Luminescence of Sm3+ in double perovskite-type Ba2SrWO6 for insect trapping.

The predominant method for pest control has been the use of pesticides, which have been shown to have detrimental effects on soil, freshwater, and crop quality. Therefore, the development of novel and sustainable crop protection strategies has become increasingly imperative. In this study, a novel orange-red emitting Ba2SrWO6: Sm3+ phosphor was synthesized using the high-temperature solid-state reaction. Under ultraviolet excitation, the phosphors showed obvious emission peaks at 575, 614, and 662 nm. The Ba2SrWO6: Sm3+ was used to fabricate a fluorescence film with polydimethylsiloxane (PDMS), and attracted twice as many insects as the blank control group under 365 nm ultraviolet light. This material holds great potential as a fluorescent agent for insect trapping in the pest control fields of tea, cotton, eggplant, rice, potato, grape, and other agricultural industries. Our findings provide an eco-friendly approach to pest management for the increment of food production.

High Velocity, Low-Voltage Collective In-Plane Switching in (100) BaTiO3 Thin Films.

Ferroelectrics are being increasingly called upon for electronic devices in extreme environments. Device performance and energy efficiency is highly correlated to clock frequency, operational voltage, and resistive loss. To increase performance it is common to engineer ferroelectric domain structure with highly-correlated electrical and elastic coupling that elicit fast and efficient collective switching. Designing domain structures with advantageous properties is difficult because the mechanisms involved in collective switching are poorly understood and difficult to investigate. Collective switching is a hierarchical process where the nano- and mesoscale responses control the macroscopic properties. Using chemical solution synthesis, epitaxially nearly-relaxed (100) BaTiO3 films are synthesized. Thermal strain induces a strongly-correlated domain structure with alternating domains of polarization along the [010] and [001] in-plane axes and 90° domain walls along the [011] or [01 1 ¯ $\bar{1}$ ] directions. Simultaneous capacitance-voltage measurements and band-excitation piezoresponse force microscopy revealed strong collective switching behavior. Using a deep convolutional autoencoder, hierarchical switching is automatically tracked and the switching pathway is identified. The collective switching velocities are calculated to be ≈500 cm s-1 at 5 V (7 kV cm-1 ), orders-of-magnitude faster than expected. These combinations of properties are promising for high-speed tunable dielectrics and low-voltage ferroelectric memories and logic.

Why it is Unfortunate that Linear Machine Learning "Works" so well in Electromechanical Switching of Ferroelectric Thin Films.

Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called "better", "faster", and "less-biased" ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) ß-variational autoencoders (ß-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. To improve the interpretability of the latent space, a variational Kullback-Leibler-divergency regularization is imposed . Finally, regularization scheduling of ß as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.

A new method for evaluating air quality using an ideal grey close function cluster correlation analysis method.

To scientifically and reasonably evaluate air quality with a large amount of monitored data, this paper proposes a new evaluation method called ideal grey close function cluster correlation analysis (IGCFCCA). Taking the air quality in Ningxia Province, China, as an example, according to China's air quality standard, SO2, NO2, PM10, PM2.5 and O3 are selected as evaluation indexes to perform the evaluation. The results show that the air quality in this region in 2018 can be divided into three classifications, among which the relatively poor air quality in March, April and May is the first classification, the better air quality in August and September is the third classification, and the air quality in other months falls under the second classification. Correlation analysis is used to qualitatively determine that these three classifications correspond to first-level air quality in China's air quality standard, and the correlation degree, which is the distance between the three classifications and the first-level air quality, is quantitatively determined. Specifically, the correlation degrees of the first-classification, second-classification and third-classification of air quality are 0.674, 0.697 and 0.71, respectively. The research results indicate potential directions and objectives for air quality management to achieve scientific management.

Morphology and Pattern Control of Diphenylalanine Self-Assembly via Evaporative Dewetting.

Self-assembled peptide nanostructures have unique physical and biological properties and promising applications in electrical devices and functional molecular recognition. Although solution-based peptide molecules can self-assemble into different morphologies, it is challenging to control the self-assembly process. Herein, controllable self-assembly of diphenylalanine (FF) in an evaporative dewetting solution is reported. The fluid mechanical dimensionless numbers, namely Rayleigh, Marangoni, and capillary numbers, are introduced to control the interaction between the solution and FF molecules in the self-assembly process. The difference in the film thickness reflects the effects of Rayleigh and Marangoni convection, and the water vapor flow rate reveals the role of viscous fingering in the emergence of aligned FF flakes. By employing dewetting, various FF self-assembled patterns, like concentric and spokelike, and morphologies, like strips and hexagonal tubes/rods, can be produced, and there are no significant lattice structural changes in the FF nanostructures.