Composite actuators consisting of magnetic nanoparticles dispersed in a crystallizable multiphase polymer system can be remotely controlled by alternating magnetic fields (AMF). These actuators contain spatially segregated crystalline domains with chemically different compositions. Here, the crystalline domain associated to low melting transition range is responsible for actuation while the crystalline domain associated to the higher melting transition range determines the geometry of the shape change. This paper reports magneto-mechanical actuators which are based on a single crystalline domain of oligo(ω-pentadecalactone) (OPDL) along with covalently integrated iron(III) oxide nanoparticles (ioNPs). Different geometrical modes of actuation such as a reversible change in length or twisting were implemented by a magneto-mechanical programming procedure. For an individual actuation mode, the degree of actuation could be tailored by variation of the magnetic field strengths. This material design can be easily extended to other composites containing other magnetic nanoparticles, e.g. with a high magnetic susceptibility.

Today, processing technologies have generated negative environmental impact as emission of toxic gases and degradation of the earth when certain products are placement in landfills leading to environmental pollution and several health risks, which damage societies to sustain the planet for future generations. As electronics waste, grain-oriented iron foils, graphite films and Mn-Zn ferrites have been identified as interesting candidates. Uncommon physical properties from such materials are available when these are converting technologically. A strategy such as Life-Cycle Assessment is employed here to taking into account all stages of the life cycle of electronics waste, including processing technology, manufacturing processes, use phase, and end-of-life routes to quantify the recycling performance as a function of the physical parameters that will characterize operability of a functional device. Hence, structure and conduction properties in waste materials are exploring by using Raman spectroscopy and electrical characterization techniques. Researching waste materials will provide theoretical basis for open-loop recycling, where trends related to green engineering must be attained with recyclable materials for practical adaptive structures using different performance principles compared to those used in silicon devices.

In the present world scenario the demand for fresh water and clean energy is driving the need to convert a microbial fuel cell (MFC) into an algal-based microbial desalination cell (MDC) that can support algal growth along with desalination of saline water. In this study, the performance of a five-chambered MDC fed with saline water having two different salt concentrations, namely 2.5 g/L and 5.0 g/L in desalination chamber, as well as MDC operated without algae in catholyte was investigated. The algal-based MDC operated with 5 g/L of total dissolved solid (TDS) in desalination chamber exhibited the best performance results among all other combinations giving a maximum power density of 45.52 mW/m2 and a desalination efficiency of 71 ± 2 %. Also, a chemical oxygen demand (COD) removal efficiency of 78 % and coulombic efficiency of 12.24 % was achieved with 5 g/L NaCl concentration in desalination chamber. Based on this experimental performance evaluation, it can be inferred that algal-based MDC can provide a promising and sustainable approach for wastewater treatment with the capability of simultaneous desalination, algal production and electrical energy recovery.

This research evidences the impact of Materials Science and Engineering Clubs as an outreach effort to expand the education and training required for a competitive Nanotechnology workforce beyond traditional STEM areas. An engineering perception questionnaire was implemented as a pre-test/post-test to track student perceptions and goals throughout the academic year to identify trends amongst gender and school level groups. Findings (107 students) show a perceived increase in student knowledge and interest for different fields of study, based on pre/post-test responses, with differences amongst gender and school level groups (middle school and high school). Also, significant differences in students’ aspirations for higher education degree were found among school level and gender. Results show that over 20% of participants increased their aspirations to higher education degrees and their interests in pursuing STEM degrees at end of the academic year. Specific findings on engineering perceptions and perceived level of knowledge and interest in science, engineering, materials, and nanotechnology as a result of club participation and student’s educational aspirations, expectations and future study plans are discussed along with implications for future STEM education.

To shorten the time for designing and developing new materials, fast evaluation of microstructure-property relation is required. In this research, we applied machine learning to perform crystal phase segmentation and extracted microstructure features of Cr-based duplex alloys from the scanning electron microscope (SEM) images. The results show that the accuracy of crystal phase segmentation was improved when the backscattered electron (BSE) images with controlled channeling contrast was used. This indicates that measurement conditions of the BSE images are important for obtaining high segmentation accuracy. The segmented images were used for the extraction of microstructure features. With the extracted features, the reliable prediction of mechanical properties of Cr-based duplex alloys was verified with a prediction error less than 10%. We demonstrated a method of using microstructure features extracted from SEM images for fast evaluation of microstructure-property relation.

Dynamic fracture of a two-dimensional MoWSe2 membrane is studied with molecular dynamics (MD) simulation. The system consists of a random distribution of WSe2 patches in a pre-cracked matrix of MoSe2. Under strain, the system shows toughening due to crack branching, crack closure and strain-induced structural phase transformation from 2H to 1T crystal structures. Different structures generated during MD simulation are analyzed using a three-layer, feed-forward neural network (NN) model. A training data set of 36,000 atoms is created where each atom is represented by a 50-dimension feature vector consisting of radial and angular symmetry functions. Hyper parameters of the symmetry functions and network architecture are tuned to minimize model complexity with high predictive power using feature learning, which shows an increase in model accuracy from 67% to 95%. The NN model classifies each atom in one of the six phases which are either as transition metal or chalcogen atoms in 2H phase, 1T phase and defects. Further t-SNE analyses of learned representation of these phases in the hidden layers of the NN model show that separation of all phases become clearer in the third layer than in layers 1 and 2.

The study of the relationship among the manufacturing process, the structure and the property of materials can help to develop the new materials. The material images contain the microstructures of materials, therefore, the quantitative analysis for the material images is the important means to study the characteristics of material structures. Generally, the quantitative analysis for the material microstructures is based on the exact segmentation of the materials images. However, most material microstructures are shown with various shapes and complex textures in images, and they seriously hinder the exact segmentation of the component elements. In this research, machine learning method and complex networks method are adopted to the challenge of automatic material image segmentation. Two segmentation tasks are completed: on the one hand, the images of the titanium alloy are segmented based on the pixel-level classification through feature extraction and machine learning algorithm; on the other hand, the ceramic images are segmented with the complex networks theory. In the first task, texture and shape features near each pixel in titanium alloy image are calculated, such as Gabor filters, Hu moments and GLCM (Gray-Level Co-occurrence Matrix) etc.. The feature vector for the pixel can be obtained by arraying these features. Then, classification is performed with the random forest model. Once each pixel is classified, the image segmentation is completed. In the second task, a complex network structure is built for the ceramic image. Then, a clustering algorithm of complex network is used to obtain network connection area. Finally, the clustered network structure is mapped back to the image and getting the contours among the component elements. The experimental results demonstrate that these methods can accurately segment material images.

Design processes of functional polymers were accelerated by adopting the Bayesian optimization; the number of trials in the process was substantially reduced. The optimization process was more than forty time accelerated to find out the target polymer compared to the random selection. The optimization efficiency was found to be successfully improved by utilizing the standard deviation of predicted probability distribution of objective function. The performance of the method was robust for dataset size in the analysis; the target polymer could be found even for a small training dataset. The proposed method is a promising tool for the high-performance polymer design, and a wide range of its applications will be expected in the polymer industry.

Tactile sense provides us with the necessary information and feedback to determined tasks. Within this context, haptic devices represent a growing and highly interesting field to be included in biomedical devices, teleoperation applications, and video games. These devices are usually developed with rigid materials, motors, and mechanisms to provide tactile feedback to individuals that corresponds to a defined task, producing pressure, tangential force or vibrations as stimuli on the skin. Here, we present a prototype of a soft pneumatic haptic device based on an inflatable hyperelastic membrane, that can provide two stimuli over skin such as pression and traction with only one input of energy. The device was fabricated using different types of silicone materials and membrane shapes. This exhibits experimentally a maximum vertical deformation of 13 mm and a tangential displacement of 10 mm at 7 kPa. These two mechanically programmed movements open the possibility of using this technology in mechano-tactile feedback for wearable devices, with low-cost hardware, soft interaction between devices and skin, and lightweight.