An Easy-to-Construct Automated Winkler Titration System

The instrument described in this report is an updated version of the high precision, automated Winkler titration system described by Friederich et al.(1984). The original instrument was based on the work of Bryan et al. (1976) who developed a colorimetric endpoint detector and on the work of Williams and Jenkinson (1982) who produced an automated system that used this detector. The goals of our updated version of the device described by Friederich et al. (1984) were as follows: 1) Move control of the system to the MS-DOS environment because HP-85 computers are no longer in production and because more user-friendly programs could be written using the IBM XT or AT computers that control the new device. 2) Use more "off the shelf" components and reduce the parts count in the new system so that it could be easily constructed and maintained. This report describes how to construct and use the new automated Winkler titration device. It also includes information on the chemistry of the Winkler titration, and detailed instructions on how to prepare reagents, collect samples, standardize and perform the titrations (Appendix I: Codispoti, L.A. 1991 On the determination of dissolved oxygen in sea water, 15pp.). A disk containing the program needed to operate the new device is also included. (pdf contains 33 pages

On some factors influencing dissolved silicon distribution over the northwest African shelf

Nitrate concentrations in the upwelling source waters observed near Cabo Corbeiro during the JOINT-I experiment were more than twice as high as dissolved silicon concentrations, but dissolved silicon concentrations in the surface layers fell below 0.5 µg-atoms..

Spin-charge separation in transport through Luttinger liquid rings

We investigate how the different velocities characterizing the low-energy spectral properties and the low-temperature thermodynamics of one-dimensional correlated electron systems (Luttinger liquids) affect the transport properties of ring-like conductors. The Luttinger liquid ring is coupled to two noninteracting leads and pierced by a magnetic flux. We study the flux dependence of the linear conductance. It shows a dip structure which is governed by the interaction dependent velocities. Our work extends an earlier study which was restricted to rather specific choices of the interaction parameters. We show that for generic repulsive two-particle interactions the number of dips can be estimated from the ratio of the charge current velocity and the spin velocity. In addition, we clarify the range of validity of the central approximation underlying the earlier study.Comment: 10 pages including figure

High‐Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels

Due to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties

2-kW Average Power CW Phase-Conjugate Solid-State Laser

We have demonstrated stable operation of a 2-kW Yb:YAG phase-conjugate master oscillator power amplifier (PC-MOPA) laser system with a loop phase-conjugate mirror (LPCM). This is the first demonstration of a continuous wave (CW)-input LPCM MOPA operating at a power greater than 1 kW with a nearly diffraction-limited output beam. The single-pass beam quality incident on the LPCM varied with the specific operating conditions, but it was typically ${sim}20$ times diffraction-limited (XDL). The measured beam quality with an MOPA output power of 1.65 kW was 1.3 XDL

Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements

Modern manufacturing systems can benefit from the use of digital tools to support both short- and long-term decisions. Meanwhile, such systems reached a high level of complexity and are frequently subject to modifications that can quickly make the digital tools obsolete. In this context, the ability to dynamically generate models of production systems is essential to guarantee their exploitation on the shop-floors as decision-support systems. The literature offers approaches for generating digital models based on real-time data streams. These models can represent a system more precisely at any point in time, as they are continuously updated based on the data. However, most approaches consider only isolated aspects of systems (e.g., reliability models) and focus on a specific modeling purpose (e.g., material flow identification). The research challenge is therefore to develop a novel framework that systematically enables the combination of models extracted through different process mining algorithms. To tackle this challenge, it is critical to define the requirements that enable the emergence of automated modeling and simulation tasks. In this paper, we therefore derive and define data requirements for the models that need to be extracted. We include aspects such as the structure of the manufacturing system and the behavior of its machines. The paper aims at guiding practitioners in designing coherent data structures to enable the coupling of model generation techniques within the digital support system of manufacturing companies

Scientific intuition inspired by machine learning-generated hypotheses

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science