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Unmanned aerial vehicles (UAVs) often face significant challenges in trajectory tracking within complex dynamic environments, where uncertainties, external disturbances, and nonlinear dynamics hinder accurate and stable control. To address this issue, a bio-inspired deep reinforcement learning (DRL) algorithm is proposed, integrating behavior cloning (BC) and long short-term memory (LSTM) networks. This method can achieve autonomous learning of high-precision control policy without establishing an accurate system dynamics model. Motivated by the memory and prediction functions of biological neural systems, an LSTM module is embedded into the policy network of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This structure captures temporal state patterns more effectively, enhancing adaptability to trajectory variations and resilience to delays or disturbances. Compared to memoryless networks, the LSTM-based design better replicates biological time-series processing, improving tracking stability and accuracy. In addition, behavior cloning is employed to pre-train the DRL policy using expert demonstrations, mimicking the way animals learn from observation. This biomimetic plausible initialization accelerates convergence by reducing inefficient early-stage exploration. By combining offline imitation with online learning, the TD3-LSTM-BC framework balances expert guidance and adaptive optimization, analogous to innate and experience-based learning in nature. Simulation experimental results confirm the superior robustness and tracking accuracy of the proposed method, demonstrating its potential as a control solution for autonomous UAVs. Full article

(This article belongs to the Special Issue Bio-Inspired Robotics and Applications 2025)

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24 pages, 1564 KB

Open AccessArticle

Cold-Plasma Method in Counteracting Prosthetic Stomatitis: Analysis of the Influence of Cold Plasma on Prosthetic Materials

by Agnieszka Mazur-Lesz, Joanna Pawłat, Piotr Terebun, Dawid Zarzeczny, Elżbieta Grządka, Agnieszka Starek-Wójcicka, Michał Kwiatkowski, Irena Malinowska, Magdalena Mnichowska-Polanowska and Monika Machoy

Materials 2025, 18(17), 4162; https://doi.org/10.3390/ma18174162 (registering DOI) - 4 Sep 2025

Abstract

The aim of this study was to determine the possibilities of using cold-plasma technology in counteracting the development of denture stomatitis (DS) in patients using different kinds of prosthetic restorations. The study focused mainly on the effect of cold atmospheric plasma on prosthetic materials, such as acryl (AR), acetal (AT), and a prosthetic metal alloy (MA). The materials were tested in terms of the effect of the plasma exposure time (5, 10, and 20 min) on changes in the chemical composition, morphology, and surface topography (FT-IR, SEM-EDS, optical profilometer) as well as changes in the color and contact angle (spectrophotometer, goniometer) after the plasma process. Furthermore, the ability of reference fungi (C. albicans and C. glabrata) to adhere to non-modified and cold atmospheric plasma (CAP)-modified dental materials was examined to evaluate the susceptibility of dental material surfaces to 12 h fungal contamination. The obtained results demonstrate that CAP appears viable for the surface modification of the acetal resin and the metal alloy, not compromising their structural integrity while variably limiting fungal overgrowth involved in the development of DS, whereas its application to the acrylic resin may be inadvisable due to morphological and optical alterations. Full article

(This article belongs to the Special Issue Advances in Plasma Treatment of Materials)

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16 pages, 2810 KB

Open AccessArticle

Assessing the Generalizability of Foundation Models for the Recognition of Motor Examinations in Parkinson’s Disease

by Christopher Gundler, Alexander Johannes Wiederhold and Monika Pötter-Nerger

Sensors 2025, 25(17), 5523; https://doi.org/10.3390/s25175523 (registering DOI) - 4 Sep 2025

Abstract

Current machine learning approaches focusing on motor symptoms in Parkinson’s disease are commonly trained on small datasets and often lack generalizability from developmental setups to clinical applications. Foundation models using large, unlabeled datasets of healthy participants through self-supervised learning appear attractive for such setups with limited samples, despite the potential impact of motoric symptoms. Acting as an exemplar, this study aims to evaluate the robustness of fine-tuned models in recognizing movements related to motor examinations across datasets and recording setups. Accelerometer data of 51 participants with Parkinson’s disease in three different training and fine-tuning setups were used to tailor the general model to the disease. Training the model on pre-trained weights, both partially (F₁ = 0.70) and fully (F₁ = 0.69), statistically significantly outperformed training the model from scratch (F₁ = 0.55) in a nested cross-validation. For evaluation, the model’s ability to process data recorded from 24 patients in clinic was tested. The models achieved lower mean F₁ scores of 0.33 (train from scratch), 0.43 for full, and 0.48 for partial fine-tuning, but demonstrated improved generalizability and robustness regarding the orientation of sensors compared to training from scratch. Utilizing foundation models for accelerometer data trained on healthy participants and fine-tuned for clinical applications in movement disorders appears as an effective strategy for optimized generalizability with small datasets. Full article

(This article belongs to the Special Issue Sensor-Based Technology for Monitoring Movement in Special Populations)

27 pages, 30539 KB

Open AccessArticle

Priori Knowledge Makes Low-Light Image Enhancement More Reasonable

by Zefei Chen, Yongjie Lin, Jianmin Xu, Kai Lu and Zihao Huang

Sensors 2025, 25(17), 5521; https://doi.org/10.3390/s25175521 - 4 Sep 2025

Abstract

This paper presents a priori knowledge-based low-light image enhancement framework, termed Priori DCE ( Priori Deep Curve Estimation). The priori knowledge consists of two key aspects: (1) enhancing a low-light image is an ill-posed task, as the brightness of the enhanced image corresponding to a low-light image is uncertain. To resolve this issue, we incorporate priori channels into the model to guide the brightness of the enhanced image; (2) during the enhancement of a low-light image, the brightness of pixels may increase or decrease. This paper explores the probability of a pixel’s brightness increasing/decreasing as its prior enhancement /suppression probability. Intuitively, pixels with higher brightness should have a higher priori suppression probability, while pixels with lower brightness should have a higher priori enhancement probability. Inspired by this, we propose an enhancement function that adaptively adjusts the priori enhancement probability based on variations in pixel brightness. In addition, we propose the Global-Attention Block (GA Block). The GA Block ensures that, during the low-light image enhancement process, each pixel in the enhanced image is computed based on all the pixels in the low-light image. This approach facilitates interactions between all pixels in the enhanced image, thereby achieving visual balance. The experimental results on the LOLv2-Synthetic dataset demonstrate that Priori DCE has a significant advantage. Specifically, compared to the SOTA Retinexformer, the Priori DCE improves the PSNR index and SSIM index from 25.67 and 92.82 to 29.49 and 93.6, respectively, while the NIQE index decreases from 3.94 to 3.91. Full article

(This article belongs to the Topic AI and Data-Driven Advancements in Industry 4.0, 2nd Edition)

21 pages, 4394 KB

Open AccessArticle

Model-in-the-Loop Design and Flight Test Validation of Flight Control Laws for a Small Fixed-Wing UAV

by Ting-Ju Shen and Chieh-Li Chen

Drones 2025, 9(9), 624; https://doi.org/10.3390/drones9090624 (registering DOI) - 4 Sep 2025

Abstract

This study provides an experimentally validated workflow for the development and model-in-the-loop (MIL) validation of flight control laws for a small, low-cost fixed-wing UAV within a model-based design (MBD) framework, addressing the limitation that previous workflow demonstrations largely remain conceptual or simulation-only and that systematic processes for low-cost UAVs are lacking. A key advantage is that control law methods or parameters can be determined prior to flight testing, avoiding on-site tuning, a major challenge in UAV deployment. The Skysurfer X8 UAV served as the experimental platform. Linearized dynamic models were derived to design rate and attitude controllers using frequency-domain techniques, where loop shaping was applied to meet U.S. military flight quality standards. The control algorithms were validated in an MIL environment, enabling early evaluation of control logic, dynamic response, and robustness under idealized and perturbed conditions. Following MIL verification, the control logic was generated via Simulink Coder and deployed on a Pixhawk 6C flight controller with the PX4 autopilot. Flight test results on the Skysurfer X8 showed good agreement with MIL simulations, confirming the reliability and consistency of the proposed methodology in both simulated and real domains, while also demonstrating a systematic workflow that fills a practical gap in low-cost UAV development. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

17 pages, 4945 KB

Open AccessArticle

Growth Behavior of Multi-Element Compound Layers During Reactive Diffusion Between Solid CoCrFeMnNi Alloy and Liquid Al

by Longtu Yang, Yufeng Yang, Zeqiang Yao, Shichao Liu and Yong Dong

Materials 2025, 18(17), 4158; https://doi.org/10.3390/ma18174158 - 4 Sep 2025

Abstract

In the present study, the diffusion couple of solid CoCrFeMnNi HEA and liquid pure Al was prepared. The microstructure evolution and relevant interdiffusion behavior of CoCrFeMnNi HEA/Al solid–liquid diffusion couple processed by different parameters were characterized and investigated. Results demonstrated that the interfacial compounds in the order of Al(Co, Cr, Fe, Mn, Ni), Al₁₃(Co, Cr, Fe, Mn, Ni)₄ and Al₄(Co, Cr, Fe, Mn, Ni) were determined in the interdiffusion area along the direction from CoCrFeMnNi HEA to Al, and the precipitated Al₄(Cr, Mn) and Al₉(Co, Fe, Ni) phases were formed in the center of Al couple. In addition, the diffusion mechanism and activation energy of growth for each diffusion layer were revealed and determined. More importantly, the growth mechanism of each diffusion layer was also investigated and uncovered in detail. Meanwhile, the activation energy of each intermetallic layer was obtained by the Arrhenius equation and the linear regression method. It is anticipated that this present study would provide a fundamental understanding and theoretical basis for the high-entropy alloy CoCrFeMnNi HEA, potentially applied as the cast mold material for cast aluminum alloy. Full article

(This article belongs to the Special Issue High-Entropy Alloys: Synthesis, Characterization, and Applications)

20 pages, 5076 KB

Open AccessArticle

Hybrid-Domain Synergistic Transformer for Hyperspectral Image Denoising

by Haoyue Li and Di Wu

Appl. Sci. 2025, 15(17), 9735; https://doi.org/10.3390/app15179735 (registering DOI) - 4 Sep 2025

Abstract

Hyperspectral image (HSI) denoising is challenged by complex spatial-spectral noise coupling. Existing deep learning methods, primarily designed for RGB images, fail to address HSI-specific noise distributions and spectral correlations. This paper proposes a Hybrid-Domain Synergistic Transformer (HDST) integrating frequency-domain enhancement and multiscale modeling. Key contributions include (1) a Fourier-based preprocessing module decoupling spectral noise; (2) a dynamic cross-domain attention mechanism adaptively fusing spatial-frequency features; and (3) a hierarchical architecture combining global noise modeling and detail recovery. Experiments on realistic and synthetic datasets show HDST outperforms state-of-the-art methods in PSNR, with fewer parameters. Visual results confirm effective noise suppression without spectral distortion. The framework provides a robust solution for HSI denoising, demonstrating potential for high-dimensional visual data processing. Full article

(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)

20 pages, 2517 KB

Open AccessArticle

Fabrication of Zein Nanoparticle-Functionalized Wheat Gluten Amyloid Fibril/Methyl Cellulose Hybrid Membranes with Efficient Performance for Water-in-Oil Emulsion Separation

by You-Ren Lai, Jun-Ying Lin, Jou-Ting Hsu, Ta-Hsien Lin, Su-Chun How and Steven S.-S. Wang

Polymers 2025, 17(17), 2409; https://doi.org/10.3390/polym17172409 - 4 Sep 2025

Abstract

Considering the high stability of water-in-oil (W/O) emulsions, contamination from emulsified pollutants poses a long-term risk to the environment. In this study, hybrid membranes composed of wheat gluten amyloid fibrils (WGAFs) and zein nanoparticles (ZNPs) were prepared and used as a separator to remove emulsified W/O droplets from the oily phase. ZNPs and WGAFs were synthesized through antisolvent method and fibrillation process. Next, a ZNP-functionalized wheat gluten AF/methyl cellulose (ZNP-WGAF/MC) hybrid membrane was fabricated, and its properties were investigated via various analytical techniques. Lastly, the separation efficiency of the ZNP-WGAF/MC hybrid membrane for various W/O emulsions was assessed using microscopy and light scattering. The formation of ZNPs or WGAFs was first verified via spectroscopic and microscopic methods. Our results indicated that the ZNP-WGAF/MC hybrid membranes were synthesized via chemical crosslinking coupled with the casting method. Furthermore, the incorporation of either WGAFs or ZNPs was found to improve the thermal stability and surface hydrophobicity of membranes. Finally, the separation efficiency of the ZNP-WGAF/MC hybrid membranes for various W/O emulsions was determined to be ~87–99%. This research demonstrates the potential of harnessing three-dimensional membranes composed of plant protein-based fibrils and nanoparticles to separate emulsified W/O mixtures. Full article

(This article belongs to the Special Issue Functional Polymer Membranes for Advanced Separation Technologies)

14 pages, 2637 KB

Open AccessArticle

Integration of High-Brightness QLED-Excited Diamond Magnetic Sensor

by Pengfei Zhao, Junjun Du, Jinyu Tai, Zhaoqi Shang, Xia Yuan and Yuanyuan Shi

Micromachines 2025, 16(9), 1021; https://doi.org/10.3390/mi16091021 - 4 Sep 2025

Abstract

The nitrogen-vacancy (NV) center magnetic sensor, leveraging nitrogen-vacancy quantum effects, enables high-sensitivity magnetic field detection via optically detected magnetic resonance (ODMR). However, conventional single-point integrated devices suffer from limitations such as inefficient regional magnetic field detection and challenges in discerning the directional variations of dynamic magnetic fields. To address these issues, this study proposes an array- based architecture that innovatively substitutes the conventional 532 nm laser with quantum-dot light-emitting diodes (QLEDs). Capitalizing on the advantages of QLEDs—including compatibility with micro/nano-fabrication processes, wavelength tunability, and high luminance—a 2 × 2 monolithically integrated magnetometer array was developed. Each sensor unit achieves a magnetic sensitivity of below 26 nT·Hz^−1/2 and a measurable range of ±120 μT within the 1–10 Hz effective bandwidth. Experimental validation confirms the array’s ability to simultaneously resolve multi-regional magnetic fields and track dynamic field orientations while maintaining exceptional device uniformity. This advancement establishes a scalable framework for the design of large-scale magnetic sensing arrays, demonstrating significant potential for applications requiring spatially resolved and directionally sensitive magnetometry. Full article

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27 pages, 10300 KB

Open AccessArticle

Investigation of Fenbendazole Solubility Using Particle Size Reduction Methods in the Presence of Soluplus^®

by Amirhossein Karimi, Pedro Barea, Óscar Benito-Román, Beatriz Blanco, María Teresa Sanz, Clement L. Higginbotham and John G. Lyons

Pharmaceutics 2025, 17(9), 1163; https://doi.org/10.3390/pharmaceutics17091163 - 4 Sep 2025

Abstract

Background/Objectives: Fenbendazole is a potential cancer treatment and a proven antiparasitic in veterinary applications. However, its poor water solubility limits its application. In this study, potential fenbendazole solubility enhancement was investigated through size reduction methods. The effect of the presence of Soluplus^® on solubility was investigated as well. Methods: Solubility enhancement was explored using microfluidization and ultrasonication techniques. These techniques were applied to fenbendazole alone and in combination with Soluplus^®. UV–Vis spectroscopy was used to determine solubility. Possible chemical reactions were checked using Fourier transform infrared spectroscopy (FT-IR). Differential scanning calorimetry (DSC) was conducted to analyze the physical structure and crystallinity of the samples. Scanning electron microscopy (SEM) was also utilized for characterization of the effect of the treated formulations and the size reduction method on morphology. The elements present in samples were identified with energy-dispersive X-ray spectroscopy (EDX) combined with SEM. A comparison of crystalline structure between the products was performed via X-ray powder diffraction (XRPD). Dynamic light scattering (DLS) was also used to measure the samples’ average particle size at different stages. Results: Both ultrasonication and microfluidization led to marginal increases in the solubility of neat fenbendazole. In contrast, formulations processed in the presence of Soluplus^® demonstrated a greater enhancement in solubility. However, solubility improvement was not retained in the dried samples. The post-drying samples, irrespective of the presence of Soluplus^®, showed nearly the same solubility as neat fenbendazole. Conclusions: Size-reduction methods, particularly when combined with Soluplus^®, improved the solubility of fenbendazole. However, drying appeared to reverse these gains, regardless of the method used. Full article

(This article belongs to the Section Physical Pharmacy and Formulation)

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25 pages, 12006 KB

Open AccessArticle

Heterogeneous Information Fusion for Robot-Based Automated Monitoring of Bearings in Harsh Environments via Ensemble of Classifiers with Dynamic Weighted Voting

by Mohammad Siami, Przemysław Dąbek, Hamid Shiri, Anna Michalak, Jacek Wodecki, Tomasz Barszcz and Radosław Zimroz

Sensors 2025, 25(17), 5512; https://doi.org/10.3390/s25175512 - 4 Sep 2025

Abstract

Modern inspection mobile robots can carry multiple sensors that can provide opportunities to take advantage of the fusion of information obtained from different sensors. In real-world condition monitoring, harsh environmental conditions can significantly affect the sensor’s accuracy. To address this issue in this paper, we introduced a fusion approach around information gaps to handle the portion of false information that can be captured by the employed sensors. To test our idea, we looked at various types of data, such as sounds, color images, and infrared images taken by a mobile robot inspecting a mining site to check the condition of the belt conveyor idlers. The RGB images are used to classify the rotating idlers as stuck ones (late-stage faults); on the other hand, the acoustic signals are employed to identify early-stage faults. In this work, the cyclostationary analysis approach is employed to process the captured acoustic data to visualize the bearing fault signature in the form of Cyclic Spectral Coherence. Since convolutional neural networks (CNNs) and their transfer learning (TL) forms are popular approaches for performing classification tasks, a comparison study of eight CNN-TL models was conducted to find the best models to classify different fault signatures in captured RGB images and acquired Cyclic Spectral Coherence. Finally, to combine the collected information, we suggest a method called dynamic weighted majority voting, where each model’s importance is regularly adjusted for each sample based on the surface temperature of the idler taken from IR images. We demonstrate that our method of combining information from multiple classifiers can work better than using just one sensor for monitoring conditions in real-world situations. Full article

(This article belongs to the Section Sensors and Robotics)

16 pages, 751 KB

Open AccessArticle

Enhancing Sensitivity of Nonparametric Tukey Extended EWMA-MA Charts for Effective Process Mean Monitoring

by Khanittha Talordphop, Yupaporn Areepong and Saowanit Sukparungsee

Symmetry 2025, 17(9), 1457; https://doi.org/10.3390/sym17091457 - 4 Sep 2025

Abstract

A control chart is a crucial statistical process control (SPC) instrument for identifying method variances that may undermine product efficacy. The combined control chart has been utilized to enhance recognition capability. When testing a methodology, nonparametric statistics make a strong and compelling case when the distribution of a quality feature is uncertain. The primary focus of monitoring this work is to offer a novel control chart to support the surveillance of mean activities. This chart will incorporate a Tukey method, an extended exponentially weighted moving average control chart, and a moving average control chart called the Nonparametric EEWMA-MA chart. The Monte Carlo simulation facilitates assessments for evaluating system performance using average run lengths (ARL) based on zero-state. The comparison analysis demonstrates that the sensitivity of the suggested chart surpasses that of the conventional control chart (including the moving average (MA) chart, the extended exponentially weighted moving average (EEWMA) chart, and the mixed extended exponentially weighted moving average-moving average (EEWMA-MA) chart) in rapidly detecting changes that fluctuate with varying parameter settings by examining the minimal ARL. A simplified monitoring scenario using data on vinyl chloride can be employed to demonstrate the feasibility of the proposed technique. Full article

(This article belongs to the Section Mathematics)

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10 pages, 2606 KB

Open AccessFeature PaperArticle

Investigating the Stability of Cu₂Se Superionic Thermoelectric Material in Air Atmosphere

by Paweł Nieroda, Małgorzata Rudnik, Marzena Mitoraj-Królikowska, Ewa Drożdż, Dawid Kozień, Juliusz Leszczyński and Andrzej Koleżyński

Materials 2025, 18(17), 4152; https://doi.org/10.3390/ma18174152 - 4 Sep 2025

Abstract

Copper selenide (Cu₂Se) has garnered significant attention as an exceptional thermoelectric material due to its high thermoelectric figure of merit (ZT values > 2). This remarkable efficiency makes it a strong candidate for various applications. However, the practical deployment of thermoelectrics often requires operation in an oxygen-containing atmosphere, which poses a significant challenge for Cu₂Se due to its environmental instability. This work investigates the environmental behavior of high-purity Cu₂Se, which was synthesized via a direct high-temperature reaction and spark plasma sintering (SPS). Our Temperature-Programmed Oxidation (TPO) studies determined that the onset of oxidation occurs at a temperature as low as 623 K. Further analysis using SEM–EDS confirmed the formation of copper oxides, Cu₂O and CuO. Critically, thermogravimetric analysis (TGA) revealed that the SeO₂ formation and sublimation process is an equally profound degradation mechanism, alongside copper oxidation, particularly within the optimal 673–973 K temperature range. Complementary XRD studies of samples annealed in air underscore this severe material degradation, which is especially devastating between 873 and 973 K. Ironically, this is the precise temperature window where Cu₂Se’s highest ZT values have been reported. Our findings demonstrate that the direct application of Cu₂Se in air is impractical, highlighting the urgent need for developing robust protective layers to unlock its full potential. Full article

(This article belongs to the Section Electronic Materials)

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20 pages, 9291 KB

Open AccessArticle

BGWL-YOLO: A Lightweight and Efficient Object Detection Model for Apple Maturity Classification Based on the YOLOv11n Improvement

by Zhi Qiu, Wubin Ou, Deyun Mo, Yuechao Sun, Xingzao Ma, Xianxin Chen and Xuejun Tian

Horticulturae 2025, 11(9), 1068; https://doi.org/10.3390/horticulturae11091068 - 4 Sep 2025

Abstract

China is the world’s leading producer of apples. However, the current classification of apple maturity is predominantly reliant on manual expertise, a process that is both inefficient and costly. In this study, we utilize a diverse array of apples of varying ripeness levels as the research subjects. We propose a lightweight target detection model, termed BGWL-YOLO, which is based on YOLOv11n and incorporates the following specific improvements. To enhance the model’s ability for multi-scale feature fusion, a bidirectional weighted feature pyramid network (BiFPN) is introduced in the neck. In response to the problem of redundant computation in convolutional neural networks, a GhostConv is used to replace the standard convolution. The Wise-Inner-MPDIoU (WIMIoU) loss function is introduced to improve the localization accuracy of the model. Finally, the LAMP pruning algorithm is utilized to further compress the model size. The experimental results demonstrate that the BGWL-YOLO model attains a detection and recognition precision rate of 83.5%, a recall rate of 81.7%, and an average precision mean of 90.1% on the test set. A comparative analysis reveals that the number of parameters has been reduced by 65.3%, the computational demands have been decreased by 57.1%, the frames per second (FPS) have been boosted by 5.8% on the GPU and 32.8% on the CPU, and most notably, the model size has been reduced by 74.8%. This substantial reduction in size is highly advantageous for deployment on compact smart devices, thereby facilitating the advancement of smart agriculture. Full article

(This article belongs to the Special Issue Application of Intelligent Technology and Equipment in Horticultural Production)

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12 pages, 1276 KB

Open AccessArticle

Delving into Process–Microstructure–Property Relationships in Cast-Extruded Polylactic Acid/Talc Composite Films: Effect of Different Screw Designs

by Giulia Bernagozzi, Chiara Gnoffo, Rossella Arrigo and Alberto Frache

J. Compos. Sci. 2025, 9(9), 483; https://doi.org/10.3390/jcs9090483 - 4 Sep 2025

Abstract

In the context of polymer-based composites, the knowledge of the correlations between the processing conditions, the microstructure, and the final properties is essential to tailor polymeric systems for specific applications. Specifically concerning the extrusion process, an accurate design of the screw profile allows for achieving composites with modulable microstructures, according to the specific properties required by the intended application. In this work, films of polylactic acid-based composites with 5 wt.% of talc were obtained by means of a single-screw extruder equipped with a flat die and a calender unit. Three different screw profiles, namely a general-purpose compression screw, a screw with a reverse flow zone, and a barrier screw, were employed for the production of films. The ability of the screw profile in varying the degree of filler dispersion and distribution was assessed through morphological and rheological analyses, demonstrating that the barrier screw is more able in disaggregating the talc lamellae. Due to the achieved microstructures, films produced using this screw profile exhibited superior barrier properties, with a decrease of about 27% in the oxygen permeability as compared to unfilled PLA. However, a concurrent decrease in material ductility as compared to the other films was observed. Finally, the thermoformability of the composites was assessed; also in this case, trays with more precise edges and corners were obtained for the film formulated through the barrier screw. Full article

(This article belongs to the Special Issue Trends and Challenges in Developing and Processing Composite Materials)

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