Fraunhofer Institute for Manufacturing Technology and Advanced Materials

• In-mould functionalization creates unique markings for anti-counterfeiting. • Coatings can improve the particle adhesion, reducing loss to few percent. • Chemical bonding through amide/urea formation ensures marking robustness. • Random particle patterns form physically unclonable functions (PUFs) • Successful transfer from mould to plastic parts via injection moulding. Unique and unclonable fluorescent particle-based markings enable the individualization of parts for track and trace applications and benefit from a more robust optical detection when compared with markerless techniques. The marking of surfaces by coating methods however requires additional fabrication steps and limits scalability. We therefore investigate a modified in-mould coating process to alleviate these drawbacks for injection moulded parts. Fluorescent particles are thereby incorporated into the surfaces of the parts by spraying them first into the mould and transferring them then permanently onto the parts surface by overmoulding so that no separate processing step for the application of a marking is needed. However, while the ability of the particles to transfer and adhere to the parts surface is considered a key factor for the usability and robustness of a marking, the highly dynamic wall temperatures and the cooling of the melt make adhesion a non-trivial task and expose a significant knowledge gap. The adhesion of the particles was therefore analysed to reveal the principal adhesion mechanisms and to investigate tailored organic particle coatings to improve adhesion. We identified mechanical interlocking and interfacial chemical reactions as key adhesion mechanisms. Peel tests showed that particle adhesion improves significantly when modifying the fluorescent particles with a polymeric multilayer shell using covalent layer-by-layer (LbL) assembly of polyethyleneimine and polyethylene-alt-maleic acid (up to four layers). Amide formation within the polymeric shell contributes to a high shell stability and establishes covalent links between the particles and the plastic part. The analysis of the interfacial reactions disclosed the formation of urethane and urea groups in case of polycarbonate. It was demonstrated that a second fluorescent particle type with identical excitation wavelength but different emission wavelength increases the information depth of product markings.

Alkaline water electrolysis (AWE) remains limited by sluggish reaction kinetics, high overpotentials, and insufficient catalyst stability. Here, we introduce an engineered hierarchical electrocatalyst in which (B, P, S)-modulated Fe–Ni–Co layered double hydroxides (LDHs) are grown on 3D-printed 316L stainless-steel microarrays with an interface of nickel nanocones (NC) to maximize active surface area and enhance mass transport. Density functional theory indicates that boron substitution at Fe sites balances oxygen-evolution reaction (OER) intermediate binding energies, creating a fully downhill free-energy profile and reducing the OER barrier. Phosphorus incorporation on Ni sites tunes the hydrogen adsorption energy toward near-thermoneutral values, optimizing hydrogen-evolution reaction (HER) kinetics. X-ray photoelectron spectroscopy confirms strong interfacial interactions, reduced oxygen vacancies, and optimized metal–anion bonding, facilitating enhanced charge transfer. The optimized asymmetric electrolyzer delivers 500 mA cm −2 at 1.93 V with Faradaic efficiencies exceeding 79 %, demonstrating a scalable route to efficient and durable AWE. • B-LDH/NC/SS achieves superior OER performance with low overpotentials and Tafel slopes. • P-LDH/NC/SS demonstrates exceptional HER activity with minimal overpotentials. • The B-LDH/NC/SS .|| P-LDH/NC/SS cell excels at water splitting with 2.03 V at 500 mA cm −2 . • DFT reveals boron incorporation optimizes electronic structure for enhanced OER. • Phosphorus promotes favorable H adsorption, lowering energy barriers for HER.

The requirements of the upcoming aircraft generation based on hybrid or electric propulsion discourage the use of Ice Protection Systems (IPSs) based on hot-air spilled from engine or demanding a large consumption of electrical power. In line with this need, a low-power IPS based on piezoelectric (PZT) technology is investigated in the current article. Its main objective is to protect an aerodynamic surface by removing ice accretions (de-icing). The idea at the basis of the concept is to drive mechanical waves at the interface between the skin and the ice layer to cause the breaking and the detachment. Moving from an assessed layout and numerical simulations providing the most effective design configuration, dedicated small-scale airfoil demonstrators (NACA 0012 with a chord of 310 mm and a span of 150 mm) were manufactured, with the aim of testing the technology within the representative environment of the IFAM Icing Wind Tunnel (IWT). The test results showed, for power consumption of 4.4 kW/m2, ice detachment levels -based on the ice-covered area- between 40 and 50% at −10 °C, about 40% at −20 °C, and a maximum of 15% at −4 °C. The results highlighted the impact of some specific parameters (environmental temperature, skin, and ice thickness) on the effectiveness of the IPS.

Hollow silica nanoparticles (HSNPs), characterized by a hollow interior enclosed within a solid mesoporous silica shell, offer several advantages, including low density, high surface area, excellent adsorption capacity, and biocompatibility, making them highly attractive for diverse applications in fields such as food, construction, electronics, imaging, and nanomedicine. To investigate the largely unexplored role of the hollow interior and surface functionality in the design of smart nanocarriers, we propose a facile, green-chemistry-based approach for the synthesis of HSNPs, utilizing polystyrene nanoparticles (64 ± 11 nm in diameter) as sacrificial templates. An ultrathin mesoporous silica shell, 10-12 nm in thickness, is conformally deposited through the controlled hydrolysis of a Si precursor, yielding a nanocarrier system that enables the high adsorption of macromolecules with a pH-sensitive desorption profile. Comprehensive analytical techniques reveal that the method of template removal significantly influences both the interior and exterior pore structures. Notably, calcination produces HSNPs with a higher specific surface area ( > 195 m² g⁻¹), a larger average pore diameter ( ~ 20 nm), and an ink-bottle-like mesoporous structure. It is shown that these structural differences, combined with tailored surface functionalities, critically modulate the triggering response of the nanocarrier. To demonstrate functionality, doxorubicin hydrochloride (DOX) was employed as a model drug. A pH-responsive desorption behavior, releasing the biomacromolecule four times faster at pH=4.5 than at pH=7.4, is presented. This finding underscores the impact of surface chemistry and pore architecture on the adsorption and desorption kinetics of macromolecules. The results of this study pave the way for the rational design of stimuli-responsive ceramic nanocarriers with enhanced adsorption efficiency and precise, controlled desorption capabilities.

Efficient integration of logistics and process control stands as a pivotal element for the economic viability and sustainability of future end-to-end factories. The vertical and horizontal networking of machines presents new opportunities for enhancing production control. Within the logistics chain, interlinking holds the potential to enable a fully automated flow of materials, thus, increasing production adaptability and efficiency. This paper proposes a novel approach to realizing a smart goods inward station by integrating a cost-efficient control logic paired with a material-flow-optimized handling of small load carriers. The developed innovative station overcomes sequential first-in-first-out delivery processes by modifying the material flow of small load carriers and parallelizing unload processes from a dynamic queue, which avoids supply bottlenecks and redundant buffers for optimizing the just-in-time supply chain and reducing non-productive times. By the addition of automated guided vehicles, unloading and delivery processes can be fully automated. A comparison with conventional first-in-first-out systems shows that the modernized smart system increases the pick-up rate through parallel removal options and automated processes, which ultimately reduce lead times. Additionally, the station is equipped with sensors and control logics, which are connected to a logistics system via cost-efficient microcontrollers as modular components. Through these components, it becomes feasible to extend existing logistics chains into Industry-4.0-compliant processes. To illustrate the effectiveness of this approach, the integration of a target-actual comparison within the goods inward station is demonstrated by using a microcontroller-based camera system and radio-frequency identification technology to ensure the completeness and end-to-end tracking of the delivered goods. In this way, the material flow can be controlled more economically, and the supply chain can be organized more efficiently.

This research investigates predicting the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO; short HL) gap of natural compounds, a crucial property for understanding molecular electronic behavior relevant to cheminformatics and materials science. To address the high computational cost of traditional methods, this study develops a high-throughput, machine learning (ML)-based approach. Using 407,000 molecules from the COCONUT database, RDKit was employed to calculate and select molecular descriptors. The computational workflow, managed by Toil and CWL on a high-performance computing (HPC) Slurm cluster, utilized Geometry – Frequency – Noncovalent – eXtended Tight Binding (GFN2-xTB) for electronic structure calculations with Boltzmann weighting across multiple conformational states. Three ensemble methods, namely Gradient Boosting Regression (GBR), eXtreme Gradient Boosting Regression (XGBR), Random Forrest Regression (RFR) and a Multi-layer Perceptron Regressor (MLPR) were compared based on their ability to accurately predict HL-gaps in this chemical space. Key findings reveal molecular polarizability, particularly SMR_VSA descriptors, as crucial for HL-gap determination in all models. Aromatic rings and functional groups, such as ketones, also significantly influence the HL-gap prediction. While the MLPR model demonstrated good overall predictive performance, accuracy varied across molecular subsets. Challenges were observed in predicting HL-gaps for molecules containing aliphatic carboxylic acids, alcohols, and amines in molecular systems with complex electronic structure. This work emphasizes the importance of polarizability and structural features in HL-gap predictive modeling, showcasing the potential of machine learning while also highlighting limitations in handling specific structural motifs. These limitations point towards promising perspectives for further model improvements.

Abstract One focus of aviation research in recent years has been the reduction of greenhouse gases through weight savings in aircraft construction, e.g. by using lightweight materials such as CFRP components. Lighter aircraft lead to less fuel consumption, which impacts the global carbon footprint and is an important step towards CO 2 -neutral flying. In order to achieve national climate goals, the environmental impact should already be assessed at the production stage. Most recently, hybrid kinematics consisting of serial kinematics (industrial robots on linear rails) and parallel kinematics (hexapod) mounted to the robot’s end effector, have been studied at Fraunhofer IFAM for the purpose of machining distorted CFRP-components. This work analyzes to which extent the redundant degrees of freedom of such hybrid kinematics can be exploited to determine energetically advantageous poses during machining. Therefore, the inverse kinematics are solved within an Optimal Control Problem (OCP) including the forward kinematics as well as minimizing the system’s energy consumption while following a reference trajectory.

Allergic diseases affect over one billion people worldwide as a common chronic condition. Conventional treatments often relieve symptoms but lack long-term efficacy or safety. Over the past decade, nanomedicine, i.e., nanoscale drugs and delivery systems, has emerged as a promising alternative that leverages the tunable physicochemical properties of nanoparticles (NPs) and enhances both diagnosis and treatment of hypersensitivity disorders. In diagnostics, nanoparticle-based biosensors have achieved detection limits as low as 42 fg/mL with specificity exceeding 90% for food and aeroallergen proteins. Therapeutic applications comprise various NPs, including gold, silver, iron oxide, carbon-based, lipid-mediated, polymeric, dendrimeric, and virus-like, as delivery vehicles and as immunomodulators. Preclinical models detect >50% reductions in pro-inflammatory cytokines (IL-4, IL-5) and two- to 3-fold reductions in eosinophil infiltration following NP-augmented allergen immunotherapy, with antigen-specific IgE titers reduced by up to 70%. Although such advancement has occurred, nanotoxicology studies highlight dose-dependent organ concentration and prolonged pulmonary half-lives that necessitate rigorous biosafety evaluation. Regulatory and manufacturability concerns remain significant hurdles for clinical translation. This article reviews up-to-date quantitative performance metrics for nanoparticle therapeutics and diagnostics in allergy control, critically examines the toxicity profiles and translational issues, and brings out directions toward individualized, safe nanotheranostic platforms.