Planar Metasurfaces Enable High‐Efficiency Colored Perovskite Solar Cells

The achievement of perfect light absorption in ultrathin semiconductor materials is not only a long‐standing goal, but also a critical challenge for solar energy applications, and thus requires a redesigned strategy. Here, a general strategy is demonstrated both theoretically and experimentally to create a planar metasurface absorber comprising a 1D ultrathin planar semiconductor film (replacing the 2D array of subwavelength elements in classical metasurfaces), a transparent spacer, and a metallic back reflector. Guided by derived formulisms, a new type of macroscopic planar metasurface absorber is experimentally demonstrated with light near‐perfectly and exclusively absorbed by the ultrathin semiconductor film. To demonstrate the power and simplicity of this strategy, a prototype of a planar metasurface solar cell is experimentally demonstrated. Furthermore, the device model predicts that a colored planar metasurface perovskite solar cell can maintain 75% of the efficiency of its black counterpart despite the use of a perovskite film that is one order of magnitude thinner. The displayed cell colors have high purities comparable to those of state‐of‐the‐art color filters, and are insensitive to viewing angles up to 60°. The general theoretical framework in conjunction with experimental demonstrations lays the foundation for designing miniaturized, planar, and multifunctional solar cells and optoelectronic devices.A type of macroscopic planar metasurface absorber with light near‐perfectly and exclusively absorbed by the ultrathin semiconductor film is theoretically and experimentally demonstrated via a general strategy. Guided by this strategy, colored perovskite solar cells are further designed to meet all the desired characteristics including high power conversion efficiency, high‐purity, tunability, and angle‐insensitive colors.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/1/advs793.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/2/advs793-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146390/3/advs793_am.pd

High‐Purity Hybrid Structural Colors by Enhancing Optical Absorption of Organic Dyes in Resonant Cavity

This work presents a novel approach of incorporating an ultrathin dye film into a classic dielectric‐absorber‐dielectric‐metal resonator configuration for generating high‐purity reflective structural colors. Utilizing a thin film of organic dye having the same color as the targeted reflection color as a part of the cavity layer in the structure, its absorption at complementary color wavelengths is significantly enhanced due to the strong cavity resonances, hence reflection at the unwanted wavelengths strongly suppressed, leading to the improved purity of the desired reflective color. This design principle can be applied to create essentially all colors, and is demonstrated by experiment to produce high‐purity blue and red colors. In addition, the fabricated device exhibits outstanding stability under UV exposure without additional protections compared to traditional organic pigments. The proposed method in this work largely simplifies the design process of high‐purity structural colors, which paves the way for more potential applications in various fields.A simple approach that incorporates an ultrathin dye film into a classic dielectric‐absorber‐dielectric‐metal multilayered structure is presented to produce high‐purity reflective colors. The enhanced optical absorptions of the colored dye layer as a result of the strong cavity resonances can effectively suppress the reflection within the unwanted wavelength range, thus significantly improving the purity of the desired reflective colors.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155972/1/adom202000317.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155972/2/adom202000317_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155972/3/adom202000317-sup-0001-SuppMat.pd

Study on tightly coupled LiDAR-Inertial SLAM for open pit coal mine environment

With the rapid development of artificial intelligence and unmanned and other related disciplines, the intelligence and unmanned of coal mining equipment has become a new trend. The application of intelligent equipment will greatly improve the productivity of coal mine operations as well as personnel safety. In this environment, the existing LIDAR-based Simultaneous localization and mapping (SLAM) solution is prone to positioning drift and large mapping errors. To address these problems, a tightly coupled SLAM algorithm based on LiDAR (Light Detection and Ranging) and IMU (Inertial Measurement Unit) is proposed, which uses both LiDAR and IMU sensors as data inputs.The front-end uses an iterative extended Kalman filter to fuse the pre-processed LiDAR feature points with the IMU data and uses backward propagation to correct the radar motion distortion, the back-end uses the LiDAR relative positional factor to use the LiDAR inter-frame alignment results as a constraint factor together with the loopback factor to complete the global factor map optimization. The robustness and accuracy of the algorithm are verified using open source dataset and open pit coal mine field dataset. The experimental results show that the accuracy of the proposed algorithm is consistent with the current LiDAR SLAM algorithm in the urban structured environment, while the proposed algorithm improves the localization accuracy by 46.00% and 23.15% with higher robustness than the FAST-LIO2 and LIO-SAM tightly coupled algorithms for the open pit coal mine field environment of more than 2000 meters long, respectively

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%

Enhancement of Innate Immune Function in Mice by Bifidobacterium bifidum FL-228.1

In this study, eight potential functional strains were selected to interfered with RAW264.7 murine macrophages and human peripheral blood mononuclear cells (PBMCs). Then, changes in phagocytic activity of RAW264.7 cells and natural killer (NK) cell activity were detected and the screened potential probiotics were further intervened in BALB/c mice to explore their immunomodulatory efficacy in vivo. In cell experiments, the results showed that the intervention of different strains significantly increased the phagocytic activity of RAW264.7 cells (P0.05). In conclusion, Bifidobacterium bifidum FL-228.1 can improve innate immune function and have a more comprehensive effect on the immune system by regulating immune cell activity, cytokine expression and mRNA levels of immune molecules related to antimicrobial peptides

Hydrogel‐Enabled Transfer‐Printing of Conducting Polymer Films for Soft Organic Bioelectronics

The use of conducting polymers such as poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) for the development of soft organic bioelectronic devices, such as organic electrochemical transistors (OECTs), is rapidly increasing. However, directly manipulating conducting polymer thin films on soft substrates remains challenging, which hinders the development of conformable organic bioelectronic devices. A facile transfer‐printing of conducting polymer thin films from conventional rigid substrates to flexible substrates offers an alternative solution. In this work, it is reported that PEDOT:PSS thin films on glass substrates, once mixed with surfactants, can be delaminated with hydrogels and thereafter be transferred to soft substrates without any further treatments. The proposed method allows easy, fast, and reliable transferring of patterned PEDOT:PSS thin films from glass substrates onto various soft substrates, facilitating their application in soft organic bioelectronics. By taking advantage of this method, skin‐attachable tattoo‐OECTs are demonstrated, relevant for conformable, imperceptible, and wearable organic biosensing.The use of hydrogels enables transfer‐printing of poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate thin films from glass substrates onto various soft substrates. Taking advantage of this technique, skin‐attachable organic electrochemical transistors (OECTs) are fabricated on commercially available tattoo paper. Wearable tattoo‐OECTs are further demonstrated with the integration of a wireless readout system.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154307/1/adfm201906016.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154307/2/adfm201906016_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154307/3/adfm201906016-sup-0001-SuppMat.pd

Electrical impedance tomography for non-invasive identification of fatty liver infiltrate in overweight individuals

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson’s correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = −0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis