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Conventional Al-air battery has many disadvantages for miniwatt applications, such as the complex water management, bulky electrolyte storage and potential leakage hazard. Moreover, the self-corrosion of Al anode continues even when the electrolyte flow is stopped, leading to great Al waste. To tackle these issues, an innovative cotton-based aluminum-air battery is developed in this study. Instead of flowing alkaline solution, cotton substrate pre-deposited with solid alkaline is used, together with a small water reservoir to continuously wet the cotton and dissolve the alkaline in-situ. In this manner, the battery can be mechanically recharged by replacing the cotton substrate and refilling the water reservoir, while the thick aluminum anode can be reused for tens of times until complete consumption. The cotton substrate shows excellent ability for the storage and transportation of alkaline electrolyte, leading to a high peak power density of 73 mW cm−2 and a high specific energy of 930 mW h g−1. Moreover, the battery discharge capacity is found to be linear to the loading of pre-deposited alkaline, so that it can be precisely controlled according to the mission profile to avoid Al waste. Finally, a two-cell battery pack with common water reservoir is developed, which can provide a voltage of 2.7 V and a power output of 223.8 mW. With further scaling-up and stacking, this cotton-based Al-air battery system with low cost and high energy density is very promising for recharging miniwatt electronics in the outdoor environment.

Millimeter wave components, such as circulators and isolators, frequently use magnetic fields to break symmetry of the signal propagation and provide unidirectional signal transmission. While effective, these components have not seen the level of miniaturization of other millimeter wave components, primarily due to the discrete nature of the magnets used in the components. Using circulators in the 40-50 GHz range as a motivating application, requirements arise for the deposited films, namely immunity to eddy currents, sufficient magnetization to act as self-biasing magnets, and out-of-plane orientation of the self-biasing field. Based on these required properties, hexaferrite materials are selected for their strong magnetocrystalline anisotropy (MCA) and low conductance. The difficulty of integrating these components monolithically with monolithic microwave integrated circuits (MMIC) originates from the incompatibility of crystal structure with standard semiconductor materials and process conditions. Additive manufacturing using a composite of strontium hexaferrite (SrFe12O19) particles and photoresist has been chosen as a method to overcome the difficulties of integrating hexaferrite material to semiconductor substrates. Due to their large internal anisotropy field, the particles of strontium hexaferrite tend to rotate to the field direction instead of changing magnetization direction under application of an external magnetic field (less than the anisotropy field). We have developed a method for 3D printing composites of high strontium hexaferrite concentration (up to 20% by volume) in a liquid photoresist, SU8. Rotation of hexaferrite particles in polymer matrix and thus magnetic anisotropy has been demonstrated in the composite, which is subsequently cured to hold the physical position and orientation of the particles. The anisotropy of the self-biasing field provided by the films has been experimentally characterized, and a ferromagnetic resonance (FMR) frequency ~43 GHz has been observed. We have also characterized the viscosity of the particle-laden polymer at different particle concentrations. 3D printing of this composite with poling will make it possible to directly print magnetic components that require out-of-plane anisotropic magnetization.

BioSentinel is the first biological CubeSat designed and developed for deep space. The main objectives of this NASA mission are to assess the effects of deep space radiation on biological systems and to engineer a CubeSat platform that can autonomously support and gather data from model organisms hundreds of thousands of kilometers from Earth. The articles in this special collection describe the extensive optimization of the biological payload system performed in preparation for this long-duration deep space mission. In this study, we briefly introduce BioSentinel and provide a glimpse into its technical and conceptual heritage by detailing the evolution of the science, subsystems, and capabilities of NASA's previous biological CubeSats. This introduction is not intended as an exhaustive review of CubeSat missions, but rather provides insight into the unique optimization parameters, science, and technology of those few that employ biological model systems.

The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy under consideration. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects. These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, this paper is the first to develop limit distribution theories for inference about the PRTE.

Policy evaluation is central to economic data analysis, but economists mostly work with observational data in view of limited opportunities to carry out controlled experiments. In the potential outcome framework, the panel data approach (Hsiao, Ching and Wan, 2012) constructs the counterfactual by exploiting the correlation between cross-sectional units in panel data. The choice of cross-sectional control units, a key step in its implementation, is nevertheless unresolved in data-rich environment when many possible controls are at the researcher's disposal. We propose the forward selection method to choose control units, and establish validity of the post-selection inference. Our asymptotic framework allows the number of possible controls to grow much faster than the time dimension. The easy-to-implement algorithms and their theoretical guarantee extend the panel data approach to big data settings., Comment: accepted for publication at the Journal of Econometrics

The biological risks of the deep space environment must be elucidated to enable a new era of human exploration and scientific discovery beyond low earth orbit (LEO). There is a paucity of deep space biological missions that will inform us of the deleterious biological effects of prolonged exposure to the deep space environment. To safely undertake long-term missions to Mars and space habitation beyond LEO, we must first prove and optimize autonomous biosensors to query the deep space radiation environment. Such biosensors must contain organisms that can survive for extended periods with minimal life support technology and must function reliably with intermittent communication with Earth. NASA's BioSentinel mission, a nanosatellite containing the budding yeast Saccharomyces cerevisiae, is such a biosensor and one of the first biological missions beyond LEO in nearly half a century. It will help fill critical gaps in knowledge about the effects of uniquely composed, chronic, low-flux deep space radiation on biological systems and in particular will provide valuable insight into the DNA damage response to highly ionizing particles. Due to yeast's robustness and desiccation tolerance, it can survive for periods analogous to that of a human Mars mission. In this study, we discuss our optimization of conditions for long-term reagent storage and yeast survival under desiccation in preparation for the BioSentinel mission. We show that long-term yeast cell viability is maximized when cells are air-dried in trehalose solution and stored in a low-relative humidity and low-temperature environment and that dried yeast is sensitive to low doses of deep space-relevant ionizing radiation under these conditions. Our findings will inform the design and development of improved future long-term biological missions into deep space.

Saat ini, isu penggunaan energi yang berlebihan telah mewabah di masyarakat. Penggunaan energi yang berlebihan banyak ditemukan pada lingkungan rumah tangga. Penerapan rumah yang hemat energi dapat dijadikan solusi dalam mengatasi masalah tersebut. Namun, beberapa masyarakat tidak memprioritaskan konsep tersebut. Penelitian ini dimaksudkan untuk menentukan bagaimana persepsi masyarakat mengenai kepentingan menerapkan konsep rumah hemat energi pada rumah tinggal sehari – hari dan menentukan hal – hal yang perlu diperhatikan dalam menciptakan konsep rumah hemat energi. Metode pengumpulan data dilakukan melalui kuesioner ke beberapa responden lapisan masyarakat baik itu karyawan, wiraswasta, maupun mahasiswa. Data yang telah terkumpul dianalisis menggunakan analisis kualitatif data teks. Hasil dari analisis tersebut akan menghasilkan faktor – faktor yang menjadi alasan kepentingan menerapkan konsep rumah hemat energi dan kriteria dalam penerapan rumah hemat energi.

Speleothem deposits in nowadays arid environments are important climate archives, as they indicate phases of enhanced precipitation and can precisely be dated by uranium-series dating. So far only very few of such archives have been found in the today hyper-arid Saharo-Arabian Desert (SAD). Therefore, the study at hand fills a gap that exists for speleothem climate archives in Northeast Africa. A new record from Saqia Cave (Central Eastern Desert, Egypt) documents for the first-time speleothem growth in Egypt for all sub-stages of MIS 5 and singular phases during MIS 6. Most important growth phases occur during periods of strong increase and maximum orbitally-forced northern hemisphere insolation, but also during phases of low insolation, which are in general attributed to aridity in North Africa. Here, at least semi-arid climate conditions are proposed for periods of low insolation during stadials of MIS 5. This suggests an impact of different possible sources of precipitation, apart from large scale shifts of the African monsoon, such as Red Sea Troughs, tropical plums and a larger spatial extent of the Mediterranean winter-rainfall zone. Concerning the spatial rainfall pattern in Egypt, we propose a considerable east to west rainfall gradient in Egypt for the Last Interglacial and suggest a stronger impact of variable moisture sources in the Eastern Desert near the Mediterranean and Red Sea in comparison to the more continental Western Desert of Egypt. This reveals more favorable pre-conditions for an enhanced land use potential in the past. Therefore, the more sustaining wetter climate in the Eastern Desert point to a recurring feasible dispersal corridor for Homo sapiens from the tropical climate into the temperate climate regime throughout MIS 5. Such a more humid climate provided an ideal basis for long-term, favorable environmental conditions east of the Nile Valley, creating a kind of contiguous landscape corridor that may have been attractive to humans and wildlife alike, linking the East African tropical climate regime with that of the temperate zone in Northeast Africa and Eurasia.

Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points in the time domain. This paper considers the situation where nonlinear time series have potential change points in the state domain. We apply a density-weighted anti-symmetric kernel function to the state domain and therefore propose a nonparametric procedure to test the existence of change points. When the existence of change points is affirmative, we further introduce an algorithm to estimate the number of change points together with their locations. Theoretical results of the proposed detection and estimation procedures are given and a real dataset is used to illustrate our methods., to appear in Journal of Econometrics

PurposeThis study aims to assess and decompose the sustainable development using the 17 sustainable development goals (SDGs) in Iran in 2018, for proposing agenda-setting of public policy.Design/methodology/approachIt ranks the SDGs not only in Iran but also in the region and the world to reveal the synergetic effects.FindingsBased on the results, subaltern-populace generally suffers from the hegemonic domination of ruling elite-bourgeois, lack of strong institutions, heterogeneous policy networks and lack of advocacy role of non-governmental organizations, due to no transparency, issues in law or no rule of law, no stringent regulation, rent, suppression and Mafia, all leading to corruption and injustice.Practical implicationsTo stop the loop of corruption-injustice, Iran should homogenize the structure of the policy network. Furthermore, the failed SDGs of the three-geographic analysis are the same in a character; all of them propose SDG 3, good health and well-being as a serious failed goal.Social implicationsIn this regard, strong evidence is the pandemic Coronavirus, COVID 19 since 2019, due to its highly-disastrous consequences in early 2020 where the public policymakers could not adopt policies promptly in the glob, particularly in Iran.Originality/valueIn Iran, in addition to this, the malfunction of health is rooted in “subjective well-being” and “traffic deaths,” respectively. Concerning the transportations system in Iran, it is underscored that it is damaging the sustainable development from all the three pillars of sustainable development including, economic, social and environmental.

Atmospheric CO2 concentrations are soaring due to the continued use of fossil fuels in energy production, an anthropogenic activity that is playing a leading role in global warming. Thus, research aimed at the capture and conversion of CO2 into value-added products, such as cyclic carbonates, is booming. While CO2 is an abundant, cheap, non-toxic, and readily accessible C1 feedstock, its thermodynamic stability necessitates the development of highly efficient catalysts that are able to promote chemical reactions under mild conditions. In this work, a novel mesoporous poly(ionic liquid) with dual active sites was synthesized through a facile method that involves co-polymerization, post-synthetic metalation, and supercritical CO2 drying. Due to a high density of nucleophilic and electrophilic sites, the as-prepared poly(ionic liquid), denoted as P2D-4BrBQA-Zn, offers excellent performance in a CO2 cycloaddition reaction using epichlorohydrin as the substrate (98.9% conversion and 96.9% selectivity). Moreover the reaction is carried out under mild, solvent-free, and additive-free conditions. Notably, P2D-4BrBQA-Zn also efficiently promotes the conversion of various other epoxide substrates into cyclic carbonates. Overall, the catalyst is found to have excellent substrate compatibility, stability, and recyclability.

Electrochemical CO2 reduction (CO2RR) over molecular catalysts is a paramount approach for CO2 conversion to CO. Herein, we report a novel phthalocyanine-derived catalyst synthesized by a two-step method with a much improved electroconductivity. Furthermore, the catalyst contains both Ni–N4 sites and highly dispersed metallic Ni nanoclusters, leading to an increased CO2RR currents by two folds. Isotope labelling study and in situ spectroscopic analysis demonstrate that the existence of metallic Ni nanoclusters is the key factor for the activity enhancement and can shift the CO2RR mechanism from being electron transfer (ET)-limited (forming ∗COO−) to concerted proton-electron transfer (CPET)-limited (forming CO).

In the conversion process of syngas-to-C2 species, the OH species are inevitably produced accompanying the production of key intermediates CHx(x = 1–3), traditionally, the function of surface OH species is generally accepted as the hydrogenating reactive species. This work for the first time proposed and confirmed the assisted catalytic mechanism of surface OH species that performed as the promoter for syngas-to-C2 species on Cu-based catalysts. DFT and microkinetic modeling results reveal that the produced OH species accompanying the intermediates CHx production on the MCu (M = Co, Fe, Rh) catalysts can stably exist to form OH/MCu catalysts, on which the presence of surface OH species as the promoter not only presented better activity and selectivity toward CHx(x = 1–3) compared to MCu catalysts, but also significantly suppressed CH3OH production, providing enough CHx sources to favor the production of C2 hydrocarbons and oxygenates. Correspondingly, the electronic properties analysis revealed the essential relationship between the electronic feature of OH/MCu catalysts and catalytic performance, attributing to the unique electronic micro−environment of the catalysts under the interaction of surface OH species. This new mechanism is called as OH-assisted catalytic mechanism, which may be applied in the reaction systems related to the generation of OH species.

Synthesizing nitrogen (N)-containing molecules from biomass derivatives is a new strategy for production of this kind of chemicals. Herein, for the first time we present the synthesis of N-substituted aryl pyrroles via reductive amination/cyclization of levulinic acid (LA) with primary aromatic amines and hydrosilanes (e.g., PMHS) over CsF, and a series of N-substituted aryl pyrroles could be obtained in good to excellent yields at 120 °C. The mechanism investigation indicates that the reaction proceeds in two steps: the cyclization between amine and LA occurs first to form intermediate 5-methyl-N-alkyl-1,3-dihydro-2H-pyrrolones and their isomeride (B), and then the chemo- and region-selective reduction of intermediates take place to produce the final products. This approach for synthesis of N-substituted aryl pyrroles can be performed under mild and green conditions, which may have promising applications.

Effective and robust electrocatalysts are mainly based on innovative materials and unique structures. Herein, we designed a flakelike cobalt phosphide-based catalyst supporting on NiCo2O4 nanorods array, which in-situ grew on the nickel foam (NF) current collector, referring as N–Co2P/NiCo2O4/NF electrode. By optimizing the microstructure and electronic structure through 3D hierarchy fabrication and nitrogen doping, the catalyst features with abundant electrochemical surface area, favorable surface wettability, excellent electron transport, as well as tailored d band center. Consequently, the as-prepared N–Co2P/NiCo2O4/NF electrode exhibits an impressive HER activity with a low overpotentials of 58 mV at 10 mA cm−2, a Tafel slop of 75 mV dec−1, as well as superior durability in alkaline medium. This work may provide a new pathway to effectively improve the hydrogen evolution performance of transition metal phosphides and to develop promising electrodes for practical electrocatalysis.

This work includes fabrication and electrical characterization of Metal/Interlayer/Semiconductor (MIS) structures with methyl violet organic film on p-InP wafer. Metal(Ag)/ Interlayer (methyl violet =MV)/Semiconductor(p-InP) MIS structure presents a rectifying contact behavior. The values of ideality factor (n) and barrier height (BH) for the Ag/MV/p-InP MIS diode by using the current-voltage (I-V) measurement have been extracted as 1.21 and 0.84 eV, respectively. It was seen that the BH value of 0.84 eV calculated for the Ag/MV/p-InP MIS structure was significantly higher than the value of 0.64 eV of Ag/p-InP control contact. This situation was ascribed to the fact that the MV organic interlayer increased the effective barrier height by influencing the space charge region of inorganic semiconductor. The values of diffusion potential and barrier height for the Ag/MV/p-InP MIS diode by using the capacitance-voltage (C-V) measurement have been extracted as 1.21 V and 0.84 eV, respectively. The interface-state density of the Ag/MV/p-InP structure was seen to change from 2.57×1013 eV-1cm-2 to 2.19×1012 eV-1cm-2.

Owing to the advantages of high operating voltage, environmental benignity, and low cost, potassium-based dual-ion batteries (KDIBs) have been considered as a potential candidate for large-scale energy storage. However, KDIBs generally suffer from poor cycling performance and unsatisfied capacity, and inactive components of conductive agents, binders, and current collector further lower their overall capacity. Herein, we prepare coral-like carbon nanowires (CCNWs) doped with nitrogen as a binder-free anode material for K+-ion storage, in which the unique coral-like porous nanostructure and amorphous/short-range-ordered composite feature are conducive to enhancing the structural stability, to facilitating the ion transfer and to boosting the full utilization of active sites during potassiation/de-potassiation process. As a result, the CCNW anode possesses a hybrid K+-storage mechanism of diffusive behavior and capacitive adsorption, and stably delivers a high capacity of 276 mAh g-1 at 50 mA g-1, good rate capability up to 2 A g-1, and long-term cycling stability with 93 % capacity retention after 2000 cycles at 1 A g-1. Further, assembling this CCNW anode with an environmentally benign expanded graphite (EG) cathode yields a proof-of-concept KDIB, which shows a high specific capacity of 134.4 mAh g-1 at 100 mA g-1, excellent rate capability of 106.5 mAh g-1 at 1 A g-1, and long-term cycling stability over 1000 cycles without negligible capacity loss. This study provides a feasible approach to developing high-performance anodes for potassium-based energy storage devices.

Co–N–C is a promising oxygen electrochemical catalyst due to its high stability and good durability. However, due to the limited adsorption ability improvement for oxygen-containing intermediates, it usually exhibits inadequate catalytic activity with 2-electron pathway and high selectivity of hydrogen peroxide. Herein, the adsorption of Co–N–C to these intermediates is modulated by constructing heterostructures using transition metals and their derivatives based on d-band theory. The heterostructured nanobelts with MoC core and pomegranate-like carbon shell consisting of Co nanoparticles and N dopant (MoC/Co–N–C) are engineered to successfully modulate the d band center of active Co–N–C sites, resulting in a remarkably enhanced electrocatalysis performance. The optimally performing MoC/Co–N–C exhibits outstanding bi-catalytic activity and stability for the oxygen electrochemistry, featuring a high wave-half potential of 0.865 V for the oxygen reduction reaction (ORR) and low overpotential of 370 mV for the oxygen evolution reaction (OER) at 10 mA cm−2. The zinc air batteries with the MoC/Co–N–C catalyst demonstrate a large power density of 180 mW cm−2 and a long cycling lifespan (2000 cycles). The density functional theory calculations with Hubbard correction (DFT + U) reveal the electron transferring from Co to Mo atoms that effectively modulate the d band center of the active Co sites and achieve optimum adsorption ability with “single site double adsorption” mode.

Plasma methods are given significant attention in the context of conditioning the producer gas derived from biomass gasification. The goal of this work is to present the impact of hydrogen on the other producer gas compounds during microwave plasma valorization. These compounds include main producer gas components (CO, CO2, CH4, N2) and minor impurities (tar compounds, H2S and NH3). The results prove a beneficial impact of hydrogen addition on the conversion of CH4 and toluene, increasing it from ca. 68%–95% and ca. 97%–100%, respectively. Additionally, the presence of hydrogen changes the distribution of the products, inhibiting soot and aromatics production and promoting C2 compounds. In the case of CO2, the conversion increases from ca. 18%–63% when compared to nitrogen plasma, with CO being the resulting product. The presence of hydrogen inhibits H2S conversion and does not affect CO and NH3

Dental calculus and other preserved microbiome substrates are an attractive target for dietary reconstruction in past populations through a variety of physical, chemical, and molecular means. Recently, studies have attempted to reconstruct diet from archaeological dental calculus using archaeogenetic techniques. While dental calculus may provide a relatively stable environment for DNA preservation, the detection of plants and animals possibly consumed by an individual through DNA analysis is primarily hindered by microbial richness and incomplete reference databases. Moreover, high genomic similarity within eukaryotic groups 1. Introduction 2. Materials and methods 3. Results 3.1. Effects of low read counts on damage patterns 3.2. Identification of dietary reads from synthetic datasets 3.3. Identification of dietary reads from real datasets 4. Discussion and conclusions 4.1. Checklist for authors and reviewers 4.1.1. Field/storage contamination 4.1.2. Is the laboratory methodology suitable for aDNA authentication? 4.1.3. Is the database and pipeline suitable? 4.1.4. Is the taxonomic resolution analyzed sufficient for reliable interpretation? 4.1.5. Have unexpected taxa been reported and evaluated? 4.1.6. Have laboratory controls been evaluated alongside samples? 4.1.7. Has evidence of aDNA authenticity been provided? 4.1.8. Is there other evidence supporting that dietary organisms were consumed?

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years. Further, the tree fruit architecture contributes to the light interception and improves tree growth, fruit quality, and fruit yield, in addition to easing the process of orchard management and harvest. Currently tree architectural traits are measured manually by researchers or growers, which is labor-intensive and time-consuming. In this study, the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders, physiologists and growers in collecting architectural traits efficiently and in a standardized manner. For this, a consumer-grade red–green–blue (RGB) camera was used to collect apple tree side-images, while an unmanned aerial vehicle (UAV) integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures. The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems (Spindle, V-trellis, Bi-axis), two rootstocks (‘WA 38 trees grafted on G41 and M9-Nic29) and two pruning methods (referred as bending and click pruning). The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model. The traits extracted from sensing data included box-counting fractal dimension (DBs), middle branch angle, number of branches, trunk basal diameter, and tree row volume (TRV). The results from ground-based sensing data indicated that there was a significant (P

In molecular communications, the direct detection of signaling molecules may be challenging due to a lack of suitable sensors and interference in the environment. Motivated by research in molecular biology, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. We consider two implementations of the proposed detection mechanism, i.e., unrestricted probe movement and probes restricted to a volume around the receiver. The reaction-diffusion equations describing the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed form. Therefore, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables of the equations in each time step. Our results show that the concentrations of the product molecules and the signalling molecules share a similar characteristic over time, i.e., a single peak and a long tail. The peak and tail values of the product molecule concentration can be controlled by choosing probes with suitable parameters. By carefully choosing the molecular probe and optimizing the decision threshold, the BER can be improved significantly and outperform that of a direct detection system., 13 pages, 11 figures, 1 table. This journal version was submitted in April 2022 to the IEEE for possible publication. A part of this article was presented at the IEEE Wireless Communications and Networking Conference 2020 and stored in the previous version on arXiv

Brood is critically important in social insect colonies. It carries the colony’s fitness through delivering future reproductive adults as well as workers that will increase the colony’s workforce. Adoption of non-nestmate brood can increase the colony workforce but entails the risk of rearing unrelated sexuals or social parasites. Thus, theory would predict that ant workers will evolve the ability to discriminate between nestmate and alien brood using the chemical cues displayed at the brood’s surface. This appears especially true for eggs and first instar (L1) larvae, which require more resources before becoming adult workers compared to older brood. However, the chemical signature of ant early brood stages and its recognition by workers remains understudied. To fill this gap, we investigated the chemical basis of early brood nestmate and cross-species recognition in six ant species. We also tested the discrimination behaviour of workers in brood retrieval trials. We observed species-level cues and discrimination against hetero-specific brood. We also found that eggs and most L1 larvae displayed a colony signature. However, only some species discriminated against non-nestmate early brood. Interestingly, these species belong to genera subject to brood parasitism. We hypothesize that non-nestmate brood discrimination could arise from species adaptations against brood parasitism.

This paper introduces an algorithmic approach to the analysis of bifurcation of limit cycles from the centers of nonlinear continuous differential systems via the averaging method. We develop three algorithms to implement the averaging method. The first algorithm allows one to transform the considered differential systems to the normal form of averaging. Here, we restricted the unperturbed term of the normal form of averaging to be identically zero. The second algorithm is used to derive the computational formulae of the averaged functions at any order. The third algorithm is based on the first two algorithms and determines the exact expressions of the averaged functions for the considered differential systems. The proposed approach is implemented in Maple and its effectiveness is shown by several examples. Moreover, we report some incorrect results in published papers on the averaging method.

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.

This is the first work that incorporates recent advancements in "explainability" of machine learning (ML) to build a routing obfuscator called ObfusX. We adopt a recent metric---the SHAP value---which explains to what extent each layout feature can reveal each unknown connection for a recent ML-based split manufacturing attack model. The unique benefits of SHAP-based analysis include the ability to identify the best candidates for obfuscation, together with the dominant layout features which make them vulnerable. As a result, ObfusX can achieve better hit rate (97% lower) while perturbing significantly fewer nets when obfuscating using a via perturbation scheme, compared to prior work. When imposing the same wirelength limit using a wire lifting scheme, ObfusX performs significantly better in performance metrics (e.g., 2.4 times more reduction on average in percentage of netlist recovery).

Influenza polymerase (FluPol) plays a key role in the viral infection cycle by transcribing and replicating the viral genome. FluPol is a multifunctional, heterotrimeric enzyme with cap-binding, endonuclease, RNA-dependent RNA polymerase and polyadenylation activities. It performs its functions in the context of the viral ribonucleoprotein particle (RNP), wherein the template viral RNA is coated by multiple copies of viral nucleoprotein. Moreover, it interacts with a number of host proteins that are essential cofactors and, consequently, adaptive mutations in the polymerase are required for crossing the avian-human species barrier. In this review, we show how mechanistic understanding of how FluPol performs its multiple functions has greatly advanced over the last decade through determination of high-resolution structures by X-ray crystallography and cryo-electron microscopy. These have revealed not only the detailed architecture of FluPol but highlighted the remarkably conformational flexibility that is inherent to its functioning as a dynamic RNA synthesis machine. Structural studies are also underpinning current attempts to develop next-generation anti-influenza drugs that directly target FluPol.

The main goal of this article is to connect some recent perspectives in the study of 4 4 -manifolds from the vantage point of singularity theory. We present explicit algorithms for simplifying the topology of various maps on 4 4 -manifolds, which include broken Lefschetz fibrations and indefinite Morse 2 2 -functions. The algorithms consist of sequences of moves, which modify indefinite fibrations in smooth 1 1 -parameter families. These algorithms allow us to give purely topological and constructive proofs of the existence of simplified broken Lefschetz fibrations and Morse 2 2 -functions on general 4 4 -manifolds, and a theorem of Auroux–Donaldson–Katzarkov on the existence of certain broken Lefschetz pencils on near-symplectic 4 4 -manifolds. We moreover establish a correspondence between broken Lefschetz fibrations and Gay–Kirby trisections of 4 4 -manifolds, and show the existence and stable uniqueness of simplified trisections on all 4 4 -manifolds. Building on this correspondence, we also provide several new constructions of trisections, including infinite families of genus- 3 3 trisections with homotopy inequivalent total spaces, and exotic same genera trisections of 4 4 -manifolds in the homeomorphism classes of complex rational surfaces.

Defect-engineered carbon materials have been emerged as promising electrocatalysts for oxygen reduction reaction (ORR) in metal-air batteries. Developing a facile strategy for the preparation of highly active nanocarbon electrocatalysts remains challenging. Herein, a low-cost and simple route is developed to synthesize defective graphene by pyrolyzing the mixture of glucose and carbon nitride. Molecular dynamics simulations reveal that the graphene formation is ascribed to two-dimensional layered feature of carbon nitride, and high compatibility of carbon nitride/glucose systems. Structural measurements suggest that the graphene possesses rich edge and topological defects. The graphene catalyst exhibits higher power density than commercial Pt/C catalyst in a primary Zn-air battery. Combining experimental results and theoretical thermodynamic analysis, it is identified that graphitic nitrogen-modified topological defects at carbon framework edges are responsible for the decent ORR performance. The strategy presented in this work can be can be scaled up readily to fabricate defective carbon materials.

Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity - a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of Structural Equation Models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale.

Synthesizing ring structure aviation fuels from biomass-derived platform molecules is challenging, especially for bridged ring structure aviation fuels which are typically achieved from fossil-derived chemicals. Herein, we report the synthesis of a series of ring structure biofuels in two steps by a combination of a solvent-free Michael-cyclization reaction and a hydrodeoxygenation (HDO) reaction from lignocellulose-derived 5,5-dimethyl-1,3-cyclohexanedione. These biofuels are obtained with high overall yields up to 90%, which exhibit high densities of 0.81 g cm−3-0.88 g cm−3 and high volumetric neat heat of combustion (VNHOC) values of 36.0 MJ L−1-38.6 MJ L−1. More importantly, bridged-ring structure hydrocarbons can also be achieved in two steps by a combination of a Robinson annulation reaction and an HDO reaction to afford the final products at high overall yields up to 90%. The bridged-ring structure products have comparable high densities and high VNHOC values to the best artificial fuel JP-10 (0.94 g cm−3 and 39.6 MJ L−1). The results demonstrate a promising way for the synthesis of high-density aviation fuels with different fuel properties at high yields.

3D flower-like hierarchical mesoporous Bi4O5I2/MoS2 Z-scheme layered heterojunction photocatalyst was fabricated by oil bath and hydrothermal methods. The heterojunction with narrow band gap of ∼1.95 eV extended the photoresponse to near-infrared region, which showed obvious photothermal effect due to the introduction of MoS2 with broad spectrum response. MoS2 nanosheets were anchored onto the surface of flower-like hierarchical mesoporous Bi4O5I2 nanosheets, thereby forming efficient layered heterojunctions, the solar-driven photocatalytic efficiency in degradation of highly toxic dichlorophenol and reduction of hexavalent chromium was improved to 98.5% and 99.2%, which was ∼4 and 7 times higher than that of the pristine Bi4O5I2, respectively. In addition, the photocatalytic hydrogen production rate reached 496.78 μmol h-1 g-1, which was ∼6 times higher than that of the pristine Bi4O5I2. The excellent photocatalytic performance can be ascribed to the promoted photothermal effect, as well as, the formation of compact Z-scheme layered heterojunctions. The 3D flower-like hierarchical mesoporous structure provided adequate surface active-sites, which was conducive to the mass transfer. Moreover, the high stability of the prepared photocatalyst further promoted its potential practical application. This strategy also provides new insights for fabricating layered Z-scheme heterojunctions photocatalysts with highly photothermal-photocatalytic efficiency.

The Ru-catalyzed carbonylation of alkenes with CO2 as a C1 surrogate and imidazole chlorides as the promotor is investigated by a combination of computational and experimental study. The conversion rate of CO2 to CO is positively correlated with the efficiency of both hydroesterification and hydroformylation, which is found facilitated in the presence of chloride additives with a decreasing order of BmimCl ∼ B3MimCl > BmmimCl ∼ LiCl. Taking the hydroesterification with MeOH as a representative example, BmimCl bearing C–H functionality at the C2 site of the cation assists the reduction of CO2 to CO as a hydrogen donor medium, with the anion and cation acting in a synergistic fashion. Subsequent insertion of CO2 into the formed Ru–H bond with the assistance of chloride anion produces the Ru–COOH species, which ultimately accelerates the activation of CO2.

Energy conversion technologies like fuel cells and metal-air batteries require oxygen reduction reaction (ORR) electrocatalysts with low cost and high catalytic activity. Herein, N-doped carbon spheres (N-CS) with rich micropore structure have been synthesized by a facile two-step method, which includes the polymerization of pyrrole and formaldehyde and followed by a facile pyrolysis process. During the preparation, zinc chloride (ZnCl2) was utilized as a catalyst to promote polymerization and provide a hypersaline environment. In addition, the morphology, defect content and activity area of the resultant N-CS catalysts could be regulated by controlling the content of ZnCl2. The optimum N-CS-1 catalyst demonstrated much better catalytic activity and durability towards ORR in alkaline conditions than commercial 20 wt% Pt/C catalysts, of which the half-wave potential reached 0.844 V vs. RHE. When applied in the Zn-air batteries as cathode catalysts, N-CS-1 showed a maximum power density of 175 mW cm-2 and long-term discharging stability of over 150 h at 10 mA cm-2, which outperformed 20 wt% Pt/C. The excellent performance could be due to its ultrahigh specific surface area of 1757 m2 g-1 and rich micropore channels structure. Meanwhile, this work provides an efficient method to synthesize an ultrahigh surface porous carbon material, especially for catalyst application.

Activated red mud (RM) has been proved to be a promising base material for the selective catalysis reduction (SCR) of NOx. The inherent low reducibility and acidity limited its low–temperature activity. In this work, molybdenum oxide, tungsten oxide, and cerium oxide were used to reconfigure the redox sites and acid sites of red mud based catalyst. When activated red mud was reconfigured by cerium-tungsten oxide (Ce–W@RM), the NOx conversion kept above 90% at 219–480 °C. The existence of Ce3+/Ce4+ redox electron pairs provided more surface adsorbed oxygen (Oα) and served as a redox cycle. Positive interactions between Ce, W species and Fe oxide in red mud occurred, which led to the formation of unsaturated chemical bond and promoted the activation of adsorbed NH3 species. WO3 and Ce2(WO4)3 (formed by solid–state reaction between Ce and W species) could provide more Bronsted acid sites (W–O modes of WO3, W O or W–O–W modes of Ce2(WO4)3). CeO2 species could provide more Lewis acid sites. The Langmuir–Hinshelwood (L-H) routes and Eley-Rideal (E-R) routes occurred in the low-temperature SCR reaction on the Ce–W@RM surface. NH4+ species on Bronsted acid sites, NH3 species on Lewis acid sites, bidentate nitrate and bridging nitrate species were key active intermediates species.

A novel strategy was developed to fabricate FeNx-doped carbon quantum dots (Fe-N-CQDs) to detect Cu2+ ions selectively as a fluorescence probe. The Fe-N-CQDs were synthesized by an efficient electrolysis of a carbon cloth electrode, which was coated with monoatomic iron-anchored nitrogen-doped carbon (Fe-N-C). The obtained Fe-N-CQDs emitted blue fluorescence and possessed a quantum yield (QY) of 7.5%. An extremely wide linear relationship between the Cu2+ concentration and the fluorescence intensity was obtained in the range from 100 nM to 1000 nM (R2 = 0.997), and the detection limit was calculated as 59 nM. Moreover, the Fe-N-CQDs demonstrated wide range pH compatibility between 2 and 13 due to the coordination between pyridine nitrogen and Fe3+, which dramatically reduced the affection of the protonation and deprotonation process between H+ and Fe-N-CQDs. It is notable that the Fe-N-CQDs exhibited a rapid response in Cu2+ detection, where stable quenching can be completed in 7 s. The mechanism of excellent selective detection of Cu2+ was revealed by energy level simulation that the LUMO level of Fe-N-CQDs (−4.37 eV) was close to the redox potential of Cu2+, thus facilitating the electron transport from Fe-N-CQDs to Cu2+.

In this paper, we propose and analyze SPARQ-SGD, an event-triggered and compressed algorithm for decentralized training of large-scale machine learning models over a graph. Each node can locally compute a condition (event) which triggers a communication where quantized and sparsified local model parameters are sent. In SPARQ-SGD, each node first takes a fixed number of local gradient steps and then checks if the model parameters have significantly changed compared to its last update; it communicates further compressed model parameters only when there is a significant change, as specified by a (design) criterion. We prove that SPARQ-SGD converges as $O\left( {\frac{1}{{nT}}} \right)$ and $O\left( {\frac{1}{{\sqrt {nT} }}} \right)$ in the strongly-convex and non-convex settings, respectively, demonstrating that aggressive compression, including event-triggered communication, model sparsification and quantization does not affect the overall convergence rate compared to uncompressed decentralized training; thereby theoretically yielding communication efficiency for ‘free’. We evaluate SPARQ-SGD over real datasets to demonstrate significant savings in communication bits over the state-of-the-art.

Biodegradable magnesium (Mg) and its alloy show huge potential as temporary bone substitute due to the favorable biocompatibility and mechanical compatibility. However, one issue deserves attention is the too fast degradation. In this work, mesoporous bioglass (MBG) with high pore volume (0.59 cc/g) and huge specific surface area (110.78 m2/g) was synthesized using improved sol-gel method, and introduced into Mg-based composite via laser additive manufacturing. Immersion tests showed that the incorporated MBG served as powerful adsorption sites, which promoted the in-situ deposition of apatite by successively adsorbing Ca2+ and HPO42−. Such dense apatite film acted as an efficient protection layer and enhanced the corrosion resistance of Mg matrix, which was proved by the electrochemical impedance spectroscopy measurements. Thereby, Mg based composite showed a significantly decreased degradation rate of 0.31 mm/year. Furthermore, MBG also improved the mechanical properties as well as cell behavior. This work highlighted the advantages of MBG in the fabrication of Mg-based implant with enhanced overall performance for orthopedic application.

Establishing highly effective charge transfer channels in carbon nitride (g-C3N4) to enhance its photocatalytic activity is still a challenging issue. Herein, the delaminated 2D Ti3C2 MXene nanosheets were employed to decorate the P-doped tubular g-C3N4 (PTCN) for engineering 1D/2D Schottky heterojunction (PTCN/TC) through electrostatic self-assembly. The optimized PTCN/TC exhibited the highest hydrogen evolution rate (565 μmol/h/g), which was 4.3 and 2.0 -fold higher than pristine bulk g-C3N4 and PTCN, respectively. Such enhancement may be primarily attributed to the phosphorus heteroatom doped and unique structure of 1D/2D g-C3N4/Ti3C2 Schottky heterojunction, enhancing the light-harvesting and charges’ separation. One-dimensional pathway of g-C3N4 tube and built-in electric field of interfacial Schottky effect can significantly facilitate the spatial separation of photogenerated charge carriers, simultaneously inhibit their recombination via Schottky barrier. In this composite, metallic Ti3C2 was served as electrons sink and photons collector. Moreover, ultrathin Ti3C2 flake with exposed terminal metal sites as a co-catalyst exhibited higher photocatalytic reactivity in H2 evolution compared to carbon materials (such as reduced graphene oxide). This work not only proposed the mechanism of tubular g-C3N4/Ti3C2 Schottky junction in photocatalysis, but also provided a feasible way to load ultrathin Ti3C2 as a co-catalyst for designing highly efficient photocatalysts.

Diphenyl carbonate (DPC) is one of the versatile carbonates, and is often used for the production of polycarbonates. In recent years, the catalytic synthesis of DPC has become an important topic but the development of a highly active metal-free catalyst is a great challenge. Herein, a series of ionic liquids-SBA-15 hybrid catalysts with different functional groups have been developed for the synthesis of DPC under solvent-free condition, which are effective and clean instead of the metal-containing catalysts. It is found that in the presence of [SBA-15-IL-OH]Br catalyst, methyl phenyl carbonate (MPC) conversion of 80.5% along with 99.6% DPC selectivity is achieved, the TOF value is thrice higher than the best value reported by using transition metal-based catalysts. Moreover, the catalyst displays remarkable stability and recyclability. This work provides a new idea to design and prepare eco-friendly catalysts in a broad range of applications for the green synthesis of carbonates.

Hydrogen energy has long been recognized as a clean alternative to conventional fossil fuels, which can be applied in a wide range of transportation and power generation applications. The rational design and engineering of high-performance and robust catalysts for hydrogen evolution reaction (HER) shows not a great significance but a challenge for efficient electrochemical water splitting. Herein, a new type of Ni-based Ni-ABDC precursor has been obtained, which leads to the formation of N-doped porous carbon nanomaterials uniformly coated with well-proportioned bimetallic AgNi alloys via a stepwise strategy. To their credit, all samples of AgNi/NC-X are structurally calcined from the pristine AgNi-ABDC-X by tuning the different concentration of AgNO3, which means all of them maintain the vermicelli-like morphology compared with Ni-ABDC. The series of AgNi/NC-X materials can be regarded as effective electrocatalysts for HER both in acidic and alkaline media, but an acid-leaching phenomenon is observed. Among them, the as-prepared AgNi/NC-2 exhibits a low overpotential of 103 mV at the current density of 10 mA cm−2 and decent durability with a high retention rate of 90.9% after 10 h in 1.0 M KOH electrolyte. The compelling HER properties of AgNi/NC-2 can be attributed to the synergistic effect between the hierarchical carbon materials, partial N-doping and abundant AgNi alloys. Meanwhile, this study provides a practicable method for the development of efficient HER electrocatalysts for energy applications, which can be conveniently prepared through the reasonable introduction of active components in the crystalline inorganic-organic precursors.

Issuing a disaster certificate, which is used to decide the contents of a victim’s support, requires accuracy and rapidity. However, in Japan at large, issuing of damage certificates has taken a long time in past earthquake disasters. Hence, the government needs a more efficient mechanism for issuing damage certificates. This study developed an estimation system of roof-damaged buildings to obtain an overview of earthquake damage based on aero-photo images using deep learning. To provide speedy estimation, this system utilized the trimming algorithm, which automatically generates roof image data using the location information of building polygons on GIS (Geographic Information System). Consequently, the proposed system can estimate, if a house is covered with a blue sheet with 97.57 % accuracy and also detect whether a house is damaged, with 93.51 % accuracy. It would therefore be worth considering the development of an image recognition model and a method of collecting aero-photo data to operate this system during a real earthquake.

Along with the environmental pollution, the scarcity of clean water seriously threatens the sustainable development of human society. Recently, the rapid development of solar evaporators has injected new vitality into the field of water purification. However, the industry faces a considerable challenge of achieving comprehensive purification of ions, especially the efficient removal of mercury ions. In this work, we introduce an ideal mercury-removal platform based on facilely and cost-effectively synthesized polysulfide nanoparticles (PSNs). Further development of PSN-functionalized reduced graphene oxide (PSN-rGO) aerogel evaporator results in achieving a high evaporation rate of 1.55 kg m−2 h−1 with energy efficiency of 90.8% under 1 sun. With the merits of interconnected porous structure and adsorption ability, the photothermal aerogel presents overall purification of heavy metal ions from wastewater. During solar desalination, salt ions can be rejected with long-term stability. Compared with traditional water purification technologies, this highly efficient solar evaporator provides a new practical method to utilize clean energy for clean water production.

In this work, Ag/Bi5O7I nanocomposite was prepared and firstly applied in piezo/photocatalytic reduction of N2 to NH3 and methyl orange (MO) degradation. Bi5O7I was synthesized via a hydrothermal-calcination method and shows nanorods morphology. Ag nanoparticles (NPs) were photo deposited on the Bi5O7I nanorods as electron trappers to improve the spatial separation of charge carriers, which was confirmed via XPS, TEM, and electronic chemical analyses. The catalytic test indicates that Bi5O7I presents the piezoelectric-like behavior, while the loading of Ag NPs can strengthen the character. Under ultrasonic vibration, the optimal Ag/Bi5O7I presents high efficiency in MO degradation. The degradation rate is determined to be 0.033 min−1, which is 4.7 folds faster than that of Bi5O7I. The Ag/Bi5O7I also presents a high performance in piezocatalytic N2 fixation. The piezocatalytic NH3 generation rate reaches 65.4 μmol L−1 g−1 h−1 with water as a hole scavenger. The addition of methanol can hasten the piezoelectric catalytic reaction. Interestingly, when ultrasonic vibration and light irradiation simultaneously act on the Ag/Bi5O7I catalyst, higher performance in NH3 generation and MO degradation is observed. However, due to the weak adhesion of Ag NPs, some Ag NPs would fall off from the Bi5O7I surface under long-term ultrasonic vibration, which would greatly reduce the piezoelectric catalytic performance. This result indicates that a strong binding force is required when preparing the piezoelectric composite catalyst. The current work provides new insights for the development of highly efficient catalysts that can use multiple energies.

A series of CoPd/KIT-6 bimetallic catalysts with various Co:Pd molar ratios at different calcination temperatures were prepared and used for the direct synthesis of H2O2 from H2 and O2. These catalysts were characterized by nitrogen adsorption-desorption, low and wide-angle X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), scanning electron microscopy (SEM), elemental mapping and energy-dispersive X-ray (EDX) methods. It was found that the particle size, electronic interactions, morphology, and textural properties of these catalysts as well as their catalytic activity in the reaction of H2 with O2 were affected by Co addition and different calcination temperatures. Also, the results showed that while the H2O2 selectivity depends on Pd2+ species, the H2 conversion is related to Pd0 active sites. Among these catalysts, CoPd/KIT-6 calcined at 350 °C (CoPd/KIT-350 catalyst) showed the best catalytic activity with 50 % of H2O2 selectivity and 51 % conversion of H2.

Adsorptive removal of heavy metal ions from wastewater is very important, and the key is the development of efficient sorbents. In this work, oxygenated alkynyl carbon materials (OACMs) were synthesized via mechanochemical reaction of CaC2 and a carbonate (CaCO3, Na2CO3, or NaHCO3) at ambient temperature. The resultant OACMs are micro mesoporous carbon nanomaterials with high specific area (>648 m2 g−1), highly crosslinked texture, and rich alkynyl and oxygenated groups. The OACMs exhibit excellent Hg(II) adsorption due to the soft acid-soft base interaction between alkynyl and Hg(II), and OACM-3 derived from CaC2 and NaHCO3 has the saturated Hg(II) adsorbance of 483.9 mg g−1 along with good selectivity and recyclability. The adsorption is mainly chemisorption following the Langmuir mode. OACM-3 also shows high adsorbance for other heavy metal ions, e.g. 256.6 mg g−1 for Pb(II), 232.4 mg g−1 for Zn(II), and 198.7 mg g−1 for Cu(II). This work expands the mechnochemical reaction of CaC2 with carbonates and possibly other oxyanionic salts, provides a new synthesis approach for functional alkynyl carbon materials with excellent adsorption performance for heavy metal ions, as well as a feasible approach for CO2 resource utilization.

Aqueous zinc-air battery (ZAB) has attractive features as the potential energy storage system such as high safety, low cost and good environmental compatibility. However, the issue of dendrite growth on zinc metal anodes has seriously hindered the development of ZAB. Herein, the N-doped carbon cloth (NC) prepared via magnetron sputtering is explored as the substrate to induce the uniform nucleation of zinc metal and suppress dendrite growth. Results show that the introduction of heteroatoms accelerates the migration and deposition kinetics of Zn2+ by boosting the desolvation process of Zn2+, eventually reducing the nucleation overpotential. Besides, theoretical calculation results confirm the zincophilicity of N-containing functional group (such as pyridine N and pyrrole N), which can guide the nucleation and growth of zinc uniformly on the electrode surface by both promoting the redistribution of Zn2+ in the vicinity of the surface and enhancing its interaction with zinc atoms. As a result, the half-cell assembled with magnetron sputtered carbon cloth achieves a high zinc stripping/plating coulombic efficiency of 98.8% and long-term stability of over 500 cycles at 0.2 mA cm-2. And the Coulombic efficiency reached about 99.5% at the 10th cycle and maintained for more than 210 cycles at a high current density of 5.0 mA cm-2. The assembled symmetrical battery can deliver 220 plating/stripping cycles with ultra-low voltage hysteresis of only 11 mV. In addition, the assembled zinc-air full battery with NC-Zn anode delivers a high special capacity of about 429 mAh gZn-1 and a long life of over 430 cycles. The effectiveness of surface functionalization in promoting the transfer and deposition kinetics of Zn2+ presented in this work shows enlightening significance in the development of metal anodes in aqueous electrolytes.

Covalent organic frameworks (COFs) are nanoporous crystalline polymers with densely conjugated structures. This work discovers that imine-linked COFs exhibit remarkable photodegradation efficiency to azo dyes dissolved in water. Visible light generates different types of radicals from COFs, and superoxide radicals break N N bonds in dye molecules, resulting in 100% degradation of azo dyes within 1 h. In contrast, these dyes cannot be degraded by conventionally used photocatalysts, for example, TiO2. Importantly, the COF photocatalysts can be recovered from the dye solutions and re-used to degrade azo dyes for multiple times without loss of degradation efficiency. This work provides an efficient strategy to degrade synthetic dyes, and we expect that COFs with designable structures may use as new photocatalysts for other important applications.

Graphene oxide (GO) is regarded as a promising candidate to construct solar absorbers for addressing freshwater crisis, but the easy delamination of GO in water poses a critical challenge for practical solar desalination. Herein, we improve the stability of GO membranes by a self-crosslinking poly (ionic liquid) (PIL) in a mild condition, which crosslinks neighbouring GO nanosheets without blemishing the hydrophilic structure of GO. By further adding carbon nanotubes (CNTs), the sandwiched GO/CNT@PIL (GCP) membrane displays a good stability in pH = 1 or 13 solution even for 270 days. The molecular dynamics simulation results indicate that the generation of water nanofluidics in nanochannels of GO nanosheets remarkably reduces the water evaporation enthalpy in GCP membrane, compared to bulk water. Consequently, the GCP membrane exhibits a high evaporation rate (1.87 kg m−2 h−1) and displays stable evaporation rates for 14 h under 1 kW m−2 irradiation. The GCP membrane additionally works very well when using different water sources (e.g., dye-polluted water) or even strong acidic solution (pH = 1) or basic solution (pH = 13). More importantly, through bundling pluralities of GCP membrane, an efficient solar desalination device is developed to produce drinkable water from seawater. The average daily drinkable water amount in sunny day is 10.1 kg m−2, which meets with the daily drinkable water needs of five adults. The high evaporation rate, long-time durability and good scalability make the GCP membrane an outstanding candidate for practical solar seawater desalination.

The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive Vector AutoRegressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is "simplest" in a certain sense. A user-specified strongly convex penalty is used to measure model simplicity, and that same penalty is then used to define an estimator that can be efficiently computed. We establish consistency of our estimators in a double-asymptotic regime. Our non-asymptotic error bound analysis accommodates both model specification and parameter estimation steps, a feature that is crucial for studying large-scale VARMA algorithms. Our analysis also provides new results on penalized estimation of infinite-order VAR, and elastic net regression under a singular covariance structure of regressors, which may be of independent interest. We illustrate the advantage of our method over VAR alternatives on three real data examples.