Zinc-Based Metal-Organic Frameworks for High-Performance Supercapacitor Electrodes:Mechanism Underlying Pore Generation

Heat treatment of metal-organic frameworks(MOFs)has provided a wide variety of functional carbons coordinated with metal compounds.In this study,two kinds of zinc-based MOF(ZMOF),C_(16)H_(10)O_(4)Zn(ZMOF1)and C_(8)H_(4)O_(4)Zn(ZMOF2),were prepared.ZMOF1 and ZMOF2 were carbonized at 1000℃,forming CZMOF1 and CZMOF2,respectively.The specific surface area(S_(BET))of CZMOF2 was~2700 m^(2)g^(−1),much higher than that of CZMOF1(~1300 m^(2)g^(−1)).A supercapacitor electrode based on CZMOF2 achieved specific capacitances of 360,278,and 221 F g^(−1)at 50,250,and 1000 mA g^(−1)in an aqueous electrolyte(H2SO_(4)),respectively,the highest values reported to date for ZMOF-derived electrodes under identical conditions.The practical applicability of the CZMOF-based supercapacitor was verified in non-aqueous electrolytes.The initial capacitance retention was 78%after 100000 charge/discharge cycles at 10 A g^(−1).Crucially,the high capacitance of CZMOF2 arises from pore generation during carbonization.Below 1000℃,pore generation is dominated by the Zn/C ratio of ZMOFs,as carbon atoms reduce the zinc oxides formed during carbonization.Above 1000℃,a high O/C ratio becomes essential for pore generation because the oxygen functional groups are pyrolyzed.These findings will provide insightful information for other metal-based MOFderived multifunctional carbons.

Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle

In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m (R^(2) = 0.99) and 0.15 m (R^(2) = 0.93) for the two orthogonal crown widths, and 3.87 m2 for CPA (R^(2) = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m (R^(2) = 0.91), 0.51 m (R^(2) = 0.74), and 4.96 m2 (R^(2) = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.

Entanglement quantification via weak measurements assisted by deep learning

Entanglement has been recognized as being crucial when implementing various quantum information tasks.Nevertheless, quantifying entanglement for an unknown quantum state requires nonphysical operations or post-processing measurement data. For example, evaluation methods via quantum state tomography require vast amounts of measurement data and likely estimation.

Ru complex and N,P-containing polymers confined within mesoporous hollow carbon spheres for hydrogenation of CO_(2)to formate

The development of reliable catalysts with both excellent activity and recyclability for carbon dioxide(CO_(2))hydrogenation is challenging.Herein,a ternary hybrid heterogeneous catalyst,involving mononuclear Ru complex,N,P-containing porous organic polymers(POPs),and mesoporous hollow carbon spheres(Ru^(3+)-POPs@MHCS)is reported for CO_(2)hydrogenation to formate.Based on comprehensive structural analyses,we demonstrated that Ru^(3+)-POPs were successfully immobilized within MHCS.The optimized Ru^(3+)-0.5POPs@MHCS catalyst,which was obtained with about 5 wt.%Ru^(3+)and 0.5 mmol POPs polymers confined into 0.3 g MHCS,exhibited high catalytic activity for CO_(2)hydrogenation to formate(turnover number(TON)>1,200 for 24 h under mild reaction conditions(4.0 MPa,120℃))and improved durability,compared to Ru^(3+)catalysts without POPs polymers(Ru^(3+)-MHCS)and unencapsulated MHCS(Ru^(3+)-0.5POPs)catalysts.The improved catalytic performance is attributed to the high surface area and large pore volume of MHCS which favors dispersion and stabilization of Ru^(3+)-POPs.Furthermore,the MHCS and POPs showed high CO_(2)adsorption ability.Ru^(3+)-POPs encapsulated into MHCS reduces the activation energy barrier for CO_(2)hydrogenation to formate.

Bandgap-tunable lateral and vertical heterostructures based on monolayer Mo1-xWxS2 alloys

The fabrication of heterostructures of two-dimensional semiconductors with specific bandgaps is an important approach to realizing the full potential of these materials in electronic and optoelectronic devices. Several groups have recently reported the direct growth of lateral and vertical heterostructures based on monolayers of typical semiconducting transition metal dichalcogenides （TMDCs） such as WSe2, MoSe2, WS2, and MoS2. Here, we demonstrate the single-step direct growth of lateral and vertical heterostructures based on bandgap-tunable Mo1-xWxS2 alloy monolayers by the sulfurization of patterned thin films of WO3 and MoO3. These patterned films are capable of generating a wide variety of concentration gradients by the diffusion of transition metals during the crystal growth phase. Under high temperatures, this leads to the formation of monolayer crystals of Mo1-xWxS2 alloys with various compositions and bandgaps, depending on the positions of the crystals on the substrates. Heterostructures of these alloys are obtained through stepwise changes in the ratio of W/Mo within a single domain during low-temperature growth. The stabilization of the monolayer Mo1-xWxS2 alloys, which often degrade even under gentle conditions, was accomplished by coating the alloys with other monolayers. The present findings demonstrate an efficient means of both studying and optimizing the optical and electrical properties of TMDC-based heterostructures to allow use of the materials in future device applications.

High-temperature formation phases and crystal structure of hot-pressed thermoelectric compounds with chalcopyrite-type structure

In this study, we introduced the temperaturedependent formation phases and crystallographic parameters of hot-pressed silver gallium telluride AgGaTe2 and copper gallium telluride CuGaTe2 with chalcopyrite structure from 300 to 800 K. These two compounds are potential thermoelectric materials in the intermediate temperature range; however, the temperature-dependent formation phases and crystallographic parameters of hotpressed samples have not yet been analyzed in detail. The crystal structure analysis based on synchrotron X-ray diffraction （SXRD） measurements clarifies that impurity phases such as Te and Ag2Te in the AgGaTe2 matrix and Te and CuTe in the CuGaTe2 matrix appear at some temperature regions above 300 K. The existence of such impurity phases could be correlated with the increases of the electrical resistivity and Seebeck coefficient of the samples after multiple measurement cycles of the temperature-dependent transport properties from 300 to 800 K. The tetragonal lattice parameters a and c, tetragonal lattice volume, thermal expansion coefficients, tetragonal distortion, anion displacement parameter, and isotropic displacement parameter of the hot-pressed AgGaTe2 and CuGaTe2 were also analyzed. These crystallographic parameters are expected to substantially affect the thermoelectric properties of AgGaTe2 and CuGaTe2. Our results provide prospect of the long-term high-temperature stability and clues of the detailed analysis on the transport properties of hot-pressed AgGaTe2 and CuGaTe2, which should aid their development for thermoelectric applications.

Charge generation in organic solar cells: Journey toward 20% power conversion efficiency

The power conversion efficiencies of organic solar cells(OSCs)have routinely lagged far behind those of their inorganic counterparts.However,owing to the enor-mous contributions of many researchers,the power conversion efficiencies of OSCs have rapidly improved and now exceed 19%.The charge generation mechanisms in OSCs have been heavily debated during this period while acquiring valuable knowl-edge.This review highlights fundamental and cutting-edge research that rationalizes why OSCs can generate photocurrent so efficiently.In particular,a photophysi-cist’s views on exciton diffusion to donor:acceptor interfaces,charge transfer at the donor:acceptor interface,and long-range spatial dissociation of charge transfer states are discussed.Although a general consensus in this area has not been reached yet,recent time-resolved spectroscopic measurements provide important photophys-ical insights that can help achieving a better understanding of the charge generation mechanism in OSCs.Based on these observations,future research directions for realizing further improvements in OSC performance are discussed.

Semisupervised Deep State-Space Model for Plant Growth Modeling

The optimal control of sugar content and its associated technology is important for producing high-quality crops more stably and efficiently.Model-based reinforcement learning(RL)indicates a desirable action depending on the type of situation based on trialand-error calculations conducted by an environmental model.In this paper,we address plant growth modeling as an environmental model for the optimal control of sugar content.In the growth process,fruiting plants generate sugar depending on their state and evolve via various external stimuli;however,sugar content data are sparse because appropriate remote sensing technology is yet to be developed,and thus,sugar content is measured manually.We propose a semisupervised deep state-space model(SDSSM)where semisupervised learning is introduced into a sequential deep generative model.SDSSM achieves a high generalization performance by optimizing the parameters while inferring unobserved data and using training data efficiently,even if some categories of training data are sparse.We designed an appropriate model combined with model-based RL for the optimal control of sugar content using SDSSM for plant growth modeling.We evaluated the performance of SDSSM using tomato greenhouse cultivation data and applied cross-validation to the comparative evaluation method.The SDSSM was trained using approximately 500 sugar content data of appropriately inferred plant states and reduced the mean absolute error by approximately 38%compared with other supervised learning algorithms.The results demonstrate that SDSSM has good potential to estimate time-series sugar content variation and validate uncertainty for the optimal control of high-quality fruit cultivation using model-based RL.

A versatile Tn7 transposon-based bioluminescence tagging tool for quantitative and spatial detection of bacteria in plants

Investigation of plant-bacteria interactions requires quantification of in planta bacterial titers by means of cumbersome and time-consuming colony-counting assays.Here,we devised a broadly applicable tool for bioluminescence-based quantitative and spatial detection of bacteria in plants.We developed vectors that enable Tn7 transposon-mediated integration of the luxCDABEluciferase operon into a specific genomic location found ubiquitously across bacterial phyla.These vectors allowed for the generation of bioluminescent transformants of various plant pathogenic bacteria from the genera Pseudomonas,Rhizobium(Agrobacterium),and Ralstonia.Direct luminescence measurements of plant tissues inoculated with bioluminescent Pseudomonas syringae pv.tomato DC3000(Pto-lux)reported bacterial titers as accurately as conventional colony-counting assays in Arabidopsis thaliana,Solanum lycopersicum,Nicotiana benthamiana,and Marchantia polymorpha.We further showed the usefulness of our vectors in converting previously generated Pto derivatives to isogenic bioluminescent strains.Importantly,quantitative bioluminescence assays using these Pto-lux strains accurately reported the effects of plant immunity and bacterial effectors on bacterial growth,with a dynamic range of four orders of magnitude.Moreover,macroscopic bioluminescence imaging illuminated the spatial patterns of Pto-lux growth in/on inoculated plant tissues.In conclusion,our vectors offer untapped opportunities to develop bioluminescence-based assays for a variety of plant-bacteria interactions.

Nanofluidics for sub-single cellular studies:Nascent progress,critical technologies,and future perspectives

In the field of cell studies,there is a burgeoning trend to further downscale the investigation from a single-cell level to a sub-single-cell level.Subcellular matter is the basic content in cells and correlates with cell heterogeneity.Sub-single cellular studies focus on the subcellular matter in single cells and aim to understand the details and heterogeneity of individual cells in terms of the subcellular matter or even at the single component/vesicle/molecule level.Hence,sub-single cellular studies can provide deeper insights into fundamental cell biology and the development of new diagnostic and therapeutic technologies and applications.Nonetheless,the contents of a single cell are not only ultra-small in volume but also extremely complex in composition,far exceeding the capabilities of most tools used in current cell studies.We believe that nanofluidics holds great potential in providing ideal tools for sub-single cellular studies,not only because of their capability to handle femtoliter/attoliter-scale samples,but also because of their possibility to manipulate and analyze subcellular matters at the single component/vesicle/molecule level in a high-throughput manner.In this review,we summarize the efforts in the field of nanofluidics for sub-single cellular studies,focusing on nascent progress and critical technologies that have the potential to overcome the technical bottlenecks.Some challenges and future opportunities to integrate with information sciences are also discussed.

Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants

The estimation of water stress is critical for the reliable production of high-quality fruits cultivated using the tacit knowledge of expert farmers.Multimodal deep neural network has achieved success in the estimation of stem diameter variations as a water stress index,calculated from leaf wilting and environmental data.However,these studies have not addressed the specific role of leaf wilting in the estimation.Revealing the role of leaf wilting not only ensures the reliability of the estimation model but also provides an opportunity for improving the estimation method.In this paper,we investigated the relationships between leaf wilting and stem diameter variations without resorting to black-box approaches such as deep neural network.To clarify the role of leaf wilting,this study uses cross-correlation analysis to analyze the time lag correlation between leaf wilting,quantified by optical flow,and stem diameter variations as a water stress index.The analysis showed that leaf wilting had a significant time lag correlation with short-term stem diameter variations,which were water stress responses in plants.As the results were consistent with known plant water transport mechanisms,it was suggested that leaf wilting quantified by optical flow can explain short-term stem diameter variations.