Software agent technology in the laboratory

The IT (Information Technology) environment in today's laboratories is characterized as being highly distributed, heterogeneous, and in some instances extremely dynamic. Larger organizations have to deal with hundreds of different systems, ranging from standalone workstations and devices in laboratories to fully integrated LIMS (Laboratory Information Management System) and ERP (Enterprise Resource Planning) systems. An information system operating in such an environment must handle several emerging problems, such as heterogeneous hardware and software platforms, as well as distributed information sources and capabilities. It is also expected that the IT infrastructure scales well, easily integrates with legacy systems, allows resource sharing, and supports day-to-day operations such as information retrieval, data storage, validation, tracking, replication, and archival in a fully automated fashion. By using real-world examples, this presentation will illustrate how software agent technology can be used to manage the ever increasing IT complexity and user demands in the laboratory of the future

DDRS4PALS: a software for the acquisition and simulation of lifetime spectra using the DRS4 evaluation board

Lifetime techniques are applied to diverse fields of study including materials sciences, semiconductor physics, biology, molecular biophysics and photochemistry. Here we present DDRS4PALS, a software for the acquisition and simulation of lifetime spectra using the DRS4 evaluation board (Paul Scherrer Institute, Switzerland) for time resolved measurements and digitization of detector output pulses. Artifact afflicted pulses can be corrected or rejected prior to the lifetime calculation to provide the generation of high-quality lifetime spectra, which are crucial for a profound analysis, i.e. the decomposition of the true information. Moreover, the pulses can be streamed on an (external) hard drive during the measurement and subsequently downloaded in the offline mode without being connected to the hardware. This allows the generation of various lifetime spectra at different configurations from one single measurement and, hence, a meaningful comparison in terms of analyzability and quality. Parallel processing and an integrated JavaScript based language provide convenient options to accelerate and automate time consuming processes such as lifetime spectra simulations

Update (v1.3) to DLTPulseGenerator: A library for the simulation of lifetime spectra based on detector-output pulses

In the last two updates v1.1 and v1.2 of DLTPulseGenerator library, we provided the simulation of distributed specific lifetimes and the simulation of lifetime spectra consisting of non-Gaussian distributed and linearly combined Instrument Response Functions (IRF). In this update v1.3, the DLTPulseGenerator library was modified to account for additional hardware influences, which significantly modify the ideal shape of a detector output-pulse defined previously by a log-normal distribution function. These influences mainly originate from the electronic parts, which accomplish the conversion of the analog voltage signal into digital numbers, i.e. the A/D converter (ADC). Eventually, this provides the modeling of authentic detector output-pulses, which leads to a spectra simulation under more realistic conditions

DLTPulseGenerator: a library for the simulation of lifetime spectra based on detector-output pulses

The quantitative analysis of lifetime spectra relevant in both life and materials sciences presents one of the ill-posed inverse problems and, hence, leads to most stringent requirements on the hardware specifications and the analysis algorithms. Here we present DLTPulseGenerator, a library written in native C++ 11, which provides a simulation of lifetime spectra according to the measurement setup. The simulation is based on pairs of non-TTL detector output-pulses. Those pulses require the Constant Fraction Principle (CFD) for the determination of the exact timing signal and, thus, the calculation of the time difference i.e. the lifetime. To verify the functionality, simulation results were compared to experimentally obtained data using Positron Annihilation Lifetime Spectroscopy (PALS) on pure tin

Update (v1.1) to DLTPulseGenerator: A library for the simulation of lifetime spectra based on detector-output pulses

In this update, the DLTPulseGenerator library was extended to allow the simulation of spectra consisting of non-discrete and distributed specific lifetimes, which follow a Gaussian, Lorentzian/Cauchy or Log-Normal distribution function. Typically, distributions of specific lifetimes are found for the lifetimes of positrons in porous materials due to the pore size distributions

Strategische Bereitstellung offener Verwaltungsdaten

Unser White Paper soll dazu beitragen, öffentliche Einrichtungen, die vor oder inmitten der Aufgabe stehen, Open Government Data in ihr Handeln zu integrieren, bei der Erarbeitung einer Strategie zur Einführung und Etablierung offener Daten zu unterstützen

Update (v1.2) to DLTPulseGenerator: A library for the simulation of lifetime spectra based on detector-output pulses

In the last update of DLTPulseGenerator library (v1.1), we realised the simulation of distributed specific lifetimes as can be found for the lifetimes of positrons (PALS) in porous materials due to the pore size distribution.However, in this update v1.2, the DLTPulseGenerator library was modified to allow the simulation of lifetime spectra consisting of non-Gaussian distributed and linearly combined Instrument Response Functions (IRF), since a Gaussian shaped instrumental response of a lifetime spectroscopy setup more likely represents an approximation as it reflects the experimentally obtained results. Eventually, this provides an improved modeling of the experimental instrumental response and, finally, leads to a more realistic simulation of lifetime spectra

Data on pure tin by Positron Annihilation Lifetime Spectroscopy (PALS) acquired with a semi-analog/digital setup using DDRS4PALS

Positron annihilation lifetime spectroscopy (PALS) provides a powerful technique for non-destructive microstructure investigations in a broad field of material classes such as metals, semiconductors, polymers or porous glasses. Even though this method is well established for more than five decades, no proper standardization for the used setup configuration and subsequent data processing exists. Eventually, this could lead to an insufficiency of data reproducibility and avoidable deviations. Here we present experimentally obtained and simulated data of positron lifetime spectra at various statistics measured on pure tin (4N-Sn) by using a semi-analog/digital setup, where the digital section consists of the DRS4 evaluation board, “Design and performance of the 6 GHz waveform digitizing chip DRS4” [1]. The analog section consists of nuclear instrument modules (NIM), which externally trigger the DRS4 evaluation board to reduce the digitization and, thus, increase the acquisition efficiency. For the experimentally obtained lifetime spectra, 22Na sealed in Kapton foil served as a positron source, whereas 60Co was used for the acquisition of the prompt spectrum, i.e. the quasi instrument response function. Both types of measurements were carried out under the same conditions. All necessary data and information regarding the data acquisition and data reduction are provided to allow reproducibility by other research groups

Sintering nickel and iron: Are there indications for defect-activated sintering?

Defects in powder metallurgical samples may enhance diffusion processes during sintering. Samples of high purity nickel reduction powder and pure carbonyl iron are prepared by interrupted sintering, i.e. removing the furnace at the target temperature and rapidly cooling the samples, which are then investigated by analytical methods like positron annihilation lifetime spectroscopy (PALS). Thus, we can follow the sintering process by changes in the microstructure. These results are compared to heavily deformed reference samples (iron taken from literature) to determine the recovery temperature (0.4 of the melting temperature), i.e. annealing of dislocations. We estimate an effective powder particle size, which can be used to model the shrinkage by a modified two-particle model. The results for the two systems will be compared. For larger samples (10-30mm in diameter) we follow the shrinkage by thermo-optical measurements to determine influences of the pressing tools by monitoring the shrinkage at different height positions during one run