Modeling of three-phase continuously operating open-cell foam catalyst packings: sugar hydrogenation to sugar alcohols

An advanced comprehensive and transient multiphase model for a trickle bed reactor with solid foam packings was developed. A new simulation model for isothermal three-phase (gas–liquid–solid) catalytic tubular reactor models was presented where axial, radial and catalyst layer effects were included. The gas, liquid and solid phase mass balances included most of the individual terms for solid foam packing (e.g. kinetics, liquid-solid and intraparticle mass transfer effects). Hydrogenation of arabinose and galactose mixture on a ruthenium catalyst supported by carbon-coated aluminum foams was applied as a fundamentally and industrially relevant case study. Parameter estimations allowed to obtain reliable and significant parameters. To test the model performance, a sensitivity analysis was performed and the effect of the kinetic parameters and the operation conditions on the arabinose and galactose conversions was studied in detail. The model described here is applicable for other three-phase continuous catalytic reactors with solid foam packings.

Preparation of Acyclovir-Containing Solid Foam by Ultrasonic Batch Technology

In recent years, the application of solid foams has become widespread. Solid foams are not only used in the aerospace field but also in everyday life. Although foams are promising dosage forms in the pharmaceutical industry, their usage is not prevalent due to decreased stability of the solid foam structure. These special dosage forms can result in increased bioavailability of drugs. Low-density floating formulations can also increase the gastric residence time of drugs; therefore, drug release will be sustained. Our aim was to produce a stable floating formula by foaming. Matrix components, PEG 4000 and stearic acid type 50, were selected with the criteria of low gastric irritation, a melting range below 70 °C, and well-known use in oral drug formulations. This matrix was melted at 54 °C in order to produce a dispersion of active substance and was foamed by different gases at atmospheric pressure using an ultrasonic homogenizer. The density of the molded solid foam was studied by the pycnometer method, and its structure was investigated by SEM and micro-CT. The prolonged drug release and mucoadhesive properties were proved in a pH 1.2 buffer. According to our experiments, a stable foam could be produced by rapid homogenization (less than 1 min) without any surfactant material.

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.

Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Dataset

This work presents a machine learning approach for computer vision-based recognition of materials inside vessels in a chemistry lab and other settings. In addition, we release a dataset associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their content is essential for performing this task. Modern machine vision methods learn recognition tasks by using datasets containing a large number of annotated images. This work presents the Vector-LabPics dataset, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder,...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this dataset. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.