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Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes. 

Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy. 

Abstract Plant cuticles are composed of hydrophobic cuticular waxes and cutin. Very long-chain fatty acids (VLCFAs) are components of epidermal waxes and the plasma membrane and are involved in organ morphogenesis. By screening a barrelclover (Medicago truncatula) mutant population tagged by the transposable element of tobacco (Nicotiana tabacum) cell type1 (Tnt1), we identified two types of mutants with unopened flower phenotypes, named unopened flower1 (uof1) and uof2. Both UOF1 and UOF2 encode enzymes that are involved in the biosynthesis of VLCFAs and cuticular wax. Comparative analysis of the mutants indicated that the mutation in UOF1, but not UOF2, leads to the increased number of leaflets in M. truncatula. UOF1 was specifically expressed in the outermost cell layer (L1) of the shoot apical meristem (SAM) and leaf primordia. The uof1 mutants displayed defects in VLCFA-mediated plasma membrane integrity, resulting in the disordered localization of the PIN-FORMED1 (PIN1) ortholog SMOOTH LEAF MARGIN1 (SLM1) in M. truncatula. Our work demonstrates that the UOF1-mediated biosynthesis of VLCFAs in L1 is critical for compound leaf patterning, which is associated with the polarization of the auxin efflux carrier in M. truncatula. 

Abstract Electrically accelerated self‐healable poly(ionic liquids) copolymers that exhibit resistor‐capacitor (RC) circuit properties are developed. At low alternating current (AC) frequencies these materials behave as a resistor (R), whereas at higher frequencies as a capacitor (C). These properties are attributed to a combination of dipolar and electrostatic interactions in (1‐[(2‐methacryloyloxy)ethyl]‐3‐butylimidazolium bis(trifluoromethyl‐sulfonyl)imide) copolymerized with methyl methacrylate (MMA) monomers to form p(MEBIm‐TSFI/MMA)] copolymers. When the monomer molar ratio (MEBIm‐TSFI:MMA) is 40/60, these copolymers are capable of undergoing multiple damage‐repair cycles and self‐healing is accelerated by the application of alternating 1.0–4.0 V electric field (EF). Self‐healing in the absence of EFs is facilitated by van der Waals (vdW) interactions, but the application of AC EF induces back and forth movement of charges against the opposing force that result in dithering of electrostatic dipoles giving rise to interchain physical crosslinks. Electrostatic inter‐ and intrachain interactions facilitated by copolymerization of ionic liquid monomers with typically dielectric acrylic‐based monomers result in enhanced cohesive energy densities that accelerate the recovery of vdW forces facilitating self‐healing. Incorporating ionic liquids into commodity polymers offers promising uses as green conducting solid polyelectrolytes in self‐healable energy storage, energy‐harvesting devices, and many other applications.