Svitlana Taranenko, Chen Wang, David Holzner
et al.
Organic solar cells have reached record efficiencies with non-fullerene acceptors, yet their translation to industrial printing remains a critical bottleneck. Here we report the highest efficiency achieved for a fully roll-to-roll-compatible gravure-printed non-fullerene organic solar cell. High-performance blends are typically optimised under laboratory coating conditions, while roll-to-roll manufacturing imposes fundamentally different constraints on ink stability, drying dynamics, and multilayer integration. Whether these constraints intrinsically limit device physics has remained unresolved. Here, we demonstrate a gravure-printed PM6:Y12 solar cell architecture using commercially available materials and establish a quantitative framework that disentangles optical, recombination, and transport losses in printed devices. We find that favourable bulk morphology and exciton harvesting can be preserved under gravure printing and non-halogenated solvents. The dominant efficiency penalties arise instead from optical interference within the printed layer stack and slow charge transport. Our results demonstrate that the performance gap between laboratory and printed solar cells is originating from device architecture rather than the intrinsic physics of modern non-fullerene systems, providing a mechanistic roadmap for roll-to-roll manufacturing of non-fullerene solar cells.
Abdelwaheb Hadou, Ahmed Belaadi, Messaouda Boumaaza
et al.
The production of natural fiber-based composites benefits various industries, including sports equipment, wind turbine blades, building materials, packaging, and medical devices, by enabling more environmentally friendly applications. Using a variety of statistical laws, the mechanical properties of newly generated fibers from the Dracaena draco fiber (DDF) plant are being examined in order to determine how the tests’ number (N) affects the tensile behavior of the fibers. The purpose of this study is to evaluate the quasi-static stress performance of DDF fibers with a gauge length of 40 mm. In order to ascertain the effect of N on the tensile resistance, rupture deformation, and Young’s modulus of 200 DDF fibers, the fibers were tensile tested in seven groups: 30, 60, 90, 120, 150, 180, and 200. The mechanical properties data were statistically analyzed using Weibull and log-normal distributions, as well as their variability was examined and estimated via maximum likelihood and least-squares prediction model with a 95% confidence level.
Science, Textile bleaching, dyeing, printing, etc.
این پژوهش عملکرد فوتوکاتالیزی کاتالیست دی اکسید تیتانیم/ اکسید آهن دوپشده با لانتانیم در حذف فوتوکاتالیزی رنگزاهای متیل رد، آلیزارین رد، متیلن بلو، متیل اورانژ و فنل فتالئین را بررسی میکند. این حذف فوتوکاتالیزی در شرایط مختلف از لحاظ اسیدیته محیط ( 11-3 pH=)، زمان انجام واکنش (min 0-35t= ) در دمای ثابت °C 45، غلظت رنگزای برابر با ppm 10 و 25/0 گرم کاتالیست انجام شد. نتایج نشان میدهد که فعالیت فوتوکاتالیزی به شدت به pH محلول و زمان واکنش بستگی دارد. برای بررسی اثرات همزمان این دو عامل بر بازده تخریب فوتوکاتالیزی از روش بهینهسازی سطح پاسخ استفاده شد. بهترین بازده تخریب فوتوکاتالیزی برای رنگزای آلیزارین رد در 3 pH= و در زمان 35 دقیقه برابر با 77/93 درصد حاصل شد. یافتهها اثربخشی کاتالیست La/TiO2-Fe2O3 را در افزایش تخریب سایر رنگزاها (متیل رد (25/46 درصد)، متیلن بلو (37/42 درصد)، متیل اورانژ (03/11 درصد) و فنل فتالئین (84/57 درصد)) در 3 pH= نشان میدهد و بینشهای ارزشمندی را برای بهینهسازی فرایندهای فوتوکاتالیزی ارائه میکند.
Building construction, Textile bleaching, dyeing, printing, etc.
Giacomo Rizzieri, Federico Lanteri, Liberato Ferrara
et al.
This work introduces ShapeGen3DCP, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are reformulated into dimensionless parameters that capture underlying physical principles. Predicted geometries are compactly represented using Fourier descriptors, which enforce smooth, closed, and symmetric profiles while reducing the prediction task to a small set of coefficients. The training dataset was synthetically generated using a well-established Particle Finite Element (PFEM) model of 3DCP, overcoming the scarcity of experimental data. Validation against diverse numerical and experimental cases shows strong agreement, confirming the framework's accuracy and reliability. This opens the way to practical uses ranging from pre-calibration of print settings, minimizing or even eliminating trial-and-error adjustments, to toolpath optimization for more advanced designs. Looking ahead, coupling the framework with simulations and sensor feedback could enable closed-loop digital twins for 3DCP, driving real-time process optimization, defect detection, and adaptive control of printing parameters.
Christian Düreth, Jan Condé-Wolter, Marek Danczak
et al.
A detailed understanding of material structure across multiple scales is essential for predictive modeling of textile-reinforced composites. Nesting -- characterized by the interlocking of adjacent fabric layers through local interpenetration and misalignment of yarns -- plays a critical role in defining mechanical properties such as stiffness, permeability, and damage tolerance. This study presents a framework to quantify nesting behavior in dry textile reinforcements under compaction using low-resolution computed tomography (CT). In-situ compaction experiments were conducted on various stacking configurations, with CT scans acquired at 20.22 $μ$m per voxel resolution. A tailored 3D{-}UNet enabled semantic segmentation of matrix, weft, and fill phases across compaction stages corresponding to fiber volume contents of 50--60 %. The model achieved a minimum mean Intersection-over-Union of 0.822 and an $F1$ score of 0.902. Spatial structure was subsequently analyzed using the two-point correlation function $S_2$, allowing for probabilistic extraction of average layer thickness and nesting degree. The results show strong agreement with micrograph-based validation. This methodology provides a robust approach for extracting key geometrical features from industrially relevant CT data and establishes a foundation for reverse modeling and descriptor-based structural analysis of composite preforms.
Twisha Chattopadhyay, Fabricio Ceschin, Marco E. Garza
et al.
The 3D printing industry is rapidly growing and increasingly adopted across various sectors including manufacturing, healthcare, and defense. However, the operational setup often involves hazardous environments, necessitating remote monitoring through cameras and other sensors, which opens the door to cyber-based attacks. In this paper, we show that an adversary with access to video recordings of the 3D printing process can reverse engineer the underlying 3D print instructions. Our model tracks the printer nozzle movements during the printing process and maps the corresponding trajectory into G-code instructions. Further, it identifies the correct parameters such as feed rate and extrusion rate, enabling successful intellectual property theft. To validate this, we design an equivalence checker that quantitatively compares two sets of 3D print instructions, evaluating their similarity in producing objects alike in shape, external appearance, and internal structure. Unlike simple distance-based metrics such as normalized mean square error, our equivalence checker is both rotationally and translationally invariant, accounting for shifts in the base position of the reverse engineered instructions caused by different camera positions. Our model achieves an average accuracy of 90.87 percent and generates 30.20 percent fewer instructions compared to existing methods, which often produce faulty or inaccurate prints. Finally, we demonstrate a fully functional counterfeit object generated by reverse engineering 3D print instructions from video.
The 3D mesh fabric is a key component of automotive seat ventilation systems as it has good compression resistance and creates channels to provide effective circulating airflow. The dimensional inconsistency of fabric sheets by laser cutting to be integrated into car seats and their unrecoverable dimension changes in subsequent cushioning applications are challenging problems. A typical commercialized 3D mesh fabric was observed to shorten and widen under compression, showing an auxetic behavior in the length–thickness section. This counterintuitive partial auxetic behavior accounts for the dimensional variation. A full-size finite element (FE) model of the fabric was established to simulate the complex fabric deformation based on the precise geometry of a unit cell obtained by X-ray micro-computed tomography (μCT) scanning. The FE simulation reproduced the planar dimension change process of the fabric. The underlying mechanism of partial auxeticity was revealed from the global to local analysis, including fabric global deformation, unit meshes deformation and unit cell geometric structure change. It was shown that buckling of initially post-buckled spacer monofilaments drives in-plane movements of monofilament loops to cause partial auxetic behavior. The partial auxeticity weakens in the compression process due to the gradual intercontact and densification of spacer monofilaments. Different constraints on monofilament loops from adjacent unit cells and multifilament inlays make the deformation uneven in the plane of fabric. It is important to fully analyze the dimensional change, especially the partial auxetic deformation, of the 3D mesh fabric under compression for its practical applications.
Materials of engineering and construction. Mechanics of materials, Chemical technology
There is a growing demand for wood as a sustainable energy source and an essential carbon sink for ecosystems. Wood formation is a dynamic process involving plant radial growth in various stages, including vascular cambial cell proliferation, xylem mother cell differentiation and expansion, secondary cell wall thickening, and programmed cell death. Wood formation is controlled by highly complex molecular regulatory networks. Identifying and analyzing the molecular mechanisms regulating key target genes is crucial for further characterizing how wood is formed. This article summarizes the current knowledge regarding wood formation and the underlying molecular regulatory mechanisms.
Science, Textile bleaching, dyeing, printing, etc.
Fabric image retrieval, a form of content based image retrieval, is a high value research with the potential to be applied in many fields, such as e-commerce and inventory management. However, this research hotspot is plagued by two major challenges, namely the high requirements for retrieval results and the peculiarities of fabric images. Unlike general image retrieval, fabric image retrieval systems have to pay more attention to texture and color features. To address these challenges, we propose a novel framework for fabric retrieval by using self-supervised and deep hashing techniques. The framework consists of two modules for feature learning and hashing learning. During the feature learning phase, the color and texture information in the image is decoupled under the drive of augmented based pretext tasks. In hashing learning, Bi-half layer is introduced to generate high-quality hash codes. The visualization results indicate that the proposed method performs well for the representation of fabric images. And the experimental results show that the proposed retrieval framework can achieve a good performance (best mAP 0.903) and outperforms other methods, including several deep hashing methods and our previous work.
Materials of engineering and construction. Mechanics of materials, Chemical technology
In this study, comprehensive analyses were used to evaluate the physical and chemical properties of basalt fibers, employing a variety of instruments. Additionally, heat treatment and solvent treatment methods were used to eliminate the sizing present on fiber surfaces. The heat treatment process involved determining the optimal temperature and duration required to remove the sizing from the basalt fibers. The appearance, chemical composition, and crystal structure of the original fibers were examined, including those subjected to heat treatment and those treated with solvents. These treated fibers were then incorporated into concrete to create basalt fiber-reinforced concrete (BFRC) specimens for mechanical tests, which assessed their compressive, flexural, and splitting tensile strengths. The results revealed that heat treatment at 300 °C for 180 min effectively removed the sizing on the basalt fibers, and the heat-treated basalt fibers exhibited uniform dispersion inside the BFRC specimens. In addition, solvent treatment primarily removed the soluble components of the sizing. The mechanical properties of specimens with sizing-removed basalt fibers were better than the specimens with original basalt fibers and the benchmark specimens. Crucially, the mechanical test results demonstrated that BFRC incorporating heat-treated basalt fibers exhibited a superior mechanical performance compared to BFRC incorporating original fibers or fibers subjected to the solvent treatment.
Chemicals: Manufacture, use, etc., Textile bleaching, dyeing, printing, etc.
In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to simulate the motion of virtual agents along the stress produced in a loaded part. Agent trajectories are then used as print trajectories. With this approach, the complex global trajectory generation problem is subdivided into a set of sequential and computationally efficient quadratic programs. Through comprehensive evaluations in both simulation and experiments, we compare our method with state-of-the-art approaches. Our results highlight a remarkable improvement in computational efficiency, achieving a 115x faster computation speed than existing methods. This efficiency, combined with the possibility to tune the trajectories spacing to match the deposition process constraints, makes the potential integration of our approach into existing 3D printing processes seamless. Additionally, the open-hole tensile specimen produced on a conventional fused filament fabrication set-up with our algorithm achieve a notable ~10% improvement in specific modulus compared to existing trajectory optimization methods.
The growing interest in 3D printing of silica glass has spurred substantial research efforts. Our prior work utilizing a liquid silica resin (LSR) demonstrated high printing accuracy and resolution. However, the resin's sensitivity to moisture posed limitations, restricting the printing environment. On the other hand, polyhedral oligomeric silsesquioxane (POSS)-based materials offer excellent water stability and sinterless features. Yet, they suffer from relatively high shrinkage due to the presence of additional organic monomers. In this study, we present a polymeric silsesquioxane (PSQ) resin with reduced shrinkage, enhanced moisture stability, and the retention of sinterless features, providing a promising solution for achieving high-resolution 3D printing of glass objects. Leveraging the two-photon polymerization (2PP) method, we realized nanostructures with feature sizes below 80 nm. Moreover, we demonstrate the tunability of the refractive index by incorporating zirconium moieties into the resin, facilitating the fabrication of glass micro-optics with varying refractive indices. Importantly, the self-welding capability observed between two individual components provides a flexible approach for producing micro-optics with multiple components, each possessing distinct refractive indices. This research represents a significant advancement in the field of advanced glass manufacturing, paving the way for future applications in micro- and nano-scale glass objects.
The advent of 3D printing has transformed the pharmaceutical industry, enabling precision drug manufacturing with controlled release profiles, dosing, and structural complexity. Additive manufacturing (AM) addresses the growing demand for personalized medicine, overcoming limitations of traditional methods. This technology facilitates tailored dosage forms, complex geometries, and real-time quality control. Recent advancements in drop-on-demand printing, UV curable inks, material science, and regulatory frameworks are discussed. Despite opportunities for cost reduction, flexibility, and decentralized manufacturing, challenges persist in scalability, reproducibility, and regulatory adaptation. This review provides an in-depth analysis of the current state of AM in pharmaceutical manufacturing, exploring recent developments, challenges, and future directions for mainstream integration.
Martin Schmitz, Dominik Schön, Henning Klagemann
et al.
Electrodermal activity (EDA) reflects changes in skin conductance, which are closely tied to human psychophysiological states. For example, EDA sensors can assess stress, cognitive workload, arousal, or other measures tied to the sympathetic nervous system for interactive human-centered applications. Yet, current limitations involve the complex attachment and proper skin contact with EDA sensors. This paper explores the concept of 3D printing electrodes for EDA measurements, integrating sensors into arbitrary 3D-printed objects, alleviating the need for complex assembly and attachment. We examine the adaptation of conventional EDA circuits for 3D-printed electrodes, assessing different electrode shapes and their impact on the sensing accuracy. A user study (N=6) revealed that 3D-printed electrodes can measure EDA with similar accuracy, suggesting larger contact areas for improved precision. We derive design implications to facilitate the integration of EDA sensors into 3D-printed devices to foster diverse integration into everyday objects for prototyping physiological interfaces.
The water absorption, chemical resistance, and biological properties are contributing factors to the overall performance of bio-composites, especially for outdoor applications. The functional properties of bio-composites are dependent on the interfacial bonding mechanism, which is controlled by the surface modification and processing parameters of natural fibers. Therefore, this study aims to investigate the potential of enhancing the mukwa/polylactide (mukwa/PLA) interface through an economic and ecological surface modification of recycled mukwa wood fibers via alkali-laccase modification. The fabricated bio-composites intended for making durable farm poles for semi-arid conditions of Southern Africa were characterized via water absorption, chemical resistance, thickness swelling, hardness, and thermal properties. Less thickness swelling and water absorption were found on the alkali-laccase/PLA composites. The less-dense (1.09 g/cm3) alkali-laccase treated composites showed better chemical resistance. Much swelling of the composites was observed on the 40% nitric acid (HNO3), while 60%NaOH shrunk the composites and PLA by <3.5%. The laccase/PLA bio-composite showed a maximum thermal stability of 733 °C. The activation energy (Ea) optimized on the laccase/PLA composite with the highest of 104 kJ mol−1. Maximum crystallinity of 45.8% was achieved on the untreated/PLA composites. The alkali-laccase modification maximized the hardness of composites with 35.45 HV on alkali-laccase/PLA.
Science, Textile bleaching, dyeing, printing, etc.
Continuous ultrasonic atomization in a closed chamber is expected to generate a mist with an equilibrium droplet concentration and size distribution. Such a mist of microdroplets with controllable mist density has been used for Aerosol Jet printing in the fabrication of a variety of additively manufactured microscale devices. Despite many unique capabilities demonstrated with the Aerosol Jet printing technology, its ultrasonic atomization behavior appears to be rather sensitive to the ink properties with gaps in our understanding of the fundamental physics underlying its operation. In this work, we investigate some basic mechanisms in the Aerosol Jet ultrasonic atomizer with a lumped-parameter kinetic coagulation model for highly concentrated mist. To mitigate the difficulty with unavailable knowledge about the complex turbulent flow inside the atomizer chamber, we present results for several orders of magnitude of the turbulent energy dissipation rates in order to examine a range of possibilities. The same approach is taken for analyzing the scavenging effect of the swirling bulk liquid. Our results also demonstrate the theoretical possibility for achieving a mist saturation condition where the mist output from the atomizer can become insensitive to process variables. As observed in experiments, such a saturated mist is highly desirable for Aerosol Jet printing with maximized and well-controlled throughput in additive manufacturing.
Franco Coltraro, Jaume Amorós, Maria Alberich-Carramiñana
et al.
In this work, we introduce a collision model specifically tailored for the simulation of inextensible textiles. The model considers friction, contacts, and inextensibility constraints all at the same time without any decoupling. Self-collisions are modeled in a natural way that allows considering the thickness of cloth without introducing unwanted oscillations. The discretization of the equations of motion leads naturally to a sequence of quadratic problems with inequality and equality constraints. In order to solve these problems efficiently, we develop a novel active-set algorithm that takes into account past active constraints to accelerate the resolution of unresolved contacts. We put to a test the developed collision procedure with diverse scenarios involving static and dynamic friction, sharp objects, and complex-topology folding sequences. Finally, we perform an experimental validation of the collision model by comparing simulations with recordings of real textiles as given by a $\textit{Motion Capture System}$. The results are very accurate, having errors around 1 cm for DIN A2 textiles (42 x 59.4 cm) even in difficult scenarios involving fast and strong hits with a rigid object.
Tom Vandekerckhove, Tom Vanackere, Jasper De Witte
et al.
High-speed Pockels modulation and second-order nonlinearities are key components in optical systems, but CMOS-compatible platforms like silicon and silicon nitride lack these capabilities. Micro-transfer printing of thin-film lithium niobate offers a solution, but suspending large areas of thin films for long interaction lengths and high-Q resonators is challenging, resulting in a low transfer yield. We present a new source preparation method that enables reliable transfer printing of thin-film lithium niobate. We demonstrate its versatility by successfully applying it to gallium phosphide and silicon, and provide an estimate of the transfer yield by subsequently printing 25 lithium niobate films without fail.
Luuk Spreeuwers, Rasmus van der Grift, Pesigrihastamadya Normakristagaluh
Finger vein pattern recognition is an emerging biometric with a good resistance to presentation attacks and low error rates. One problem is that it is hard to obtain ground truth finger vein patterns from live fingers. In this paper we propose an advanced method to create finger vein phantoms using 3D printing where we mimic the optical properties of the various tissues inside the fingers, like bone, veins and soft tissues using different printing materials and parameters. We demonstrate that we are able to create finger phantoms that result in realistic finger vein images and precisely known vein patterns. These phantoms can be used to develop and evaluate finger vein extraction and recognition methods. In addition, we show that the finger vein phantoms can be used to spoof a finger vein recognition system. This paper is based on the Master's thesis of Rasmus van der Grift.