Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery

Preparation, Proximate Composition and Culinary Properties of Yellow Alkaline Noodles from Wheat and Raw/Pregelatinized Gadung (Dioscorea Hispida Dennst) Composite Flours

The steady increase of wheat flour price and noodle consumptions has driven researchers to find substitutes for wheat flour in the noodle making process. In this work, yellow alkaline noodles were prepared from composite flours comprising wheat and raw/pregelatinized gadung (Dioscorea hispida Dennst) flours. The purpose of this work was to investigate the effect of composite flour compositions on the cooking properties (cooking yield, cooking loss and swelling index) of yellow alkaline noodle. In addition, the sensory test and nutrition content of the yellow alkaline noodle were also evaluated for further recommendation. The experimental results showed that a good quality yellow alkaline noodle can be prepared from composite flour containing 20% w/w raw gadung flour. The cooking yield, cooking loss and swelling index of this noodle were 10.32 g, 1.20 and 2.30, respectively. Another good quality yellow alkaline noodle can be made from composite flour containing 40% w/w pregelatinized gadung flour. This noodle had cooking yield 8.93 g, cooking loss 1.20, and swelling index of 1.88. The sensory evaluation suggested that although the color, aroma and firmness of the noodles were significantly different (p ≤ 0.05) from wheat flour noodle, but their flavor remained closely similar. The nutrition content of the noodles also satisfied the Indonesian National Standard for noodle. Therefore, it can be concluded that wheat and raw/pregelatinized gadung composite flours can be used to manufacture yellow alkaline noodle with good quality and suitable for functional food

Designing optimal mixtures using generalized disjunctive programming: Hull relaxations

A general modeling framework for mixture design problems, which integrates Generalized Disjunctive Programming (GDP) into the Computer-Aided Mixture/blend Design (CAMbD) framework, was recently proposed (S. Jonuzaj, P.T. Akula, P.-M. Kleniati, C.S. Adjiman, 2016. AIChE Journal 62, 1616–1633). In this paper we derive Hull Relaxations (HR) of GDP mixture design problems as an alternative to the big-M (BM) approach presented in this earlier work. We show that in restricted mixture design problems, where the number of components is fixed and their identities and compositions are optimized, BM and HR formulations are identical. For general mixture design problems, where the optimal number of mixture components is also determined, a generic approach is employed to enable the derivation and solution of the HR formulation for problems involving functions that are not defined at zero (e.g., logarithms). The design methodology is applied successfully to two solvent design case studies: the maximization of the solubility of a drug and the separation of acetic acid from water in a liquid-liquid extraction process. Promising solvent mixtures are identified in both case studies. The HR and BM approaches are found to be effective for the formulation and solution of mixture design problems, especially via the general design problem

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Rapid Determination of Saponins in the Honey-Fried Processing of Rhizoma Cimicifugae by Near Infrared Diffuse Reflectance Spectroscopy.

ObjectiveA model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae.MethodsShengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model.ResultsThe determination coefficient R² was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130.ConclusionThis method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae