. Although VAs are especially successful in home automation, they also show great potential as artificial intelligence-driven laboratory assistants. Possible applications include stepwise reading of standard operating procedures (SOPs) and recipes, recitation of chemical substance or reaction parameters to a control, and readout of laboratory devices and sensors. In this study, we present a retrofitting approach to make standard laboratory instruments part of the Internet of Things (IoT). We established a voice user interface (VUI) for controlling those devices and reading out specific device data. A benchmark of the established infrastructure showed a high mean accuracy (95% ± 3.62) of speech command recognition and reveals high potential for future applications of a VUI within the laboratory. Our approach shows the general applicability of commercially available VAs as laboratory assistants and might be of special interest to researchers with physical impairments or low vision. The developed solution enables a hands-free device control, which is a crucial advantage within the daily laboratory routine.

To observe and control cultivation processes, optical sensors are used increasingly. Important variables for controlling such processes are cell count, cell size distribution and the morphology of cells. Among turbidity measurement methods, imaging procedures are applied for determining these process values. A disadvantage of most previously developed imaging procedures is that they are only available offline, which requires sampling. On the other hand, available imaging inline probes can only deliver a limited number of process values so far. This contribution gives an overview of optical procedures for the inline determination of cell count, cell size distribution and other variables. In particular, by in situ microscopy, an imaging procedure will be described, which allows the determination of direct and non-direct cell variables in real time without sampling.

Over the last two decades, more and more applications of sophisticated sensor technology have been described in the literature on upstreaming and downstreaming for biotechnological processes (Middendorf et al. J Biotechnol 31:395-403, 1993; Lausch et al. J Chromatogr A 654:190-195, 1993; Scheper et al. Ann NY Acad Sci 506:431-445, 1987), in order to improve the quality and stability of these processes. Generally, biotechnological processes consist of complex three-phase systems--the cells (solid phase) are suspended in medium (liquid phase) and will be streamed by a gas phase. The chemical analysis of such processes has to observe all three phases. Furthermore, the bioanalytical processes used must monitor physical process values (e.g. temperature, shear force), chemical process values (e.g. pH), and biological process values (metabolic state of cell, morphology). In particular, for monitoring and estimation of relevant biological process variables, image-based inline sensors are used increasingly. Of special interest are sensors which can be installed in a bioreactor as sensor probes (e.g. pH probe). The cultivation medium is directly monitored in the process without any need for withdrawal of samples or bypassing. Important variables for the control of such processes are cell count, cell-size distribution (CSD), and the morphology of cells (Höpfner et al. Bioprocess Biosyst Eng 33:247-256, 2010). A major impetus for the development of these image-based techniques is the process analytical technology (PAT) initiative of the US Food and Drug Administration (FDA) (Scheper et al. Anal Chim Acta 163:111-118, 1984; Reardon and Scheper 1995; Schügerl et al. Trends Biotechnol 4:11-15, 1986). This contribution gives an overview of non-invasive, image-based, in-situ systems and their applications. The main focus is directed at the wide application area of in-situ microscopes. These inline image analysis systems enable the determination of indirect and direct cell variables in real time without sampling, but also have application potential in crystallization, material analysis, polymer research, and the petrochemical industry.