Heteronuclear Dual Metal Atom Electrocatalysts for Water-Splitting Reactions.

Hydrogen is considered a promising substitute for traditional fossil fuels because of its widespread sources, high calorific value of combustion, and zero carbon emissions. Electrocatalytic water-splitting to produce hydrogen is also deemed to be an ideal approach; however, it is a challenge to make highly efficient and low-cost electrocatalysts. Single-atom catalysts (SACs) are considered the most promising candidate to replace traditional noble metal catalysts. Compared with SACs, dual-atom catalysts (DACs) are capable of greater attraction, including higher metal loading, more versatile active sites, and excellent catalytic activity. In this review, several general synthetic strategies and structural characterization methods of DACs are introduced, and recent experimental advances in water-splitting reactions are discussed. The authors hope that this review provides insights and inspiration to researchers regarding DACs in electrocatalytic water-splitting.

PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.

Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model.

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production, which benefits food production. Object detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating diseases. However, existing methods are still limited to single crop disease diagnosis. More importantly, the existing model has a large number of parameters, which is not conducive to deploying it to agricultural mobile devices. Nonetheless, reducing the number of model parameters tends to cause a decrease in model accuracy. To solve these problems, we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple crops. In detail, we design 2 strategies to build 4 different lightweight models as student models: the YOLOR-Light-v1, YOLOR-Light-v2, Mobile-YOLOR-v1, and Mobile-YOLOR-v2 models, and adopt the YOLOR model as the teacher model. We develop a multistage knowledge distillation method to improve lightweight model performance, achieving 60.4% mAP@ .5 in the PlantDoc dataset with small model parameters, outperforming existing methods. Overall, the multistage knowledge distillation technique can make the model lighter while maintaining high accuracy. Not only that, the technique can be extended to other tasks, such as image classification and image segmentation, to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart agriculture. Our code is available at https://github.com/QDH/MSKD.

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis.

Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.

Ta3N5 Nanobelt-Loaded Ru Nanoparticle Hybrids' Electrocatalysis for Hydrogen Evolution in Alkaline Media.

Electrochemical hydrogen evolution is a highly efficient way to produce hydrogen, but since it is limited by high-cost electrocatalysts, the preparation of high-efficiency electrocatalysts with fewer or free noble metals is important. Here, Ta3N5 nanobelt (NB)-loaded Ru nanoparticle (NP) hybrids with various ratios, including 1~10 wt% Ru/Ta3N5, are constructed to electrocatalyze water splitting for a hydrogen evolution reaction (HER) in alkaline media. The results show that 5 wt% Ru/Ta3N5 NBs have good HER properties with an overpotential of 64.6 mV, a Tafel slope of 84.92 mV/dec at 10 mA/cm2 in 1 M of KOH solution, and good stability. The overpotential of the HER is lower than that of Pt/C (20 wt%) at current densities of 26.3 mA/cm2 or more. The morphologies and structures of the materials are characterized by scanning electron microscopy and high-resolution transmission electron microscopy, respectively. X-ray photoelectron energy spectroscopy (XPS) demonstrates that a good HER performance is generated by the synergistic effect and electronic transfer of Ru to Ta3N5. Our electrochemical analyses and theoretical calculations indicate that Ru/Ta3N5 interfaces play an important role as real active sites.

Synthesis and solar blind photosensitivity of crystalline boron nanowires.

Herein, single crystalline boron nanowires (BNWs) have been synthesized by chemical vapor transport using boron element as boron source, iodine as transport agent, and Au as catalyst. The results demonstrate that BNWs can be all formed at 600 °C-950 °C for 2 h, and possess rhombohedral crystal structure (ß-boron). The NWs have diameters from several to hundreds of nanometers, and lengths from several to hundreds of microns. A single nanowire has been fabricated to field effect transistor (FET) which shows excellent solar blind photosensitivity and selectivity. The photo/dark current ratio and photoresponsitity is 1.14 and 97.6 mA W-1at a bias of 5 V under light illumination of 254 nm with 0.42 mW cm-2, respectively, and both the rising and decay time of the on-off currents are 4.6 s and 10.3 s, respectively. When the FET is used as a personal breath sensor, the ratio of exsufflating and inhaling currents is 2.7, rising and decay time of the breath currents are 0.4 s and 2.2 s, respectively. So the BNWs are important sense materials.

Correction to "Application of Nanocellulose in Oilfield Chemistry".

[This corrects the article DOI: 10.1021/acsomega.1c02095.].

Application of Nanocellulose in Oilfield Chemistry.

The preparation and classification of nanocellulose are briefly introduced, and the modification of nanocellulose and the application of modified nanocellulose in oilfield chemistry are reviewed. The principles and methods of surface modification, including surface adsorption, oxidation, acetylation, silanization, etherification, and polymer grafting, are summarized. Meanwhile, this paper focuses on the application of nanocellulose research progress in drilling fluid, enhanced oil recovery, and oilfield sewage treatment. In addition, the application issues and natural advantages of nanocellulose are analyzed, and suggestions and ideas on how to expand its application are put forward. Finally, the development and potential application of nanocellulose in oilfield chemistry are proposed.

Visualization Study of Produced Oil Based on Magnetic Resonance Imaging Technology.

In recent years, visualization technology based on magnetic resonance imaging has been widely used in core flooding experiments and mechanism research. In this research, the visualization of produced oil is realized by subtracting from image signals collected in different displacement stages. The distribution images of the produced oil can be used to clearly analyze the oil displacement characteristics of cores with different permeabilities and chemical agents with different functions. The distribution image of the produced oil shows advantageous and hard-to-reach areas of water flooding in cores with different permeabilities. The SMG dispersion by blocking the dominant water pathway produced remaining oil areas that were difficult to be swept water flooding and then enlarged the swept volume. The produced oil is concentrated at both ends and dispersed in the middle of the core. The polymer flooding by improving the viscosity of water flooding sharply increased the injection pressure and then swept all areas. The distribution image of the produced oil has the characteristics of filamentous continuous advancement.

Fe-modified Co2(OH)3Cl microspheres for highly efficient oxygen evolution reaction.

Surface self-reconstruction by the electrochemical activation is considered as an effective strategy to increase the oxygen evolution reaction (OER) performance of transition metal compounds. Herein, uniform Co2(OH)3Cl microspheres are developed and present an activation-enhanced OER performance caused by the etching of lattice Cl- after 500 cyclic voltammetry (CV) cycles. Furthermore, the OER activity of Co2(OH)3Cl can be further enhanced after small amounts of Fe modification (Fe2+ as precursor). Fe doping into Co2(OH)3Cl lattices can make the etching of surface lattice Cl- easier and generate more surface vacancies to absorb oxygen species. Meanwhile, small amounts of Fe modification can result in a moderate surface oxygen adsorption affinity, facilitating the activation of intermediate oxygen species. Consequently, the 10% Fe-Co2(OH)3Cl exhibits a superior OER activity with a lower overpotential of 273 mV at 10 mA cm-2 (after 500 CV cycles) along with an excellent stability as compared with commercial RuO2.

Ag nanoparticles decorated urchin-like cobalt carbonate hydroxide composites for highly efficient oxygen evolution reaction.

Herein, a novel composite of small amounts of Ag nanoparticles (NPs) decorated urchin-like cobalt carbonate hydroxide hydrate (CCHH) was developed for highly-efficient alkaline oxygen evolution reaction (OER). Not only can Ag colloids, as template agents, modify the morphologies of urchin-like CCHH microspheres to expose more active sites available, but also the supported Ag NPs formed by Ag colloids can transfer the electron to CCHH surfaces, accelerating the transformation of surface CoII to CoIII/CoIV (proton-coupled electron transfer (PCET) process). The urchin-like Ag/CCHH (0.013 mmol) precatalyst (before cyclic voltammetry (CV) activation) exhibits a better OER performance (a low overpotential of 273 mV at 10 mA cm-2 and small Tafel slope of 65 mV dec-1) as compared with commercial RuO2. Furthermore, the dynamic surface self-reconstruction (surface CO3 2- and OH - exchange) can further enhance the activities of Ag/CCHH precatalysts. Consequently, the optimal Ag/CCHH (0.013 mmol) catalyst presents a superior activity (a lower overpotential of 267 mV at 10 mA cm-2 and markedly reduced Tafel slope to 56 mV dec-1) along with an excellent stability after CV cycles. The study provides a feasible strategy to fully realize the low overpotential of CCHH-based OER electrocatalysts.

Plasmonic Modulation of the Upconversion Luminescence Based on Gold Nanorods for Designing a New Strategy of Sensing MicroRNAs.

Upconversion nanoparticles (UCNPs) have potential applications in biosensing and bioimaging. However, the UCNPs-based sensors constructed by luminescence resonance energy transfer (LRET) always suffer from low quenching efficiency, hindering their application. Therefore, exploring a new strategy to resolve this issue is highly desirable. Herein, a strategy based on the surface plasmon resonance (SPR) effect of gold nanorods (AuNRs) is presented. The luminescence of UCNPs was modulated by adjusting the SiO2 thickness of AuNRs@SiO2 and the structure of UCNPs; an enhancement factor of ≈50 times was obtained. Based on the results of the SPR effect of AuNRs, we designed two kinds of potential upconversion microRNA sensors using microRNA-21 as a model to resolve the problem of the lower quenching efficiency resulting from a dye as a quencher. Studies revealed that the proposed strategy could be successfully used to construct upconversion microRNA sensors for avoiding the limitation of the low quenching efficiency. The sensitivity was ≈10 000 times higher than that of the upconversion sensor using dyes as quenchers. Importantly, the assay of microRNA-21 was successfully achieved using this sensor in human serum samples and human breast cancer cell (MCF-7) lysates. It provides a new method for designing upconversion microRNA sensors and may have potential for use in biosensing and bioimaging.

Dual-Templating Approaches to Soybeans Milk-Derived Hierarchically Porous Heteroatom-Doped Carbon Materials for Lithium-Ion Batteries.

Biomass derived carbon materials are widely available, cheap and abundant resources. The application of these materials as electrodes for rechargeable batteries shows great promise. To further explore their applications in energy storage fields, the structural design of these materials has been investigated. Hierarchical porous heteroatom-doped carbon materials (HPHCs) with open three-dimensional (3D) nanostructure have been considered as highly efficient energy storage materials. In this work, biomass soybean milk is chosen as the precursor to construct N, O co-doped interconnected 3D porous carbon framework via two approaches by using soluble salts (NaCl/Na2CO3 and ZnCl2/Mg5(OH)2(CO3)4, respectively) as hard templates. The electrochemical results reveal that these structures were able to provide a stable cycling performance (710 mAh ⋅ g-1 at 0.1 A ⋅ g-1 after 300 cycles for HPHC-a, and 610 mAh ⋅ g-1 at 0.1 A ⋅ g-1 after 200 cycles for HPHC-b) in Li-ion battery and Na-ion storage (210 mAh ⋅ g-1 at 0.1 A ⋅ g-1 after 900 cycles for HPHC-a) as anodes materials, respectively. Further comparative studies showed that these improvements in HPHC-a performance were mainly due to the honeycomb-like structure containing graphene-like nanosheets and high nitrogen content in the porous structures. This work provides new approaches for the preparation of hierarchically structured heteroatom-doped carbon materials by pyrolysis of other biomass precursors and promotes the applications of carbon materials in energy storage fields.

Effect of the Formation of Diglycerides/Monoglycerides on the Kinetic Curve in Oil Transesterification with Methanol Catalyzed by Calcium Oxide.

Many researchers reported that a sigmoid kinetic curve was obtained in oil transesterification with methanol catalyzed by CaO and gave different explanations for this formation. In this paper, heterogeneously catalyzed transesterification of soybean oil with methanol using CaO has been investigated. The solid catalyst and the liquid reaction mixture under different reaction time periods were characterized by X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, and high-performance liquid chromatography (HPLC) to reveal the formation of an S-shape kinetic curve. The appearance of calcium hydroxide, calcium methoxide, calcium glyceroxide, fatty acid calcium, diglycerides, and monoglycerides and their contributions to the kinetic curve have been discussed. The low reaction rate in the induction period can be attributed to mass transfer in this three-phase system. However, the formation of surfactants, diglycerides and monoglycerides, promotes the emulsification of the reaction mixture and numerous emulsion reactors are generated. These emulsion reactors can improve the contact of the solid catalyst with the reactants and thus accelerate the reaction.

Antifriction and Antiwear Effect of Lamellar ZrS2 Nanobelts as Lubricant Additives.

In this study, the tribological behavior of lamellar ZrS2 nanobelts as lubricant additives was investigated under different concentrations, normal load, velocity, and temperature. The friction and wear tests were performed using a tribometer and with a reciprocating motion. The results indicate that the lamellar ZrS2 nanobelt additives can effectively reduce the coefficient of friction and running-in time during the running-in period. With the addition of ZrS2, the wear volumes decrease significantly. The wear is mostly influenced by the tribological performance throughout the running-in period. The lower the running-in time and coefficient of friction are during the running-in period, the less amount of wear is shown. ZrS2 can significantly increase the load-carrying capacity of oil. The 1.0 wt% concentration of ZrS2 yields the best antifriction effect, antiwear performance, and load-carrying capacity. The ZrS2 additives can increase the working temperature of the oil. The friction-reducing and antiwear mechanisms of lamellar ZrS2 were discussed.

Transport mechanism of deformable micro-gel particle through micropores with mechanical properties characterized by AFM.

Deformable micro-gel particles (DMP) have been used to enhanced oil recovery (EOR) in reservoirs with unfavourable conditions. Direct pore-scale understanding of the DMP transport mechanism is important for further improvements of its EOR performance. To consider the interaction between soft particle and fluid in complex pore-throat geometries, we perform an Immersed Boundary-Lattice Boltzmann (IB-LB) simulation of DMP passing through a throat. A spring-network model is used to capture the deformation of DMP. In order to obtain appropriate simulation parameters that represent the real mechanical properties of DMP, we propose a procedure via fitting the DMP elastic modulus data measured by the nano-indentation experiments using Atomic Force Microscope (AFM). The pore-scale modelling obtains the critical pressure of the DMP for different particle-throat diameter ratios and elastic modulus. It is found that two-clog particle transport mode is observed in a contracted throat, the relationship between the critical pressure and the elastic modulus/particle-throat diameter ratio follows a power law. The particle-throat diameter ratio shows a greater impact on the critical pressure difference than the elastic modulus of particles.

Enhancing intracellular microRNA imaging: a new strategy combining double-channel exciting single colour fluorescence with the target cycling amplification reaction.

A microRNA (miRNA) fluorescence (FL) nanoprobe based on PDA-UCNPs@FAM-LLD@AuNPs was developed, which exhibited an enhanced signal for imaging intracellular miRNA by combining double-channel exciting single colour FL (DCESCF) with the target cycling amplification reaction. Through this strategy, normal cells and cancer cells could be successfully discriminated.

Lighting Up MicroRNA in Living Cells by the Disassembly of Lock-Like DNA-Programmed UCNPs-AuNPs through the Target Cycling Amplification Strategy.

Intracellular microRNAs imaging based on upconversion nanoprobes has great potential in cancer diagnostics and treatments. However, the relatively low detection sensitivity limits their application. Herein, a lock-like DNA (LLD) generated by a hairpin DNA (H1) hybridizing with a bolt DNA (bDNA) sequence is designed, which is used to program upconversion nanoparticles (UCNPs, NaYF4 @NaYF4 :Yb, Er@NaYF4 ) and gold nanoparticles (AuNPs). The upconversion emission is quenched through luminescence resonance energy transfer (LRET). The multiple LLD can be repeatedly opened by one copy of target microRNA under the aid of fuel hairpin DNA strands (H2) to trigger disassembly of AuNPs from the UCNP, resulting in the lighting up of UCNPs with a high detection signal gain. This strategy is verified using microRNA-21 as model. The expression level of microRNA-21 in various cells lines can be sensitively measured in vitro, meanwhile cancer cells and normal cells can be easily and accurately distinguished by intracellular microRNA-21 imaging via the nanoprobes. The detection limit is about 1000 times lower than that of the previously reported upconversion nanoprobes without signal amplification. This is the first time a nonenzymatic signal amplification method has been combined with UCNPs for imaging intracellular microRNAs, which has great potential for cancer diagnosis.

A Universal Upconversion Sensing Platform for the Sensitive Detection of Tumour-Related ncRNA through an Exo III-Assisted Cycling Amplification Strategy.

Here, a sensitive and universal noncoding RNA (ncRNA) upconversion sensing nanoplatform is developed. Gold nanoparticles bearing one hairpin DNA (Hp) molecule are conjugated to the linker DNA modified NaYF4 :Yb, Er@NaYF4 upconversion nanoparticles by DNA hybridization, leading to quenching of the upconversion emission through fluorescence resonance energy transfer. A signal DNA (SDNA) sequence is designed to open Hp, recovering the upconversion emission. To achieve universality and high sensitivity of the nanoprobe, an exonuclease III (Exo III)-assisted cycling amplification strategy is introduced. A multifunctional hairpin DNA (mHp) containing ncRNA recognition sequence and SDNA sequence is designed to recognize ncRNA and trigger Exo III as a biocatalyst to stepwise disintegrate itself, releasing both ncRNA and SDNA. The released ncRNA can be reused to release more SDNA, which greatly improves the sensing sensitivity. By changing the recognition portion of mHp, various ncRNA can be detected. The sensitive detection of both homeobox (HOX) transcript antisense RNA segment and miR-21 is achieved with this novel strategy, even in human serum, indicating the universality and sensitivity of the proposed strategy. Additionally, the expression level of miR-21 in human breast cancer cell (MCF-7) lysate is successfully measured, suggesting its potential in clinical diagnosis.

Visible light nonlinear absorption and optical limiting of ultrathin ZrSe3 nanoflakes.

The nonlinear absorption and nonlinear refractive properties of ZrSe3 nanoflakes were studied with a 6.5 ns pulse laser at 532 nm. Open-aperture Z-scan curves reveal that ZrSe3 nanoflakes have a strong reverse saturable absorption property, and close-aperture Z-scan curves show that ZrSe3 dispersions possess a positive nonlinear refractive index caused by self-focusing. The nonlinear absorption coefficient, the nonlinear refraction coefficient, and the figures of merit (FOM) of ZrSe3 dispersed in water with linear transmittances of 0.86 at input energy of 18 µJ are 6.35 × 10-10 m W-1 15.73 × 10-17 m2 W-1, and 10.09 × 10-11 esu · cm respectively. In addition, nonlinear optical (NLO) performance of ZrSe3 nanoflakes depends on organic solvent dispersions. ZrSe3 nanoflakes in water dispersions have the largest FOM of 10.27 × 10-11 esu · cm, while the FOM in ethanol dispersions is 5.41 × 10-11 esu · cm at the same input energy of 26.5 µJ. The optical limiting threshold Fth of ZrSe3 nanosheet is 2.2 J cm-2 under picosecond laser pulse. The Results imply that ZrSe3 nanoflakes are an extraordinarily promising material for novel nanophotonic devices like optical limiters.