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Geometric crystal structure analysis using three-dimensional Voronoi tessellation provides intuitive insights into the ionic transport behavior of metal-ion electrode materials or solid electrolytes by mapping the void space in a framework onto a network. The existing tools typically consider only the local voids by mapping them with Voronoi polyhedra vertices and then define the mobile ions pathways using the Voronoi edges connecting these vertices. We show that in some structures mobile ions are located on Voronoi polyhedra faces and thus cannot be located by a standard approach. To address this deficiency, we extend the method to include Voronoi faces in the constructed network. This method has been implemented in the CAVD python package. Its effectiveness is demonstrated by 99% recovery rate for the lattice sites of mobile ions in 6,955 Li-, Na-, Mg- and Al-containing ionic compounds extracted from the Inorganic Crystal Structure Database. In addition, various quantitative descriptors of the network can be used to identify and rank the materials and further used in materials databases for machine learning.

In this work, we prepared flexible carbon-fiber/semimetal Bi nanosheet arrays from solvothermalsynthesized carbon-fiber/Bi 2 O 2 CO 3 nanosheet arrays via a reductive calcination process. The flexible carbon-fiber/semimetal Bi nanosheet arrays can function as photocatalysts and photoelectrocatalysts for 2,4-dinitorphenol oxidation. Comparing with carbon-fiber/Bi 2 O 2 CO 3 nanosheet arrays, the newly designed flexible carbon-fiber/semimetal Bi nanosheet arrays show enhanced ultraviolet-visible (UV-vis) light absorption efficiency, photocurrent, photocatalytic and photoelectrocatalytic activities. Photocatalytic analyses indicate that the surface plasmon resonance (SPR) of semimetal Bi occurs under solar-simulated light irradiation during photocatalytic process. The carbon fiber traps the hot electrons exerted from the SPR of semimetal Bi and creates holes in the semimetal Bi nanosheets, which boosts photocatalytic activity of the carbon fiber through plasmonic sensitization. Both photocatalytic experiments and density functional theory (DFT) calculations indicate that the electrons transferred to carbon fiber and the holes created in semimetal Bi contribute to the formation of and •OH, O 2 respectively. The synergistic effect between electrocatalysis and photocatalysis under the solar-simulated light results in almost complete degradation of 2,4-dinitorphenol during the photoelectrocatalytic process.This work realizes a non-noble-metal plasmonic catalyst and provides a new avenue for the commercializaiton of photocatalysis and photoelectrocatalysis using the separable and recyclable carbonfiber/semimetal Bi nanosheet arrays in the environment-related field.

Data-driven machine learning is widely employed in the analysis of materials structure-activity relationship, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, machine learning models encounter the issue of the mismatch between high dimension of feature space and small sample size (for traditional machine learning models) or the mismatch between model parameters and sample size (for deep learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation, and specific machine learning approaches and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of machine learning, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate the materials design and discovery based on machine learning.

TiO2 microtubes with tunable wall thickness have been synthesized by a one-step electrospinning method linked with a calcination process. The wall thickness of TiO2 microtubes can be easily tuned by altering the dosage of liquid paraffin. The influence of the thickness on the light-harvesting ability and separation efficiency of the photogenerated carriers was studied using ultraviolet-visible (UVvis) diffuse reflectance spectroscopy, photoluminescence emission spectroscopy, and photocurrent density measurements. Results show that TiO2 microtubes with an appropriate thickness exhibit enhanced light scattering effect, UV-vis light-harvesting ability, charge separation efficiency, and photocatalytic performance. The degradation rates of rhodamine B and 2,4-dinitrophenol by using TiO2 microtubes synthesized at a dosage of 0.14 g/mL liquid paraffin are 99.9 % within 60 minutes and 97.8 % within 40 minutes, respectively, which are higher than most of the reported values. All these results suggest that our work provides an ideal strategy for adjusting the wall thickness of TiO2 microtubes and new approach to enhance the photocatalytic performance of TiO2.

m-Cresol purple functionalized SERS chips for sensitive detection of pH in the neutral pH range relying on the SERS to SERRS mechanism.

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