Tissue engineering has been recognized as a translational approach to replace damaged tissue or whole organs. Engineering tissue, however, faces an outstanding knowledge gap in the challenge to fully recapitulate complex organ-specific features. Major components, such as cells, matrix, and architecture, must each be carefully controlled to engineer tissue-specific structure and function that mimics what is found in vivo. Here we review different methods to engineer tissue, and discuss critical challenges in recapitulating the unique features and functional units in four major organs—the kidney, liver, heart, and lung, which are also the top four candidates for organ transplantation in the USA. We highlight advances in tissue engineering approaches to enable the regeneration of complex tissue and organ substitutes, and provide tissue-specific models for drug testing and disease modeling. We discuss the current challenges and future perspectives toward engineering human tissue models.

Silver nanoparticle inks printed on temperature-sensitive substrates can be converted into structures with high electrical conductivities within fractions of a second by photonic flash sintering. The key principle is the selective heating of the ink by the absorption of strongly focused pulsed light for which the substrate is transparent. The influence of process parameters like intensity and flashing frequency on the sintering speed is investigated. Furthermore, a setup is demonstrated for monitoring the temperature development in an ink during flash sintering, revealing that the substrate's glass transition point is exceeded only for very short time intervals, which prevents deformation.

Cu2ZnSn(S,Se)4 (CZTSSe) photovoltaics (PV) have long been considered promising candidates for large-scale PV deployment due to the availability of constituent elements and steady improvements in device efficiency over time. The key limitation to high efficiency in this technology remains a deficit in the open-circuit voltage with respect to the band gap. The past decade has seen significant progress toward understanding how the various material properties such as bulk and surface composition, point defects (intrinsic and extrinsic), and grain boundaries all impact the optoelectronic properties of CZTSSe materials, and consequently device performance. This paper aims to summarize what is known about the CZTSSe bulk and surfaces, and how these material properties may be related to the Voc deficit.

This Prospective covers an overview of the injection molding process and the importance of mold design and tooling considerations, important material requirements and thermal properties for molds, polymer material requirements for injection molding, mold flow analysis, and the promise of using the 3D printing process for mold fabrication. The second part demonstrates the injection molding process using 3D-printed polymer molds and its suitability for low-run productions. 3D-printed molds using stereolithography and fused filament fabrication have been injected with polylactic acid, and the quality of the injected parts was assessed in terms of dimensional accuracy and the damage mechanisms during fabrication.

Tetrahedrite-structure compounds, of general composition Cu12−xZnxSb4S13, are an earth-abundant alternative to PbTe for thermoelectric power generation applications in the intermediate high-temperature range (300–400°C). Tetrahedrites can be synthesized in the laboratory using a multi-step process involving long annealing times. However, this compound also exists in natural mineral form, and, in fact, is one of the most abundant copper-bearing minerals in the world. We show here that by simply mixing natural mineral tetrahedrite with pure elements through high-energy ball milling without any further heat treatment, we can successfully obtain material with figure of merit near unity at 723 K.

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 software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refines the model parameters using phase equilibrium data through Bayesian parameter estimation within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu–Mg system down to 0 K using unary descriptions based on segmented regression. The model parameter uncertainties are quantified and propagated to the Gibbs energy functions.

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.

The long-term stability of mechanically exfoliated MoS2 flakes was compared for storage in the air and storage under vacuum. Significant changes in MoS2 flakes were observed for samples stored in the air, whereas similar flakes on samples stored in vacuum underwent no change. Small speckles were observed to appear on the surface of flakes stored in the air, followed by thinning and eventual decomposition of MoS2 flakes. The speckles are suspected to be formed by oxidation of MoS2 in the presence of atmospheric oxygen and water molecules, resulting in the formation of hydrated MoO3.

Polymeric nanostructures can be synthesized where the catalytic motif is covalently attached within the core domain and protected from the environment by a polymeric shell. Such nanoreactors can be easily recycled, and have shown unique properties when catalyzing reactions under pseudohomogeneous conditions. Many examples of how these catalytic nanostructures can act as nanosized reaction vessels have been reported in the literature. This prospective will focus on the exclusive features observed for these catalytic systems and highlight their potential as enzyme mimics, as well as the importance of further studies to unveil their full potential.

Screen-printed organic electrochemical transistors (OECTs) were tested as glucose and lactate sensors. The intrinsic amplification of the device allowed it to detect metabolites in low molecular range and validation tests were made on real human sweat. The development of an organically modified sol–gel solid electrolyte paves the way for all printed OECT-based biosensors.

Yarn-type supercapacitors should have high energy density in small given spaces, and the one attempt among many is to comprise the electrodes asymmetrically. However, the low capacitance of conventional materials causes the widened operating voltage useless. In this study, we have utilized a novel material MXene with carbon nanotubes (CNTs) to make highly loaded MXene/CNT yarn electrodes, which exhibited a remarkable areal capacitance. With MnO2/CNT biscrolled cathode and PVA/LiCl gel electrolyte, the plied asymmetric yarn supercapacitor had energy density of 100 µWh/cm2. The yarn supercapacitor could operate under mechanical deformations without performance degradation.

This paper explores thin films of the entropy-stabilized oxide (ESO) composition MgxNixCoxCuxZnxScxO (x ~ 0.167) grown by laser ablation in incremental gas pressures and O2/Ar ratios to modulate particle kinetic energy and plume reactivity. Low pressures supporting high kinetic energy adatoms favor the kinetic stabilization of a single rocksalt phase, while high pressures (low kinetic energy adatoms) result in phase separation. The pressure threshold for phase separation is a function of O2/Ar ratio. These findings suggest large kinetic energies facilitate the assembly and quench of metastable ESO phases that may require immoderate physical or chemical conditions to synthesize using near-equilibrium techniques.

Ultra-thin optical structures, known as metasurfaces, have shown promising light controlling capability at the nanoscale. In this paper, we study their particular case, a periodic array of high-refractive-index nanoparticles with electric and magnetic resonances. The main result of the work is a numerical demonstration that the lattice effect in the periodic arrangement of nanoparticles changes the resonance position even if the resonances are above the diffraction wavelength (Rayleigh anomaly). We show that the disk resonance changes can be achieved not only by varying periods of the array under normal light incidence but also by changing the incident angle.

The recent observations of ferromagnetic order in several two-dimensional (2D) materials have generated an enormous interest in the physical mechanisms underlying 2D magnetism. In the present Prospective Article, we show that Density Functional Theory combined with either classical Monte Carlo simulations or renormalized spin-wave theory can predict Curie temperatures for ferromagnetic insulators that are in quantitative agreement with experiments. The case of materials with in-plane anisotropy is then discussed, and it is argued that finite size effects may lead to observable magnetic order in macroscopic samples even if long range magnetic order is forbidden by the Mermin–Wagner theorem.

Three-dimensional (3D) printed poly (ethylene glycol) diacrylate (PEGDA) objects have been reinforced with 1%, 3% and 5% silica (SiO2) nanoparticles. Rheological characterizations were conducted for each formulation and 3D-printed using a stereolithographic apparatus (SLA) 3D printer. The tensile and compressive properties of the as-printed nanocomposites were investigated and compared with unreinforced samples. Additionally, the mechanical properties of the objects before and after swelling the samples in deionized water were compared with as-printed ones. Adding SiO2 increased the tensile and compressive strengths of the 3D-printed PEGDA. The tensile and compressive strengths of swollen PEGDA/SiO2 nanocomposite specimens were generally higher than the unswollen specimens.

Machine learning (ML) has revolutionized disciplines within materials science that have been able to generate sufficiently large datasets to utilize algorithms based on statistical inference, but for many important classes of materials the datasets remain small. However, a rapidly growing number of approaches to embedding domain knowledge of materials systems are reducing data requirements and allowing broader applications of ML. Furthermore, these hybrid approaches improve the interpretability of the predictions, allowing for greater physical insights into the factors that determine material properties. This review introduces a number of these strategies, providing examples of how they were implemented in ML algorithms and discussing the materials systems to which they were applied.

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.

Herein we describe the use of a new DNAzyme/graphene hybrid material as a biointerfaced sensing platform for optical detection of pathogenic bacteria. The hybrid consists of a colloidal graphene nanomaterial and an Escherichia coli-activated RNA-cleaving DNAzyme and is prepared via non-covalent self-assembly of the DNAzyme onto the graphene surface. Exposure of the hybrid material to E. coli-containing samples results in the release of the DNAzyme, followed by the cleavage-mediated production of a fluorescent signal. Given that specific RNA-cleaving DNAzymes can be created for diverse bacterial pathogens, direct interfacing of graphene materials with such DNAzymes represents a general and attractive approach for real-time, sensitive, and highly selective detection of pathogenic bacteria.

In this perspective, the authors challenge the status quo of polymer innovation. The authors first explore how research in polymer design is conducted today, which is both time consuming and unable to capture the multi-scale complexities of polymers. The authors discuss strategies that could be employed in bringing together machine learning, data curation, high-throughput experimentation, and simulations, to build a system that can accurately predict polymer properties from their descriptors and enable inverse design that is capable of designing polymers based on desired properties.