Background: Collar-related pressure ulcers (CRPU) are a problem in trauma patients with a suspicion of cervical cord injury patients. Indentation marks (IM), skin temperature (Tsk) and comfort could play a role in the development of CRPU. Two comparable cervical collars are the Stifneck® and Philadelphia®. However, the differences between them remain unclear. Aim: To determine and compare occurrence and severity of IM, Tsk and comfort of the Stifneck® and Philadelphia® in immobilized healthy adults. Methods: This single-blinded randomized controlled trial compared two groups of immobilized participants in supine position for 20 min. Results: All participants (n = 60) generated IM in at least one location in the observed area. Total occurrence was higher in the Stifneck®-group (n = 95 versus n = 69; p = .002). Tsk increased significantly with 1.0 °C in the Stifneck®-group and 1.3 °C in the Philadelphia®-group (p = .024). Comfort was rated 3 on a scale of 5 (p = .506). Conclusion: The occurrence of IM in both groups was high. In comparison to the Stifneck®, fewer and less severe IM were observed from the Philadelphia®. The Tsk increased significantly with both collars; however, no clinical difference in increase of Tsk between them was found. The results emphasize the need for a better design of cervical collars regarding CRPU.
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Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are important on a specific measured outcome or which parameter values we should expect to observe given a set of input parameters. At the same time, however, these models rely heavily on assumptions (e.g., stationarity) or are “black box” (e.g., deep neural networks), meaning that they lack interpretability of their internal working and can be viewed only in terms of their inputs and outputs. An interpretable alternative to the black box models and with considerably less assumptions is symbolic regression (SR). SR searches for the optimal model structure while simultaneously optimizing the model’s parameters without relying on an a priori model structure. In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight, in order to uncover meaningful algebraic relationships between the EGT and other measurable engine parameters. The experimental results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3°C compared to the ground truth and demonstrating consistency from an engineering perspective.
From the article: "A facile approach for the fabrication of large-scale interdigitated nanogap electrodes (nanogap IDEs) with a controllable gap was demonstrated with conventional micro-fabrication technology to develop chemocapacitors for gas sensing applications. In this work, interdigitated nanogap electrodes (nanogap IDEs) with gaps from 50–250 nm have been designed and processed at full wafer-scale. These nanogap IDEs were then coated with poly(4-vinyl phenol) as a sensitive layer to form gas sensors for acetone detection at low concentrations. These acetone sensors showed excellent sensing performance with a dynamic range from 1000 ppm to 10 ppm of acetone at room temperature and the observed results are compared with conventional interdigitated microelectrodes according to our previous work. Sensitivity and reproducibility of devices are discussed in detail. Our approach of fabrication of nanogap IDEs together with a simple coating method to apply the sensing layer opens up possibilities to create various nanogap devices in a cost-effective manner for gas sensing applications"
MULTIFILE
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.
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.
Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
