In this study, an injectable bone substitute (IBS) was produced by mixing a liquid and powder phase. The liquid phase consisted of 8 wt% methylcellulose (MC), 2.5% gelatin, and different amounts of graphene oxide (GO). The powder phase was composed of tetracalcium phosphate (TTCP), dicalcium phosphate dihydrate (DCPD), and calcium sulfate dihydrate (CSD). The results showed that 1 and 1.5 wt% GO added IBS samples showed higher stability, injectability, rheological properties, and biocompatibility than the other GO added IBS samples. GO addition significantly decreased the setting time, but it did not significantly affect the compressive strength of the samples.

The development of more sustainable construction materials is a crucial step toward the reduction of CO2 emissions to mitigate climate change issues and minimize environmental impacts of the associated industries. Therefore, there is a growing demand for bio-based binders which are not only safer toward human and environmental health but also facilitate cleaner disposal of the construction materials and enable their compostability. Here, we summarize the most relevant bio-based polymers and molecules with applications in the construction sector. Due to the biologic nature of these materials, the existing biotechnologic processes, including synthetic biology, for their development and production have been evaluated.

In this topical review of two-photon stereolithography (TPS), we discuss novel materials and demonstrate applications of this technology. Two-photon-initiated chemical processes are used to fabricate arbitrary three-dimensional structures in TPS. In the first part of this article, the development of novel photoactive materials to fabricate pure inorganic or organic–inorganic hybrid microstructures is discussed. The second part discusses the fabrication of functional microstructures for highly specific applications to demonstrate the importance of TPS in different fields of science.

Applying artificial intelligence to materials research requires abundant curated experimental data and the ability for algorithms to request new experiments. ESCALATE (Experiment Specification, Capture and Laboratory Automation Technology)—an ontological framework and open-source software package—solves this problem by providing an abstraction layer for human- and machine-readable experiment specification, comprehensive and extensible (meta-) data capture, and structured data reporting. ESCALATE simplifies the initial data collection process, and its reporting and experiment generation mechanisms simplify machine learning integration. An initial ESCALATE implementation for metal halide perovskite crystallization was used to perform 55 rounds of algorithmically-controlled experiment plans, capturing 4336 individual experiments.

We propose a device to secure random data in analog format, so that it is taken off the digital grid. Such action will turn off the means by which remote hackers violate security. A physical “rock” manufactured through 3D printing technology, constructed on the basis of high-grade randomness, which is packed into the comprising materials of that rock. The rock functions as an oracle, and does not allow any massive copy of its content. Thus, a major claim of this Prospective is that materials science and engineering may hold the keys to the future of cryptography.

A crucial target in the printed electronics technologies is to realize all-printed thin-film transistors (TFTs), as being applicable to the industry. Here, the authors report printed polymer TFTs through the integration of the SuPR-NaP technique, a promising way for manufacturing ultrafine printed silver electrodes, with printed polymer semiconductor layers. The authors used a class of donor–acceptor-type copolymer, PDVT-10, and found that the devices exhibit excellent TFT characteristics. The devices allow the transfer length method measurements with high accuracy, where the estimated contact resistance is considerably small (4.7 kΩ cm) among the bottom-contact TFTs using printed silver electrodes, with also showing short-channel effects.

Synthetic biology has a huge potential to produce the next generation of advanced materials by accessing previously unreachable (bio)chemical space. In this prospective review, we take a snapshot of current activity in this rapidly developing area, focusing on prominent examples for high-performance applications such as those required for protective materials and the aerospace sector. The continued growth of this emerging field will be facilitated by the convergence of expertise from a range of diverse disciplines, including molecular biology, polymer chemistry, materials science, and process engineering. This review highlights the most significant recent advances and addresses the cross-disciplinary challenges currently being faced.

Machine-learning (ML) approaches have proven to be of great utility in modern materials innovation pipelines. Generally, ML models are trained on predetermined past data and then used to make predictions for new test cases. Active-learning, however, is a paradigm in which ML models can direct the learning process itself through providing dynamic suggestions/queries for the “next-best experiment.” In this work, the authors demonstrate how an active-learning framework can aid in the discovery of polymers possessing high glass transition temperatures (Tg). Starting from an initial small dataset of polymer Tg measurements, the authors use Gaussian process regression in conjunction with an active-learning framework to iteratively add Tg measurements of candidate polymers to the training dataset. The active-learning framework employs one of three decision making strategies (exploitation, exploration, or balanced exploitation/exploration) for selection of the “next-best experiment.” The active-learning workflow terminates once 10 polymers possessing a Tg greater than a certain threshold temperature are selected. The authors statistically benchmark the performance of the aforementioned three strategies (against a random selection approach) with respect to the discovery of high-Tg polymers for this particular demonstrative materials design challenge.

Natural living conductive biofilms transport electrons between electrodes and cells, as well as among cells fixed within the film, catalyzing an array of reactions from acetate oxidation to CO2 reduction. Synthetic biology offers tools to modify or improve electron transport through biofilms, creating a new class of engineered living conductive materials. Engineered living conductive materials could be used in a range of applications for which traditional conducting polymers are not appropriate, including improved catalytic coatings for microbial fuel-cell electrodes, self-powered sensors for austere environments, and next-generation living components of bioelectronic devices that interact with the human microbiome.

A review is given of the future device processing needs for Ga2O3 power electronics. The two main devices employed in power converters and wireless charging systems will be vertical rectifiers and metal oxide semiconductor field effect transistors (MOSFETs). The rectifiers involve thick epitaxial layers on conducting substrates and require stable Schottky contacts, edge termination methods to reduce electric field crowding, dry etch patterning in the case of trench structures, and low resistance Ohmic contacts in which ion implantation or low bandgap interfacial oxides are used to minimize the specific contact resistance. The MOSFETs also require spatially localized doping enhancement for low source/drain contact resistance, stable gate insulators with acceptable band offsets relative to the Ga2O3 to ensure adequate carrier confinement, and enhancement mode capability. Attempts are being made to mitigate the absence of p-type doping capability for Ga2O3 by developing p-type oxide heterojunctions with n-type Ga2O3. Success in this area would lead to minority carrier devices with better on-state performance and a much-improved range of functionality, such as p-i-n diodes, Insulated Gate Bipolar Transistors, and thyristors.

A piezoelectric biomedical microelectromechanical system (bioMEMS) cantilever device was designed and fabricated to act as either a sensing element for muscle tissue contraction or as an actuator to apply mechanical force to cells. The sensing ability of the piezoelectric cantilevers was shown by monitoring the electrical signal generated from the piezoelectric aluminum nitride in response to the contraction of iPSC-derived cardiomyocytes cultured on the piezoelectric cantilevers. Actuation was demonstrated by applying electrical pulses to the piezoelectric cantilever and observing bending via an optical detection method. This piezoelectric cantilever device was designed to be incorporated into body-on-a-chip systems.

The authors developed a Behler–Parrinello-type neural network (NN) to improve the density-functional tight-binding (DFTB) energy and force prediction. The Δ-machine learning approach was adopted and the NN was designed to predict the energy differences between the density functional theory (DFT) quantum chemical potential and DFTB for a given molecular structure. Most notably, the DFTB-NN method is capable of improving the energetics of intramolecular hydrogen bonds and torsional potentials without modifying the framework of DFTB itself. This improvement enables considerably larger simulations of complex chemical systems that currently could not easily been accomplished using DFT or higher level ab initio quantum chemistry methods alone.

Direct urea fuel cells were fabricated using CuNi-plated polymer cloth for anode catalyst and current collector, and Pt-black for cathode catalyst. The output power was significantly enhanced by coating the CuNi cloth with a conducting polymer, poly(3,4-ethylenedioxythiophene)polystyrene sulfonate (PEDOT*PSS). The open circuit voltage, 0.80 V and the maximum output power, 3.0 mW/cm2 were obtained for the fuel of 0.5 M urea water solution under ambient conditions. Improvement over the cell structure demonstrated to lighten a light emitting diode.

This prospective offers a new Bayesian framework that could guide the systematic application of the emerging toolsets of machine learning in the efforts to address two of the central bottlenecks encountered in materials innovation: (i) the capture of core materials knowledge in reduced-order forms that allow one to rapidly explore the vast materials design spaces, and (ii) objective guidance in the selection of experiments or simulations needed to identify the governing physics in the materials phenomena of interest. The framework builds on recent advances in the low-dimensional representation of the statistics describing the material's hierarchical structure.

The extending market of concentrated solar power plants requires high-temperature materials for solar surface receivers that would ideally heat an air coolant beyond 1300 K. This work presents investigation on high-temperature alloys with ceramic coatings (AlN or SiC/AlN stacking) to combine the properties of the substrate (creep resistance, machinability) and coating (slow oxidation kinetics, high solar absorptivity). The first results showed that high-temperature oxidation resistance and optical properties of metallic alloys were improved by the different coatings. However, the fast thermal shocks led to high stress levels not compatible due to the differences in thermal expansion coefficients.

Our ability to describe crystal structure features is of crucial importance when attempting to understand structure–property relationships in the solid state. In this paper, the authors introduce robocrystallographer, an open-source toolkit for analyzing crystal structures. This package combines new and existing open-source analysis tools to provide structural information, including the local coordination and polyhedral type, polyhedral connectivity, octahedral tilt angles, component-dimensionality, and molecule-within-crystal and fuzzy prototype identification. Using this information, robocrystallographer can generate text-based descriptions of crystal structures that resemble descriptions written by human crystallographers. The authors use robocrystallographer to investigate the dimensionalities of all compounds in the Materials Project database and highlight its potential in machine learning studies.

The glass surfaces used for optical devices are necessary to have high transparency. Here we propose to take advantage of tube-like SiO2 textures to trap lubricant liquid inside aiming to prepare novel slippery liquid-infused porous surfaces (SLIPS). As a consequence, SLIPS with high transparency were synthesized on glass substrate successfully. The capillary action of unique tubular structure induces the ion migration of adjacent Krytox 100, thus endowing SLIPS with the self-healing property. Moreover, the remarkable slip behavior enables these surfaces to possess the self-cleaning and anti-biofouling performances. The current work might provide a promising candidate for long-term transparent optical devices.

Increasing fluorination of organosilyl nitrile solvents improves ionic conductivities of lithium salt electrolytes, resulting from higher values of salt dissociation. Ionic conductivities at 298 K range from 1.5 to 3.2 mS/cm for LiPF6 salt concentrations at 0.6 or 0.7 M. The authors also report on solvent blend electrolytes where the fluoroorganosilyl (FOS) nitrile solvent is mixed with ethylene carbonate and diethyl carbonate. Ionic conductivities of the FOS solvent/carbonate blend electrolytes increase achieving ionic conductivities at 298 K of 5.5–6.3 mS/cm and salt dissociation values ranging from 0.42 to 0.45. Salt dissociation generally decreases with increasing temperature.

The authors develop a computational approach that integrates machine learning (ML) and density functional theory (DFT) with experimental data to predict formable and thermodynamically stable iodine-containing apatites. This is an important problem because radioactive iodine is toxic and capturing it in solid waste forms have implications in remediation treatments. The authors train ML models using 336 compositions and screen 54 iodine-containing compounds in apatite stoichiometry. ML models predict 18 as formable and 24 as nonformable in the apatite structure; 12 compounds were identified to be uncertain. DFT convex hull predicted two to be thermodynamically stable, one as metastable, and nine as unstable.

Materials design and discovery can be represented as selecting the optimal structure from a space of candidates that optimizes a target property. Since the number of candidates can be exponentially proportional to the structure determination variables, the optimal structure must be obtained efficiently. Recently, inspired by its success in the Go computer game, several approaches have applied Monte Carlo tree search (MCTS) to solve optimization problems in natural sciences including materials science. In this paper, we briefly reviewed applications of MCTS in materials design and discovery, and analyzed its future potential.