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In this paper, a microheater that can absorb thermal stress and has a large heating area is demonstrated by optimizing the structure and process of the microheater. Four symmetrically distributed elongated support beam structures were machined around the microheater via deep silicon etching. This design efficiently mitigates the deformation of the heated region caused by thermal expansion and enhances the structural stability of the microheater. The updated microheater no longer converts the work area into a thin film; instead, it creates a stable heating platform that can uniformly heat a work area measuring 10 × 10 mm2. The microheater is verified to have high temperature uniformity and structural stability in finite element simulation. Finally, thorough investigations of electrical–thermal–structural characterization were conducted. The test findings show that the new microheater can achieve 350 °C with a power consumption of 6 W and a thermal reaction time of 22 s. A scan of its whole plane reveals that the surface of the working area of the new microheater is flat and does not distort in response to variations in temperature, offering good structural stability.

With the use of Li-ion batteries, Li-ion batteries will experience unavoidable aging, which can cause battery safety issues, performance degradation, and inaccurate SOC estimation, so it is necessary to predict the state of health (SOH) of Li-ion batteries. Existing methods for Li-ion battery state of health assessment mainly focus on parameters such as constant voltage charging time, constant current charging time, and discharging time, with little consideration of the impact of changes in Li-ion battery temperature on the state of health of Li-ion batteries. In this paper, a new prediction method for Li-ion battery health state based on the surface difference temperature (DT), incremental capacity analysis (ICA), and differential voltage analysis (DVA) is proposed. Five health factors are extracted from each of the three curves as input features to the model, respectively, and the weights, thresholds, and number of hidden layers of the Elman neural network are optimized using the Whale of a Whale Algorithm (WOA), which results in an average decrease of 43%, 49%, and 46% in MAE, RMSE, and MAPE compared to the Elman neural network. For the problem where the three predictions depend on different sources, the features of the three curves are fused using the weighted average method and predicted using the WOA–Elman neural network, whose MAE, RMSE, and MAPE are 0.00054, 0.0007897, and 0.06547% on average. The results show that the proposed method has an overall error of less than 2% in SOH prediction, improves the accuracy and robustness of the overall SOH estimation, and reduces the computational burden to some extent.

Cadmium (Cd) accumulation in soil is a serious form of heavy metal pollution affecting environmental safety and human health. In order to clarify the tolerance mechanisms to Cd-contaminated soils under N deposition, changes in plant growth, root architecture and physiological characteristics of Eleocarpus glabripetalus seedlings under combined nitrogen (N) and cadmium (Cd) treatments were determined in this study. The results indicated that Cd-induced negative effects inhibited the growth of E. glabripetalus seedlings through increased underground biomass allocation, and affected transpiration and respiratory processes, resulting in a decreased soluble sugars concentration in leaves and non-structural carbohydrates (NSC) in the roots. Root systems might play a major role in Cd absorption. Cd stress restricted the growth of fine roots (

The authors wish to make the following correction to their paper [...]

Hypericum acmosepalum belongs to the Hypericum genus of the Guttiferae family. The characteristic components in Hypericum are mainly a series of polycyclic polyprenylated acylphloroglucinols (PPAPs), flavonoids, and xanthones. Among them, the PPAPs have received much attention due to their novel structures and diverse pharmacological activities and have become hot spots in organic chemistry and medicinal chemistry. However, there are few reports about the chemical constituents of Hypericum acmosepalum at present, especially the PPAPs. This research is dedicated to the study of the air-dried aerial parts of Hypericum acmosepalum, which were extracted with 95% EtOH under reflux, then suspended and successively partitioned with petroleum ether and ethyl acetate. Five PPAP derivatives were obtained using various chromatographic techniques, and their structures were determined by NMR spectroscopic data, including two new phloroglucinol derivatives, hyperacmosin A (1) and hyperacmosin B (2). Those compounds were evaluated for their neuroprotective effect using two models.