Automation systems with applied robotics have already been established in industrial applications for many years. In the field of life sciences, a comparable high level of automation can be found in the areas of bioscreening and high-throughput screening. Strong deficits still exist in the development of flexible and universal fully automated systems in the field of analytical measurement. Reasons are the heterogeneous processes with complex structures, which include sample preparation and transport, analytical measurements using complex sensor systems, and suitable data analysis and evaluation. Furthermore, the use of nonstandard sample vessels with various shapes and volumes results in an increased complexity. The direct use of existing automation solutions from bioscreening applications is not possible. A flexible automation system for sample preparation, analysis, and data evaluation is presented in this article. It is applied for the determination of cholesterol in biliary endoprosthesis using gas chromatography–mass spectrometry (GC-MS). A dual-arm robot performs both transport and active manipulation tasks to ensure human-like operation. This general robotic concept also enables the use of manual laboratory devices and equipment and is thus suitable in areas with a high standardization grade.

The continued growth in life sciences is being accompanied by the constantly rising demand for robotic systems. Today, bioscreening and high–throughput screening processes are well automated. In contrast, a deficit can be found in the area of analytical measurements with complex and frequently changing processes. Robots undertake not only transportation tasks, but also direct sample manipulation and subsequent analytical measurements. Due to their human-like structure, dual-arm robots perform such processes similar to human operation. Liquid handling is required to transfer chemicals, to prepare standard solutions, or to dilute samples. Two electronic pipettes with different volume ranges (5–200 µL and 50–1000 µL) were integrated into a dual–arm robotic system. The main focus in this publication is the software interface for alternating robot and pipette control as well as the high–level process control system. The performance using a dual–arm robot equipped with electronic pipettes and conventional manual pipettes was determined and compared. The automation system presented is the first integration of a dual-arm robot in analytical measurement processes. Conventional manual laboratory pipettes and electronic pipettes are simultaneously used for liquid-handling tasks. The software control system enables a flexible and user-friendly process generation.

The measurement of air quality parameters for indoor environments is of increasing importance to provide sufficient safety conditions for workers, especially in places including dangerous chemicals and materials such as laboratories, factories, and industrial locations. Indoor air quality index (IAQ-index) and total volatile organic Compounds (TVOC) are two important parameters to measure air impurities or air pollution. Both parameters are widely used in gases sensing applications. In this paper, the IAQ-index and TVOCs have been investigated to identify the best and most flexible solution for air quality threshold selection of hazardous/toxic gases detection and alarming systems. The TVOCs from the SGP30 gas sensor and the IAQ-index from the SGP40 gas sensor were tested with 12 different organic solvents. The two gas sensors are combined with an IoT-based microcontroller for data acquisition and data transfer to an IoT-cloud for further processing, storing, and monitoring purposes. Extensive tests of both sensors were carried out to determine the minimum detectable volume depending on the distance between the sensor node and the leakage source. The test scenarios included static tests in a classical chemical hood, as well as tests with a mobile robot in an automated sample preparation laboratory with different positions.

The endoprosthetic care of hip and knee joints introduces multiple materials into the human body. Metal containing implant surfaces release degradation products such as particulate wear and corrosion debris, metal-protein complexes, free metallic ions, inorganic metal salts or oxides. Depending on the material composition of the prostheses, a systemic exposure occurs and may result in increasing metal concentrations in body fluids and tissues especially in the case of malfunctions of the arthroplasty components. High concentrations of Cr, Co, Ni, Ti and Al affect multiple organs such as thyroid, heart, lung and cranial nerves and may lead to metallosis, intoxications, poly-neuropathy, retinopathy, cardiomyopathy and the formation of localized pseudo tumors. The determination of the concentration of metals in body fluids and tissues can be used for predicting failure of hip or knee replacements to prevent subsequent severe intoxications. A semi-automated robot-assisted measurement system is presented for the determination of heavy metals in human tissue samples using inductively coupled plasma mass spectrometry (ICP-MS). The manual and automated measurement processes were similarly validated using certified reference material and the results are compared and discussed. The automation system was successfully applied in the determination of heavy metals in human tissue; the first results are presented.

Air pollution and leakages of hazardous and toxic gases and chemicals are among the dangers that frequently occur at automated chemical and life science laboratories. This type of accident needs to be processed as soon as possible to avoid the harmful side effects that can happen when a human is exposed. Nitrogen oxides and volatile organic compounds are among the most prominent indoor air pollutants, which greatly affect the lifestyles in these places. In this study, a commercial MOX gas sensor, SGP41, was embedded in an IoT environmental sensor node for hazardous gas detection and alarm. The sensor can detect several parameters, including nitrogen oxide index (NOx-Index) and volatile organic compound index (VOC-Index). Several tests were conducted to detect the leakage of nitrogen oxides and volatile organic compounds in different concentrations and volumes, as well as from different leakage distances, to measure the effect of these factors on the response speed and recovery time of the sensors used. These factors were also compared between the different sensors built into the sensor node to give a comprehensive picture of the system used. The system testing results revealed that the SGP41 sensor is capable of implementing the design purposes for the target parameters, can detect a small NO₂ gas leakage starting from 0.3% volume, and can detect all the tested VOC solvents ≥ 100 µL