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Continuous in situ monitoring of ammonia in bioprocesses using a novel scattering fiber-optic sensor
(2026)
This study presents a laboratory proof-of-concept for a novel scattering-based fiber-optic sensor for continuous in situ monitoring of free NH3 in highly loaded aqueous bioprocess media. The sensing element consists of a thin phenyl-modified sol–gel coating with covalently immobilized bromocresol green on fused silica fiber rods. The experimental observations are consistent with covalent immobilization of the dye within the hybrid matrix, which appears to suppress classical pH-dependent indicator behavior and enables a largely pH-independent response within the biogas-relevant range of 6.8–8.5. Nanoscale structural inhomogeneities within the high-refractive-index sol–gel layer are expected to contribute to weak light scattering and to enhance the effective optical interaction length. Upon exposure to free NH3, the sensor shows reversible spectral changes, with the strongest response observed at 748 nm. The sensor exhibited an exponentially saturating calibration behavior between 20 and 200 mg/L free NH3 at 24 °C, response times of t90 less than 2 min, and good reversibility over more than 50 cycles. The deliberately minimalist optical setup consists of a white LED, the coated fiber element, and a compact spectrometer. It operated at low power without reagents, membranes, or consumables and achieved an AGREE greenness score of 0.88. Preliminary tests in real biogas digestate suggest promising matrix compatibility for continuous in situ monitoring in anaerobic digestion and related nitrogen-rich bioprocesses.
The integration of artificial intelligence into video-based human behavior analysis enables contactless and continuous monitoring of both motor dynamics and facial reactions. This paper proposes a dual-stream multimodal framework for synchronized modeling of facial expression dynamics and skeletal motion during physical movement from monocular RGB video. The framework consists of two coordinated streams: the motor stream, based on 2D skeletal keypoints, and the facial stream, which extracts features associated with discomfort and affective responses. Person and face detection are performed using YOLO11, while specialized deep learning models handle pose estimation and facial expression recognition. Temporal dependencies and cross-modal relationships are modeled via a bidirectional LSTM, enabling unified temporal modeling of skeletal and facial dynamics. This novel approach allows investigation of how physical movement patterns relate to facial reactions during dynamic activities. By integrating heterogeneous facial and skeletal features in a synchronized temporal model, the framework enables consistent cross-modal analysis of dynamic human behavior. The framework was trained and validated using FER2013 and AffectNet for facial expression recognition, and UI-PRMD and FineRehab for skeletal motion modeling. It achieves 91.2% accuracy in facial expression classification, 94.8% mean Intersection over Union for human detection, and an F1 score of 0.89 for multimodal state assessment. Operating in real-time at 18–28 FPS on standard GPU hardware without requiring wearable sensors, the framework supports applications in behavioral monitoring and safety analysis.
Categorization of Sustainable Leadership in Sustainable Manufacturing to Promote Industry 5.0
(2026)
A shift towards Industry 5.0 with a focus on sustainability, human centricity, and resilience requires the implementation of sustainable manufacturing (SM). Implementing SM is challenging as it requires commitment to sustainable practices at all organizational levels. Leadership is a crucial success factor but is largely neglected in the literature on SM. To address this gap, we introduce a novel approach focusing on multiple leadership dimensions. The aim is to develop effective leadership perspectives by integrating sustainable leadership (SL) theory into SM. A systematic literature review and qualitative content analysis are used to identify and analyze 79 peer-reviewed articles from the Scopus and Web of Science databases. SL serves as a frame of reference to identify relevant aspects for sustainable leaders in the scientific discourse on SM. The findings are summarized in 24 categories for integrated SM. They form a conceptual framework comprising three levels: context; systems and processes; and leadership and social aspects. Examining these aspects with interrelationships enriches the SM discourse and sheds a new, human-centered light on it. The findings can support training and knowledge transfer, thereby enabling leaders to navigate operational complexity. Furthermore, the categorization provides a foundation for developing socio-technical models and assessments for Industry 5.0 from a systemic perspective.
Indium gallium nitride (InGaN) nanowire structures were investigated as photoelectrochemical transducers for bioanalytical applications. Chopped-light voltammetry in HEPES buffer (pH 7.0) revealed a light intensity-dependent anodic photocurrent that varies with applied potential. In the presence of hypoxanthine unmodified InGaN showed no photocurrent changes when electrode potentials around 0 mV vs Ag/AgCl or below have been applied. The three-dimensional surface of the nanowires was then used to adsorb the enzyme xanthine dehydrogenase (XDH). After this modification, the InGaN electrode exhibited a distinct increase in anodic photocurrent in the presence of hypoxanthine. The photocurrent showed a clear concentration-dependent behavior. This is indicative for a direct electron transfer from XDH to the semiconductor material. These findings highlight the suitability of InGaN nanowires for coupling with redox enzymes and their potential for developing light-driven biosensing platforms.
Additive manufacturing enables the development of low-cost, self-built robotic systems; however, their performance is typically not characterized by validated metrics. The paper presents a photogrammetric concept intended for system-independent application to characterize planar positioning accuracy and repeatability without access to internal controller data. The method is based on a Raspberry Pi 4 camera system, image processing in Python 3.12.0 and OpenCV 4.12.0, and a universal additively manufactured robot tool attachment. Two position estimation strategies are investigated: a marker-based approach using ArUco markers and a markerless blob-analysis method based on a ruby sphere. Camera calibration is evaluated using different patterns, with a compact CharUco board exhibiting the lowest RMS reprojection error (~1 px). Experimental validation follows selected elements of ISO 9283:1998 and comprises 30 repetitions at five target poses for linear and axial motion strategies. The results show lower positional deviations for marker-based methods compared to the markerless approach, with a two-marker configuration yielding the lowest mean deviation under the investigated conditions. Sub-millimeter positioning accuracy and repeatability are achieved, and linear motion exhibits lower repeatability deviations than axial motion. The proposed approach provides a cost-effective and flexible solution for external robot characterization, particularly suited for self-built and resource-constrained systems.
Electrospinning is a versatile technique for producing polymer nanofibers with high ratios of surface area to volume and tunable porosity. Conventional approach to the optimization of processing parameters such as voltage and flow rate frequently encounters limitations in reproducibility and scalability. This review proposes a comprehensive framework that integrates macromolecular design principles with established electrohydrodynamic theories. We analyze how intrinsic molecular traits, specifically chain entanglement density, molecular weight distribution (MWD), topological architecture, and polymer–solvent thermodynamic interactions, define the boundaries of jet stability and solidification. Key findings highlight that while molecular weight establishes a baseline for spinnability, the MWD dictates the dynamic response under extreme deformation. Notably, high-molecular-weight fractions act as elastic load-bearers that suppress capillary breakup. Furthermore, we discuss here how molecular architecture and solvent-mediated segmental mobility determine whether molecular orientation is kinetically trapped or relaxed during the nanosecond timescales of jet flight. By establishing a hierarchical design logic prioritizing molecular and formulation variables over processing parameters, this framework provides a robust strategy to overcome challenges in scalability and reproducibility, positioning electrospinning as a sensitive probe for macromolecular dynamics under extreme elongation.
We report on the design and performance of two solid-state Raman lasers, pumped by a frequency-doubled Nd:YAG laser at 532 nm (8 ns FWHM, 10 Hz, up to 10 mJ), that generate either the first or second Stokes radiation with wavelengths of 563 nm and 598 nm. Barium nitrate crystals with a 1 cm² face area and lengths of 3 cm and 5 cm were used as the Raman-active medium in stable semi-spherical resonators. The Raman lasers produced first Stokes and second Stokes pulses, achieving pulse energies up to 2.3 mJ and conversion efficiencies up to 35% (first Stokes, 5 cm crystal) and 21% (second Stokes, 5 cm crystal). The output beams exhibit nearly Gaussian profiles with beam quality factors of M² = 1.67 and M² = 1.83, respectively. The proposed design and characterization demonstrated a Raman laser with high efficiency and superior beam quality. After subsequent frequency doubling, the output appears well dedicated for future differential absorption LiDAR (DIAL) applications.
On the background of the supplementary applicant-job fit theory, this study tests the application of a weighted closed vocabulary approach as introduced by Ostendorf on a large corpus of online job advertisements (OJA). Therefore, a large dataset of online job advertisements (OJAs) was scraped from a German job portal (N = 151k), and a sample (n = 3,239) was selected based upon Porter’s Value Chain and corresponding job functions. The dataset was pre-processed with techniques of natural language processing, and the predefined words extracted from the OJAs were weighted with the average subject matter expert rating, as offered in the vocabulary of Ostendorf. The results reveal that, first, 0 to 12 words describing personality are used across OJAs, second, that HR, sales, and finance positions show values higher than sample mean (3.22), third, that HR positions show loadings across all dimensions of personality above the sample means (extroversion: 2.32, agreeableness: 3.98, conscientiousness: 4.28, emotional stability: 3.54, openness: 2.0) followed by sales. The job profiles being significantly parallel on equal levels are HR clerk and referee, as well as IT, R&D, and procurement. While conscientiousness is reported to be an important predictor of performance, our study showed only low to average importance in OJAs.
The efficiency of demand responsive transport (DRT) services, such as ridepooling, has garnered significant attention across various (simulation) studies. However, a deeper look into the metrics employed to assess system performance exposes notable discrepancies in their utilization. Notably, certain indicators, such as the pooling rate, demonstrate a susceptibility to manipulation based on input parameters, potentially skewing results to give a more favorable impression of service performance. In this paper, we show that, under such circumstances, achieving a fair comparison between study outcomes and with traditional (public) transport modes becomes challenging.
In light of these challenges, this study introduces a novel operational performance indicator: Operational System Efficiency (OSE). OSE is tailored to evaluate the operational efficiency of ridepooling systems in a holistic way. In particular, it combines important operational indicators such as detour factor and empty kilometers share, which are considered in numerous analyses. Hereby, OSE is fostering a more equitable assessment of service performance. We applied this methodology and calculated the indicators provided in other studies to two real-world trip datasets from ridepooling services in Berlin and Münster, Germany, and compared the results. Distinct disparities emerge in comparison with conventional efficiency indicators. Consequently, the proposed OSE holds promise for stakeholders, including service providers, public transport companies, and regulatory authorities, as a valuable tool for determining the suitability of a ridepooling service for a given locale relative to other transport modalities, while also providing a transparent assessment of its efficiency.
This publication presents an improved manufacturing method for tetrahedral metal effect pigment particles that demonstrates reduced flowlines in injection-molded polymer components compared with conventional platelet-shaped pigment particles. The previously published cold forming process for tetrahedral particles, made entirely from aluminum, faced manufacturing challenges, resulting in a high reject rate due to particle adhesion to the micro-structured mold roller. In contrast, this study introduces a new manufacturing method for tetrahedral particles, now consisting of metallized UV-cured thermoset polymer. These particles, dispersed in amorphous matrix thermoplastics, have shown to maintain their shape during the injection molding process. The manufacturing technique for these novel particles is based on UV imprint lithography, omitting the reject rates compared with the previously presented cold rolling process of tetrahedral full aluminum particles. Thus, the novel manufacturing technique for tetrahedral pigment particles shows increased potential for automation through roll-to-roll manufacturing in the future.
