Red Carbon Mediated Formation of Cu2O Clusters Dispersed on the Oxocarbon Framework by Fehling's Route and their Use for the Nitrate Electroreduction in Acidic Conditions.

The oligomers of carbon suboxide, known as red carbon, exhibit a highly conjugated structure and semiconducting properties. Upon mild heat treatment, it transforms into a carbonaceous framework rich in oxygen surface terminations, called oxocarbon. In this study, the abundant oxygen functionalities are harnessed as anchors to create oxocarbon-supported nanohybrid electrocatalysts. Starting with single atomic Cu (II) strongly coordinated to oxygen atoms on red carbon, the Fehling reaction leads to the formation of Cu2O clusters. Simultaneously, a covalent oxocarbon framework emerges via cross-linking, providing robust support for Cu2O clusters. Notably, the oxocarbon support effectively stabilizes Cu2O clusters of very small size, ensuring their high durability in acidic conditions and the presence of ammonia. The synthesized material exhibits a superior electrocatalytic activity for nitrate reduction under acidic electrolyte conditions, with a high yield rate of ammonium (NH4 +) at 3.31 mmol h-1 mgcat -1 and a Faradaic efficiency of 92.5% at a potential of -0.4 V (vs RHE).

Comprehensive identification and analysis of DUF640 genes associated with rice growth.

Protein domains with conserved amino acid sequences and uncharacterized functions are called domains of unknown function (DUF). The DUF640 gene family plays a crucial role in plant growth, particularly in light regulation, floral organ development, and fruit development. However, there exists a lack of systematic understanding of the evolutionary relationships and functional differentiation of DUF640 within the Oryza genus. In this study, 61 DUF640 genes were identified in the Oryza genus. The expression of DUF640s is induced by multiple hormonal stressors including abscisic acid (ABA), cytokinin (CK), ethylene (ETH), and indole-3-acetic acid (IAA). Specifically, OiDUF640-10 expression significantly increased after ETH treatment. Transgenic experiments showed that overexpressing OiDUF640-10 lines were sensitive to ETH, and seedling length was obstructed. Evolutionary analysis revealed differentiation of the OiDUF640-10 gene in O. sativa ssp. indica and japonica varieties, likely driven by natural selection during the domestication of cultivated rice. These results indicate that OiDUF640-10 plays a vital role in the regulation of rice seedling length.

Unraveling the Activity and Mechanism of TM@g-C4N3 Electrocatalysts in the Oxygen Reduction Reaction.

In this work, a high-throughput screening strategy and density functional theory (DFT) are jointly employed to identify high-performance TM@g-C4N3 (TM = 3d, 4d, 5d transition metals) single-atom catalysts (SACs) for the oxygen reduction reaction (ORR). Comprehensive studies demonstrated that Cu@, Zn@, and Ag@g-C4N3 show high ORR catalytic activities under both acidic and alkaline conditions with favorable overpotentials (ηORR) of 0.70, 0.89, and 0.89 V, respectively; among them, Cu@g-C4N3 is the best candidate. The ORR follows a four-electron mechanism with the final product H2O/OH-. Cu@, Zn@, and Ag@g-C4N3 catalysts also exhibit good thermal (500 K) and electrochemical (0.93-3.14 V) stabilities. Cu@, Zn@, and Ag@g-C4N3 demonstrate superior activities with low ηORR due to its moderate adsorption strength of *OH. The ηORR and the Gibbs free energy changes of *OH (ΔG4(acidic)/ΔG4(alkaline)) resemble a volcano-type relationship under acidic/alkaline conditions, respectively. Additionally, the O-O bond length in *OOH emerged as an effective structural descriptor for rapidly identifying the promising electrocatalysts. This research provides valuable insights into the origin of the ORR activity on TM@g-C4N3 and offers useful guidance for the efficient exploration of high-performance catalyst candidates.

Emerging Two-Dimensional Carbonaceous Materials for Electrocatalytic Energy Conversions: Rational Design of Active Structures through High-Temperature Chemistry.

Electrochemical energy conversion and storage technologies involving controlled catalysis provide a sustainable way to handle the intermittency of renewable energy sources, as well as to produce green chemicals/fuels in an ecofriendly manner. Core to such technology is the development of efficient electrocatalysts with high activity, selectivity, long-term stability, and low costs. Here, two-dimensional (2D) carbonaceous materials have emerged as promising contenders for advancing the chemistry in electrocatalysis. We review the emerging 2D carbonaceous materials for electrocatalysis, focusing primarily on the fine engineering of active structures through thermal condensation, where the design, fabrication, and mechanism investigations over different types of active moieties are summarized. Interestingly, all the recipes creating two-dimensionality on the carbon products also give specific electrocatalytic functionality, where the special mechanisms favoring 2D growth and their consequences on materials functionality are analyzed. Particularly, the structure-activity relationship between specific heteroatoms/defects and catalytic performance within 2D metal-free electrocatalysts is highlighted. Further, major challenges and opportunities for the practical implementation of 2D carbonaceous materials in electrocatalysis are summarized with the purpose to give future material design guidelines for attaining desirable catalytic structures.

Anti-Freezing Electrolytes in Aqueous Multivalent Metal-Ion Batteries: Progress, Challenges, and Optimization Strategies.

Aqueous rechargeable multivalent metal-ion batteries (ARMMBs) have attracted considerable attention due to their high capacity, high energy density, and low cost. However, their performance is often limited by low temperature operation, which requires the development of anti-freezing electrolytes. In this review, we summarize the anti-freezing mechanisms and optimization strategies of anti-freezing electrolytes for aqueous batteries (especially for Zn-ion batteries). Besides, we investigate the possible interactions and side reactions between electrolytes and electrodes. We also analyze the problems between electrolytes and electrodes at low temperature, and propose possible solutions. The research progress in the field of low temperature energy storage for aqueous Mg-ion, Ca-ion, and Al-ion batteries, and the challenges faced in their anti-freezing electrolytes are investigated in detail. Last but not least, the outlook on the energy storage applications of ARMMBs is provided to guide the future research.

Tin (II) Chloride Salt Melts as Non-Innocent Solvents for the Synthesis of Low-Temperature Nanoporous Oxo-Carbons for Nitrate Electrochemical Hydrogenation.

Carbonaceous electrocatalysts offer advantages over metal-based counterparts, being cost-effective, sustainable, and electrochemically stable. Their high surface area increases reaction kinetics, making them valuable for environmental applications involving contaminant removal. However, their rational synthesis is challenging due to the applied high temperatures and activation steps, leading to disordered materials with limited control over doping. Here, a new synthetic pathway using carbon oxide precursors and tin chloride as a p-block metal salt melt is presented. As a result, highly porous oxygen-rich carbon sheets (with a surface area of 1600 m2 g-1) are obtained at relatively low temperatures (400 °C). Mechanistic studies reveal that Sn(II) triggers reductive deoxygenation and concomitant condensation/cross-linking, facilitated by the Sn(II) → Sn(IV) transition. Due to their significant surface area and oxygen doping, these materials demonstrate exceptional electrocatalytic activity in the nitrate-to-ammonia conversion, with an ammonia yield rate of 221 mmol g-1 h-1 and a Faradic efficiency of 93%. These results surpass those of other carbon-based electrocatalysts. In situ Raman studies reveal that the reaction occurs through electrochemical hydrogenation, where active hydrogen is provided by water reduction. This work contributes to the development of carbonaceous electrocatalysts with enhanced performance for sustainable environmental applications.

Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Roots of Rice Seedlings under Nitrogen Deficiency.

Long non-coding RNAs (lncRNAs) regulate gene expression in eukaryotic organisms. Research suggests that lncRNAs may be involved in the regulation of nitrogen use efficiency in plants. In this study, we identified 1628 lncRNAs based on the transcriptomic sequencing of rice roots under low-nitrogen (LN) treatment through the implementation of an integrated bioinformatics pipeline. After 4 h of LN treatment, 50 lncRNAs and 373 mRNAs were significantly upregulated, and 17 lncRNAs and 578 mRNAs were significantly downregulated. After 48 h LN treatment, 43 lncRNAs and 536 mRNAs were significantly upregulated, and 42 lncRNAs and 947 mRNAs were significantly downregulated. Moreover, the interaction network among the identified lncRNAs and mRNAs was investigated and one of the LN-induced lncRNAs (lncRNA24320.6) was further characterized. lncRNA24320.6 was demonstrated to positively regulate the expression of a flavonoid 3'-hydroxylase 5 gene (OsF3'H5). The overexpression of lncRNA24320.6 was shown to improve nitrogen absorption and promote growth in rice seedlings under LN conditions. Our results provide valuable insights into the roles of lncRNAs in the rice response to nitrogen starvation.

Enhancing Local CO2 Adsorption by L-histidine Incorporation for Selective Formate Production Over the Wide Potential Window.

Electrochemical carbon dioxide reduction reaction (CO2 RR) to produce valuable chemicals is a promising pathway to alleviate the energy crisis and global warming issues. However, simultaneously achieving high Faradaic efficiency (FE) and current densities of CO2 RR in a wide potential range remains as a huge challenge for practical implements. Herein, we demonstrate that incorporating bismuth-based (BH) catalysts with L-histidine, a common amino acid molecule of proteins, is an effective strategy to overcome the inherent trade-off between the activity and selectivity. Benefiting from the significantly enhanced CO2 adsorption capability and promoted electron-rich nature by L-histidine integrity, the BH catalyst exhibits excellent FEformate in the unprecedented wide potential windows (>90 % within -0.1--1.8 V and >95 % within -0.2--1.6 V versus reversible hydrogen electrode, RHE). Excellent CO2 RR performance can still be achieved under the low-concentration CO2 feeding (e.g., 20 vol.%). Besides, an extremely low onset potential of -0.05 VRHE (close to the theoretical thermodynamic potential of -0.02 VRHE ) was detected by in situ ultraviolet-visible (UV-Vis) measurements, together with stable operation over 50 h with preserved FEformate of ≈95 % and high partial current density of 326.2 mA cm-2 at -1.0 VRHE .

Cation Substitution Strategy for Developing Perovskite Oxide with Rich Oxygen Vacancy-Mediated Charge Redistribution Enables Highly Efficient Nitrate Electroreduction to Ammonia.

The electrocatalytic nitrate (NO3-) reduction reaction (eNITRR) is a promising method for ammonia synthesis. However, its efficacy is currently limited due to poor selectivity, largely caused by the inherent complexity of the multiple-electron processes involved. To address these issues, oxygen-vacancy-rich LaFe0.9M0.1O3-δ (M = Co, Ni, and Cu) perovskite submicrofibers have been designed from the starting material LaFeO3-δ (LF) by a B-site substitution strategy and used as the eNITRR electrocatalyst. Consequently, the LaFe0.9Cu0.1O3-δ (LF0.9Cu0.1) submicrofibers with a stronger Fe-O hybridization, more oxygen vacancies, and more positive surface potential exhibit a higher ammonia yield rate of 349 ± 15 µg h-1 mg-1cat. and a Faradaic efficiency of 48 ± 2% than LF submicrofibers. The COMSOL Multiphysics simulations demonstrate that the more positive surface of LF0.9Cu0.1 submicrofibers can induce NO3- enrichment and suppress the competing hydrogen evolution reaction. By combining a variety of in situ characterizations and density functional theory calculations, the eNITRR mechanism is revealed, where the first proton-electron coupling step (*NO3 + H+ + e- → *HNO3) is the rate-determining step with a reduced energy barrier of 1.83 eV. This work highlights the positive effect of cation substitution in promoting eNITRR properties of perovskites and provides new insights into the studies of perovskite-type electrocatalytic ammonia synthesis catalysts.

Corrigendum: Comprehensive evolutionary analysis of growth-regulating factor gene family revealing the potential molecular basis under multiple hormonal stress in Gramineae crops.

[This corrects the article DOI: 10.3389/fpls.2023.1174955.].

Heteroatom-doped noble carbon-tailored mixed matrix membranes with ultrapermeability for efficient CO2 separation.

Membranes with ultrapermeability for CO2 are desired for future large-scale carbon capture projects, because of their excellent separative productivity and economic efficiency. Herein, we demonstrate that a membrane with ultrapermeability for CO2 can be constructed by combining N/O para-doped noble carbons, C2NxO1-x, with high-permeability polymer PIM-1. The optimal PIM-1/C2NxO1-x membranes exhibit superior CO2 permeability (22110 Barrer) with a CO2/N2 selectivity of 15.5, and an unprecedented CO2 permeability of 37272 Barrer can be obtained after a PEG activation treatment, far surpassing the 2008 upper bound. Both broad experiments and molecular dynamics simulations reveal that the numerous ordered polar channels of C2NxO1-x and their excellent compatibility with PIM-1 are responsible for the superior CO2 separation performance of the membrane. Although this is the first study on C2N-type gas separation membranes, the outstanding results indicate that noble carbon building blocks may pave a new avenue to advance high-performance CO2 separation membranes.

Comprehensive evolutionary analysis of growth-regulating factor gene family revealing the potential molecular basis under multiple hormonal stress in Gramineae crops.

Growth-regulating factors (GRFs) are plant-specific transcription factors that contain two highly conserved QLQ and WRC domains, which control a range of biological functions, including leaf growth, floral organ development, and phytohormone signaling. However, knowledge of the evolutionary patterns and driving forces of GRFs in Gramineae crops is limited and poorly characterized. In this study, a total of 96 GRFs were identified from eight crops of Brachypodium distachyon, Hordeum vulgare, Oryza sativa L. ssp. indica, Oryza rufipogon, Oryza sativa L. ssp. japonica, Setaria italic, Sorghum bicolor and Zea mays. Based on their protein sequences, the GRFs were classified into three groups. Evolutionary analysis indicated that the whole-genome or segmental duplication plays an essential role in the GRFs expansion, and the GRFs were negatively selected during the evolution of Gramineae crops. The GRFs protein function as transcriptional activators with distinctive structural motifs in different groups. In addition, the expression of GRFs was induced under multiple hormonal stress, including IAA, BR, GA3, 6BA, ABA, and MeJ treatments. Specifically, OjGRF11 was significantly induced by IAA at 6 h after phytohormone treatment. Transgenic experiments showed that roots overexpressing OjGRF11 were more sensitive to IAA and affect root elongation. This study will broaden our insights into the origin and evolution of the GRF family in Gramineae crops and will facilitate further research on GRF function.

Judgment Prediction Based on Tensor Decomposition With Optimized Neural Networks.

In the field of smart justice, handling legal cases through artificial intelligence technology is a research hotspot. Traditional judgment prediction methods are mainly based on feature models and classification algorithms. The former is difficult to describe cases from multiple angles and capture the correlation information between different case modules, while requires a wealth of legal expertise and manual labeling. The latter is unable to accurately extract the most useful information from case documents and produce fine-grained predictions. This article proposes a judgment prediction method based on tensor decomposition with optimized neural networks, which consists of OTenr, GTend, and RnEla. OTenr represents cases as normalized tensors. GTend decomposes normalized tensors into core tensors using the guidance tensor. RnEla intervenes in a case modeling process in GTend by optimizing the guidance tensor, so that core tensors represent tensor structural and elemental information, which is most conducive to improving the accuracy of judgment prediction. RnEla consists of the similarity correlation Bi-LSTM and optimized Elastic-Net regression. RnEla takes the similarity between cases as an important factor for judgment prediction. Experimental results on real legal case dataset show that the accuracy of our method is higher than that of the previous judgment prediction methods.

Highly Anisotropic Second-Order Nonlinear Optical Effects in the Chiral Lead-Free Perovskite Spiral Microplates.

Chiral metal halide perovskites with intrinsic asymmetric structures have drawn increased research interest for the application of second-order nonlinear optics (NLO). However, designing chiral perovskites with the features of a large NLO coefficient, high laser-induced damage thresholds (LDT), and environmental friendliness remains a major challenge. Herein, we have synthesized two chiral hybrid bismuth halides: (R/S-MBA)4Bi2Br10 spiral structure microplates, templated by chiral (R/S)-methylbenzylamine (R/S-MBA). The as-grown chiral lead-free perovskite spiral microplates exhibit a recorded second harmonic generation (SHG) effect with a large effective second-order NLO coefficient (deff) of 11.9 pm V-1 and a high LDT of up to 59.2 mJ cm-2. More importantly, the twisted screw structures show competitive circular polarization sensitivity at 1200 nm with an anisotropy factor (gSHG-CD) of 0.58, which is about 3 times higher than that of reported Pb-based chiral perovskites. These findings provide a new platform to design multifunctional lead-free chiral perovskites for nonlinear photonic applications.

Spatiotemporal biocontrol and rhizosphere microbiome analysis of Fusarium wilt of banana.

The soil-borne fungus Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) causes Fusarium wilt of banana (FWB), which devastates banana production worldwide. Biocontrol is considered to be the most efficient approach to reducing FWB. Here we introduce an approach that spatiotemporally applies Piriformospore indica and Streptomyces morookaensis strains according to their respective strength to increase biocontrol efficacy of FWB. P. indica successfully colonizes banana roots, promotes lateral root formation, inhibits Foc TR4 growth inside the banana plants and reduces FWB. S. morookaensis strain Sm4-1986 secretes different secondary compounds, of which xerucitrinin A (XcA) and 6-pentyl-α-pyrone (6-PP) show the strongest anti-Foc TR4 activity. XcA chelates iron, an essential nutrient in pathogen-plant interaction that determines the output of FWB. 6-PP, a volatile organic compound, inhibits Foc TR4 germination and promotes banana growth. Biocontrol trials in the field demonstrated that application of S. morookaensis lead to improvement of soil properties and increase of rhizosphere-associated microbes that are beneficial to banana growth, which significantly reduces disease incidence of FWB. Our study suggests that optimal utilization of the two biocontrol strains increases efficacy of biocontrol and that regulating iron accessibility in the rhizosphere is a promising strategy to control FWB.

The diurnal emission of floral scent in Oncidium hybrid orchid is controlled by CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) through the direct regulation on terpene synthase.

BACKGROUND: To adapt the periodic fluctuation of environmental factors, plants are subtle to monitor the natural variation for the growth and development. The daily activities and physiological functions in coordination with the natural variation are regulated by circadian clock genes. The circadian emission of floral scents is one of the rhythmic physiological activities controlled by circadian clock genes. Here, we study the molecular mechanism of circadian emission pattern of ocimene and linalool compounds in Oncidium Sharry Baby (Onc. SB) orchid. RESULTS: GC-Mass analysis revealed that Onc. SB periodically emitted ocimene and linalool during 6 to 14 o'clock daily. Terpene synthase, one of the key gene in the terpenoid biosynthetic pathway is expressed in coordination with scent emission. The promoter structure of terpene synthase revealed a circadian binding sequence (CBS), 5'-AGATTTTT-3' for CIRCADIAN CLOCK ASSOCIATED1 (CCA1) transcription factor. EMSA data confirms the binding affinity of CCA1. Transactivation assay further verified that TPS expression is regulated by CCA1. It suggests that the emission of floral scents is controlled by CCA1. CONCLUSIONS: The work validates that the mechanism of circadian emission of floral scents in Onc. Sharry Baby is controlled by the oscillator gene, CCA1(CIRCADIAN CLOCK ASSOCIATED 1) under light condition. CCA1 transcription factor up-regulates terpene synthase (TPS) by binding on CBS motif, 5'-AGATTTTT-3' of promoter region to affect the circadian emission of floral scents in Onc. SB.

Decision Fusion Networks for Image Classification.

Convolutional neural networks, in which each layer receives features from the previous layer(s) and then aggregates/abstracts higher level features from them, are widely adopted for image classification. To avoid information loss during feature aggregation/abstraction and fully utilize lower layer features, we propose a novel decision fusion module (DFM) for making an intermediate decision based on the features in the current layer and then fuse its results with the original features before passing them to the next layers. This decision is devised to determine an auxiliary category corresponding to the category at a higher hierarchical level, which can, thus, serve as category-coherent guidance for later layers. Therefore, by stacking a collection of DFMs into a classification network, the generated decision fusion network is explicitly formulated to progressively aggregate/abstract more discriminative features guided by these decisions and then refine the decisions based on the newly generated features in a layer-by-layer manner. Comprehensive results on four benchmarks validate that the proposed DFM can bring significant improvements for various common classification networks at a minimal additional computational cost and are superior to the state-of-the-art decision fusion-based methods. In addition, we demonstrate the generalization ability of the DFM to object detection and semantic segmentation.

Constructing Interfacial Boron-Nitrogen Moieties in Turbostratic Carbon for Electrochemical Hydrogen Peroxide Production.

The electrochemical oxygen reduction reaction (ORR) provides a green route for decentralized H2 O2 synthesis, where a structure-selectivity relationship is pivotal for the control of a highly selective and active two-electron pathway. Here, we report the fabrication of a boron and nitrogen co-doped turbostratic carbon catalyst with tunable B-N-C configurations (CNB-ZIL) by the assistance of a zwitterionic liquid (ZIL) for electrochemical hydrogen peroxide production. Combined spectroscopic analysis reveals a fine tailored B-N moiety in CNB-ZIL, where interfacial B-N species in a homogeneous distribution tend to segregate into hexagonal boron nitride domains at higher pyrolysis temperatures. Based on the experimental observations, a correlation between the interfacial B-N moieties and HO2 - selectivity is established. The CNB-ZIL electrocatalysts with optimal interfacial B-N moieties exhibit a high HO2 - selectivity with small overpotentials in alkaline media, giving a HO2 - yield of ≈1787 mmol gcatalyst -1 h-1 at -1.4 V in a flow-cell reactor.

Structure-Aware Prototypical Neural Process for Few-Shot Graph Classification.

Graph classification plays an important role in a wide range of applications from biological prediction to social analysis. Traditional graph classification models built on graph kernels are hampered by the challenge of poor generalization as they are heavily dependent on the dedicated design of handcrafted features. Recently, graph neural networks (GNNs) become a new class of tools for analyzing graph data and have achieved promising performance. However, it is necessary to collect a large number of labeled graph data for training an accurate GNN, which is often unaffordable in real-world applications. Therefore, it is an open question to build GNNs under the condition of few-shot learning where only a few labeled graphs are available. In this article, we introduce a new Structure-aware Prototypical Neural Process (SPNP for short) for a few-shot graph classification. Specifically, at the encoding stage, SPNP first employs GNNs to capture graph structure information. Then, SPNP incorporates such structural priors into the latent path and the deterministic path for representing stochastic processes. At the decoding stage, SPNP uses a new prototypical decoder to define a metric space where unseen graphs can be predicted effectively. The proposed decoder, which contains a self-attention mechanism to learn the intraclass dependence between graphs, can enhance the class-level representations, especially for new classes. Furthermore, benefited from such a flexible encoding-decoding architecture, SPNP can directly map the context samples to a predictive distribution without any complicated operations used in previous methods. Extensive experiments demonstrate that SPNP achieves consistent and significant improvements over state-of-the-art methods. Further discussions are provided toward model efficiency and more detailed analysis.

Epidemic Risk Assessment by a Novel Communication Station Based Method.

The COVID-19 pandemic has caused serious consequences in the last few months and trying to control it has been the most important objective. With effective prevention and control methods, the epidemic has been gradually under control in some countries and it is essential to ensure safe work resumption in the future. Although some approaches are proposed to measure people's healthy conditions, such as filling health information forms or evaluating people's travel records, they cannot provide a fine-grained assessment of the epidemic risk. In this paper, we propose a novel epidemic risk assessment method based on the granular data collected by the communication stations. We first compute the epidemic risk of these stations in different intervals by combining the number of infected persons and the way they pass through the station. Then, we calculate the personnel risk in different intervals according to the station trajectory of the queried person. This method could assess people's epidemic risk accurately and efficiently. We also conduct extensive simulations and the results verify the effectiveness of the proposed method.