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This work reports on the development of collagen films with graphene oxide nanoparticles (GO NPs), aiming toward the development of a new generation of functional sustainable sensors. For this purpose, different GO NP contents up to 3 wt % were incorporated into a collagen matrix, and morphological, thermal, mechanical and electrical properties were evaluated. Independently of the GO NP content, all films display an increase in thermal stability as a result of the increase in the structural order of collagen, as revealed by XRD analysis. Further, the inclusion of GO NPs into collagen promotes an increase in the intensity of oxygen characteristic absorption bands in FTIR spectra, due to the abundant oxygen-containing functional groups, which lead to an increase in the hydrophilic character of the surface. GO NPs also influence the mechanical properties of the composites, increasing the tensile strength from 33.2 ± 2.4 MPa (collagen) to 44.1 ± 1.0 MPa (collagen with 3 wt % GO NPs). Finally, the electrical conductivity also increases slightly with GO NP content, allowing the development of resistive bending sensors.

This review focuses on the combination of metal-organic frameworks (MOFs) and ionic liquids (ILs) to obtain composite materials to be used as solid electrolytes in metal-ion battery applications. Benefiting from the controllable chemical composition, tunable pore structure and surface functionality, MOFs offer great opportunities for synthesizing high-performance electrolytes. Moreover, the encapsulation of ILs into porous materials can provide environmentally benign solid-state electrolytes for electrochemical devices. Due to the versatility of MOF-based materials, in this review we also explore their use as anodes and cathodes in Li- and Na-ion batteries. Finally, solid IL@MOF electrolytes and their implementation into Li and Na batteries have been analyzed, as well as the design and advanced manufacturing of solid IL@MOF electrolytes embedded on polymeric matrices.

Machine vision systems are widely used in assembly lines for providing sensing abilities to robots to allow them to handle dynamic environments. This paper presents a comparison of 3D sensors for evaluating which one is best suited for usage in a machine vision system for robotic fastening operations within an automotive assembly line. The perception system is necessary for taking into account the position uncertainty that arises from the vehicles being transported in an aerial conveyor. Three sensors with different working principles were compared, namely laser triangulation (SICK TriSpector1030), structured light with sequential stripe patterns (Photoneo PhoXi S) and structured light with infrared speckle pattern (Asus Xtion Pro Live). The accuracy of the sensors was measured by computing the root mean square error (RMSE) of the point cloud registrations between their scans and two types of reference point clouds, namely, CAD files and 3D sensor scans. Overall, the RMSE was lower when using sensor scans, with the SICK TriSpector1030 achieving the best results (0.25 mm ± 0.03 mm), the Photoneo PhoXi S having the intermediate performance (0.49 mm ± 0.14 mm) and the Asus Xtion Pro Live obtaining the higher RMSE (1.01 mm ± 0.11 mm). Considering the use case requirements, the final machine vision system relied on the SICK TriSpector1030 sensor and was integrated with a collaborative robot, which was successfully deployed in an vehicle assembly line, achieving 94% success in 53,400 screwing operations.

Bin picking is a challenging task involving many research domains within the perception and grasping fields, for which there are no perfect and reliable solutions available that are applicable to a wide range of unstructured and cluttered environments present in industrial factories and logistics centers. This paper contributes with research on the topic of object segmentation in cluttered scenarios, independent of previous object shape knowledge, for textured and textureless objects. In addition, it addresses the demand for extended datasets in deep learning tasks with realistic data. We propose a solution using a Mask R-CNN for 2D object segmentation, trained with real data acquired from a RGB-D sensor and synthetic data generated in Blender, combined with 3D point-cloud segmentation to extract a segmented point cloud belonging to a single object from the bin. Next, it is employed a re-configurable pipeline for 6-DoF object pose estimation, followed by a grasp planner to select a feasible grasp pose. The experimental results show that the object segmentation approach is efficient and accurate in cluttered scenarios with several occlusions. The neural network model was trained with both real and simulated data, enhancing the success rate from the previous classical segmentation, displaying an overall grasping success rate of 87.5%.

Mesenchymal stem cells (MSCs) osteogenic commitment before injection enhances bone regeneration therapy results. Piezoelectric stimulation may be an effective cue to promote MSCs pre-differentiation, and poly(vinylidene) fluoride (PVDF) cell culture supports, when combined with CoFe2O4 (CFO), offer a wireless in vitro stimulation strategy. Under an external magnetic field, CFO shift and magnetostriction deform the polymer matrix varying the polymer surface charge due to the piezoelectric effect. To test the effect of piezoelectric stimulation on MSCs, our approach is based on a gelatin hydrogel with embedded MSCs and PVDF-CFO electroactive microspheres. Microspheres were produced by electrospray technique, favouring CFO incorporation, crystallisation in β-phase (85%) and a crystallinity degree of around 55%. The absence of cytotoxicity of the 3D construct was confirmed 24 h after cell encapsulation. Cells were viable, evenly distributed in the hydrogel matrix and surrounded by microspheres, allowing local stimulation. Hydrogels were stimulated using a magnetic bioreactor, and no significant changes were observed in MSCs proliferation in the short or long term. Nevertheless, piezoelectric stimulation upregulated RUNX2 expression after 7 days, indicating the activation of the osteogenic differentiation pathway. These results open the door for optimising a stimulation protocol allowing the application of the magnetically activated 3D electroactive cell culture support for MSCs pre-differentiation before transplantation.

Circular economy paradigms will benefit materials with multifunctional properties allowing to be implemented in more than one application, after processed, or in one application after already being used in another. In this context, membranes based on TiO2/poly(vinylidene fluoride‐co‐trifluoroethylene) (PVDF‐TrFE) with different filler contents were prepared for battery and photocatalytic degradation of pollutants by a temperature‐induced phase separation process (TIPS). It has been shown that the morphological, thermal and mechanical properties, porosity, contact angle, the ionic conductivity and lithium transference number are dependent on the TiO2 content. The membranes with 10 wt% TiO2 content show an ionic conductivity of 0.59 mS cm−1, lithium transference number of 0.72, discharge capacity of 128 mAh g−1 and low capacity fade (17%) at 2C. Further, this membrane presents a high photocatalytic response, being able to be used for ciprofloxacin remediation from water after 300 min of exposure to UV. Further, a second life was confirmed for this membrane by being implemented as a lithium ion battery separator after its use for water remediation applications. This work explores a new concept for the efficient use of the same membranes in very different applications, demonstrating the multifunctionality of the material in the scope of the circular economy paradigm.

Summary: Lithium-ion batteries (LIBs) are currently the fastest growing segment of the global battery market, and the preferred electrochemical energy storage system for portable applications. Magnetism is one of the forces that can be applied improve performance, since the application of magnetic fields influences electrochemical reactions through variation of electrolyte properties, mass transportation, electrode kinetics, and deposits morphology. This review provides a description of the magnetic forces present in electrochemical reactions and focuses on how those forces may be taken advantage of to influence the LIBs components (electrolyte, electrodes, and active materials), improving battery performance. The different ways that magnetic forces can interact with LIBs components are discussed, as well as their influence on the electrochemical behavior. The suitable control of these forces and interactions can lead to higher performance LIBs structures and to the development of innovative concepts.

There is a growing demand for lithium ion batteries (LIBs) fabricated with environmentally-friendly materials to transition toward a more sustainable society based on a circular economy. Battery separator, typically a porous petroleum-polymer, plays a pivotal role as it serves to efficiently transfer ions between electrodes while preventing electrical short-circuits. To reduce our dependence on fossil resources, cellulose and its derivatives are being used as sustainable battery separators thanks to its easily controllable porosity, suitable mechanical and thermal properties, non-toxicity and inherent hydrophilicity. Here we first present the structure, physico-chemical properties and various types of cellulose derivatives, as well as the different manufacturing approaches to obtain porous cellulose membranes. Further, the most recent developments in the field of cellulose and its derivates for lithium ion battery separators and solid polymer electrolytes are discussed. Finally, the main issues and properties to be improved in the near future concerning cellulosic separators are shown.

Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.

Environmental issues related to energy consumption are mainly associated with the strong dependence on fossil fuels. To solve these issues, renewable energy sources systems have been developed as well as advanced energy storage systems. Batteries are the main storage system related to mobility, and they are applied in devices such as laptops, cell phones, and electric vehicles. Lithium-ion batteries (LIBs) are the most used battery system based on their high specific capacity, long cycle life, and no memory effects. This rapidly evolving field urges for a systematic comparative compilation of the most recent developments on battery technology in order to keep up with the growing number of materials, strategies, and battery performance data, allowing the design of future developments in the field. Thus, this review focuses on the different materials recently developed for the different battery components—anode, cathode, and separator/electrolyte—in order to further improve LIB systems. Moreover, solid polymer electrolytes (SPE) for LIBs are also highlighted. Together with the study of new advanced materials, materials modification by doping or synthesis, the combination of different materials, fillers addition, size manipulation, or the use of high ionic conductor materials are also presented as effective methods to enhance the electrochemical properties of LIBs. Finally, it is also shown that the development of advanced materials is not only focused on improving efficiency but also on the application of more environmentally friendly materials.

Manufacturing and production industries are increasingly turning to robots to carry out repetitive picking operations in an efficient manner. This paper focuses on tackling the novel challenge of automating the bin picking process for entangled objects, for which there is very little research. The chosen case study are sets of freely curved tubes, which are prone to occlusions and entanglement. The proposed algorithm builds a representation of the tubes as an ordered list of cylinders and joints using a point cloud acquired by a 3D scanner. This representation enables the detection of occlusions in the tubes. The solution also performs grasp planning and motion planning, by evaluating post-grasp trajectories via simulation using Gazebo and the ODE physics engine. A force/torque sensor is used to determine how many items were picked by a robot gripper and in which direction it should rotate to solve cases of entanglement. Real-life experiments with sets of PVC tubes and rubber radiator hoses showed that the robot was able to pick a single tube on the first try with success rates of 99% and 93%, respectively. This study indicates that using simulation for motion planning is a promising solution to deal with entangled objects.

Cellulose nanocrystals (CNCs) were incorporated into poly (vinylidene fluoride) (PVDF) to tailor the mechanical and dielectric properties of this electroactive polymer. PVDF/CNC nanocomposites with concentrations up to 15 wt.% were prepared by solvent-casting followed by quick vacuum drying in order to ensure the formation of the electroactive γ-phase. The changes induced by the presence of CNCs on the morphology of PVDF and its crystalline structure, thermal properties, mechanical performance and dielectric behavior are explored. The results suggest a relevant role of the CNC surface −OH groups, which interact with PVDF fluorine atoms. The real dielectric constant ε’ of nanocomposites at 200 Hz was found to increase by 3.6 times up to 47 for the 15 wt.% CNC nanocomposite due to an enhanced ionic conductivity provided by CNCs. The approach reported here in order to boost the formation of the γ-phase of PVDF upon the incorporation of CNCs serves to further develop cellulose-based multifunctional materials.

The separator membrane is an essential component of lithium-ion batteries, separating the anode and cathode, and controlling the number and mobility of the lithium ions. Among the polymer matrices most commonly investigated for battery separators are poly(vinylidene fluoride) (PVDF) and its copolymers poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE), poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP), and poly(vinylidene fluoride-cochlorotrifluoroethylene) (PVDF-CTFE), due to their excellent properties such as high polarity and the possibility of controlling the porosity of the materials through binary and ternary polymer/solvent systems, among others. This review presents the recent advances on battery separators based on PVDF and its copolymers for lithium-ion batteries. It is divided into the following sections: single polymer and co-polymers, surface modification, composites, and polymer blends. Further, a critical comparison between those membranes and other separator membranes is presented, as well as the future trends on this area.

Chronic venous disease (CVeD) is a prevalent condition with a significant socioeconomic burden, yet the pathophysiology is only just beginning to be understood. Previous studies concerning the dysregulation of matrix metalloproteinases (MMPs) and their inhibitors (tissue inhibitors of metalloproteinases (TIMPs)) within the varicose vein wall are inconsistent and disregard clinical progression. Moreover, it is highly plausible that MMP and TIMP expression/activity is affected by transforming growth factor (TGF)-β1 and its signaling receptors (TGFβRs) expression/activity in the vein wall. A case–control study was undertaken to analyze genetic and immunohistochemical differences between healthy (n = 13) and CVeD (early stages: n = 19; advanced stages: n = 12) great saphenous vein samples. Samples were grouped based on anatomic harvest site and subjected to quantitative polymerase chain reaction for MMP1, MMP2, MMP8, MMP9, MMP12, MMP13, TIMP1, TIMP2, TIMP3, TIMP4, TGFβR1, TGFβR2, and TGFβR3 gene expression analysis, and then to immunohistochemistry for immunolocalization of MMP2, TIMP2, and TGFβR2. Decreased gene expression of MMP12, TIMP2, TIMP3, TIMP4, and TGFβR2 was found in varicose veins when compared to controls. Regarding CVeD clinical progression, two facts arose: results across anatomical regions were uneven; decreased gene expression of MMP9 and TGFβR3 and increased gene expression of MMP2 and TIMP3 were found in advanced clinical stages. Most immunohistochemistry results for tunica intima were coherent with qPCR results. In conclusion, decreased expression of TGFβRs might suggest a reduction in TGF-β1 participation in the MMP/TIMP imbalance throughout CVeD progression. Further studies about molecular events in the varicose vein wall are required and should take into consideration the venous anatomical region and CVeD clinical progression.

Chronic venous insufficiency and varicose veins occur commonly in affluent countries and are a socioeconomic burden. However, there remains a relative lack of knowledge about venous pathophysiology. Various theories have been suggested, yet the molecular sequence of events is poorly understood. Transforming growth factor-beta one (TGF-β1) is a highly complex polypeptide with multifunctional properties that has an active role during embryonic development, in adult organ physiology and in the pathophysiology of major diseases, including cancer and various autoimmune, fibrotic and cardiovascular diseases. Therefore, an emphasis on understanding its signaling pathways (and possible disruptions) will be an essential requirement for a better comprehension and management of specific diseases. This review aims at shedding more light on venous pathophysiology by describing the TGF-β1 structure, function, activation and signaling, and providing an overview of how this growth factor and disturbances in its signaling pathway may contribute to specific pathological processes concerning the vessel wall which, in turn, may have a role in chronic venous insufficiency.