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We developed a magnesium/sodium (Mg/Na) hybrid battery using a hierarchical disk-whisker FeSe2architecture (HD-FeSe2) as the cathode material and a modified dual-ion electrolyte. The polarizable Se2–anion reduced the Mg2+migration barrier, and the 3D configuration possessed a large surface area, which facilitated both Mg2+/Na+cation diffusion and electron transport. The dual-ion salts with NaTFSI in ether reduced the Mg plating/stripping overvoltage in a symmetric cell. The hybrid battery exhibited an energy density of 260.9 Wh kg–1and a power density of 600.8 W kg–1at 0.2 A g–1. It showed a capacity retention of 154 mAh g–1and a Coulombic efficiency of over 99.5% under 1.0 A g–1after 800 long cycles. The battery also displayed outstanding temperature tolerance. The findings of 3D architecture as cathode material and hybrid electrolyte provide a pathway to design a highly reliable Mg/Na hybrid battery.

An experimental design by combining hydrogel and ethylenediaminetetraacetic acid disodium salt (EDTA) for visually displaying chemical complexation reactions is developed. The Na+ions on the α-l-guluronic acid (G) unit of the sodium alginate molecule undergo an ion exchange with a divalent cation, so that the G units are stacked to create a cross-linked network, forming a hydrogel. EDTA is inserted into the as-prepared hydrogel, producing transparent hydrogel microspheres containing EDTA, which are considered to be color-changeable pearls on the basis of the chelation of EDTA when reacting with several metal ions. By using different stability constants of the same typed coordination complexes, the color of pearls is changeable. The process and mechanism of the complex reaction are investigated by using elemental mapping, Fourier transform infrared reflection (FTIR) and Raman spectra. Moreover, all the instruments and chemicals for this experiment are included in one single portable experimental kit. The kit is small, easy to carry, cheap, and safe, and it enables easy display of related chemical reactions. By using the developed kit, the students’ hands-on experience can be improved. The experimental operation is simple, green, safe, and interesting, which enables the engineering of color-changeable pearls to be broadly used including the chemical experiment courses in universities, experimental teaching in schools, and the chemical popularization activities for the public.

Despite extensive studies, the development of solid-state batteries (SSBs) has not yet met expectations, owing mainly to the lack of suitable solid electrolytes (SEs) that exhibit low electronic conductivity (σe), high ionic conductivity (σi), and good stability. Here, we propose an effective target-driven framework for holistic identifying promising garnet-type SEs. Using artificial intelligence (AI) technologies, we accurately predict the σewith a mean absolute error of 0.25 eV, achieving a computed speed that is ~109faster than ab initio calculations. Successfully, from 29,008 garnets, we discovered 12 promising super Li-ion conductors for SEs with σe< 3.6 × 10−30S cm−1, σi> 10−4S cm−1(up to 3.24 S cm−1), and good thermal stability at room temperature and high temperature based on rigorous ab initio validation. These emerging SEs are expected to be used in Li-ion SSBs, thus improving the safety, performance, and lifetime of state-of-the-art energy storage technology. This approach directly cuts across at least 95 years of computational cycles to screen SEs, resulting in significant cost savings and helping us enter an electrified future that relies less on fossil fuels.

Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trial-and-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is ∼108faster than ab initio calculations. From ∼23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.0–2.0 eV), where two have direct band gaps and low effective masses. They all show good thermal stabilities that are hopefully able to be synthesized. The proposed ML workflow immensely shortens the screening cycle for double perovskites, which will greatly promote the development and application of photovoltaic devices.

Machine learning (ML) can replace mechanism-based solutions, such as first-principle calculation, for speeding up fundamental researches. Although ML has the benefits of representing the material's properties with critical descriptors without involving the physical/chemical mechanisms, the reliability of data-driven models remain a great challenge because of the scarcity and irregular distribution of data sets. Here, we develop several models with different input features and ML methods. We find the artificial neural network (ANN) with reasonable features that can greatly alleviate these two challenges by a case study of early prediction of supercapacitors (SCs) cycle lives. We generate a comprehensive data set consisting 88 commercial SCs cycled under different charging strategies, with widely varying cycle lives up to 10,000 cycles. Based on the ANN model, we achieve the early prediction of SCs' cycle life with the test errors less than 10.9%, only using the first 16% cycles, and such error could be further tuned by the data set. The proposed model is suitable for training widely distributed data set and has accurate early diagnosis and prediction ability for the performance of complex SC systems.