Quality Control in the Production Process of SMC Lightweight Material

The use of sheet molding compounds (SMC) in diverse applications requires different specific material properties for each type of finished parts. These material properties have to be assured by a reliable quality control, which does not only have to be performed for the prefabricated SMC itself but also during the production process of the semi-finished material. This is of high importance because quality fluctuations and defects can already occur during the production of the semi-finished SMC. This results in high scrap rates as well as machine failure and can additionally cause further problems in the following process steps. Hence, an inline quality control can help to establish objective quality criteria for semi-finished SMC and can enable controlled and stable production processes. Therefore, this paper deals with quality assurance in the production process of semi-finished sheet molding compounds. Air entrapping and fiber distribution are identified as two parameters that influence the quality of the semi-finished product significantly. In addition, the early detection of a pending carrier foil failure can help to establish a stable process. The focus of this paper lies on how various, individually adapted metrology systems can be used for the detection of the respective characteristics and integrated into the production process of the semi-finished SMC. In particular, optical systems, such as area scan cameras and laser stripe sensors as well as thermographic sensors are discussed and possibilities for application-related sensor data evaluation are shown. This helps to reduce the scrap rates of parts and to establish a further automated production process

Iron-Catalyzed Oxidative α-Amination of Ketones with Primary and Secondary Sulfonamides

We report the iron-catalyzed α-amination of ketones with sulfonamides. Using an oxidative coupling approach, ketones can be directly coupled with free sulfonamides, without the need for prefunctionalization of either substrate. Primary and secondary sulfonamides are both competent coupling partners, with yields from 55% to 88% for deoxybenzoin-derived substrates

Mechanism of Iron-Catalyzed Oxidative α-Amination of Ketones with Sulfonamides

We report the mechanism of the iron-catalyzed oxidative α-amination of ketones with sulfonamides. Using linear free energy relationships, competition experiments, and identification of reaction intermediates, we have found that the mechanism of this reaction proceeds through rate-limiting electron transfer to 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) from an iron enolate in the process of forming an α-DDQ adduct. The adduct then serves as the electrophile for substitution with sulfonamide nucleophiles, accelerated by iron and additional DDQ. This mechanistic study rules out formation of an α-carbocation intermediate and purely radical mechanistic hypotheses

Influence of Alkali Metal Cations on the Oxygen Reduction Activity of Pt\u3csub\u3e5\u3c/sub\u3eY and Pt\u3csub\u3e5\u3c/sub\u3eGd Alloys

Electrolyte species can significantly influence the electrocatalytic performance. In this work, we investigate the impact of alkali metal cations on the oxygen reduction reaction (ORR) on active Pt5Gd and Pt5Y polycrystalline electrodes. Due to the strain effects, Pt alloys exhibit a higher kinetic current density of ORR than pure Pt electrodes in acidic media. In alkaline solutions, the kinetic current density of ORR for Pt alloys decreases linearly with the decreasing hydration energy in the order of Li+ \u3e Na+ \u3e K+ \u3e Rb+ \u3e Cs+, whereas Pt shows the opposite trend. To gain further insights into these experimental results, we conduct complementary density functional theory calculations considering the effects of both electrode surface strain and electrolyte chemistry. The computational results reveal that the different trends in the ORR activity in alkaline media can be explained by the change in the adsorption energy of reaction intermediates with applied surface strain in the presence of alkali metal cations. Our findings provide important insights into the effects of the electrolyte and the strain conditions on the electrocatalytic performance and thus offer valuable guidelines for optimizing Pt-based electrocatalysts

Technology for Behavioral Change in Rural Older Adults with Obesity

Background: Mobile health (mHealth) technologies comprise a multidisciplinary treatment strategy providing potential solutions for overcoming challenges of successfully delivering health promotion interventions in rural areas. We evaluated the potential of using technology in a high-risk population. Methods: We conducted a convergent, parallel mixed-methods study using semi-structured interviews, focus groups, and self-reported questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting. We developed codes informed by thematic analysis and assessed the quantitative data using descriptive statistics. Results: All groups expressed that mHealth could improve health behaviors. Older adults were optimistic that mHealth could track health. Participants believed they could improve patient insight into health, motivating change and assuring accountability. Barriers to using technology were described, including infrastructure. Conclusions: Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist

Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.Comment: 12 pages, 7 figures. Extension of our ISBI 2022 paper (arXiv:2201.12528) (Best Paper Award Finalist