Ductilization of Nanoporous Ceramics by Crystallinity Control.

Synthesizing ceramic materials with a significant amount of deformability is one of the most important engineering pursuits. In this study, we demonstrate the emergence of metal-like plasticity through the crystallinity control in the monolithic zirconia with the vertically aligned honeycomb-like periodic nanopore structures fabricated using the anodizing technique. The crystalline orders of the nanoporous zirconia films vary between monoclinic, tetragonal, and amorphous phases after the heat treatment and/or proton irradiation, whereas the vertical pore structures are maintained. The micropillar compression tests on those samples reveal a large amount of plasticity, more than 20% of total stains, in the as-anodized and proton-irradiated samples, both of which contain the amorphous phase. In contrast, the fully crystallized zirconia that resulted from annealing at 500 °C shows the brittle failure, the typical characteristic of conventional ceramic foams. These results offer a new opportunity for the nanoporous ceramic materials to be used in various applications, benefited from the tunable structural stability.

A novel route to the formation of 3D nanoflower-like hierarchical iron oxide nanostructure.

The present work reports the formation of 3D nanoflower-like morphology of iron alkoxide via the anodization of Fe sheet in ethylene glycol (EG) electrolyte. XRD, FESEM, EDX, XPS, Raman and FTIR are applied to characterize the samples. SEM results show that the as-anodized sample is composed of 3D nanoflowers with hierarchical nanosheets beneath it. The average width of the nanoflower petal is ∼25 nm and the length is about 1 µm. The 3D nanoflowers are transformed into spherical nanoparticles (NPs) with uniform size when calcined at elevated temperature. XRD and Raman results indicate that the 3D nanoflowers consist of akaganeite, which transforms into magnetite and hematite by annealing. XPS and FTIR results confirm that the nanoflowers contain significant amounts of F, C and OH, which are drastically decreased after annealing. The formation of 3D nanoflower-like morphology can be attributed to EG. A possible formation mechanism of 3D nanoflowers and their transformation into NPs is proposed. We showed that the morphology of the as-anodized iron oxide can be tailored simply by changing the electrolyte. The anodization of Fe sheet in glycerol-based electrolyte under identical conditions produced nanotubes.

Plasmon-enhanced photocatalytic hydrogen production over visible-light responsive Cu/TiO2.

Cheap and visible-light responsive Cu/TiO2 photocatalysts were fabricated by illuminating ultraviolet (UV) to a mixture of TiO2 nanoparticles (NPs) and Cu2O NPs in an evacuated reaction chamber. The Cu2O NPs were reduced by UV in an oxygen-free reaction chamber, and hence, metallic Cu NPs with size less than 5 nm were uniformly loaded on TiO2. Due to the plasmon resonance of the Cu NPs, the Cu/TiO2 exhibited a good performance of water-splitting hydrogen production under visible light in the presence of glycerol as a hole scavenger. The optimum hydrogen production rate of Cu/TiO2 was 0.24 mmol h(-1) g(-1). The Cu/TiO2 also showed high stability of the photocatalytic performance in the evacuated chamber; however, the visible-light responsive photocatalytic properties dramatically and rapidly decreased when exposed to air.

1.5 million materials narratives generated by chatbots.

The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered material examples in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical attributes. To address this imbalance, we generated a dataset consisting of 1,453,493 natural language-material narratives from OQMD, Materials Project, JARVIS, and AFLOW2 databases based on ab initio calculation results that are more evenly distributed across the periodic table. The generated text narratives were then scored by both human experts and GPT-4, based on three rubrics: technical accuracy, language and structure, and relevance and depth of content, showing similar scores but with human-scored depth of content being the most lagging. The integration of multimodal data sources and large language models holds immense potential for AI frameworks to aid the exploration and discovery of solid-state materials for specific applications of interest.

Deep contrastive learning of molecular conformation for efficient property prediction.

Data-driven deep learning algorithms provide accurate prediction of high-level quantum-chemical molecular properties. However, their inputs must be constrained to the same quantum-chemical level of geometric relaxation as the training dataset, limiting their flexibility. Adopting alternative cost-effective conformation generative methods introduces domain-shift problems, deteriorating prediction accuracy. Here we propose a deep contrastive learning-based domain-adaptation method called Local Atomic environment Contrastive Learning (LACL). LACL learns to alleviate the disparities in distribution between the two geometric conformations by comparing different conformation-generation methods. We found that LACL forms a domain-agnostic latent space that encapsulates the semantics of an atom's local atomic environment. LACL achieves quantum-chemical accuracy while circumventing the geometric relaxation bottleneck and could enable future application scenarios such as inverse molecular engineering and large-scale screening. Our approach is also generalizable from small organic molecules to long chains of biological and pharmacological molecules.

Machine learning traction force maps of cell monolayers.

Cellular force transmission across a hierarchy of molecular switchers is central to mechanobiological responses. However, current cellular force microscopies suffer from low throughput and resolution. Here we introduce and train a generative adversarial network (GAN) to paint out traction force maps of cell monolayers with high fidelity to the experimental traction force microscopy (TFM). The GAN analyzes traction force maps as an image-to-image translation problem, where its generative and discriminative neural networks are simultaneously cross-trained by hybrid experimental and numerical datasets. In addition to capturing the colony-size and substrate-stiffness dependent traction force maps, the trained GAN predicts asymmetric traction force patterns for multicellular monolayers seeding on substrates with stiffness gradient, implicating collective durotaxis. Further, the neural network can extract experimentally inaccessible, the hidden relationship between substrate stiffness and cell contractility, which underlies cellular mechanotransduction. Trained solely on datasets for epithelial cells, the GAN can be extrapolated to other contractile cell types using only a single scaling factor. The digital TFM serves as a high-throughput tool for mapping out cellular forces of cell monolayers and paves the way toward data-driven discoveries in cell mechanobiology.

Fabrication of Uniform Nanoporous Oxide Layers on Long Cylindrical Zircaloy Tubes by Anodization Using Multi-Counter Electrodes.

We have presented a method to prepare a uniform anodic nanoporous oxide film on the surface of a cylindrical zircaloy (Zr) tube. The distribution of the electric field around the Zr tube determines the distribution of the thickness of the anodic nanoporous oxide film. The electric field generated when a cylindrical Zr tube is electrochemically anodized was simulated by using commercial code COMSOL. When four Pt wires were used as counter electrodes, a uniform electric field was achieved with minimal use of Pt. Based on the simulation results, a cylindrical Zr tube was anodized and the distribution of the thickness of the anodic nanoporous oxide layer was measured by FESEM. Also, mass production of uniform nanoporous anodic oxide films was possible by symmetrically arranging the zircaloy tubes and Pt wires.

Controlled Fabrication of Nanoporous Oxide Layers on Zircaloy by Anodization.

We have presented a mechanism to explain why the resulting oxide morphology becomes a porous or a tubular nanostructure when a zircaloy is electrochemically anodized. A porous zirconium oxide nanostructure is always formed at an initial anodization stage, but the degree of interpore dissolution determines whether the final morphology is nanoporous or nanotubular. The interpore dissolution rate can be tuned by changing the anodization parameters such as anodization time and water content in an electrolyte. Consequently, porous or tubular oxide nanostructures can be selectively fabricated on a zircaloy surface by controlling the parameters. Based on this mechanism, zirconium oxide layers with completely nanoporous, completely nanotubular, and intermediate morphologies between a nanoporous and a nanotubular structure were controllably fabricated.

Formation of self-organized Zircaloy-4 oxide nanotubes in organic viscous electrolyte via anodization.

This work reports the formation of self-organized Zircaloy-4 (Zr-4) oxide nanotubes in viscous organic ethylene glycol (EG) electrolyte containing a small amount of fluoride salt and deionized (DI) water via an electrochemical anodization. The structure, morphology, and composition of the Zr-4 oxide nanotubes were studied using X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), EDX, and XPS. SEM results showed that the length of the nanotubes is approximately 13 µm, and TEM results showed that the inner diameter of the Zr-4 oxide nanotubes is approximately 20 nm with average wall thickness of approximately 7 nm. XRD and selected area electron diffraction pattern (SAED) results confirmed that the as-anodized Zr-4 oxide nanotubes have cubic crystalline structure. Both cubic and monoclinic phases were found after annealing of Zr-4 oxide nanotubes. The tubular structure morphology of Zr-4 oxide nanotubes did not remain intact after annealing which is attributed to the elimination of F species from the annealed nanotubes.