Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

1.42-fold enhancement of formate selectivity by linker conversion on the Zn-based metal organic framework catalyst

Electro-reduction of carbon dioxide(ERCO_(2)) is considered an effective method to alleviate the greenhouse effect and produce value-added chemicals.Achieving the dominant selectivity of Zn-based catalysts for formate remains a challenge.In this article,the ZnIn-E_(12) catalyst is successfully prepared by solvent assisted ligand exchange(SALE) method to convert organic ligands,achieving a Faradaic efficiency of 72.28% for formate at-1.26 V vs.RHE(V_(RHE)),which is 1.42 times higher than the original catalyst.Evidence shows that the successful conversion of organic ligands can transform the catalyst from the original large size polyhedron to cross-linked network of particles with a diameter of about 30 nm.The increased specific surface area can expose more active sites and facilitate the electrocatalytic conversion of CO_(2) to formate.This work is expected to provide inspiration for the regulation of formate selectivity and catalyst size in Zn-based catalysts.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Valley transport in Kekulé structures of graphene

Valleytronics is an emergent discipline in condensed matter physics and offers a new way to encode and manipulate information based on the valley degree of freedom in materials. Among the various materials being studied, Kekulé distorted graphene has emerged as a promising material for valleytronics applications. Graphene can be artificially distorted to form the Kekulé structures rendering the valley-related interaction. In this work, we review the recent progress of research on Kekulé structures of graphene and focus on the modified electronic bands due to different Kekulé distortions as well as their effects on the transport properties of electrons. We systematically discuss how the valley-related interaction in the Kekulé structures was used to control and affect the valley transport including the valley generation, manipulation, and detection. This article summarizes the current challenges and prospects for further research on Kekulé distorted graphene and its potential applications in valleytronics.

Geometries and electronic structures of Zr_(n)Cu(n=2–12) clusters: A joint machine-learning potential density functional theory investigation

Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.

An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

Luminescence regulation of Sb^(3+)in 0D hybrid metal halides by hydrogen bond network for optical anti-counterfeiting

The Sb^(3+) doping strategy has been proven to be an effective way to regulate the band gap and improve the photophysical properties of organic-inorganic hybrid metal halides(OIHMHs).However,the emission of Sb^(3+) ions in OIHMHs is primarily confined to the low energy region,resulting in yellow or red emissions.To date,there are few reports about green emission of Sb^(3+)-doped OIHMHs.Here,we present a novel approach for regulating the luminescence of Sb^(3+) ions in 0D C_(10)H_(2)_(2)N_(6)InCl_(7)·H_(2)O via hydrogen bond network,in which water molecules act as agents for hydrogen bonding.Sb^(3+)-doped C_(10)H_(2)2N_(6)InCl_(7)·H_(2)O shows a broadband green emission peaking at 540 nm and a high photoluminescence quantum yield(PLQY)of 80%.It is found that the intense green emission stems from the radiative recombination of the self-trapped excitons(STEs).Upon removal of water molecules with heat,C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7) generates yellow emis-sion,attributed to the breaking of the hydrogen bond network and large structural distortions of excited state.Once water molecules are adsorbed by C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7),it can subsequently emit green light.This water-induced reversible emission switching is successfully used for optical security and information encryption.Our findings expand the under-standing of how the local coordination structure influences the photophysical mechanism in Sb^(3+)-doped metal halides and provide a novel method to control the STEs emission.

Implications of the Stellar Mass Density of High-z Massive Galaxies from JWST on Warm Dark Matter

A significant excess of the stellar mass density at high redshift has been discovered from the early data release of James Webb Space Telescope(JWST),and it may require a high star formation efficiency.However,this will lead to large number density of ionizing photons in the epoch of reionization(EoR),so that the reionization history will be changed,which can arise tension with the current EoR observations.Warm dark matter(WDM),via the free streaming effect,can suppress the formation of small-scale structure as well as low-mass galaxies.This provides an effective way to decrease the ionizing photons when considering a large star formation efficiency in high-z massive galaxies without altering the cosmic reionization history.On the other hand,the constraints on the properties of WDM can be derived from the JWST observations.In this work,we study WDM as a possible solution to reconcile the JWST stellar mass density of high-z massive galaxies and reionization history.We find that,the JWST high-z comoving cumulative stellar mass density alone has no significant preference for either CDM or WDM model.But using the observational data of other stellar mass density measurements and reionization history,we obtain that the WDM particle mass with mw=0.51_(-0.12)^(+0.22) keV and star formation efficiency parameter f_(*)^(0)> 0.39 in 2σ confidence level can match both the JWST high-z comoving cumulative stellar mass density and the reionization history.

Structural, Magnetic and Heating Efficiency of Ball Milled γ-Fe2O3/Gd2O3 Nanocomposite for Magnetic Hyperthermia

The preparation of γ-Fe2O3/Gd2O3 nanocomposite for possible use in magnetic hyperthermia application was done by ball milling technique. The nanocomposite was characterized by X-ray diffraction (XRD) and vibrating sample magnetometer (VSM). The heating efficiency and the effect of milling time (5 h and 30 h) on the structural and magnetic properties of the nanocomposite were reported. XRD analysis confirms the formation of the nanocomposite, while magnetization measurements show that the milled sample present hysteresis with low coercivity and remanence. The specific absorption rate (SAR) under an alternating magnetic field is investigated as a function of the milling time. A mean heating efficiency of 68 W/g and 28.7 W/g are obtained for 5 h and 30 h milling times respectively at 332 kHz and 170 Oe. The results showed that the obtained nanocomposite for 5 h milling time is a promising candidate for magnetic hyperthermia due to his properties which show an interesting magnetic behavior and high specific absorption rate.

A Review of In‑Situ Techniques for Probing Active Sites and Mechanisms of Electrocatalytic Oxygen Reduction Reactions

Electrocatalytic oxygen reduction reaction(ORR)is one of the most important reactions in electrochemical energy technologies such as fuel cells and metal–O2/air batteries,etc.However,the essential catalysts to overcome its slow reaction kinetic always undergo a complex dynamic evolution in the actual catalytic process,and the concomitant intermediates and catalytic products also occur continuous conversion and reconstruction.This makes them difficult to be accurately captured,making the identification of ORR active sites and the elucidation of ORR mechanisms difficult.Thus,it is necessary to use extensive in-situ characterization techniques to proceed the real-time monitoring of the catalyst structure and the evolution state of intermediates and products during ORR.This work reviews the major advances in the use of various in-situ techniques to characterize the catalytic processes of various catalysts.Specifically,the catalyst structure evolutions revealed directly by in-situ techniques are systematically summarized,such as phase,valence,electronic transfer,coordination,and spin states varies.In-situ revelation of intermediate adsorption/desorption behavior,and the real-time monitoring of the product nucleation,growth,and reconstruction evolution are equally emphasized in the discussion.Other interference factors,as well as in-situ signal assignment with the aid of theoretical calculations,are also covered.Finally,some major challenges and prospects of in-situ techniques for future catalysts research in the ORR process are proposed.

Selective sulfur conversion with surface engineering of electrocatalysts in a lithium-sulfur battery

The sluggish kinetics of multiphase sulfur conversion with homogeneous and heterogeneous electrochemical processes,causing the“shuttle effect”of soluble polysulfide species(PSs),is the challenges in terms of lithium-sulfur batteries(LSBs).In this paper,a Mn_(3)O_(4-x) catalyst,which has much higher activity for heterogeneous reactions than for homogeneous reactions(namely,preferentialactivity catalysts),is designed by surface engineering with rational oxygen vacancies.Due to the rational design of the electronic structure,the Mn_(3)O_(4-x) catalyst prefers to accelerate the conversion of Li2S4 into Li_(2)S_(2)/Li_(2)S and optimize Li_(2)S deposition,reducing the accumulation of PSs and thus suppressing the“shuttle effect.”Both density functional theory calculations and in situ X-ray diffraction measurements are used to probe the catalytic mechanism and identify the reaction intermediates of MnS and Li_(y)Mn_(z)O_(4-x) for fundamental understanding.The cell with Mn_(3)O_(4-x) delivers an ultralow attenuation rate of 0.028% per cycle over 2000 cycles at 2.5 C.Even with sulfur loadings of 4.93 and 7.10mg cm^(-2) in a lean electrolyte(8.4μL mg s^(-1)),the cell still shows an initial areal capacity of 7.3mAh cm^(-2).This study may provide a new way to develop preferential-activity heterogeneous-reaction catalysts to suppress the“shuttle effect”of the soluble PSs generated during the redox process of LSBs.

The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Human speech indirectly represents the mental state or emotion of others.The use of Artificial Intelligence(AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech.In this study,we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN).About 2800 audio files were extracted from the Toronto emotional speech set(TESS)database for this study.A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data.A total of seven types of emotions;Angry,Disgust,Fear,Happy,Neutral,Pleasant-surprise,and Sad were used in this study.Energy,Fundamental frequency,and Mel Frequency Cepstral Coefficient(MFCC)have been used to extract the emotion features,and these features resulted in 97.5%accuracy in the mixed LSTM+CNN model.This mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from speech.It also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing.

Low light intensity elongates period and defers peak time of photosynthesis: a computational approach to circadian-clock-controlled photosynthesis in tomato

Photosynthesis is involved in the essential process of transforming light energy into chemical energy.Although the interaction between photosynthesis and the circadian clock has been confirmed,the mechanism of how light intensity affects photosynthesis through the circadian clock remains unclear.Here,we propose a first computational model for circadian-clock-controlled photosynthesis,which consists of the light-sensitive protein P,the core oscillator,photosynthetic genes,and parameters involved in the process of photosynthesis.The model parameters were determined by minimizing the cost function(δ=8.56),which is defined by the errors of expression levels,periods,and phases of the clock genes(CCA1,PRR9,TOC1,ELF4,GI,and RVE8).The model recapitulates the expression pattern of the core oscillator under moderate light intensity(100μmol m^(-2) s^(-1)).Further simulation validated the dynamic behaviors of the circadian clock and photosynthetic outputs under low(62.5μmol m^(-2) s^(-1))and normal(187.5μmol m^(-2) s^(-1))intensities.When exposed to low light intensity,the peak times of clock and photosynthetic genes were shifted backward by 1–2 hours,the period was elongated by approximately the same length,and the photosynthetic parameters attained low values and showed delayed peak times,which confirmed our model predictions.Our study reveals a potential mechanism underlying the circadian regulation of photosynthesis by the clock under different light intensities in tomato.

Detecting HI Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference

In the neutral hydrogen(H I)galaxy survey,a significant challenge is to identify and extract the H I galaxy signal from the observational data contaminated by radio frequency interference(RFI).For a drift-scan survey,or more generally a survey of a spatially continuous region,in the time-ordered spectral data,the H I galaxies and RFI all appear as regions that extend an area in the time-frequency waterfall plot,so the extraction of the H I galaxies and RFI from such data can be regarded as an image segmentation problem,and machine-learning methods can be applied to solve such problems.In this study,we develop a method to effectively detect and extract signals of H I galaxies based on a Mask R-CNN network combined with the PointRend method.By simulating FAST-observed galaxy signals and potential RFI impact,we created a realistic data set for the training and testing of our neural network.We compared five different architectures and selected the best-performing one.This architecture successfully performs instance segmentation of H I galaxy signals in the RFI-contaminated time-ordered data,achieving a precision of 98.64%and a recall of 93.59%.

Tailoring the separation performance of a carbon nanotube-based mixed matrix membrane decorated with metal–organic framework

Synthesis of mixed matrix membranes(MMM)using carbon nanotubes(CNTs)has shown great prospects for achieving excellent selective separation because of its special structure.Nevertheless,the preparation of highly selective MMM faces challenges,which is attributed to the obstacles encountered by CNTs dispersion in polymer matrix and elimination of interface defects.A novel CNT-based composite decorated with metal–organic framework(MOF)was synthesized and applied to the preparation of MMM.MOF was post modified,and then carboxyl groups were inserted on the outer surface of CNTs.The synthetic MMM(Cu-MOF-en@MWCNT)not only has selective adsorption on dyes,but also has selective photodegradation on dyes.The method of using CNTs to wrap the outside of MOF has great potential in dye separation.The performance of MMM was further improved by decorating MOF on the filler to improve the selectivity to the designated dye.

Effect of Shot Peening on Surface Damage Evolution Behavior of Cu-19Ni Alloy

Shot peening is a surface modification technology with the metal surface nano machine(SNC),which can modify the surface microstructure and extend the fatigue life of Cu-19Ni alloy.The hardness,damage evolution and mechanical properties were investigated and characterized by scanning electron microscope(SEM),laser confocal microscope(LSM)and material surface performance tester(CFT).The results showed that the surface roughness and friction coefficient of Cu-19Ni alloy decreased with the increase of shot peening duration and diameter,while the microhardness and strength increased.Moreover,with the increase in shot peening duration and diameter,SEM observation showed that the fracture dimples became smaller,meanwhile,with the increase of small cleavage planes,shear tearing ridges and the thickness of the surface nano layer,the fracture mode gradually evolved from plastic to brittle fracture.The uniaxial tensile test of shot peened Cu-19Ni alloy was carried out by MTS testing machine combined with digital image correlation technology(DIC).The evolution of Cu-19Ni surface damage was analyzed,and the evolution equations describing the damage of large deformation zone and small deformation zone were established.The effect of shot peening on the damage evolution behavior of Cu-19Ni alloy was revealed.

New Cases of Superflares on Slowly Rotating Solar-type Stars and Large Amplitude Superflares in G-and M-type Main Sequence Stars

In our previous work,we searched for superflares on different types of stars while focusing on G-type dwarfs using entire Kepler data to study statistical properties of the occurrence rate of superflares.Using these new data,as a byproduct,we found 14 cases of superflare detection on 13 slowly rotating Sun-like stars with rotation periods of24.5–44 days.This result supports the earlier conclusion by others that the Sun may possibly undergo a surprise superflare.Moreover,we found 12 and seven new cases of detection of exceptionally large amplitude superflares on six and four main sequence stars of G-and M-type,respectively.No large-amplitude flares were detected in A,F or K main sequence stars.Here we present preliminary analysis of these cases.The superflare detection,i.e.,an estimation of flare energy,is based on a more accurate method compared to previous studies.We fit an exponential decay function to flare light curves and study the relation between e-folding decay time,τ,versus flare amplitude and flare energy.We find that for slowly rotating Sun-like stars,large values ofτcorrespond to small flare energies and small values ofτcorrespond to high flare energies considered.Similarly,τis large for small flare amplitudes andτis small for large amplitudes considered.However,there is no clear relation between these parameters for large amplitude superflares in the main sequence G-and M-type stars,as we could not establish clear functional dependence between the parameters via standard fitting algorithms.

Precipitation scale effect of the TRMM satellite in Tianshan,China

High-resolution precipitation data is conducive to objectively describe the spatial-temporal variability of regional precipitation,and the study of downscaling techniques and spatial scale effects can provide technical and theoretical support to improve the spatial resolution and accuracy of satellite precipitation data.In this study,we used a machine learning algorithm combined with a regression algorithm RF-PLS(Random Forest-Partial Least Squares)to construct a downscaling model to obtain three types of high-resolution TRMM(Tropical Rainfall Measuring Mission)downscaled precipitation data for the years 2000-2017 at 250 m,500 m,and 1km.The scale effects with topographic and geomorphological features in the study area were analysed.Finally,we described the spatial and temporal variation of precipitation based on the optimal TRMM downscaled precipitation data.The results showed that:1)The linear relationships between the TRMM downscaled precipitation data obtained by each of the three downscaled models(PLS,RF,and RF-PLS)and the precipitation at the observation stations were improved compared to the linear relationships between the original TRMM data and the precipitation at the observation stations.The accuracy of the RF-PLS model was better than the other two models.2)Based on the RF-PLS model,the resolution of the TRMM data was increased to three different scales(250 m,500 m,and 1 km),considering the scale effects with topographic and geomorphological features.The precipitation simulation effect with a spatial resolution of 500 m was better than the other two scales.3)The annual precipitation was the highest in the areas with extremely high mountains,followed by the mediumhigh mountain,high mountain,medium mountain,medium-low mountain,plain,low mountain,and basin.

Prediction of Short Stellar Activity Cycles using Derived and Established Empirical Relations between Activity and Rotation Periods

In our previous work,we investigated the occurrence rate of super-flares on various types of stars and their statistical properties,with a particular focus on G-type dwarfs,using entire Kepler data.The said study also considered how the statistics change with stellar rotation period,which in turn,had to be determined.Using such new data,as a by-product,we found 138 Kepler IDs of F-and G-type main sequence stars with rotation periods less than a day(P_(rot)<1 day).On one hand,previous studies have revealed short activity cycles in F-type and G-type stars and the question investigated was whether or not short-term activity cycles are a common phenomenon in these stars.On the other hand,extensive studies exist which establish an empirical connection between a star's activity cycle and rotation periods.In this study,we compile all available Kepler data with P_(rot)<1 day,and rely on an established empirical relation between P_(cyc)and P_(rot)with the aim to provide predictions for very short 5.09≤P_(cyc)≤38.46 day cases in a tabular form.We propose an observation to measure P_(cyc)using a monitoring program of stellar activity(e.g.,activity-related chromospheric emission S-index)or a similar means for the Kepler IDs found in this study in order put the derived empirical relations between P_(cyc)and P_(rot)derived here to the test.We also propose an alternative method for measuring very short P_(cyc),using flare-detection algorithms applied to future space mission data.

Emitter discharge characteristics of vertical tube irrigation affected by various factors

To examine the working principle of vertical tube irrigation, variations in vertical tube emitter discharge and their causes were analyzed in the laboratory experiment. The effects of the pressure head, initial soil water content, and tube diameter on the emitter discharge of the vertical tube were studied. The results show that quantitative relationship between the time and cumulative infiltration and emitter discharge of the vertical tube is obtained, and R 2 is more than 0.98. Emitter discharge exhibits a positive and negative correlation with the pressure head and soil water content, respectively. Tube dia- meter has a nonsignificant effect on the emitter discharge. Changes of the soil water content around the emitter water outlet are the main causes of emitter discharge variations. In the experiments, the range of vertical tube emitter discharge is 0.056-1.102 L/h. The emitter of vertical tube irrigation automatically adjusts the soil water content and maintains the root zone soil water content within an appropriate range, which achieves continuous irrigation, and further achieves the effect of water-saving.