We previously demonstrated that electrode architectures comprising nanoscale birnessite-like MnOx affixed to three-dimensional carbon nanofoam (CNF) scaffolds offer performance advantages when used as cathodes in rechargeable zinc-ion cells. To discern chemical and physical changes at the MnOx@CNF electrode upon deep charge/discharge in aqueous Zn2+-containing electrolytes, we deploy electroanalytical methods and ex situ characterization by microscopy, elemental analysis, x-ray photoelectron spectroscopy, x-ray diffraction, and x-ray pair distribution function analyses. Our findings verify that redox processes at the MnOx are accompanied by reversible precipitation/dissolution of crystalline zinc hydroxide sulfate (Zn4(OH)6(SO4)·xH2O), mediated by the more uniformly reactive electrode structure inherent to the CNF scaffold.

This work established the feasibility of flexible solution-processed radiation sensors prepared from an organic scintillator (1-phenyl-3-mesityl-2-pyrazoline) and a biocompatible semiconducting polymer (violanthrone-79). Absorbance, steady-state, and time-resolved photoluminescence measurements demonstrated a high efficiency for the transfer of absorbed energy from the scintillator to the semiconductor. Blended nanoparticles containing both materials were fabricated in order to reduce the intermolecular distance between molecules, creating a highly efficient energy transfer pathway. Radiation-sensing devices were then constructed from the materials. These exhibited successful sensitivity for gamma radiation from a 137Cs source that was not present for the control semiconducting polymer alone.

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.

Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the trade-off between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low-fidelity, B3LYP, and high-accuracy, G4MP2, atomization energies and predict the G4MP2 atomization energy to 0.005 eV (mean absolute error) for molecules with less than nine heavy atoms (training set of 117,232 entries, test set 13,026) and 0.012 eV for a small set of 66 molecules with between 10 and 14 heavy atoms. Our two best models, which have different accuracy/speed trade-offs, enable the efficient prediction of G4MP2-level energies for large molecules and are available through a simple web interface.

The authors report on pulsed laser powder bed fusion fabrication of nitinol (NiTi) shape memory materials. The authors first performed single-track laser parameter sweeps to assess melt pool stability and determine energy parameters and hatch spacing for larger builds. The authors then assessed the melt pool chemistry as a function of laser energy density and build plate composition. Brittle intermetallics were found to form at the part/build plate interface for both N200 and Ti-6-4 substrates. The intermetallic formation was reduced by building on a 50Ni–50Ti substrate, but delamination still occurred due to thermal stresses upon cooling. The authors were able to overcome delamination on all substrates and fabricate macroscopic parts by building a lattice support structure, which is both compliant and controls heat transfer into the build plate. This approach will enable scalable fabrication of complex NiTi parts.

Advancements in temporal and spatial resolutions of microscopes promise to expand the frontiers of understanding in materials science. Imaging techniques produce images at a high-frame rate, streaming out a tremendous amount of data. Analysis of all these images is time-consuming and labor intensive, creating a bottleneck in material discovery that needs to be overcome. This paper summarizes recent progresses in machine learning and data science for expediting and automating material image analysis. The discussion covers both static image and dynamic image analyses, followed by remarks concerning ongoing efforts and future needs in automated image analysis that accelerates material discovery.

Ab initio microkinetic modeling was performed to study ethanol conversion to acetaldehyde on Pt-based bimetallic alloys in a non-oxidative environment. Alloying Pt with Au, Ag, Cu, Co, Ni, Zn, Cd, Al, Ga, In, Tl, Ge, Sn, Pb, As, and Sb showed an increase in product turnover by at least an order of magnitude compared with Pt at 423 K. This was correlated to the increased stabilization of CH3CHO species over these alloys. Among the alloy candidates; Pt3Cu, Pt3Zn, Pt3Ga, Pt3Ge, Pt3Sb, and Pt3Pb were found to be more active than Pt.

Insects have more than a million described species and represent more than half of all known living organisms. However, little is known about the operation and functions of the insect body, particularly their remarkable leg joints. This study is focused on partly filling this knowledge gap by using nanoindentation instruments to characterize the mechanical properties of leg joints from three different insects: a beetle, a mantis, and a dragonfly nymph. For all insect species, the tibia joint had the largest effective elastic moduli, followed by the femur joint, while the exocuticle had the smallest values.

Machine learning (ML) has been perceived as a promising tool for the design and discovery of novel materials for a broad range of applications. In this prospective paper, we summarize recent progress in the applications of ML to composite materials modeling and design. An overview of how different types of ML algorithms can be applied to accelerate composite research is presented. This framework is envisioned to revolutionize approaches to design and optimize composites for the next generation of materials with unprecedented properties.

The authors report the microwave-assisted hydrothermal synthesis of α-La(IO3)3 nanocrystals doped with Yb3+ and Er3+ ions, along with their structural and optical characterizations. 50-nm-sized α-La0.9−xYb0.1Erx(IO3)3 nanocrystals with x = 0.005, 0.01, and 0.02 were synthesized and dispersed in ethylene glycol. The as-obtained suspensions exhibit both second harmonic generation (SHG) signal and up-conversion photoluminescence (UC-PL) without interplay between the two signals under near-infrared resonant excitation. The relative intensity of SHG and UC-PL emission can be modulated according to the excitation wavelength. The most intense UC-PL signal is obtained from a 980-nm excitation, thanks to the sensitization of Er3+ by Yb3+.

The authors demonstrate that gold-binding peptides displayed on the outer membrane of Escherichia coli enhance bioelectrochemical charge transfer by binding gold nanoparticles. Microbial fuel cells were run with different gold-binding peptides displayed and with different nanoparticle sizes, and the results were correlated with transmission electron microscopy (TEM) imaging of nanoparticle binding. When a gold-binding peptide is displayed and 5 nm gold nanoparticles are present, up to 4× power generation over E. coli not displaying a gold-binding peptide is observed. While an enhanced current is observed using the previously published M6G9, the largest enhancement is observed when a new longer peptide named M9G18 is used.

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.

Multifunctional materials with excellent biocompatibility and electron-transport properties are critical for the pursuit of point-of-care biosensing devices. The authors report the synthesis of zinc oxide–reduced graphene oxide (ZnO–rGO) nanocomposite for the fabrication of an electrochemical immunosensing test-bed for noninvasive onsite detection of oral cancer biomarker (interleukin-8, IL8). The immunosensor showed successful detection of IL8 at low concentration ranges, i.e., 100 fg/mL–5 ng/mL with a sensitivity of 12.46 ± 0.82 µA mL/ng and a detection limit of 51.53 ± 0.43 pg/mL. These results have been validated through in vitro investigations using real saliva samples spiked with IL8.

The effective charge of an element is a parameter characterizing the electromigration effect, which can determine the reliability of interconnection in electronic technologies. In this work, machine learning approaches were employed to model the effective charge (z*) as a linear function of physically meaningful elemental properties. Average fivefold (leave-out-alloy-group) cross-validation yielded root-mean-square-error divided by whole data set standard deviation (RMSE/σ) values of 0.37 ± 0.01 (0.22 ± 0.18), respectively, and R2 values of 0.86. Extrapolation to z* of totally new alloys showed limited but potentially useful predictive ability. The model was used in predicting z* for technologically relevant host–impurity pairs.

We report on the structural evolution of diamond-like carbon (DLC) films by the nanosecond pulsed laser annealing process. DLC film is coated on cemented carbide (WC-Co) by cathodic arc ion plating, which is then annealed by ArF laser (193 nm, 20 ns) at different laser fluences (0.9–1.7 J/cm2). Upon laser annealing, Raman spectra divulge higher sp3 fractions accompanied by a blue shift in the G-peak position, which indicates the changes of sp2 sites from rings to chains. At higher fluence (>1.2 J/cm2), the film converts into reduced graphene oxide confirmed by its Raman-active vibrational modes: D, G, and 2D.

We report the synthesis of a novel polymer gel electrolyte primarily based on cellulose extracted from wood along with gelatin, polyacrylic acid (PAA) and potassium hydroxide (KOH) added as additives in minute amounts in various stages. We also study and report the variation of ionic conductivity with variation of various additives. We found that, with variation of additives to hydrogel, its stability and degree of crystallinity are varied. The results were confirmed using x-ray diffraction and Fourier transform infrared spectra studies. An average best ionic conductivity of 96.89mS/cm was reported for a hydrogel: gelatin: PAA: KOH system, which is one of the best reported values of ionic conductivity for gel electrolytes.

Inkjet printing is a promising technology providing cost-effective method for processing various materials on deformable substrates. In this work, linear and serpentine inkjet printed interconnects on two different substrates were fabricated and electromechanically characterized. A particular attention was given to the optimization of the process parameters; high quality can be achieved only printing slowly in vertical direction and optimizing the drop spacing to the specific pattern. The electromechanical results showed that the geometrical layout and printing direction strongly affect the printing quality and the electromechanical response; serpentine shapes should be preferred to straight interconnects as better gauge factors are obtained.

A method for rapid quantitative imaging of dopant distribution using secondary ion mass spectrometry (SIMS) is described. The method is based on SIMS imaging of the cross-section of a reference sample with a known concentration profile. It is demonstrated for the case of boron quantification in silicon in a SIMS imaging mode. A nonlinear relationship between the secondary ion intensity and the concentration is observed. A detection limit of 3 (±2) × 1017 at./cm3 (~6 ppm) is determined with 39 nm pixel-size for the used experimental conditions. As an application example, a boron concentration profile in a passivating contact deposited on a textured Si surface is analyzed.

In the three-phase (pure donor, pure acceptor, and mixed phases) morphologies of organic solar cells, the mixed phases produce an energy cascade that promotes the generation of free carriers. However, how to optimize the content of the mixed phases is a challenging problem. The authors proposed to control different content of mixed phases in DRTB-T and IDIC blends by additive and solvent vapor annealing (SVA). The authors first formed the largest extent amount of mixed phases by the additive cinene (2%) to inhibit the crystallization of DRTB-T and IDIC. And then, different amounts of mixed phases were achieved by further SVA for different times (from 0 to 50 s) to increase the content of pure DRTB-T and IDIC phases. The energetic offsets (ΔE) of pure and mixed phases gradually decrease from 0.529 to 0.477 eV for different content of mixed phases. When ΔE was 0.498 eV, the highest photocurrent density (Jsc) was obtained. The power conversion efficiency was increased from 3.23% (without any treatment) to 8.54%. Therefore, the authors demonstrated that the optimized content of the mixed phases is critical to device performance.

There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties.