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The Nordtest method evaluates the moisture buffer value (MBV) of materials that are being exposed to indoor air. The test simulates daily variations (high RH of 75% for 8 hours, low RH of 33% for 16 hours) in MBV, which in this context refers to the moisture uptake/release in the material when it is exposed to relative humidity (RH) in a square wave. We are looking for ways to increase clay plasterâs ability to buffer moisture. Which material has simultaneously a higher ability to buffer moisture and can be added to the clay plaster mixture? The recipe for the specimen included the following: waste paper (newspaper paper), glue (methylcellulose), clay and water. Our research showed that adding paper plaster mixture to the clay plaster mixture increases the moisture buffering ability of the plaster. The bigger the amount of added paper plaster, the better the plasterâs ability to buffer moisture.

Summary: Paper substrate has many advantages, such as low cost, bendable, foldable, printable, and environmentally friendly recycling. Nowadays, paper has been further extended as a flexible platform to deliver electronic information with the integration of organic optoelectronic devices, such as organic thin-film transistor, organic solar cell, organic electrochromic device, and organic light-emitting device. It has great potential to become the new generation of flexible substrate. Given rough surface and porous of paper, many efforts have been underway in recent years to enable the compatibility between optoelectronics and paper substrate. In this review, we present the development history of paper and its physicochemical properties, and summarize the current development of paper-based organic optoelectronic devices. We also discuss the challenges that need to be addressed before practical uses of paper-based organic optoelectronic devices.

The generation of three-dimensional superb packages of compact hexagonal periodic assembly with multifaceted layers was demonstrated using Poly (styrene-butylcrylate-acrylic acid) on glass substrates. The synthesized P(St-BA-AA) microspheres were used to formulate a fast drying UV curable terpolymer microspheres printing ink for the generation of colloidal crystal microdots on ink-jet paper with remarkable colours. The terpolymer microspheres and their formulated terpolymer ink undergo self-assembly to form blue monochromatic and viewing angle dependent tunable colours, thus affirming the photonic nature of the generated colloidal crystal films. Unlike other printing techniques which usually make use of specialized tools, this study generated a well ordered coloured tunable assembly of spherical shaped core-shell colloidal crystal microdots on the surface of an inkjet-paper by manually writing the as-synthesized P(St-BA-AA) UV curable printing ink microdots on it. The TEM analysis showed core and shell sizes of 198/50nm and 176/30nm for P(St-BA-AA)1 and P(St- BA-AA)2 respectively. Whenever the prepared terpolymer microspheres are used within the heating and transition temperatures of 383 °C and 110 °C, their thermal stability is retained. This simple technique of generating crystals microdots on inkjet paper may find use in optical devices, security applications and other colour coating applications.

This study evaluates the effect of treated and untreated jute fiber/eggshell particulate reinforced cement-paper matrix composites for ceiling board application. Treated jute fiber (TJF) was obtained by immersing untreated jute fiber (UJF) into 1.25 M sodium hydroxide (NaOH) solution in a shaker water bath maintained at 40 °C for 4 h. Eggshells (ESP) were pulverized and sieved to -75μm. Samples were prepared by varying the fiber volume fraction from 0.5 to 2.5 wt.% in the composites. While other constituents such as the binder (cement) and eggshell were kept constant. An hydraulic press cold compaction molder was utilized in the production of the hybrid composites in a predetermined mix ratio designed based on previous research. The samples produced were cured for 7 and 14 days, then sundried for 36 h. The physical, thermal, mechanical and wear behaviour of the produced composites were evaluated while the surface morphology of the fractured splitting tensile samples were analyzed. The result reveals that TJF/ESP hybrid composites had better performance than UJF/ESP hybrid composites in most of the tests carried out. Increase in the number of curing days was found to also enhance the properties of the composite produced in majority of the test evaluated. The 0.5 wt.% UJF/ESP gave the least performance of all the composites developed. While 2.5 wt. % TJF/ESP showed an optimum properties among the composites tested. When compared with standard, it is concluded that the hybrid composites developed can be suitable for ceiling boards and also find possible application in wall partitioning.

The crumpling of precursor materials to form dense three dimensional geometries offers an attractive route towards the utilisation of minor-value waste materials. Crumple-forming results in a mesostructured system in which mechanical properties of the material are governed by complex cross-scale deformation mechanisms. Here we investigate the physical and mechanical properties of dense compacted structures fabricated by the confined uniaxial compression of a cellulose tissue to yield crumpled mesostructuring. A total of 25 specimens of various densities were tested under compression. Crumple formed specimens exhibited densities in the range 0.8–1.3 g cm−3, and showed high strength to weight characteristics, achieving ultimate compressive strength values of up to 200 MPa under both quasi-static and high strain rate loading conditions and deformation energy that compares well to engineering materials of similar density. The materials fabricated in this work and their mechanical attributes demonstrate the potential of crumple-forming approaches in the fabrication of novel energy-absorbing materials from low-cost precursors such as recycled paper. Stiffness and toughness of the materials exhibit density dependence suggesting this forming technique further allows controllable impact energy dissipation rates in dynamic applications.

Thermomechanical pulp (TMP) fibers, generally used to produce newspapers and carton materials, have poor inter- and intra-fiber bonding contributing to low strength properties. Poly(vinyl alcohol) (PVOH) was applied as a co-additive of 1,2,3,4-butanetetracarboxylic acid (BTCA), in the presence of sodium hypophosphite (SHP) as a catalyst, to esterify paper sheets based on TMP fibers. Fourier transform infrared (FTIR) spectroscopy technique was used to confirm the formation of the ester bond. The effects of curing temperature, molar mass and mass amount of the PVOH on the tensile index were investigated. The increasing of the curing temperature improved further the wet tensile index. The presence of PVOH increased both the dry and the wet tensile index of the paper sheets. The rise of both the molar mass and the mass amount of PVOH improved the tensile index.

Herein, short review paper investigated the rapidly progress in fluorescent biosensors field and their significant influences on analytical chemistry applications. An electrical signal can be produced from a biological reaction using an integrated receptor-transducer device called a biosensor. Because there are so many uses for biosensors in the medical field, including medication delivery, environmental monitoring, water and food quality monitoring, and illness detection, biosensor design and development have become a top priority for scientists and researchers in the last ten years. In the beginning, we started to explain the basic principles of fluorescence. Then, we moved to discuss about the current advancements, creative sensor designs, and the fusion of materials science and nanotechnology. The paper highlighted the sensitivity of fluorescent biosensors by addressing a wide range of applications, including biological research, environmental monitoring, and medical diagnostics. This review paper censoriously assessed the present difficulties, such as interference and constrained dynamic range, and provided explanations into ongoing research. Advanced material and nanotechnology integration is emerging as a force that improves biosensor performance and broadens their uses. This study also discussed the future possible breakthroughs in biosensor technology and its broad use in a variety of scientific and societal fields.

Representative large models and their related applications, such as Bidirectional encoder representations from transformers (BERT), Generative pretrained transformer (GPT), Segment anything model (SAM), ChatGPT, DALL-E, Wenxin, and Pangu, have made astounding strides and exerted considerable influence across various fields domestically and abroad. They constantly attract the attention and follow-up of diverse societal sectors, including enterprises, universities, and research institutions. Large model applications have been successfully applied in scenarios such as biology, medicine, law, and social governance. Designing, modifying, and constructing domain-specific large models are crucial for truly harnessing their application value. Therefore, this paper provides inspiration for the application of large models in materials science. First, it provides an overview of large models, introducing their basic concepts, developmental process, technical classification, and features. Second, from the perspectives of the general domain and specific large models, this paper summarizes the applications of large models and analyzes the application scenarios and functions of various types of large models. Subsequently, considering the specific needs and current state of research in the field of materials science, this paper reviews the application of large language models, large visual models, and large multimodal models. It integrates engineering strategies and zero-shot knowledge transfer learning from specific tasks in natural language processing and computer vision and referencing typical application cases, clarifying current research paradigms and limiting factors for applying large models to materials science. To verify the effectiveness and potential of the visual large model, basal experiments of image segmentation and key structure extraction are performed on the microscopic image data of four types of materials using improved SAM, including Ni-superalloy, superalloy, polycrystalline pure iron grain, and Inconel 939. The experimental results reveal that the zero-shot segmentation capability of SAM has enormous potential for accurate and efficient representation of material microstructures. With the help of tailored prompt engineering, precise masks of the precipitated phase, grain boundaries, and cracks can be outputted without any label. Finally, this paper proposes future research opportunities for technologies and methods related to large models in materials science. This paper assesses the feasibility and technical challenges for the development and tuning of unimodal to comprehensive multimodal large models. With continuous innovations and collaborations, the horizon for large models in materials science seems boundlessly promising. The integration of these models can produce a new era of advanced research, leading to advancements that were previously considered unattainable. The symbiosis between materials science and large models can pave the way for unforeseen discoveries, enriching our scientific prowess.

There is an increasing need for compact low-cost NMR apparatus that can be used on the laboratory bench and in the field. There are four main usage variants of usage: (a) time-domain apparatus, particularly for physical measurements; (b) frequency-domain apparatus, particularly for chemical analysis, (c) NMR Cryoporometry apparatus for measuring pore-size distributions; and (d) MRI apparatus for imaging. For all of these, variable temperature capability may be vital. We have developed compact low-cost apparatus targeted at these applications. We discuss a hand-held NMR Spectrometer, and three different holdable NMR magnets, with sufficiently large internal bores for the Lab-Tools compact Peltier thermo-electric cooled variable-temperature probes. Currently, the NMR Spectrometer is very suitable for (a) NMR time-domain relaxation and (c) NMR Cryoporometry. With a suitable high-homogeneity magnet, it is also appropriate for simple use (b), spectral analysis, or, with a suitable gradient set, (d) MRI. Together, the NMR Spectrometer, one of the NMR variable-temperature probes, and any of these NMR magnets make excellent NMR Cryoporometers, as demonstrated by this paper and previously published research. Equally, they make versatile general-purpose variable-temperature NMR systems for materials science.

Abstract Materials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these papers to find material-related data is a daunting task. In information processing, named entity recognition (NER) plays a crucial role as it can automatically extract entities in the field of materials science, which have significant value in tasks such as building knowledge graphs. The typically used sequence labeling methods for traditional named entity recognition in material science (MatNER) tasks often fail to fully utilize the semantic information in the dataset and cannot effectively extract nested entities. Herein, we proposed to convert the sequence labeling task into a machine reading comprehension (MRC) task. MRC method effectively can solve the challenge of extracting multiple overlapping entities by transforming it into the form of answering multiple independent questions. Moreover, the MRC framework allows for a more comprehensive understanding of the contextual information and semantic relationships within materials science literature, by integrating prior knowledge from queries. State-of-the-art (SOTA) performance was achieved on the Matscholar, BC4CHEMD, NLMChem, SOFC, and SOFC-Slot datasets, with F1-scores of 89.64%, 94.30%, 85.89%, 85.95%, and 71.73%, respectively in MRC approach. By effectively utilizing semantic information and extracting nested entities, this approach holds great significance for knowledge extraction and data analysis in the field of materials science, and thus accelerating the development of material science. Scientific contribution We have developed an innovative NER method that enhances the efficiency and accuracy of automatic entity extraction in the field of materials science by transforming the sequence labeling task into a MRC task, this approach provides robust support for constructing knowledge graphs and other data analysis tasks.

A two-dimensional crystalline allotrope of boron, Borophene, has attracted much interest lately because of its unique electrical characteristics and possible uses in electronic devices. This thorough analysis examines the opportunities and difficulties related to the bandgap creation in boron, which is essential for its incorporation into semiconductor technologies. An introduction to the structural features of Borophene is given at the outset of the text, emphasizing its fascinating hexagonal lattice and tunable electronic properties. The review thoroughly explores the range of techniques used in synthesizing Borophene, covering both theoretical and experimental methods. It assesses how growth conditions, post-synthesis treatments, and substrate interactions affect the establishment of the bandgap in Borophene. The study also looks at how strain engineering, flaws, and impurities affect the bandgap, highlighting the necessity of exact control over these elements to get desirable electrical properties. We go into great length about the difficulties in Borophene bandgap engineering, including stability, scalability, and repeatability problems. The study critically evaluates the current body of knowledge, pointing out knowledge gaps and suggesting possible directions for further research. In addition, the paper discusses how external elements like humidity and temperature affect the stability of Borophene electrical characteristics, which complicates practical application. To sum up, this thorough analysis offers insightful information about the development of the borophene bandgap formation and a road map for scientists and engineers hoping to utilize borophene to its maximum potential in the future generation of electronic devices. The difficulties in synthesis and the complex interaction of several factors influencing bandgap creation highlight the necessity of ongoing multidisciplinary work to realize the technological potential of borophene.

Summary: Inspired by advancements in natural language processing, we utilize self-supervised learning and an equivariant graph neural network to develop a unified platform for training generative models capable of generating inorganic crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To mitigate the challenge of evaluating the reliability of generated structures during training, we employ a generative adversarial network (GAN) with its discriminator being a cost-effective reliability evaluator, significantly enhancing model performance. We demonstrate the utility of our model in optimizing crystal structures under predefined conditions. Without external properties acquired experimentally or numerically, our model further displays its capability to help understand inorganic crystal formation by grouping chemically similar elements. This paper extends an invitation to further explore the scientific understanding of material structures through generative models, offering a fresh perspective on the scope and efficacy of machine learning in material science.

The aim of this work is to explore the possibility of using a magnetic ionic liquid as both a lubricant and sealant in a vacuum pump. The current lubricant in use is due to become obsolete due to incoming legislation on fluorinated compounds. The long-term goal is to use a magnetic field to hold the novel lubricant in place to create a better seal and thus reduce pressure more effectively whilst also lubricating the necessary mechanical parts. This paper explores physical properties (contact angle, viscosity, coefficient of friction (COF) and magnetism) and chemical properties (thermal stability and chemical stability as deduced by X-ray photoelectron spectroscopy (XPS)) of three ionic liquids based on imidazolium cations with varying length of alkyl side chain and a tetrachloroferrate anion along with two control lubricants which are used currently in the field and based upon perfluoropolyether (PFPE). The work shows that the three magnetic ionic liquids possess a lower COF value than the two control lubricants when measured using a Bruker UMT TriboLab suggesting they may perform better in the real life vacuum pump. Upon analysis of the three ionic liquid samples after tribological testing using XPS, it was shown that the ionic liquid samples displayed minor differences in spectra suggesting good stability with no obvious decomposition or degradation of the sample.

Artificial Intelligence (AI) has revolutionized technological development globally, delivering relatively more accurate and reliable solutions to critical challenges across various research domains. This impact is particularly notable within the field of materials science and engineering, where artificial intelligence has catalyzed the discovery of new materials, enhanced design simulations, influenced process controls, and facilitated operational analysis and predictions of material properties and behaviors. Consequently, these advancements have streamlined the synthesis, simulation, and processing procedures, leading to material optimization for diverse applications. A key area of interest within materials science is the development of hydrogen-based electrochemical systems, such as fuel cells and electrolyzers, as clean energy solutions, known for their promising high energy density and zero-emission operations. While artificial intelligence shows great potential in studying both fuel cells and electrolyzers, existing literature often separates them, with a clear gap in comprehensive studies on electrolyzers despite their similarities. This review aims to bridge that gap by providing an integrated overview of artificial intelligence's role in both technologies. This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way, establishing a foundational knowledge base for further discussion. Subsequently, it explores the role of artificial intelligence in materials science, highlighting the critical applications and drawing on examples from recent literature to build on the discussion. The paper then examines how artificial intelligence has propelled significant advancements in studying various types of fuel cells and electrolyzers, specifically emphasizing proton exchange membrane (PEM) based systems. It thoroughly explores the artificial intelligence tools and techniques for characterizing, manufacturing, testing, analyzing, and optimizing these systems. Additionally, the review critically evaluates the current research landscape, pinpointing progress and prevailing challenges. Through this thorough analysis, the review underscores the fundamental role of artificial intelligence in advancing the generation and utilization of clean energy, illustrating its transformative potential in this area of research.

This paper addresses the numerical characterization of microstructures by the concept of tortuosity. After a brief review of geometric tortuosities, some definitions are considered for a benchmarking analysis. The focus is on the M-tortuosity definition, which is revised by expliciting the link to percolation theory, among other things. This operator fits with the analysis of real samples of materials whatever their complexity. A contribution of this paper is a new formulation of the M-tortuosity, making it generic to many situations. Additionally, the comparison of the various tortuosimetric descriptors, state-of-the-art definitions and M-tortuosity, is proposed by considering several scenarios thanks to stochastic multi-scale models of complex materials. The relationships with porosity, morphological heterogeneity and structural anisotropy are investigated. The results highlight the similarities and differences between the descriptors while attesting that the M-tortuosity is equivalent to the state-of-the-art definitions, for a potential use in diffusion and conductivity analyses. Moreover, the M-tortuosity handles correctly situations where state-of the-art algorithms fail. The anisotropic case highlights some limitations of the state-of-the-art definitions behaving differently according to the given propagation direction. In the case of unknown propagation and irregular piece of materials, the M-tortuosity provides a unique tortuosity value representative of the whole microstructure while detecting the anisotropy. These operators are freely available within the plug im! platform.

Summary: Nitrogen doped carbon materials have been studied as catalyst support for ammonia decomposition. There are 4 different types of nitrogen environments (graphitic, pyrrolic, pyridinic and nitrogen oxide) on the amorphous support identified. In this paper, we report a 5%Ru on MgCO3 pre-treated nitrogen doped carbon catalyst with high content of edge nitrogen-containing sites which displays an ammonia conversion rate of over 90% at 500°C and WHSV = 30,000 mL gcat−1 h−1. It also gives an impressive hydrogen production rate of 31.3 mmol/(min gcat) with low apparent activation energy of 43 kJ mol−1. Fundamental studies indicate that the distinct average Ru-N4 coordination site on edge regions is responsible for such high catalytic activity. Ammonia is stepwise decomposed via a Ru-N(H)-N(H)-Ru intermediate. This associative mechanism circumvents the direct cleavage of energetic surface nitrogen from metal to form N2 hence lowering the activation barrier for the decomposition over this catalyst.

In this paper, we introduce our approach in using the web-based application Kadi4Mat (KadiWeb) as an electronic laboratory notebook (ELN) combined with an integrated instrument database to facilitate Findable - Accessible - Interoperable - Reusabe (FAIR) research data. Facing transmission electron microscopy (TEM), focused ion beam (FIB), atom probe tomography (APT), or scanning electron microscopy (SEM) tasks, including sample preparation challenges, we developed a strategy to document the complex processes in our user facility KNMFi. To create appropriate records in Kadi4Mat we are comprising one central record for the material/sample to be investigated, a record for the sample preparation, a record for the investigation/experiment, and a record for the data evaluation. Therefore, a set of appropriate templates for the categories ‘sample preparation general,’ ‘sample preparation for TEM,’ ‘Focused Ion Beam and Scanning Electron Microscopy,’ ‘Transmission Electron Microscopy,’ ‘Atom Probe Tomography,’ and ‘Data Evaluation’ was developed in ‘atomistic units.’ The templates can be combined easily and have been designed to be user-friendly, but at the same time requesting the relevant metadata in a structured and standardized way. The documentation process, including MaTeLiS-instrument database, is demonstrated in a use-case with several sample preparation steps and different investigation methods. The developed templates can be exported in JSON-format and might be used as models for other tasks.

Summary: Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in applied disciplines. This study introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different methods, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine-learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and material development. The bigger picture: Big data’s importance in materials science is clear, yet its effective use is challenging due to the sheer volume and complexity of the data. Natural language processing (NLP) offers a solution by transforming unstructured text into structured formats, facilitating tasks such as extraction and summarization. In materials science, this means converting information from scientific papers into structured datasets, a process often slowed by the continuous influx of new data. To circumvent the inefficiencies of multi-step NLP workflows, there is a growing need for streamlined, one-step NLP methods. Employing fine-tuned large language models could be key, allowing for the rapid updating of datasets and providing valuable training data for further model development. This approach not only expedites research but also accelerates material prediction, leading to faster scientific breakthroughs.

Summary: Battery health assessment and recuperation play crucial roles in the utilization of second-life Li-ion batteries. However, due to ambiguous aging mechanisms, it is challenging to estimate battery health and devise an effective strategy for cell rejuvenation. This paper presents aging and reconditioning experiments of 62 commercial lithium iron phosphate cells, which allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity. An average test error of 16.84% ± 1.87% (mean absolute percentage error) for cycle life prediction is achieved by gradient boosting regressor. Some of the recoverable lost capacity is found to be attributed to the non-uniformity in electrodes. An experimentally validated equivalent circuit model is built to demonstrate how such non-uniformity can be accumulated, and how it can give rise to recoverable capacity loss. Furthermore, Shapley additive explanations (SHAP) analysis also reveals that battery operation history significantly affects the capacity recovery.

Introduction. The development of aerated concrete with a high content of open pores, using polymineral binders with reduced cement content and significant utilization of local technogenic resources (in particular, enriched waste from thermal power plants) is relevant for the modern construction industry. To systematize modern methods for studying the characteristics of sound insulation and sound protection of buildings, it is necessary to take into account the diversity of international building codes. The aim of the work is to develop a scientifically based technological solution that provides effective aerated concrete based on fly ash from China with improved sound-absorbing characteristics.Main part. In this paper, a methodological basis, which provides for the comprehensive use of fundamental approaches in building materials science, was used. Integrated design to develop composite materials of a given quality at the nano-, micro- and macrolevels was used. The physical and mechanical properties of raw materials and the required characteristics of the developed materials using physicochemical methods of analysis, including laser granulometry, X-ray phase analysis, scanning electron microscopy, thermal methods, etc were studied. The experimental studies in the laboratories of V.G. Shukhov BSTU, as well as Chinese universities were carried out.Conclusions. In this work, scientific approaches for the design and synthesis of aerated concrete with improved acoustic characteristics in various regions of the country, taking into account local raw materials were formulated. Further research can be aimed at expanding the range of lightweight materials for building envelopes in order to ensure a safe human environment. This can be achieved by synthesizing multicomponent composite binders that affect the formation of nano-, micro- and macrostructures of materials.

Summary: Concern for the environment is one of the main factors that are increasing the demand for compact and energy-efficient electronic devices. Recent research has made advances in reducing the power consumption of field-effect transistors, including the use of high-dielectric insulators, low-voltage operation, and selective power-conservation strategies. This paper introduces a revolutionary air-friction-driven rotating gate transistor that operates without the need for a conventional gate voltage. This new device offers the advantages of wear resistance, a slim and flexible design (achieved through low-temperature solution processing), and a simplified three-layer structure that streamlines manufacturing and reduces potential carbon emissions. This device’s wear resistance and ease of fabrication render the device a promising technology with applications in various fields, including electronics, vehicles, aviation, and wearable devices. This study provides evidence of the device’s feasibility for use in real-world vehicular scenarios, underscoring its potential for future innovation and widespread adoption.

This paper reports the synthesis and physical properties of graphene oxide/neodymium oxide (GO/Nd2O3) composites. The GO/Nd2O3 composite was prepared by a simple Sonochemical Method, and graphene oxide was synthesized by a modified hummers method. The synthesized composites were characterized by using X-Ray Diffraction (XRD), Fourier Transform Infra-Red (FT-IR), Scanning Electron Microscopy-Energy Dispersive X-ray (SEM-EDX), Raman Spectroscopy, and UV–Vis Spectroscopy. The XRD data confirmed that the GO/Nd2O3 composite had a hexagonal phase structure and showed that the more Nd2O3 nanoparticles were added to GO, the more the crystallinity index increased. The FT-IR spectra showed six types of bonds of functional groups of the GO/Nd2O3 composite, namely the hydroxyl (-OH), carbonyl (C]O), (C]C), (C–O–H), (C–O–C), and the absorption band at 678 cm−1 shows REOs (Nd2O3). Raman spectroscopic analysis showed that the intensity of the ratio of ID/IG was (∼0.85) with the size of sp2 graphitic area of ∼22.6 nm. The addition of Nd2O3 particles to GO did not affect the intensity ratio of ID/IG and was almost the same for the GO and GO/Nd2O3 composite. SEM results show that the composite has a porous three-dimensional carbon network structure. The optical properties were analyzed using UV–Visible spectroscopy. The results of Urbach Energy showed that the GO/Nd2O3 composite is a semiconductor material with an Urbach Energy value of 0.050–0.063 eV, showing the potential application of GO/Nd2O3 composites in electronic devices.

In this study, we present an approach to create a visual research topic map for materials science researchers from a large collection of archived research papers using natural language processing (NLP). We apply this approach on SAMURAI, a directory service for the researchers of the National Institute for Materials Science (NIMS), Japan. Visualization of research content will support exploratory searches by maximizing the information absorbance and intuitively capturing the research characteristics of each materials science researcher. In addition, a research topic map can connect researchers with similar topics aiming to find potential collaborators. Collaboration can support the advance of scientific research. We analyze all available publications of a researcher using frequent term analysis. In addition, materials science knowledge resources were utilized including dictionaries and automatic extraction for the material names. Noise reduction was implemented using stop word filtering. The topics were then visualized using a word cloud technique for each researcher. An analysis of the topic similarity was conducted to find researchers that share similar topics leading to the creation of a topic map for each researcher. The approach aims at maximizing information absorbance for public knowledge by applying NLP approaches to information mining from materials science research papers. NLP analysis and visualization code are available https://github.com/ThaerDieb/Topic_map.

Summary: In this paper, the carbon electrode materials were prepared by the KOH-HNO3 low-temperature activation technique using cheap residual carbon from gasification fine slag (CK) as raw materials. The results showed that the prepared material (CKN-2) which obtained by dry-wet sequential activation at 500°C for 1.5 h at carbon to KOH ratio of 1:2 and further at 80°C for 1 h in 2 mol/L HNO3 solution. The specific capacitance of CKN-2 reached 142 F/g at a current density of 0.5 A/g. CKN-2 was used to assemble a symmetrical (CKN-2//CKN-2) supercapacitor, which exhibited an energy density of 6.80 Wh/kg at a power density of 244.8 W/kg. The CKN-2//CKN-2 capacitor was tested for stability after 10,000 cycles, with a capacitance retention rate of 97%. These results demonstrate that residual carbon from gasification fine slag can be effectively used to produce high-performance carbon electrode materials for supercapacitors using the KOH-HNO3 low-temperature sequential co-activation technique.

Summary: This paper proposed a method based on heart rate variability (HRV) for evaluating the accuracy of wearable devices in measuring heart rate. HRV refers to the variation in time intervals between successive heartbeats, widely used in many fields such as clinical and sports fields. Wearable devices such as Electrocardiogram (ECG) electrode patches have gained popularity due to their portability and ease of use. However, they can be prone to measurement interference caused by environmental noise, human respiration, etc. The proposed method consists of four main components: selection of “gold standard measurement devices”, identification of HRV measurement metrics, construction of an HRV evaluation framework, and quantification of measurement errors. The method is validated through simulated experiments using ECG patches. The evaluation framework and quantification model established in this method have significant implications in establishment of industry standards and diagnosis of diseases in clinical practice.

Summary: Hotspots in electronic devices can cause overheating and reduce performance. Enhancing the thermal spreading ability is critical for reducing device temperature to improve the reliability. However, as devices shrink, phonon ballistic effects can increase thermal resistance, making conventional optimization methods less effective. This paper presents a topology optimization method that combines the phonon Boltzmann transport equation with solid isotropic material with penalization method to optimize high thermal conductivity (HTC) material distributions for thermal spreading problems. Results show that the contraction-expansion structure can effectively reduce thermal resistance. Optimal distributions differ from that based on Fourier’s heat conduction law, and only the trunk structure appears in optimized layouts due to the size effect. Additionally, HTC material with longer mean free paths tends to be filled around the heat source with a gap in a ballistic-diffusive regime. This work deepens understanding of thermal spreading and aids in thermal optimization of microelectronic chips.

Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1000 scientific papers focused on biological materials into a comprehensive ontological knowledge graph. Through an in-depth structural analysis of this graph, we have calculated node degrees, identified communities along with their connectivities, and evaluated clustering coefficients and betweenness centrality of pivotal nodes, uncovering fascinating knowledge architectures. We find that the graph has an inherently scale-free nature, shows a high level of connectedness, and can be used as a rich source for downstream graph reasoning by taking advantage of transitive and isomorphic properties to reveal insights into unprecedented interdisciplinary relationships that can be used to answer queries, identify gaps in knowledge, propose never-before-seen material designs, and predict material behaviors. Using a large language embedding model we compute deep node representations and use combinatorial node similarity ranking to develop a path sampling strategy that allows us to link dissimilar concepts that have previously not been related. One comparison revealed detailed structural parallels between biological materials and Beethoven’s 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. In another example, the algorithm proposed an innovative hierarchical mycelium-based composite based on integrating path sampling with principles extracted from Kandinsky’s ‘Composition VII’ painting. The resulting material integrates an innovative set of concepts that include a balance of chaos and order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a context-dependent heterarchical interplay of constituents. Because our method transcends established disciplinary boundaries through diverse data modalities (graphs, images, text, numerical data, etc), graph-based generative AI achieves a far higher degree of novelty, explorative capacity, and technical detail, than conventional approaches and establishes a widely useful framework for innovation by revealing hidden connections.

Summary: Materials science literature has grown exponentially in recent years making it difficult for individuals to master all of this information. This constrains the formulation of new hypotheses that scientists can come up with. In this work, we explore whether materials science knowledge can be automatically inferred from textual information contained in journal papers. Using a data set of 0.5 million polymer papers, we show, using natural language processing methods that vector representations trained for every word in our corpus can indeed capture this knowledge in a completely unsupervised manner. We perform time-based studies through which we track popularity of various polymers for different applications and predict new polymers for novel applications based solely on the domain knowledge contained in our data set. Using co-relations detected automatically from literature in this manner thus, opens up a new paradigm for materials discovery.

Summary: Incorporating graphite/graphene into a Mg alloy matrix is a promising approach for developing lightweight heat dissipation materials. However, carbon material is inherently incompatible with Mg because of their distinctly different surface characteristics, resulting in the challenge of composite fabricating and interface controlling. Herein, a new strategy of in situ interfacial modification was proposed to achieve excellent thermal conductivity and mechanical properties in graphite/Mg composites. A super-nano CaCO3 interfacial layer was reported in this paper. The detailed interfacial structure, reaction thermodynamics and kinetics, and interface strengthening mechanisms were analyzed and discussed. Several preferential epitaxial relationships of the Mg/CaCO3 interface were revealed, which are conducive to minimize the interfacial energy, stabilize and strengthen the interface. Moreover, strong ionic bond of graphite/CaCO3 interface was demonstrated. The strong chemical interface bonding of graphite-Mg via in situ interface modification facilitates both the interfacial cohesion and interfacial thermal conduction, which endows the graphite/Mg composites with superior strength–thermal conductivity synergy.

Biodegradable modified natural polymers have great potential in curbing the threat of plastic pollution, but are still uncompetitive to petrochemical-based plastic. In this study, starch was hydrophobized by treating starch-dimethyl sulfoxide solutions with soybean oil at high temperature in the presence of sodium carbonate, then spray-coated on paper. The modified starch was evaluated by Fourier-transform infrared spectroscopy analysis and contact angle value measurement of coated paper. FTIR analysis confirmed the substitution of hydroxyl groups with fatty acid ester and provided an estimate of the degree of substitution. The contact angle value of starch-coated paper surfaces was 121°, and was 111° after 10 min, demonstrating the high hydrophobicity and potential of the modified starch coating as a water-resistant treatment. The high hydrophobicity of the coated paper was due to formation of a textured surface with two levels of roughness, caused by the deposition of rough hydrophobic starch particles on paper fibers.

Efforts to reduce pressure on use of wood in particleboard formulation have included the use of non-wood materials such as crop residues. Physical and mechanical properties are determined by the number of the hydroxyl (-OH) groups. Hydroxyl (-OH) groups attracts water molecules through hydrogen bonding affecting water absorption (WA) and thickness swelling (TS). WA and TS affect curing process of adhesive. Curing process of adhesives affects the mechanical characteristics of formulated particleboards. These challenges have been acted upon continuously through research. This review paper presents crop residues used as alternative lignocellulose material source in particleboard formulation and the various advances that have been made to improve on the properties of the resultant particleboards. Improvement over time of the non-wood material in composite materials focusses on increasing water resistance and compatibility between lignocellulose and binder. Crop residues-based are used in making medium and low density particleboards. These boards have shown good mechanical characteristics which include modulus of rupture (MOR), modulus of elasticity (MOE) and internal bonding (IB). MOR, MOE and IB have over time been improved by enhancing chemical compatibility of lignocellulose material and the binders. Water absorption and thickness swelling remain challenge. This review paper further explored various methods of improving water absorption and thickness swelling of crop-residue based particleboards.

The material selection process for producing a horizontal axis wind turbine blade for sustainable energy generation is a vital issue when using Nigeria as a case study. Due to the challenge faced with the low wind speed variations. However, this paper focuses on implementing MCDM for the material selection process for a suitable material for developing a horizontal wind turbine blade. This paper used a quantitative research approach using AHP and TOPSIS multi-criteria decision method. The study put into consideration the environmental conditions for the material selection process when designing the questionnaire. The authors extracted the data used for the selection process from the 130 research questionnaire distributed to materials engineers and renewable energy professionals. This research considered four alternatives that is, aluminum alloy, stainless steel, glass fiber, and mild steel to determine the best material for the wind turbine blade. Also, the model has four criteria and eight sub-criteria used for developing the pair-wise matrix and the performance score used for the ranking process of the alternatives. The result shows that a consistency index of 0.056 and a consistency ratio of 0.062 gotten via the AHP method is workable for material selection practice. 78%, 43%, 67%, and 25% are the performance scores for the four alternatives via the TOPSIS techniques. In conclusion, aluminum alloy is the best material, followed by glass fibre. Therefore, the decision-makers recommended aluminum alloy; hence, manufacturers should apply aluminum alloy to develop the wind turbine blade for sustainable energy generation.

The effects of electrochemical aging on the mechanical properties of electrodes in lithium-ion batteries are challenging to measure and are largely unknown. Mechanochemical degradation processes occur at different scales within an electrode and understanding the correlation between the degradation of mechanical properties, electrochemical aging, and morphological changes is crucial for mitigating battery performance degradation. This paper explores the evolution of mechanical and electrochemical properties at the layer level in a Ni-rich positive electrode during the initial stages of electrochemical cycling. The investigation involves complementary cross-section analyses aimed at unraveling the connection between observed changes on both macroscopic and microscopic scales. The macroscopic constitutive properties were assessed using a U-shaped bending test method that had been previously developed. The compressive modulus exhibited substantial dependency on both the porous structure and binder properties. It experienced a notable reduction with electrolyte wetting but demonstrated an increase with cycling and aging. During the initial stages of aging, electrochemical impedance spectra revealed increased local resistance near the particle–electrolyte interface. This is likely attributable to factors such as secondary particle grain separation and the redistribution of carbon black. The swelling of particles, compression of the binder phase, and enhanced particle contact were identified as probable factors adding to the elevation of the elastic modulus within the porous layer as a result of cycling.

Electric field enhancement mediated through sharp tips in scattering-type scanning near-field optical microscopy (s-SNOM) enables optical material analysis down to the 10-nm length scale and even below. Nevertheless, the out-of-plane electric field component is primarily considered here due to the lightning rod effect of the elongated s-SNOM tip being orders of magnitude stronger than any in-plane field component. Nonetheless, the fundamental understanding of resonantly excited near-field coupled systems clearly allows us to take profit from all vectorial components, especially from the in-plane ones. In this paper, we theoretically and experimentally explore how the linear polarization control of both near-field illumination and detection can constructively be implemented to (non-)resonantly couple to selected sample permittivity tensor components, e.g., explicitly to the in-plane directions as well. When applying the point-dipole model, we show that resonantly excited samples respond with a strong near-field signal to all linear polarization angles. We then experimentally investigate the polarization-dependent responses for both non-resonant (Au) and phonon-resonant (3C-SiC) sample excitations at a 10.6 µm and 10.7 µm incident wavelength using a tabletop CO2 laser. Varying the illumination polarization angle thus allows one to quantitatively compare the scattered near-field signatures for the two wavelengths. Finally, we compare our experimental data to simulation results and thus gain a fundamental understanding of the polarization’s influence on the near-field interaction. As a result, the near-field components parallel and perpendicular to the sample surface can be easily disentangled and quantified through their polarization signatures, connecting them directly to the sample’s local permittivity.

The paper covers some measurement aspects of transport of electrons through metals and semiconductors in magnetic field – magnetotransport – allowing for the determination of electrical parameters characteristic of three-dimensional (3D) topological insulators (TI) (i.e. those that behave like an insulator inside their volume and have a conductive layer on their surface). A characteristic feature of the 3D TI is also a lack of differences between the chemical composition of the conductive surface and the interior of the material tested and the fact that the electron states for its surface conductivity are topologically protected. In particular, the methods of generating strong magnetic fields, obtaining low temperatures, creating electrical contacts with appropriate geometry were presented, and the measurement methods were reviewed. In addition, the results of magnetotransport measurements obtained for two volumetric samples based on the HgCdTe compound grown with the molecular beam epitaxy method are presented.

Summary: CO2-responsive foaming has been drawing huge attention due to its unique switching characteristics in academic research and industrial practices, whereas its stability remains questionable for further applications. In this paper, a new CO2-switchable foam was synthesized by adding the preferably selected hydrophilic nanoparticle N20 into the foaming agent C12A, through a series of analytical experiments. Overall, the synergy between cationic surfactants and nanoparticles with a contact angle of 37.83° is the best. More specifically, after adding 1.5 wt% N20, the half-life of foam is 14 times longer than that of pure C12A foam. What’s more, the C12A-N20 solution is validated to own distinctive CO2-N2 switching features because very slight foaming degradations are observed in terms of the foaming volume and half-life time even after three cycles of CO2-N2 injections. This study is of paramount importance pertaining to future CO2 foam research and applications in energy and environmental practices.

Creep test is a useful tool to study thermal aging and deformation mechanisms of semi-crystalline polymers, such as polyether-ether-ketone (PEEK). Hou and Chen proposed a power law to fit creep data of PEEK aged at different temperatures and the master curve built from those data. This paper attempts to complement that analysis by introducing Kohlrausch function as an alternative to the fractional Maxwell's model associated to the power law. Although the fitting of experimental data and the mathematical conditions imposed to equations that describe curves that can be superimposed by translations, are obeyed by both models, this paper demonstrates that Kohlrausch function provides a better phenomenological description of the creep response of PEEK due to the physical interpretation of the fitting parameters and their dependence on the aging time and temperature.

Summary: Rechargeable Mg|O2 batteries (RMOBs) offer several advantages over alkali metal-based battery systems owing to Mg’s ease of transport/storage in ambient environment, low cost originating from its high abundance, as well as the high theoretical specific energy of RMOBs. However, research on RMOBs has been stagnant for the past decade, largely owing to unacceptably poor electrochemical performance. Here, we present a RMOB that employs Mg anode, Mg((CF3SO2)2N)2-MgCl2 in diglyme (G2) electrolyte, and commercial Pt/C on carbon fiber paper (Pt/C@CFP) oxygen cathode. This battery demonstrates unparalleled improvement over existing RMOBs by rendering a discharge capacity over 1.6 mAh cm−2, achieving cycle lives up to 35 cycles with a cumulative energy density of ∼3.2 mWh cm−2 at room temperature. This RMOB system seeks to reignite the pursuit of novel electrochemical systems based on Mg-O2 chemistries.

This paper focuses on studying the rheological properties of polydimethylsiloxane (PDMS). This polymer has been used to fabricate membranes and filters in engineering. The analysis of the rheological properties of this polymer is required for a further investigation of its mechanical behavior. In this study, the rheological behavior of PDMS is reported at different temperatures. This polymer showed steady shear viscosity during a short duration. However, this behavior changed with time and increased more with increasing temperature. The impact of the temperature increase was also observed when the shear viscosity of PDMS increased with shear strain. The increase of torque with shear strain and time was observed at different temperatures. Shear stress increased linearly with the shear rate at 20 °C and 40 °C. As expected, the deformation of the polymer required less shear stress with the increase of temperature. However, the change of shear stress with the shear rate at 60 °C was not linear, and the slope of the curve increased more at high shear rates. The results of this investigation can provide the required information for a better fabrication of membranes and filters with this polymer.

In this study, the three structures of the symmetric paper supercapacitors based on the carbon nanotubes (CNTs), graphite nanoparticles (GNPs) and graphene electrodes have been fabricated. In the supercapacitors was used of polyvinyl alcohol (PVA)/phosphoric acid (H3PO4) as a gel electrolyte and the BaTiO3 film as a separator film. The carbon nanomaterials, gel electrolyte surface, and electrode films were characterized by scanning electron microscopy (SEM) and transmission electron microscope (TEM). The specific capacitance of the symmetric paper supercapacitors using charge-discharge technique and C-V curves at the voltage scan rates 20 mV/s and 150 mV/s have been investigated. The symmetric paper supercapacitor based on the CNTs electrode showed higher specific capacitance 411 F g−1, compared to GNPs and graphene supercapacitors. Also by electrochemical impedance spectroscopy, the Nyquist curves of the symmetric paper supercapacitors have been plotted. For the symmetric paper supercapacitors based on the GNPs, graphene and CNTs electrodes the equivalent series resistance (ESR) resistance was 210 Ω, 96 Ω and 101 Ω respectively. The flexible symmetric paper supercapacitor based on BaTiO3/PVA/CNTs structure denotes a new type of the flexible supercapacitor that can be applied to the soft electronic.

Aramid Fibre Reinforced Plastic composites are difficult to be drilled due to anisotropic material properties. Currently, soft computing techniques are used as alternatives to conventional mathematical models, which is robust and can deal with inaccuracy and uncertainty. In this paper, drilling of Aramid Fibre Reinforced Plastics (AFRPs) was carried out using Taguchi L54 experimental layout. Drilling tool used in this experiment was solid carbide. The purpose of this study was to find optimum combination of drilling parameters to obtain minimum thrust and torque force to reduce the delamination. Also, this paper proposed a prediction model of Multilayer Perception Neural Network optimized by Genetic Algorithm (MLPNN-GA). Moreover, RSM technique was used to evaluate the influence of process parameters (spindle speed, feed rate, drill point angle and drill diameter on thrust force and torque. The prediction capability of both RSM and MLPNN-GA was compared with Response optimizer for thrust force and torque. The investigation demonstrated that drill point angle is the primary factor affecting thrust force and drill diameter influences the torque force on the drill bit. Overall, this study recommends the use of high speed and low feed combination and drill point angles of 90°–118° to reduce the delamination of the materials in the drilling of AFRP composites.

Summary: Designing optical structures for generating structural colors is challenging because of the complex relationship between the optical structures and the color perceived by human eyes. Machine learning-based approaches have been developed to expedite this design process. However, existing methods solely focus on structural parameters of the optical design, which could lead to suboptimal color generation because of the inability to optimize the selection of materials. To address this issue, an approach known as Neural Particle Swarm Optimization is proposed in this paper. The proposed method achieves high design accuracy and efficiency on two structural color design tasks; the first task is designing environment-friendly alternatives to chrome coatings, and the second task concerns reconstructing pictures with multilayer optical thin films. Several designs that could replace chrome coatings have been discovered; pictures with more than 200,000 pixels and thousands of unique colors can be accurately reconstructed in a few hours.

Summary: Vehicle electrification has always been a hot topic and gradually become a major role in the automobile manufacturing industry over the last two decades. This paper presented comprehensive discussions and insightful evaluations of both conventional electric vehicle (EV) batteries (such as lead-acid, nickel-based, lithium-ion batteries, etc.) and the state-of-the-art battery technologies (such as all-solid-state, silicon-based, lithium-sulphur, metal-air batteries, etc.). Battery major component materials, operating characteristics, theoretical models, manufacturing processes, and end-of-life management were thoroughly reviewed. Different from other reviews focusing on theoretical studies, this review emphasized the key aspects of battery technologies, commercial applications, and lifecycle management. Useful battery managing technologies such as health prediction, charging and discharging, as well as thermal runaway prevention were thoroughly discussed. Two novel hexagon radar charts of all-round evaluations of most reigning and potential EV battery technologies were created to predict the development trend of the EV battery technologies. It showed that lithium-ion batteries (3.9 points) would be still the dominant product for the current commercial EV power battery market in a short term. However, some cutting-edge technologies such as an all-solid-state battery (3.55 points) and silicon-based battery (3.3 points) are highly likely to be the next-generation EV onboard batteries with both higher specific power and better safety performance.

In the construction industry, there is growing attention of using effective external strengthening techniques such as bonding of Carbon Fiber Reinforced Polymers (CFRP) composites onto the external deficient faces of the structural members due to their ease of installation, low invasiveness, high corrosion resistance, and high strength to weight ratio. As a result, the center of consideration of the majority of previously published studies was either only on the impact of fibers on the structural behavior of reinforced concrete elements or using CFRP composite as external strengthening for flexural or shear. The intent was to arrive at the vital CFRP strengthening technique that provides an effective increase in the flexural and shear strength while maintaining ductile failure mode. Therefore, this paper investigated the behavior of simply supported RC beams strengthened using CFRP and subjected to combined bending and torsion using Nonlinear Finite Element Analysis (NLFEA). Twenty-six models have been constructed and divided into six groups to scrutinize the effect of clear span to depth ratio; CFRP length; CFRP strip spacing; and CFRP depth. The results showed that the increase in the clear span to depth ratio as well as length of CFRP leads to a notable increase in the ductility and decreases the ultimate load. The models with zero spacing CFRP strips (Fully) showed a higher considerable effect than the models with strips wrapping. Furthermore, this enhancement was the highest for group six which contains the models with the highest CFRP depth.

Sugarcane bagasse ash re-utilisation has been advocated as a silica-rich feed for zeolites, pozzolans in cements and concretes, and geopolymers. However, many papers report variable success with the incorporation of such materials in these products as the ash can be inconsistent in nature. Therefore, understanding what variables affect the ash quality in real mills and understanding the processes to characterise ashes is critical in predicting successful ash waste utilisation. This paper investigated sugarcane bagasse ash from three sugar mills (Northern NSW, Australia) where two are used for the co-generation of electricity. Data shows that the burn temperatures of the bagasse in the high-efficiency co-generation boilers are much higher than those reported at the temperature measuring points. Silica polymorph transitions indicate the high burn temperatures of ≈1550 °C, produces ash dominated α −quartz rather than expected α-cristobilite and amorphous silica; although α-cristobilite, and amorphous silica are present. Furthermore, burn temperatures must be ≤1700 °C, because of the absence of lechatelierite where silica fusing and globulisation dominates. Consequently, silica-mineralogy changes deactivate the bagasse ash by reducing silica solubility, thus making bagasse ash utilisation in synthetic zeolites, geopolymers, or a pozzolanic material in mortars and concretes more difficult. For the ashes investigated, use as a filler material in cements and concrete has the greatest potential. Reported mill boiler temperatures discrepancies and the physical characteristics of the ash, highlight the importance of accurate temperature monitoring at the combustion seat if bagasse ash quality is to be prioritised to ensure a usable final ash product.

Summary: The accumulation of ice will reduce the performance of the base material and lead to all kinds of damage, even a threat to people's life safety. Recent increasing studies suggest that superhydrophobic surfaces (SHSs) originating from nature can remove impacting and condensing droplets from the surface before freezing to subzero temperatures, and it can be seen that hydrophobic/SH coating has good freezing cold resistance. But such anti-icing performances and developments in practical applications are restricted by various factors. In this paper, the mechanism and process of surface icing phenomenon are introduced, as well as how to prevent surface icing on SHS. The development of SH materials in the aspect of anti-icing in recent years is described, and the existing problems in the aspect of anti-icing are analyzed, hoping to provide new research ideas and methods for the research of anti-icing materials.

Summary: Heat dissipation materials in which fillers are dispersed in a polymer matrix typically do not exhibit both high thermal conductivity (k) and processability due to a trade-off. In this paper, we fabricate heat dissipation composites which overcome the trade-off using liquid metal (LM). By exceeding the conventional filler limit, ten times higher k is achieved for a 90 vol% LM composite compared with k of 50 vol% LM composite. Further, an even higher k is achieved by introducing h-BN between the LM droplets, and the highest k in this study was 17.1 W m−1 K−1. The LM composite is processable at room temperature and used as inks for 3D printing. This combination of high k and processability not only allows heat dissipation materials to be processed on demand under ambient conditions but it also increases the surface area of the LM composite, which enables rapid heat dissipation.

The ability to non-destructively measure the structural properties of devices, either in situ or operando, are now possible using an intense X-ray synchrotron source combined with specialized equipment. This tool attracted researchers, in particular metallurgists, to attempt more complex and ambitious experiments aimed at answering unresolved questions in formation mechanisms, phase transitions, and magnetism complex alloys for industrial applications. In this paper, we introduce the diffraction diffusion anomale multi-longueur d’onde (D2AM) beamline, a French collaborating research group (CRG) beamline at the European Synchrotron Radiation Facility (ESRF), partially dedicated to in situ X-ray scattering experiments. The design of the beamline combined with the available equipment (two-dimensional fast photon counting detectors, sophisticated high precision kappa diffractometer, a variety of sample environments, continuous scanning for X-ray imaging, and specific software for data analysis) has made the D2AM beamline a highly efficient tool for advanced, in situ synchrotron characterization in materials science, e.g., single crystal or polycrystalline materials, powders, liquids, thin films, or epitaxial nanostructures. This paper gathers the main elements and equipment available at the beamline and shows its potential and flexibility in performing a wide variety of temporally, spatially, and energetically resolved X-ray synchrotron scattering measurements in situ.

Summary: Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses’ literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA) to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots is presented using the caption cluster plot (CCP), providing direct access to images buried in the papers. Finally, we combine the LDA and CCP with chemical elements to present an elemental map, a topical and image-wise distribution of elements occurring in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition–structure–processing–property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery. The bigger picture: Most knowledge generated through scientific enquiry in materials domain is presented in the form of unstructured data. Among the available sources such as online websites, digital data, and publications, peer-reviewed journals serve as the undisputed source of reliable information regarding materials synthesis, characterization, and properties. Despite the availability of large data, only a limited fraction is compiled in the form of machine-readable databases, most of which are manually curated. Here, applying natural language processing on a large corpus of journal publications on inorganic glasses, we present a framework of information extraction from text and images, which answers queries related to synthesis and characterization techniques, and even chemical elements used. The scalable approach presented here can be applied to other domains for efficient information retrieval from scientific literature.

Summary: This paper addresses the design optimization process of an energy harvesting device for scavenging energy from an e-gadget, utilizing its ”rocking” motion. The plucking mechanism inspired by the frequency up-conversion technique provides initial displacement exciting piezoelectric beams and increases the total number of excitations multiple times. The harvester is designed in conjunction with the multidimensional surrogate optimization algorithm to maximize the device’s performance considering the geometrical features of the concept and the constrained operating environment. The established numerical model is validated first using a set of experimental data. The obtained numerical results demonstrate that the developed 10.2″ size device produces 55 mJ in half-period when inclined at 45°, which is equivalent to generating 0.3 W. Considering that an iPad of the same size consumes around 3 W, the proposed energy harvester is capable of extending its battery life by 10%.