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Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBH) is a bio-based polyester with the potential to replace some common polymers of fossil origin. However, PHBH presents serious limitations, such as low stiffness, tendency to undergo crystallization over long time periods and low resistance to thermal degradation during processing. In this work, we studied the use of alumina nanowires to generate PHBH–alumina nanocomposites, modifying the properties of PHBH to improve its usability. Solvent casting and melt blending were used to produce the nanocomposites. Then, their physicochemical properties and aquatic toxicity were measured. Finally, LCA was used to evaluate and compare the environmental impacts of several scenarios relevant to the processing and end of life (EoL) conditions of PHBHs. It was observed that, at low concentrations (3 wt.%), the alumina nanowires have a small positive impact on the stiffness and thermal degradation for the samples. However, for higher concentrations, the observed effects differed for each of the applied processing techniques (solvent casting or melt blending). The toxicity measurements showed that PHBH alone and in combination with alumina nanowires (10 wt.%) did not produce any impact on the survival of brine shrimp larvae after 24 and 48 h of exposure. The 18 impact categories evaluated by LCA allowed defining the most environmentally friendly conditions for the processing and EoL of PHBHs, and comparing the PHBH-related impacts to those of some of the most common fossil-based plastics. It was concluded that the preferable processing technique for PHBH is melt blending and that PHBH is unquestionably more environmentally friendly than every other analyzed plastic. This research was funded by 4gune/KSIgune “Siemens-Gamesa Students4Sustainability 2021, the Basque Government (consolidated research group IT1743-22), the Spanish Ministry of Science and Innovation project FIERA (PID2021-128600OB-I00) and the University of the Basque Country (UPV/EHU) (recognized research group GIU20/004).

Aprotic lithium–oxygen (Li–O₂) batteries are a prominent example of ultrahigh energy density batteries. Although Li–O₂ batteries hold a great potential for large-scale electrochemical energy storage and electric vehicles, their implementation is lagging due to the complex reactions occurring at the cathode. Great effort has been applied to find practical cathodes through the incorporation of different materials acting as catalysts. Here we tap into the quantification of the environmental footprint of seven high-performance Li–O₂ batteries. The batteries were standardized to feed a 60 kWh electric vehicle. Life cycle assessment (LCA) methodology is applied to determine and compare how different batteries and respective components contribute to environmental footprints, categorized in 18 groups. To get a bigger picture, results are compared with the environmental burdens of a reference lithium ion battery, reference sodium ion battery, and the average value of lithium–sulfur batteries. Overall, Li–O₂ batteries present lower environmental burdens in 9 impact categories, with similar impacts in 5 categories in comparison with lithium–sulfur and lithium ion batteries. With an average value of 55.76 kg·CO₂ equiv in Global Warming Potential for the whole Li–O₂ battery, the cathode is the major contributor, with a relative weight of 44.5%. These results provide a road map to enable the practical design of sustainable aprotic Li–O₂ batteries within a circular economy perspective.

The objective of performing transmission electron microscopy (TEM) tomography (TEMT) on poly (lactic acid) (PLA)/clay samples is to characterize their 3D microstructure by obtaining the dispersion distribution and orientation of the dimensions of the clays. This information cannot be elucidated from a qualitative TEM analysis or from conventional characterization techniques such as X-ray diffraction. The nanocomposites are obtained by mixing PLA with Cloisite 20A and 30B at different extrusion shear rates which have been analyzed in 3D. Quantitative TEMT is performed to all the nanocomposites and the resulting 3D quantitative characterization (geometry of clay particles misalignment degree and distribution) is used for a more realistic comprehension of the mechanical behavior of the nanocomposites.

[EN]With the emergence of portable electronics and electric vehicle adoption, the last decade has witnessed an increasing fabrication of lithium-ion batteries (LIBs). The future development of LIBs is threatened by the limited reserves of virgin materials, while the inadequate management of spent batteries endangers environmental and human health. According to the Circular Economy principles aiming at reintroducing end-of-life materials back into the economic cycle, further attention should be directed to the development and implementation of battery recycling processes. To enable sustainable paths for graphite recovery, the environmental footprint of state-of-the-art graphite recycling through life cycle assessment is analyzed quantifying the contribution of nine recycling methods combining pyrometallurgical and hydrometallurgical approaches to indicators such as global warming, ozone layer depletion potential, ecotoxicity, eutrophication, or acidification. Laboratory-scale recycling is scaled up into pilot-scale processes able to treat 100 kg of spent graphite. With values ranging from 0.53 to 9.76 kg.CO2 equiv. per 1 kg of graphite, energy consumption and waste acid generation are the main environmental drivers. A sensitivity analysis demonstrates a 20-73% impact reduction by limiting to one-fourth the amount of H2SO4. Combined processes involving hydrometallurgy and pyrometallurgy give environmentally preferable results. The electrochemical performance of regenerated graphite is also compared with virgin battery-grade graphite. This work provides cues boosting the environmentally sustainable recycl The authors are grateful for Open Access funding provided by the University of Basque Country (UPV/EHU).

Since 2002, the University of the Basque Country (UPV/EHU) has supported several teaching experiences related to the so-called Life Cycle Thinking or Ecodesign in collaboration with local Institutions. The kick off was the Ecodesign Learning Center aiming to promote environmental education at the Faculty of Engineering in Bilbao. In this framework, the last effort has been the implementation of a Master’s Degree entitled Circular Economy: Business Application. This course has been successfully implemented in the 2019–2020 academic year and has been specifically designed to provide training in circular economy for people with backgrounds as varied as product manufacturing engineering, environmental engineering or economics. These studies are aimed to become a European reference in its goal of promoting circular economy, life cycle thinking, ecodesign and sustainable development. This paper analyzes the learning issues and characteristics of this Master's degree placing a special emphasis on its novel competencies and learning outcomes for our society. It can be concluded that the Master's degree is a pioneering teaching experience, being the forefront of Circular Economy Education in Southwestern Europe.

In carbon black reinforced rubbers, the shape of the carbon black aggregates has a very significant influence on the final properties of the material. Accurately classifying these particles by shape has proven to be difficult, but the results of the classification would allow to model the final mechanical properties of the material. In this work, 21 features are measured from 7714 isolated filler images obtained from TEM images and used for the classification. Support vector machines and artificial neural network techniques are used to classify the aggregates using a methodology to tune the algorithm parameters to improve the performance of the models. Also, genetic algorithms are applied to make a feature selection in order to get most robust and accurate models. It is demonstrated that the combination of genetic algorithms with support vector machines and artificial neural network improves the classification results and minimizes the complexity of the resulting model.

Poly (lactic acid) (PLA) is one the most promising bio-based and biodegradable polymer. However, its properties limit the range of applications it reaches. The neat PLA can be modified with additives that will alter its final properties. In this work the mechanical properties of PLA and PLA/clay (Cloisite 20A and Cloisite 30B) nanocomposites are studied, as a function of the composition of the nanocomposites and a wide range of processing conditions. The novelty of this work is the development of a methodology using TEM-AIA techniques to statistically representatively quantify the microstructure. The length and the thickness of the clay particles, their distribution and the misalignment are quantified and introduced into micromechanical models in order predict the elastic modulus of the nanocomposites. The study proves that by including the effective clay particle and the misalignment degree (parameters calculated from the TEM-AIA analysis) into the models the predictions improve.

A method is proposed and verified for selecting the optimum segmentation of a TEM reconstruction among the results of several segmentation algorithms. The selection criterion is the accuracy of the segmentation. To do this selection, a parameter for the comparison of the accuracies of the different segmentations has been defined. It consists of the mutual information value between the acquired TEM images of the sample and the Radon projections of the segmented volumes. In this work, it has been proved that this new mutual information parameter and the Jaccard coefficient between the segmented volume and the ideal one are correlated. In addition, the results of the new parameter are compared to the results obtained from another validated method to select the optimum segmentation.

We present an efficient method for the encapsulation and fixation of octadecylamine coated CdSe/ZnS quantum dots. This is achieved by incorporating the QDs into core-shell particles made of PS/DVB (core)/PMMA/DVB (shell) cross linked polymeric phases synthesized in two stages. The first stage comprises the production of a hybrid PS/DVB core with QD nanoparticles located at the interface of the polymer particle and aqueous phase. In the second stage a PMMA/DVB shell is produced on the hybrid cores, burying the QDs within the composite particle and substantially minimizing the migration of the QDs out of the particles. The stability of the QDs within the core-shell particles is proven by assessing the fluorescence over long periods of time. Contrary to what happens to the core particle dispersion that lost fluorescence in few weeks, the fluorescence emission of the core-shell particles preserves the optical properties of the neat QDs for more than 46 weeks.

Bio-based polymeric nanocomposites (NCs) with enhanced electrical conductivity and rigidity were obtained by adding multi-walled carbon nanotubes (CNTs) to a commercial bio-based polyamide 4,10 (PA410). Two different types of commercial CNTs (Cheap Tubes and Nanocyl NC7000TM) and two different preparation methods (using CNTs in powder form and a PA6-based masterbatch, respectively) were used to obtain melt-mixed PA410/CNT NCs. The effect of the preparation method as well as the degree of dispersion and aspect ratio of the CNTs on the electrical and mechanical properties of the processed NCs was studied. Superior electrical and mechanical behavior was observed in the Nanocyl CNTs-based NCs due to the enhanced dispersion and higher aspect ratio of the nanotubes. A much more significant reduction in aspect ratio was observed in the Cheap Tubes CNTs than in the Nanocyl CNTs. This was attributed to the fact that the shear stress applied during melt processing reduced the length of the CNTs to similar lengths in all cases, which pointed to the diameter of the CNTs as the key factor determing the properties of the NCs. The PA6 in the ternary PA410/PA6/CNT system led to improved Young&rsquo, s modulus values because the reinforcing effect of CNTs was greater in PA6 than in PA410.

21st century engineers have to confront global society’s competitiveness, so technical and technological competences have to be enriched with critical thinking, problem solving, collaboration, communication and creativity skills. To this end, teaching methodologies like Project-Based Learning (PBL) together with cooperative work, to cover the educational needs of future engineering graduates, should be promoted in class. This paper presents the SimABP project, an action where we simulate a work experience in the CAD subject presenting a real challenge to the students, tightly linking academy to real labour reality. In addition, we also define specific rubrics for evaluating the competences and we show the most significant results obtained. Our action has increased the satisfaction and motivation of the students, and the marks obtained in technical skills have been higher.

Which didactic methodology is the most appropriate considering the contents of my subject? What are the activities that suit my possibilities? What evaluation method is fair and formative for the competences of my subject? The teaching team of the subject of Graphic Expression in the Engineering of the Engineering School of Bilbao (UPV/EHU) decided to act in order to improve the teaching-learning process. For this purpose a restructuring of the entire subject that would be carried out by making a disruptive approach to redesign the subjetc was thought. The main objective was to capture the students’ interest in the subject and, as a result, improve the success rate. For this purpose, a restructuring of the whole subject was sought by applying educational methodologies and updated technologies according to the new requirements by the students and the teachers. The main idea is to give importance to the knowledge and ability of extracting the necessary data to represent graphically the reality, identifying the real problem posed with a method of theoretical resolution. At the same time, to minimize the importance of memorizing steps, data can be consulted in the bibliography. With the objective of implementing the main ideas above, the teaching team inquired about pedagogic models, and found that the Flipped Classroom suited the necessary characteristics, mainly the effective use of time in the classroom. Finally, it is important to notice that the creative process followed has allowed the teaching team to unify criteria and agree on results. This way, all members have felt involved in the project.

This work applies statistical analysis, and classical and advanced machine learning algorithms to classify 7714 aggregates into four categories according to their shape. The aggregates under study are obtained from several grades of carbon black: Vulcan XC 605, Vulcan XC 72, CSX 691, Printex 25, N990, and N762. The classification of the shape is of great significance in order to explain and predict the end-use properties of the composite materials, like mechanical properties. The proposed approach combines transmission electron microscopy and automated image analysis to obtain the dataset of the morphological features that defines the shape of the aggregate, and statistical analysis and machine learning techniques to create the classification models using feature transformation and reduction, parameter tuning, and validation methods in order to achieve robust classification models. The best result is obtained from a classification tree based on evolutionary algorithms with a principal component analysis-based feature reduction that reports an acceptable accuracy, thereby validating both the final chosen model and the methodology.

The present work describes a methodology to implement microstructural data obtained from TEM tomography into finite element (FE) simulation software. In this first approach the tomographic reconstruction is treated with different software to create a 3D realistic model of the microstructure of the composite. This realistic model is compared against 2D image-based models via FE simulations in order to compare the mechanical behavior achieved with both procedures. After this first study the results obtained with both types of model are similar. These results are a promising starting point for the development of this novel methodology for obtaining nanocomposite FE models from TEM tomography with realistic results.

This work compares several automatic classification methods with visual expert decision classification. The classification is based on carbon black aggregates into one of the four morphological classes that were proposed by Herd. The automatic methodologies were performed in several steps: transmission electron microscopy image processing to compute a set of twenty-one morphological characteristics, multivariate analysis of the dataset to avoid the curse of dimensionality in classification problems, and built, test and validate machine-learning techniques to classify the aggregates. In the other hand six expert researchers made a visual classification of the aggregates and later, a statistical analysis was conducted to classify the aggregates according to some defined weights. And finally, a comparison of the methods decided which method obtained the highest accuracy and was the most reliable between the compared ones.

While manufacturing composite materials, reinforcement fillers inevitable collide with each other and subsequently they congregate to aggregates with different shapes. The shape of these nanoparticles aggregates are of great significance for the mechanical material properties and in consequence, knowing the percentage of aggregates of each shape within of a specific group of shapes can give an idea of the final properties of the material. This work classifies aggregates, a new dataset of 5713 carbon black aggregates gathered based on transmission electron microscopy image processing, using several classification trees and rule-based methods. Based on these methods several models are built, trained and tested to perform the classification. And then, the most reliable and accurate model to classify aggregates is selected, obtaining a testing accuracy of the 74.57% according to their shape.

A wide range of mechanical properties of carbon-black reinforced rubber blends are usually studied to evaluate their performance according to the initial material composition. Different amount of each composition element generate rubber blends with different mechanical properties, subsequently model the relationship between composition and mechanical properties could contribute useful information and could also save manufacturer industries significant amounts of capital. This study models tensile strength property using linear regression techniques and low errors were obtained in comparison with the values obtained from real experiments. Linear regression and generalized linear regression techniques, simple and enhanced with Gradient Boosting techniques, were used to create linear models with RMSE errors of approximately 25.33% in tensile strength prediction.

The evolution of the morphology of an acrylic/CeO2 hybrid latex synthesized by a two step seeded semibatch emulsion polymerization process was investigated. The seed was produced by batch miniemulsion polymerization and in the second step a neat monomer preemulsion was fed to the seed to increase the solids content and encapsulate the inorganic material. The morphology of the hybrid miniemulsion droplets, the seed and the final latex was analyzed by TEM. The morphologies achieved could be explained by theoretical equilibrium morphology maps based on the interfacial tensions of the monomeric, polymeric, inorganic and aqueous phases. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2015, 53, 792–799

Mastering the relationship between the final mechanical properties of carbon black reinforced rubber blends and their composition is a key advantage for an efficient design of the composition of the blend. In this work, some models to predict three relevant physical attributes of rubber blends — modulus at 100% deformation, Shore A hardness, and tensile strength — are built by machine learning methods and subsequently evaluated. Linear regression, artificial neural networks, support vector machine, and regression trees are used to generate the models. The number of used samples and the values for the input variables is determined by a Taguchi design of experiments, and prior to the modeling the uncertainty of the experimental data was analyzed.

An original method is presented to classify nano-particle aggregates into one of the morphological classes that were previously proposed in the literature. Carbon black was selected as a study case in this work, since it is a nanoreinforcement used massively in many industries, but the proposed method can be applied to other particulate aggregates as well. This methodology consists of three steps: (i) transmission electron microscopy image processing to compute a set of twenty-one morphological characteristics for each aggregate including the new attributes proposed herein, (ii) a multivariate analysis of the dataset to reduce the problem dimensionality and (iii) creation and assessment of decision trees based on evolutionary algorithms to classify the aggregates. The effectiveness of the method was proven for a balanced-per-class sample of forty-eight selected aggregates. The original dimension of the problem is reduced to three principal components, which explain 90% of the total variance. The best model classifies an aggregate into one of the four morphological classes in a simple comparison process. The accuracy of the applied model to classify new aggregates was 75%.

Poly(lactic acid) (PLA) is one of the most promising bio-based and biodegradable polymers. However, its processability and range of applications are limited by its low thermal stability. The novelty of this work is studying the thermal stability of two PLA/clay nanocomposites (PLA/Cloisite 20A and PLA/Cloisite 30B) as a function of the composition and the microstructure, which is exhaustively studied by performing quantitative electron tomography (ET). Quantitative ET involves image acquisition, reconstruction of the volume and a subsequent segmentation of the objects under study. In this work, an objective segmentation is applied to compare the two PLA/clay microstructures. Then, relevant descriptors of the 3D microstructure (dimensions of the reconstructed objects, their specific surface area and orientation) are measured and used to quantitatively explain the effect of the clay particles on the thermal stability of the nanocomposites. The tortuous path has been calculated—based on the descriptors of the microsturecture—and proved to be larger for PLA/Cloisite 30B nanocomposite compared to that of PLA/Cloisite 20A. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 134, 44691.

In the present work, the influence of multiwalled carbon nanotubes (MWCNTs) on the flame retardancy and rheological, thermal and mechanical properties of polybutilen terephthalate (PBT) and polypropylene (PP) matrixes has been investigated. The carbon nanotube content in the thermoplastic materials was 2 and 5 wt-%. The nanocomposites were obtained by diluting a masterbatch containing 20 wt-% nanotubes using a twin-screw extruder and the thermal properties were analysed by differential scanning calorimetry and thermogravimetric analysis; thermomechanical properties were determined by dynamic mechanical thermal analysis and the rheological behaviour was studied by a Thermo Haake Microcompounder. The results concerning the flame retardancy show that the MWCNTs are not equally effective as flame retardants in PP and PBT. The ignition time is increased only for PBT whereas the extinguishing time is decreased for PP and PBT. The reinforcement of the thermoplastics with multiwall carbon nanotubes is improved regarding the mechanical and thermal properties of the nanocomposites compared to pristine materials and the behaviour of thermoplastic nanocomposites regarding fire retardancy depends on the nature of the polymeric matrix.

The effects of deep cryogenic treatment (DCT) on the wear behavior of different tool steels have been widely reported in the scientific literature with uneven results. Some tool steels show a significant improvement in their wear resistance when they have been cryogenically treated while others exhibit no relevant amelioration or even a reduction in their wear resistance. In this study, the influence of DCT was investigated for a grade that has been barely studied in the scientific literature, the AISI A8 air-hardening medium-alloy cold work tool steel. Several aspects were analyzed in the present work: the wear resistance of the alloy, the internal residual stress, and finally the secondary carbide precipitation in terms of lengths and occupied area and its distribution into the microstructure. The results revealed a reduction in the wear rate of about 14% when the AISI A8 was cryogenically treated before tempering. The number of carbides that precipitated into the microstructure was 6% higher for the cryogenically treated samples, increasing from 0.68% to 0.73% of the total area they covered. Furthermore, the distribution of the carbides into the microstructure was more homogenous for the cryogenically treated samples. This research was funded by the Department of Industry, Innovation, Trade, and Tourism of the Basque Country Government through the S-PE12UN080 SAIOTEK project.

When talking about trybology, the election of the laboratory experiment type is a common problem of discussion. Laboratory wear methods are not designed to exactly reproduce the real working conditions of the analyzed part itself but serve to engineers and researcher to extrapolate the laboratory results to the real application. In order to shed some light on this issue, two wear tests have been analyzed following an ASTM standard and using the same experimental parameters and testing pair-materials in order to be able to make a comparison: Pin-on-Disk (PoD) against Ball-on-Disk (BoD). Three different tool steel have been analyzed in this study, AISI D2, AISI A8 and AISI H13, used to produce metal cutting shears. Metal on metal dry sliding tests were designed in order to reproduce the tool working conditions. These three materials were cryogenically treated and compared against no cryogenically treated ones to measure the improvement on their wear resistance due to cryogenic treatment. Finally, the wear rates obtained with both laboratory tests were compared against some real production metal cutting tools wear data. Results revealed an improvement of the wear resistance for cryo-treated samples of around 20% with the BoD test and around 6% with the PoD test. Real production tools wear was calculated for one of the tool steels and for two different applications. The improvement was approximately the one revealed by the BoD test. So, for the studied case, the BoD laboratory test gives more realistic prediction of real tool life improvement due to the cryogenic treatment.

The microstructure of laminar bio-nanocomposites (Poly (lactic acid)(PLA)/clay) depends on the amount of clay platelet opening after integration with the polymer matrix and determines the final properties of the material. Transmission electron microscopy (TEM) technique is the only one that can provide a direct observation of the layer dispersion and the degree of exfoliation. However, the orientation of the clay platelets, which affects the final properties, is practically immeasurable from a single 2D TEM image. This issue can be overcome using transmission electron tomography (ET), a technique that allows the complete 3D characterization of the structure, including the measurement of the orientation of clay platelets, their morphology and their 3D distribution. ET involves a 3D reconstruction of the study volume and a subsequent segmentation of the study object. Currently, accurate segmentation is performed manually, which is inefficient and tedious. The aim of this work is to propose an objective/auto...

Poly(lactic acid) (PLA) is one the most promising bio-based and biodegradable polymer. However, its low thermal stability limits the range of applications and complicates its transformation via the most industrial common processes. The novelty of this work is studying the thermal stability of PLA and PLA/clay nanocomposites during use, as a function of the composition and using a wide range of extrusion and injection moulding processing parameters. To improve the thermal stability of the PLA, laminar silicates containing different organomodifications have been added (Cloisite 20A and Cloisite 30B). The results show that the processing conditions and composition define the morphology of the bio-nanocomposites, which plays key role in defining final thermal properties of the material. In general, clays improve the thermal stability of the processed material, increasing the degradation temperature and decreasing the degradation rate under a wide range of processing conditions. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40747.

Covalently crosslinked semi-crystalline polyalkenamer-based shape memory polymers (SMPs) were prepared and characterized. Thermal and thermo-mechanical properties of thermo-sensitive polymers manufactured by melt compounding were investigated, and shape memory features demonstrated. For remote activation of shape recovery properties, electromagnetic inductive heating of a series of iron-based ferromagnetic microparticles was evaluated for subsequent incorporation into a shape memory polymeric matrix. The inductive heating capacity of micro-sized iron-filled polyalkenamers with different volume fraction contents was optimized and a comparison of thermo-mechanical properties of filled and unfilled shape memory polymeric networks was performed. Electromagnetically triggered shape memory properties of easily formed composites were documented and shape memory recovery rates comparable to those obtained by conventional heating methods were demonstrated for further research and design of new types of applications.