Plastics components are degrading over time to small entities mostly known as microplastics (MP). Microplastics particles (MPPs) (by definition <5 mm) can enter the human food chain on different levels. MPPs have been found in several organisms and human heart tissue. MPPs also occur in the plant world. MPPs can be analyzed by light microscopy (LM). The different MP hydrocarbons distinguish from biomaterial matrices. But the degradation doesn’t stop. Microplastics can degrade further to nanoparticles (NPs), being smaller than cells. These nanoplastics particles (NPPs) require higher efforts to be detected. Electron microscopy (EM) is suitable to depict those nanoscale regions. Metal nanoparticles (MNPs) are often well visible in bulk biomaterial with scanning electron microscopy (SEM). But polymeric nanoparticles (PNPs) are more difficult to be detected in biomaterials than MNPs. Dependent on experiment and imaging conditions, PNPs can be distinguished from bulk biomaterial with SEM. Thus, we approach this issue with correlative microscopy (CM) to develop reliable NPP analytics. Using a plant model system with PNPs, combining LM and fluorescence microscopy (FM) with SEM data in a correlative light and electron microscopy (CLEM) approach, non-uniform patch formation of NPPs in- and outside the biomaterial matrix is observed. Energy-dispersive X-ray spectroscopy (EDS), Raman, micro-computer tomography (uCT), ultramicrotomy (UM) and focused ion beam (FIB) data provide additional information. Specific discovery of contaminated plant biomaterial areas is thus possible with single NP resolution. The investigation of the biomaterial matrix behaviour exposed to different PNP sizes and materials is so enabled. The procedural correlative microscopy approach reveals that PNPs seem to be tightly attached to the biomaterial. It indicates further that basic food cleaning procedures might be insufficient for PNP or NPPs removal. The model system can reliably detect PNPs, showing solution pathways in general NPPs analytics.

A systematic sequence of materials was investigated to develop phytic-acid (Phyt)–based flame retarded poly(lactide acid) (PLA), while factoring in molecular weight (MW), crystallinity and mechanical properties. Synergistic approaches were developed based on combinations with lignin and expandable graphite (EG), as well as by applying different Phyt salts of melamine (Mel), piperazine (Pip), and arginine (Arg). Compounds were twin screw extruded, injection molded, hot pressed and investigated with thermal analysis, size exclusion chromatography, infrared spectroscopy, tensile testing, limited oxygen index (LOI), UL 94, cone calorimeter, and scanning electron microscope. 16.7 wt.% flame retardant (FR) slightly enhances crystallization while MW remains unchanged in PLA Phyt Arg and PLA Phyt Mel. LOI was improved to 43.7 vol.% for PLA Phyt Arg, UL 94 V0 achieved for PLA Phyt Pip. Cone calorimeter results show total heat evolved reduced by 14 %, maximum average rate of heat emission 43 % lower, and peak heat release rate reduced by 50 % for PLA Phyt Mel. Phyt Mel combined with EG increased the char yield of PLA to 20 wt.% and 15.5 wt.% at 600 and 900 °C, respectively. Phyt is exploited to enhance char yield, stabilize the intumescent char, and lower the apparent effective heat of combustion. The combination of Phyt Mel and EG was proposed as an efficient FR for PLA via an evidence based developing route.

This study explores the unique operating behavior of an alkaline water electrolysis cell equipped with an ion-solvating membrane, operated with a diluted alkaline electrolyte, specifically 1-M potassium hydroxide (1M KOH), in anode feed mode. Our investigations reveal several key insights. Charge transport: In an ion-solvating membrane, charge transport occurs through both the cations and anions of the electrolyte. Due to electro-osmosis, cation transport to the cathode results in a combined hydrogen-electrolyte discharge from the cathode compartment of the electrolysis cell. The discharged electrolyte is more concentrated than the electrolyte supplied to the anode. The concentration and flow rate of the electrolyte increase with current density and electrolyte temperature. Current density dependence: Since only a fraction of the total charge is transferred by hydroxide ions within the membrane, current density strongly depends on the electrolyte flow through the anode compartment. Membrane stability and performance: The membrane’s mechanical and chemical stability enables operation at high temperatures, up to 80 ◦C. This stability enables increased current density at a given cell voltage. Effects of catalyst use: Using cathode catalysts with high surface areas, such as Raney-Ni, enhances current density because highly concentrated liquid potassium hydroxide forms at the cathode during operation. Anode catalysts with high surface areas increase current density, but only if the flow of hydroxide ions is not impeded. Otherwise, the jV-curve exhibits transport-limited behavior.

Abstract - Shape memory polymers (SMP) can be used in numerous applications, for example in medical engineering, control technology, or in the automotive industry. Various triggers can be used to "remember" a programmed shape, such as temperature, pH value, humidity or light. One example of SMP is polyvinyl alcohol (PVA), which is characterised by biocompatibility, high hydrophilicity and non-toxicity as well as efficient shape memory behaviour. PVA reacts to the stimulus of temperature. In this work, the influence of incorporated nanoparticles on the shape memory effect of PVA will be investigated. For this purpose, the glass transition temperature of PVA containing different iron nanoparticles is determined and compared by means of Differential Scanning Calorimetry (DSC). In addition to the impact of the integrated nanoparticles on the switching temperature, consideration is also given to the quality of the resetting characteristics linked with the shape memory effect. These studies employ thermomechanical analysis as a tool to gain insights into this phenomenon. The inclusion of iron nanoparticles has been observed to result in a slight alteration of the switching temperature, while significantly influencing the shape memory effect and reducing the strain recovery rate by approximately 15%. The strain fixity rate simultaneously increases by approximately 50%. However, the size and structure of the iron nanoparticles showed no discernible impact on the observed phenomenon.

Laser sintering, also known as powder bed fusion—laser beam, employs a laser to sinter-powdered polymeric materials, such as Polyamide 12. Despite the process, a significant portion of the powder remains unsintered. However, due to elevated temperatures, material degradation occurs, altering its chemical characteristics. Industrially, a common practice involves utilizing a mixture of virgin and aged powder, with the latter having undergone multiple thermal cycles without sintering. Developing an effective powder recycling methodology is fundamental for fully realizing the potential of Polyamide 12. This work focuses the attention on reused powder to manufacture parts in Polyamide 12 through selective laser sintering and evaluates the correlation between the properties of reused powder and the strength of manufactured part through six consecutive printing processes. To achieve this, a wide experimental approach was planned and developed to arrive to establish a clear relationship between the degree of crystallinity of the powder and the ultimate tensile strength of the part made from that powder. This relationship may be used to choose the kind of powder based on the strength required to the manufactured part in exercise. At the same time, if only a kind of powder is available, this relationship allows to predict the mechanical strength of the part manufactured.

The dependencies between process parameters and the resulting geometrical accuracy of additively manufactured parts are usually highly non-linear and thus complex to investigate and mathematically quantify. Therefore, the application of artificial intelligence techniques is promising to generate mathematical models that reduce effort and increase the prediction quality. The overall goal is to establish a procedure to automatically determine the optimal settings of the manufacturing process parameters to guarantee the highest geometrical accuracy of parts in additive manufactured production. This paper presents the first step towards this fully automatic procedure – the training and evaluation of a mathematical model based on artificial neural networks to quantify the effects of varying process parameters of a material extrusion process on both macro- and micro-geometrical performances. Therefore, a dataset is established based on the Design of Experiment of an additively manufactured part made from Polylactic Acid filament. The dataset is then used to train an artificial neural network that predicts the dimensional and micro-geometrical deviations of the manufactured parts. Finally, the evaluation of the network's prediction quality and reliability indicate that it is possible to predict the parameters linked to resulting print quality with a mean absolute error from 0.0004 to 0.036.