No abstract

Low-dimensional organic–inorganic halide perovskites have attracted interest for their properties in exciton dynamics, broad-band emission, magnetic spin selectivity. However, there is no quantitative model for predicting the structure-directing effect of organic cations on the dimensionality of these low-dimensional perovskites. Here, we report a machine learning (ML)-assisted approach to predict the dimensionality of lead iodide-based perovskites. A literature review reveals 86 reported amines that are classified into “2D”-forming and “non-2D”-forming based on the dimensionality of their perovskites. Machining learning models were trained and tested based on the classification and descriptor features of these ammonium cations. Four structural features, including steric effect index, eccentricity, largest ring size, and hydrogen-bond donor, have been identified as the key controlling factors. On the basis of these features, a quantified equation is created to calculate the probability of forming 2D perovskite for a selected amine. To further illustrate its predicting capability, the built model is applied to several untested amines, and the predicted dimensionality is verified by growing single crystals of perovskites from these amines. This work represents a step toward predicting the crystal structures of low dimensional hybrid halide perovskites using ML as a tool.

Exploring reactions of methanol on TiO2 surfaces is of great importance in both C1 chemistry and photocatalysis. Reported herein is a combined experimental and theoretical calculation study of methanol adsorption and reaction on a mineral anatase TiO2(001)-(1×4) surface. The methanol-to-dimethyl ether (DME) reaction was unambiguously identified to occur by the dehydration coupling of methoxy species at the fourfold-coordinated Ti(4+) sites (Ti(4c)), and for the first time confirms the predicted higher reactivity of this facet compared to other reported TiO2 facets. Surface chemistry of methanol on the anatase TiO2(001)-(1×4) surface is seldom affected by co-chemisorbed water. These results not only greatly deepen the fundamental understanding of elementary surface reactions of methanol on TiO2 surfaces but also show that TiO2 with a high density of Ti(4c) sites is a potentially active and selective catalyst for the important methanol-to-DME reaction.

A new conceptual method termed as suspension 3D printing is demonstrated using self‐healing hydrogel support to create macroscopic structures of liquid metal that exhibits properties indicative of a nonprintable object. The relationships between the process parameters, supporting gel concentration, and the deposited microdroplet geometry are clarified. The smaller nozzle inner diameter, lower flow rate, and higher printing speed will lead to a smaller droplets size. The gel concentration plays a significant role on patterning the droplets space. The results presented can be applied to design the target feature and further optimize the input parameters. Besides, this paper also illustrates the capability and potential application of the method in constructing 3D macrostructures and stereo electronic systems using these liquid metal droplets. Based on this strategy, it is possible to print liquid metal into sophisticated multidimensional and shape transformable functional structures ignoring the effects of fluid instability, gravity, and surface tension. Furthermore, this work can help remove the limits of materials and technical barriers to enable a wide variety of materials to be printed into arbitrary shapes. It is expected that further practices of the methodology will facilitate the advancements in multiscale droplets generation, flexible electronics, encapsulation technologies, biology and medicine, etc.

We present a detailed investigation of the adsorption and oxidation of CO by O 2 at rutile TiO 2 (110). PBE, PBE+U, PBE+U-D methods were tested in calculations to allow a direct comparison of their accuracies. By utilizing PBE+U-D method, we found that the agreement between theoretical and experimental results has been improved. We then adopted PBE+U-D method on rutile TiO 2 (110) with different defects, i.e. Ti interstitials, O vacancies, or both. It is found that (i) CO adsorbs most favorably at the surface site that undergoes the most significant relaxation upon reduction by such defects; (ii) O 2 molecule adsorbs as O 2 -or O 2 2-besides the defects, and its dissociation is favorable only when two O 2-can occur by accepting the four excess electrons brought by a Ti interstitial; (iii) the catalytic cycle of CO oxidation by O adatoms from dissociated O 2 can be maintained with the help of Ti interstitials. These results are of importance to understand the role of different defects in metal oxide catalysts.

Post-treatment is one of the facile and effective approaches to stabilize organic–inorganic hybrid perovskites. In this work, we apply a machine learning technique to study the trend of reactivity of different types of amines, which are used for the post-treatment of organic–inorganic hybrid perovskite films. Fifty amines are classified based on their compatibility with the methylammonium lead iodide films. Machine learning models are constructed from the classification of these amines and their molecular descriptor features. The model has achieved 86% accuracy on predicting the outcomes of whether perovskite films are maintained after post-treatment. By analyzing the constructed models, it was found that amines with fewer hydrogen bond donors and acceptors, more steric bulk, secondary, tertiary amines, and pyridine derivatives tend to have high compatibility with perovskite films.

A novel approach to modulating the chemical state of dopants by engineering the topological features of a glass matrix is presented. The method allows selective stabilization of dopants on a wide range of length scales, from dispersed ions to aggregated clusters to nanoparticles, leading to various intriguing optical phenomena, such as great emission enhancement and ultra-broadband optical amplification.

Strong saturable absorption was observed in MoS2 nanoflowers, which were synthesized by a facile solvothermal method. A MoS2 nanoflower-based saturable absorber with a high modulation depth of 51.8% and a large saturable intensity of 275.5 GW cm(-2) was introduced to the application of passively Q-switched fiber laser generation. Stable passively Q-switched fiber laser pulses at 1.56 μm with a low threshold power of 16.10 mW, high signal-to-noise ratio of 52.5 dB and short pulse duration of 1.9 μs were obtained. More importantly, a high output power of 3.10 mW related to a large pulse energy of about 51.84 nJ can be attained at a very low pump power. The efficiency of the laser reaches 4.71%, which is larger than that of the prepared layered MoS2 and recently reported MoS2-based passively Q-switching operations. Such results imply that the MoS2 nanoflowers are an excellent candidate for a saturable absorber in passively Q-switched fiber lasers at a low pump intensity.