Zoekresultaten

3D printed scoliosis brace; a material challenge

Recycling of plastics plays an important role to reach a climate neutral industry. To come to a sustainable circular use of materials, it is important that recycled plastics can be used for comparable (or ugraded) applications as their original use. QuinLyte innovated a material that can reach this goal. SmartAgain® is a material that is obtained by recycling of high-barrier multilayer films and which maintains its properties after mechanical recycling. It opens the door for many applications, of which the production of a scoliosis brace is a typical example from the medical field. Scoliosis is a sideways curvature of the spine and wearing an orthopedic brace is the common non-invasive treatment to reduce the likelihood of spinal fusion surgery later. The traditional way to make such brace is inaccurate, messy, time- and money-consuming. Because of its nearly unlimited design freedom, 3D FDM-printing is regarded as the ultimate sustainable technique for producing such brace. From a materials point of view, SmartAgain® has the good fit with the mechanical property requirements of scoliosis braces. However, its fast crystallization rate often plays against the FDM-printing process, for example can cause poor layer-layer adhesion. Only when this problem is solved, a reliable brace which is strong, tough, and light weight could be printed via FDM-printing. Zuyd University of Applied Science has, in close collaboration with Maastricht University, built thorough knowledge on tuning crystallization kinetics with the temperature development during printing, resulting in printed products with improved layer-layer adhesion. Because of this knowledge and experience on developing materials for 3D printing, QuinLyte contacted Zuyd to develop a strategy for printing a wearable scoliosis brace of SmartAgain®. In the future a range of other tailor-made products can be envisioned. Thus, the project is in line with the GoChem-themes: raw materials from recycling, 3D printing and upcycling.

A green process to make carboxylated cellulose starting from levulinates

Carboxylated cellulose is an important product on the market, and one of the most well-known examples is carboxymethylcellulose (CMC). However, CMC is prepared by modification of cellulose with the extremely hazardous compound monochloracetic acid. In this project, we want to make a carboxylated cellulose that is a functional equivalent for CMC using a greener process with renewable raw materials derived from levulinic acid. Processes to achieve cellulose with a low and a high carboxylation degree will be designed.

A portable compact device for routine micro-/nanoparticle measurement

Size measurement plays an essential role for micro-/nanoparticle characterization and property evaluation. Due to high costs, complex operation or resolution limit, conventional characterization techniques cannot satisfy the growing demand of routine size measurements in various industry sectors and research departments, e.g., pharmaceuticals, nanomaterials and food industry etc. Together with start-up SeeNano and other partners, we will develop a portable compact device to measure particle size based on particle-impact electrochemical sensing technology. The main task in this project is to extend the measurement range for particles with diameters ranging from 20 nm to 20 um and to validate this technology with realistic samples from various application areas. In this project a new electrode chip will be designed and fabricated. It will result in a workable prototype including new UMEs (ultra-micro electrode), showing that particle sizing can be achieved on a compact portable device with full measuring range. Following experimental testing with calibrated particles, a reliable calibration model will be built up for full range measurement. In a further step, samples from partners or potential customers will be tested on the device to evaluate the application feasibility. The results will be validated by high-resolution and mainstream sizing techniques such as scanning electron microscopy (SEM), dynamic light scattering (DLS) and Coulter counter.

A SAMSUNG VIRTUAL REALITY

Developing and testing several AR and VR concepts for SAMSUNG (Benelux) Samsung and Breda University of Applied Sciences decided to work together on developing and testing several new digital media concepts with a focus on VR and gaming. This collaboration has led to several innovative projects and concepts, among others: the organisation of the first Samsung VR jam in which game and media students developed new concepts for SAMSUNG GEAR in 24 hours, the pre-development of a VR therapy concept (Fear of Love) created by CaptainVR, the Samsung Industry Case in which students developed new concepts for SAMSUNG GEAR (wearables), the IGAD VR game pitch where over 15 VR game concepts were created for SAMSUNG VR GEAR and numerous projects in which VR concepts are developed and created using new SAMSUNG technologies. Currently we are co-developing new digital HRM solutions.

Adaptive Robotic Binpicking - AEROBIC -

AdaptSort: Adaptive AI for Sorting E-Waste

The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.