The Heat shock factor 20-HSF4-Cellulose synthase A2 module regulates heat stress tolerance in maize.

Temperature shapes the geographical distribution and behavior of plants. Understanding the regulatory mechanisms underlying the plant heat response is important for developing climate-resilient crops, including maize (Zea mays). To identify transcription factors that may contribute to the maize heat response, we generated a dataset of short- and long-term transcriptome changes following a heat treatment time course in the inbred line B73. Co-expression network analysis highlighted several transcription factors, including the class B2a heat shock factor (HSF) ZmHSF20. Zmhsf20 mutant seedlings exhibited enhanced tolerance to heat stress. Furthermore, DNA affinity purification sequencing and Cleavage Under Targets and Tagmentation (CUT&Tag) assays demonstrated that ZmHSF20 binds to the promoters of Cellulose synthase A2 (ZmCesA2) and three class A Hsf genes, including ZmHsf4, repressing their transcription. We showed that ZmCesA2 and ZmHSF4 promote the heat response, with ZmHSF4 directly activating ZmCesA2 transcription. In agreement with the transcriptome analysis, ZmHSF20 inhibited cellulose accumulation and repressed the expression of cell wall-related genes. Importantly, the Zmhsf20 Zmhsf4 double mutant exhibited decreased thermotolerance, placing ZmHsf4 downstream of ZmHsf20. We proposed an expanded model of the heat stress response in maize, whereby ZmHSF20 lowers seedling heat tolerance by repressing ZmHsf4 and ZmCesA2, thus balancing seedling growth and defense.

In Situ Surface Reconstruction toward Planar Heterojunction for Efficient and Stable FAPbI3 Quantum Dot Solar Cells.

Pure-phase α-FAPbI3 quantum dots (QDs) are the focus of an increasing interest in photovoltaics due to their superior ambient stability, large absorption coefficient, and long charge-carrier lifetime. However, the trap states induced by the ligand-exchange process limit the photovoltaic performances. Here, a simple post treatment using methylamine thiocyanate is developed to reconstruct the FAPbI3 -QD film surface, in which a MAPbI3 capping layer with a thickness of 6.2 nm is formed on the film top. This planar perovskite heterojunction leads to a reduced density of trap-states, a decreased band gap, and a facilitated charge carrier transport. As a result, a record high power conversion efficiency (PCE) of 16.23% with negligible hysteresis is achieved for the FAPbI3 QD solar cell, and it retains over 90% of the initial PCE after being stored in ambient environment for 1000 h.

Thermal-Induced Performance Decay of the State-of-the-Art Polymer: Non-Fullerene Solar Cells and the Method of Suppression.

Improving thermal stability is of great importance for the industrialization of polymer solar cells (PSC). In this paper, we systematically investigated the high-temperature thermal annealing effect on the device performance of the state-of-the-art polymer:non-fullerene (PM6:Y6) solar cells with an inverted structure. Results revealed that the overall performance decay (19% decrease) was mainly due to the fast open-circuit voltage (VOC, 10% decrease) and fill factor (FF, 10% decrease) decays whereas short circuit current (JSC) was relatively stable upon annealing at 150 °C (0.5% decrease). Pre-annealing on the ZnO/PM6:Y6 at 150 °C before the completion of cell fabrication resulted in a 1.7% performance decrease, while annealing on the ZnO/PM6:Y6/MoO3 films led to a 10.5% performance decay, indicating that the degradation at the PM6:Y6/MoO3 interface is the main reason for the overall performance decay. The increased ideality factor and reduced built-in potential confirmed by dark J - V curve analysis further confirmed the increased interfacial charge recombination after thermal annealing. The interaction of PM6:Y6 and MoO3 was proved by UV-Vis absorption and XPS measurements. Such deep chemical doping of PM6:Y6 led to unfavorable band alignment at the interface, which led to increased surface charge recombination and reduced built-in potential of the cells after thermal annealing. Inserting a thin C60 layer between the PM6:Y6 and MoO3 significantly improved the cells' thermal stability, and less than 2% decay was measured for the optimized cell with 3 nm C60.

Microstructure Design of High-Entropy Alloys Through a Multistage Mechanical Alloying Strategy for Temperature-Stable Megahertz Electromagnetic Absorption.

Developing megahertz (MHz) electromagnetic wave (EMW) absorption materials with broadband absorption, multi-temperature adaptability, and facile preparation method remains a challenge. Herein, nanocrystalline FeCoNiCr0.4Cu0.2 high-entropy alloy powders (HEAs) with both large aspect ratios and thin intergranular amorphous layers are constructed by a multistage mechanical alloying strategy, aiming to achieve excellent and temperature-stable permeability and EMW absorption. A single-phase face-centered cubic structure with good ductility and high crystallinity is obtained as wet milling precursors, via precisely controlling dry milling time. Then, HEAs are flattened to improve aspect ratios by synergistically regulating wet milling time. FeCoNiCr0.4Cu0.2 HEAs with dry milling 20 h and wet milling 5 h (D20) exhibit higher and more stable permeability because of larger aspect ratios and thinner intergranular amorphous layers. The maximum reflection loss (RL) of D20/SiO2 composites is greater than - 7 dB with 5 mm thickness, and EMW absorption bandwidth (RL < - 7 dB) can maintain between 523 and 600 MHz from - 50 to 150 °C. Furthermore, relying on the "cocktail effect" of HEAs, D20 sample also exhibits excellent corrosion resistance and high Curie temperature. This work provides a facile and tunable strategy to design MHz electromagnetic absorbers with temperature stability, broadband, and resistance to harsh environments.

Highly insulating thermoplastic nanocomposites based on a polyolefin ternary blend for high-voltage direct current power cables.

Octyl-silane-coated Al2O3 nanoparticles are found to be a promising conductivity-reducing additive for thermoplastic ternary blends comprising low-density polyethylene (LDPE), isotactic polypropylene and a styrenic copolymer. The ternary blend nanocomposites were prepared by compounding the blend components together with an LDPE-based masterbatch that contained the nanoparticles. The nanoparticles did not affect the superior stiffness of the ternary blends, compared to neat LDPE, between the melting temperatures of the two polyolefins. As a result, ternary blend nanocomposites comprising 38 wt% polypropylene displayed a storage modulus of more than 10 MPa up to at least 150 °C, independent of the chosen processing conditions. Moreover, the ternary blend nanocomposites featured a low direct-current electrical conductivity of about 3 × 10-15 S m-1 at 70 °C and an electric field of 30 kV mm-1, which could only be achieved through the presence of both polypropylene and Al2O3 nanoparticles. This synergistic conductivity-reducing effect may facilitate the design of more resistive thermoplastic insulation materials for high-voltage direct current (HVDC) power cables.

FeCoNiCr0.4CuX High-Entropy Alloys with Strong Intergranular Magnetic Coupling for Stable Megahertz Electromagnetic Absorption in a Wide Temperature Spectrum.

Electromagnetic (EM) absorbers serving in the megahertz (MHz) band and a wide temperature range (from -50 to 150 °C) require high and temperature-stable permeability for outstanding EM absorption performance. Herein, FeCoNiCr0.4CuX high-entropy alloy (HEA) powders with a unique nanocrystalline structure separated by a thin amorphous layer (NTA) are designed to improve permeability and enhance intergranular coupling. Simultaneously, the long-range anisotropy is introduced via devising the preparation process and tuning the chemical composition, such that the intergranular exchange interaction is further strengthened for stable permeability and EM wave absorption in a wide temperature range. FeCoNiCr0.4Cu0.2 HEAs exhibit a near-zero permeability temperature coefficient (5.7 × 10-7 °C-1) a in wide temperature range. The maximum reflection loss (RL) of FeCoNiCr0.4Cu0.2 HEAs is higher than -7 dB with 5 mm thickness at -50-150 °C, and the absorption bandwidth (RL < -7 dB) can almost cover 400-1000 MHz. Furthermore, FeCoNiCr0.4Cu0.2 HEAs also have a high Curie temperature (770 °C) and distinguished oxidation resistance. The permeability temperature dependence of FeCoNiCr0.4CuX HEAs is investigated in-depth in light of the microstructural change induced by tuning the chemical composition, and a new inspiration is provided for the design of magnetic applications serving in wide temperature, such as transformers, sensors, and EM absorbers.

Simultaneously Achieving Highly Efficient and Stable Polymer:Non-Fullerene Solar Cells Enabled By Molecular Structure Optimization and Surface Passivation.

Despite the tremendous efforts in developing non-fullerene acceptor (NFA) for polymer solar cells (PSCs), only few researches are done on studying the NFA molecular structure dependent stability of PSCs, and long-term stable PSCs are only reported for the cells with low efficiency. Herein, the authors compare the stability of inverted PM6:NFA solar cells using ITIC, IT-4F, Y6, and N3 as the NFA, and a decay rate order of IT-4F > Y6 ≈ N3 > ITIC is measured. Quantum chemical calculations reveal that fluorine substitution weakens the C═C bond and enhances the interaction between NFA and ZnO, whereas the ß-alkyl chains on the thiophene unit next to the C═C linker blocks the attacking of hydroxyl radicals onto the C═C bonds. Knowing this, the authors choose a bulky alkyl side chain containing molecule (named L8-BO) as the acceptor, which shows slower photo bleaching and performance decay rates. A combination of ZnO surface passivation with phenylethanethiol (PET) yields a high efficiency of 17% and an estimated long T80 and Ts80 of 5140 and 6170 h, respectively. The results indicate functionalization of the ß-position of the thiophene unit is an effective way to improve device stability of the NFA.

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification.

Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods. However, the model trained on the interpreted logging data is not effective in predicting new exploration well due to the data distribution discrepancy. In this article, we aim to train a lithology identification model for the target well using a large amount of source-labeled logging data and a small amount of target-labeled data. The challenges of this task lie in three aspects: 1) the distribution misalignment; 2) the data divergence; and 3) the cost limitation. To solve these challenges, we propose a novel active adaptation for logging lithology identification (AALLI) framework that combines active learning (AL) and domain adaptation (DA). The contributions of this article are three-fold: 1) the domain-discrepancy problem in intelligent logging lithology identification is first investigated in this article, and a novel framework that incorporates AL and DA into lithology identification is proposed to handle the problem; 2) we design a discrepancy-based AL and pseudolabeling (PL) module and an instance importance weighting module to query the most uncertain target information and retain the most confident source information, which solves the challenges of cost limitation and distribution misalignment; and 3) we develop a reliability detecting module to improve the reliability of target pseudolabels, which, together with the discrepancy-based AL and PL module, solves the challenge of data divergence. Extensive experiments on three real-world well-logging datasets demonstrate the effectiveness of the proposed method compared to the baselines.

Structural modifications on indole and pyrimidine rings of osimertinib lead to high selectivity towards L858R/T790M double mutant enzyme and potent antitumor activity.

EGFR inhibitors represent a significant milestone for treatment of non-small cell lung cancer, however, they suffer from the acquired drug resistance. Utilizing osimertinib as the lead compound, this work has explored the structural modifications on the indole and pyrimidine rings of osimertinib to generate novel osimertinib derivatives. The in vitro enzymatic and cellular studies showed that the derivatives possessed high selectivity towards double mutant EGFR and potent antitumor activity. Particularly, compound 6b-1, the most active compound, exhibited excellent inhibitory activity against double mutant EGFR (IC50 = 0.18 nM) and wild-type EGFR (IC50 = 2.89 nM) as well as H1975 cells (IC50 = 1.44 nM). Western blot analysis showed that 6b-1 completely inhibited double mutant EGFR and Erk phosphorylation. In vivo test using xenograft model indicated that compound 6b-1 had better antitumor efficacy than osimertinib. More importantly, 6b-1 displayed many advantages in the pharmacokinetic study, including better oral bioavailability and metabolism character.

Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment.

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method.

Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same probability distribution; however, the variations in sedimentary environment and well-logging technique might cause the data drift problem; i.e., data of different wells have different probability distributions. Therefore, the model trained on old wells does not perform well in predicting the lithologies in newly-coming wells, which motivates us to propose a transfer learning method named the data drift joint adaptation extreme learning machine (DDJA-ELM) to increase the accuracy of the old model applying to new wells. In such a method, three key points, i.e., the project mean maximum mean discrepancy, joint distribution domain adaptation, and manifold regularization, are incorporated into extreme learning machine. As found experimentally in multiple wells in Jiyang Depression, Bohai Bay Basin, DDJA-ELM could significantly increase the accuracy of an old model when identifying the lithologies in new wells.

Large nonlinear optical effect in tungsten bronze structures via Li/Na cross-substitutions.

By a simple cross-substitution of A-site Li/Na in tetragonal tungsten bronze (TTB) structures, we successfully synthesized a new niobate compound, Pb2.15(Li0.25Na0.75)0.7Nb5O15, with a superstructure. This compound exhibits a strong second harmonic generation (SHG) up to ∼47 × KDP. The large SHG response is related to strengthened local distortion, manifesting cross-substitution as a possibly general route to improve the NLO effect in stiff and low symmetric structures.

The Role of the Hydrogen Bond between Piperazine and Fullerene Molecules in Stabilizing Polymer:Fullerene Solar Cell Performance.

Piperazine has been recently reported as a stabilizer for polymer:fullerene solar cells that can minimize the "burn-in" degradation of the cell. In this paper, the influence of N-substituents on the stabilization effect of piperazine in P3HT:PC61BM cells was investigated. Results confirmed that only piperazine derivatives (PZs) with N-H bonds showed the stabilization effect, whereas the bis-alkyl-substituted piperazine compounds cannot improve the stability. An efficient photon-induced electron transfer (PET) process between PZ and PC61BM was only detected for the N-H-containing PZ:PC61BM blends, corresponding very well to the stabilization effect of the PZs, which indicates that the PET process between PZ and PC61BM stabilizes the cell performance, and the N-H bond plays a critical role ensuring the PET process and the consequent stabilization effect. Both 1H-NMR spectroscopy and theoretical calculations confirmed the formation of N-H···O-C and N-H···π bonds for the PC61BM:piperazine adduct, which was considered as the driving force that promotes the PET process between these two components. In addition, comparison of the calculated electron affinity energy (EA) and excitation energy (EEx) of PC61BM with/without piperazine confirmed that piperazine doping is able to promote the electron transfer (which leads to the formation of PC61BM anions) than the energy transfer (leads to the formation of PC61BM excitons) between P3HT and PC61BM, which is beneficial for the performance and stability improvement.

Efficiency above 12% for 1 cm2 Flexible Organic Solar Cells with Ag/Cu Grid Transparent Conducting Electrode.

With the rapid progress of organic solar cells (OSCs), improvement in the efficiency of large-area flexible OSCs (>1 cm2) is crucial for real applications. However, the development of the large-area flexible OSCs severely lags behind the growth of the small-area OSCs, with the electrical loss due to the large sheet resistance of the electrode being a main reason. Herein, a high conductive and high transparent Ag/Cu composite grid with sheet resistance <1 Ω sq-1 and an average visible light transparency of 84% is produced as the transparent conducting electrode of flexible OSCs. Based on this Ag/Cu composite grid electrode, a high efficiency of 12.26% for 1 cm2 flexible OSCs is achieved. The performances of large-area flexible OSCs also reach 7.79% (4 cm2) and 7.35% (9 cm2), respectively, which are much higher than those of the control devices with conventional flexible indium tin oxide electrodes. Surface planarization using highly conductive PEDOT:PSS and modification of the ZnO buffer layer by zirconium acetylacetonate (ZrAcac) are two necessary steps to achieve high performance. The flexible OSCs employing Ag/Cu grid have excellent mechanical bending resistance, maintaining high performance after bending at a radius of 2 mm.

Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers.

Autonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%.

The glucose metabolite methylglyoxal inhibits expression of the glucose transporter genes by inactivating the cell surface glucose sensors Rgt2 and Snf3 in yeast.

Methylglyoxal (MG) is a cytotoxic by-product of glycolysis. MG has inhibitory effect on the growth of cells ranging from microorganisms to higher eukaryotes, but its molecular targets are largely unknown. The yeast cell-surface glucose sensors Rgt2 and Snf3 function as glucose receptors that sense extracellular glucose and generate a signal for induction of expression of genes encoding glucose transporters (HXTs). Here we provide evidence that these glucose sensors are primary targets of MG in yeast. MG inhibits the growth of glucose-fermenting yeast cells by inducing endocytosis and degradation of the glucose sensors. However, the glucose sensors with mutations at their putative ubiquitin-acceptor lysine residues are resistant to MG-induced degradation. These results suggest that the glucose sensors are inactivated through ubiquitin-mediated endocytosis and degraded in the presence of MG. In addition, the inhibitory effect of MG on the glucose sensors is greatly enhanced in cells lacking Glo1, a key component of the MG detoxification system. Thus the stability of these glucose sensors seems to be critically regulated by intracellular MG levels. Taken together, these findings suggest that MG attenuates glycolysis by promoting degradation of the cell-surface glucose sensors and thus identify MG as a potential glycolytic inhibitor.