University of Kaiserslautern

Aminoacyl-tRNA synthetases mediate the activation and transfer of amino acids to their cognate tRNA, which constitutes one of the initial events in protein biosynthesis. Even though different mechanisms of action have been proposed for the catalysis of these enzymes, their entire catalytic cycle remains elusive. Here, we used electron paramagnetic resonance spectroscopy in vitro and in cells in combination with molecular dynamics simulations to study the role of amino acid interactions in the catalytic cycle of pyrrolysyl-tRNA synthetases (PylRS), a widely used tool for genetic code expansion. Experiments using the paramagnetic non-canonical amino acid SLK-1 revealed the presence and occupation of secondary amino acid binding sites in PylRS located at the intermonomer interface, distant from the catalytic binding site. Based on our results, we propose a model that assumes an alternating mode of action of the two PylRS monomers for the catalytic cycle of PylRS.

Laser-based directed energy deposition (DED-LB) with multiple powder feeders enables additive manufacturing of functionally graded materials (FGMs) through controlled variation of powder compositions during deposition. However, changes in powder composition affect spatial powder stream distributions, influencing focal morphology and position that can lead to laser-powder focus misalignment. Such a misalignment can induce process anomalies that result in part defects such as porosity or dimensional inaccuracies. While numerical methods (e.g., computational fluid dynamics) have provided insights into powder flow physics, they remain computationally expensive and rely on idealized conditions without capturing machine-specific behaviors. Therefore, a data-driven approach is needed that is computationally efficient while capturing variations in process parameters and varying powder compositions. This work presents a deep learning approach using a convolutional neural network (CNN) for predicting powder stream distributions for a specific machine configuration under varying carrier gas flow rate, shield gas flow rate, total powder mass flow rate, and powder composition mixtures of 316L and 17-4PH stainless steels. The model is evaluated using experimental validation on the test dataset, demonstrating strong predictive performance with $$\varvec{R}^{\varvec{2}}$$ scores averaging 0.939 and structural similarity of 0.97 across meaningful signal regions. To assess the practical utility of the CNN predictions for downstream applications, the predicted powder streams are integrated into coupled thermal-geometric process simulations of DED-LB. These simulations yield part geometries with mean dimensional deviations below 1.5% (max below 2.5%) of those obtained using ground-truth powder stream measurements (PowderSpy). This close agreement between simulations using predicted versus experimental powder distributions validates the model’s suitability for process modeling of manufacturing FGMs with implications on in-situ process optimization and control systems given the computational efficiency.

Abstract Context‐centric proactive information delivery (PID) is a relatively underexplored domain within recommender systems (RS) aimed at enhancing Knowledge Workers' productivity by proactively providing relevant information during digital tasks. These RS anticipate user needs by leveraging personal knowledge modeling, context recognition, and recommendation techniques to deliver timely and relevant resources without requiring explicit searches. Developing such RS faces various challenges in addition to common challenges within the broader RS landscape. These challenges range from data‐related issues, such as handling heterogeneous and noisy data, to user‐centric concerns, including privacy, explainability, and the lack of explicit feedback, as well as system and algorithmic challenges like cross‐application context modeling and scalability. This paper explores the opportunities, challenges, and future directions for PID, outlining key advancements and enabling technologies that support its development.

We demonstrate a grazing-incidence X-ray platform that simultaneously records time-resolved grazing-incidence small-angle X-ray scattering (GISAXS) and grazing-incidence X-ray diffraction (GID) from a femtosecond-laser-irradiated gold film above the melting threshold, with picosecond resolution using an X-ray free-electron laser (XFEL). By tuning the X-ray incidence angle, the probe depth is set to tens of nanometres, enabling depth-selective sensitivity to near-surface dynamics. GISAXS resolves ultrafast changes in surface nanomorphology (correlation length, roughness), while GID quantifies subsurface lattice compression, grain orientation, melting and recrystallization. The approach overcomes photon-flux limitations of synchrotron grazing-incidence geometries and provides stringent, time-resolved benchmarks for complex theoretical models of ultrafast laser-matter interaction and warm dense matter. Looking ahead, the same depth-selective methodology is well suited to inertial confinement fusion (ICF): it can visualize buried-interface perturbations and interfacial thermal resistance on micron to sub-micron scales that affect instability seeding and burn propagation.

Current plastic research focuses on particulate water-insoluble polymers such as polyethylene and polystyrene. Synthetic hydrophilic polymers, a class of anthropogenic materials with an annual global production of over 35 Mt, have received little scientific attention. Synthetic hydrophilic polymers are used for the controlled release of agrochemicals and for seed coating. Despite being intentionally added to soil, little is known about their occurrence or fate in soil.This study aimed to address this knowledge gap by developing and validating a pyrolysis gas chromatography/mass spectrometry (Py-GC/MS) method for identifying and quantifying synthetic hydrophilic polymers in soil. We focused on the most common synthetic hydrophilic polymers: polyacrylic acid (PAA), polyethylene glycol (PEG), polyvinyl alcohol (PVOH) and polyvinyl pyrrolidone (PVP) in agricultural model soil.The method was validated by measuring one solution containing all four polymers between 1 and 200 µg/mL using pyrolysis gas chromatography/mass spectrometry (Py-GC/MS). Intra-day repeatability was determined by 10-fold measurement of a 150 µg/mL standard. To test polymer recovery from three agricultural model soils (5–47% clay, 1–3% organic carbon), 5 g of soil were spiked with 1 mL of hydrophilic polymer solution (2 mg/mL). Further, three extraction agents were tested: aqueous NH3 solution (pH = 11), aqueous H3PO4 (pH = 3) solution and concentrated sodium pyrophosphate solution (TSPP, 0.1 M, pH = 9). 10 mL of extraction agent were added and agitated for up to 28 days. Samples were taken every seven days, to assess the optimal extraction time and the best possible recovery rate. Non-spiked reference soil was used as a blank. Samples and blanks were measured as duplicates.Limits of detection (LODs) for PEG, PVOH and PVP were below 1 µg/mL and limits of quantification (LOQs) ranged from 68 to 87 µg/mL. LOD and LOQ for PAA were the highest with 25 µg/ml and 94 µg/mL, respectively. The pyrolysis of PAA and PVOH partly resulted in similar pyrolysis products, challenging the simultaneous and selective quantification of both polymers. The intra-day repeatability was 8–16%. The best recovery rates ranged from 20 to 133% and were achieved with TSPP. While the acidic solution led to recovery rates of 21–115% in soils with 5-16% clay and 1% organic carbon, polymer concentrations in a soil with 47% clay and 2.6% SOM were below LOD. The alkaline solution recovered 5–144% of the polymers. The optimal extraction time varied among soil types. On average, a 14-day extraction yielded the best recoveries with TSPP solution. Blank signals for PVP and PEG were below 10% of the sample spikes. For PVOH and PAA the blank signals were 26–71% and 15–63%, respectively. These results demonstrate the significant challenges of analyzing PVOH and PAA simultaneously, as both polymers produce similar pyrolysis products.Py-GC/MS is a promising tool for identifying and quantifying synthetic hydrophilic polymers. However, further experiments using complementary analytical methods are required to improve analytical robustness.

Background/Objectives: Pes planovalgus is one of the most common misalignments in children. In this study the established biomechanical foot orthoses (BMFO) are being compared with a more recent treatment: sensorimotor foot orthoses (SMFO). SMFO are a more recent treatment and aim to correct malalignment by specifically modulating muscle activity rather than relying solely on passive mechanical support, as is the case with BMFO. Methods: Thirty-two children and adolescents aged six to six-teen participated in this study. After randomized group allocation, the rearfoot angle was analyzed by two-dimensional gait analysis in the SMFO-group (n=18) and the BMFO group (n=14) under three conditions: without foot orthoses (baseline), with foot orthoses (immediate) and after four weeks of use. Results: (1) SMFO and BMFO significantly improved the rearfoot angle immediately after application, (2) the achieved correction was maintained over four weeks in both groups, and (3) despite baseline differences, the superior rate of improvement in the SMFO-group resulted in comparable rearfoot alignment between SMFO and BMFO at the four-week follow-up. Conclusions: Based on the results, it can be concluded that SMFO and BMFO are comparable methods for treating pes planovalgus in children and adolescents.

ABSTRACT This study investigates wetting behavior, condensation dynamics, and humid‐air condensation heat transfer on stainless steel (SS), an untreated thermally conductive graphite composite (GC), and a hierarchically structured, superhydrophobic graphite composite (SHGC) under varying relative humidity (RH). Static and dynamic contact angle measurements confirm distinct wetting regimes, with SS being hydrophilic (), GC hydrophobic (), and SHGC superhydrophobic (). Condensation experiments reveal that performance is not governed by wettability alone but by a trade‐off between dropwise renewal, droplet pinning, and additional interfacial thermal resistances introduced by hierarchical textures and low‐surface‐energy functionalization. Under saturated conditions (100% RH), GC achieves the highest vapor‐side heat transfer coefficient of , followed by SHGC with , while SS reaches . The lower performance of SHGC compared to GC is consistent with Wenzel‐type pinning and an increased effective thermal resistance during operation. Computational fluid dynamics simulations confirm laminar internal flow in the cooling‐water channel and provide an independent cross‐check of the water‐side heat transfer coefficient used in the resistance‐based evaluation of vapor‐side HTCs. Overall, the results establish graphite composites as promising condenser substrates and highlight that maximizing apparent superhydrophobicity alone does not necessarily maximize condensation heat transfer under humid‐air conditions.

This paper explores the process of microwelding thin stainless steel foils using an ultrashort pulsed (USP) laser. The production of practical, miniaturized pipelines that allow liquids to flow smoothly presents numerous challenges. Usually, coatings are applied to the inner walls of the pipe. An innovative approach involves the use of high-precision laser processing. The utilization of USP laser processing is used both to create functional surfaces and to produce miniaturized pipes through an overlap welding process. This study demonstrates a successful approach using a USP laser for overlap welding of thin foils to produce miniaturized pipes. Overlap welding was performed on two nontransparent 1.4301 stainless foils with a foil thickness of 20 μm using a picosecond laser. The authors identified optimal process parameters by varying power density and pulse overlap and by optimizing the sample holder to achieve high-quality weld seams with a reduced heat-affected zone.

Biarticular actuators can enhance efficiency and stability in legged locomotion by transferring energy between joints. Their effectiveness depends strongly on the lever arm ratio—the ratio of the actuator’s moment arm at one joint to its moment arm at another—which governs how torque is distributed across joints during movement. Inspired by biomechanics, early robotic studies implemented biarticular actuators to improve energy efficiency, joint coordination, and positional control, primarily in planar or single-joint systems, leaving a gap in fully 3D robotic legs. Here, we present a geometry optimization framework for a robotic leg incorporating both biarticular and monoarticular actuators. Using human motion capture and joint torque data, we optimized the linkage mechanisms so that the system can maintain the required joint torques while keeping biarticular actuator moment arm ratios near their optimal values during walking and running. The optimized leg achieved a minimum achievable cost of transport of approximately 0.41 J/(kg·m) for walking and 0.62 J/(kg·m) for running.