Effect of liquid surface depression size on discharge characteristics and chemical distribution in the plasma-liquid anode system

Atmospheric pressure plasma-liquid interactions exist in a variety of applications,including wastewater treatment,wound sterilization,and disinfection.In practice,the phenomenon of liquid surface depression will inevitably appear.The applied gas will cause a depression on the liquid surface,which will undoubtedly affect the plasma generation and further affect the application performance.However,the effect of liquid surface deformation on the plasma is still unclear.In this work,numerical models are developed to reveal the mechanism of liquid surface depressions affecting plasma discharge characteristics and the consequential distribution of plasma species,and further study the influence of liquid surface depressions of different sizes generated by different helium flow rates on the plasma.Results show that the liquid surface deformation changes the initial spatial electric field,resulting in the rearrangement of electrons on the liquid surface.The charges deposited on the liquid surface further increase the degree of distortion of the electric field.Moreover,the electric field and electron distribution affected by the liquid surface depression significantly influence the generation and distribution of active species,which determines the practical effectiveness of the relevant applications.This work explores the phenomenon of liquid surface depression,which has been neglected in previous related work,and contributes to further understanding of plasma-liquid interactions,providing better theoretical guidance for related applications and technologies.

Nonlinear change of ion-induced secondary electron emission in theκ-Al_(2)O_(3) surface charging from first-principle modelling

Secondary electron emission(SEE)induced by the positive ion is an essential physical process to influence the dynamics of gas discharge which relies on the specific surface material.Surface charging has a significant impact on the material properties,thereby affecting the SEE in the plasma-surface interactions.However,it does not attract enough attention in the previous studies.In this paper,SEE dependent on the charged surface of specific materials is described with the computational method combining a density functional theory(DFT)model from the first-principle theory and the theory of Auger neutralization.The effect ofκ-Al2O3 surface charge,as an example,on the ion-induced secondary electron emission coefficient(SEEC)is investigated by analyzing the defect energy level and band structure on the charged surface.Simulation results indicate that,with the surface charge from negative to positive,the SEEC of a part of low ionization energy ions(such as Ei=12.6 eV)increases first and then decreases,exhibiting a nonlinear changing trend.This is quite different from the monotonic decreasing tendency observed in the previous model which simplifies the electronic structure.This irregular increase of the SEEC can be attributed to the lower escaped probability of orbital energy.The results further illustrate that the excessive charge could cause the bottom of the conduction band close to the valence band,thus leading to the decrease of the orbital energy occupied by the excited electrons.The nonlinear change of SEEC demonstrates a more realistic situation of how the electronic structure of material surface influences the SEE process.This work provides an accurate method of calculating SEEC from specific materials,which is urgent in widespread physical scenarios sensitive to surface materials,such as increasingly growing practical applications concerning plasma-surface interactions.

Increased prognostic value of clinical–reproductive model in Chinese female patients with esophageal squamous cell carcinoma

BACKGROUND In China,it has been well recognized that some female patients with esophageal squamous cell carcinoma(ESCC)have different overall survival(OS)time,even with the same tumor-node-metastasis(TNM)stage,challenging the prognostic value of the TNM system alone.An effective predictive model is needed to accurately evaluate the prognosis of female ESCC patients.AIM To construct a novel prognostic model with clinical and reproductive data for Chinese female patients with ESCC,and to assess the incremental prognostic value of the full model compared with the clinical model and TNM stage.METHODS A new prognostic nomogram incorporating clinical and reproductive features was constructed based on univariatie and Cox proportional hazards survival analysis from a training cohort(n=175).The results were recognized using the internal(n=111)and independent external(n=85)validation cohorts.The capability of the clinical–reproductive model was evaluated by Harrell’s concordance index(C-index),Kaplan–Meier curve,time-dependent receiver operating characteristic(ROC),calibration curve and decision curve analysis.The correlations between estrogen response and immune-related pathways and some gene markers of immune cells were analyzed using the TIMER 2.0 database.RESULTS A clinical–reproductive model including incidence area,age,tumor differentiation,lymph node metastasis(N)stage,estrogen receptor alpha(ESR1)and beta(ESR2)expression,menopausal age,and pregnancy number was constructed to predict OS in female ESCC patients.Compared to the clinical model and TNM stage,the time-dependent ROC and C-index of the clinical–reproductive model showed a good discriminative ability for predicting 1-,3-,and 5-years OS in the primary training,internal and external validation sets.Based on the optimal cut-off value of total prognostic scores,patients were classified into high-and low-risk groups with significantly different OS.The estrogen response was significantly associated with p53 and apoptosis pathways in esophageal cancer.CONCLUSION The clinical–reproductive prognostic nomogram has an incremental prognostic value compared with the clinical model and TNM stage in predicting OS in Chinese female ESCC patients.

Phase classification of multi-principal element alloys via interpretable machine learning

There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs).In this paper,we develop a machine learning (ML) approach built on the foundations of ensemble learning,post hoc model interpretability of black-box models,and clustering analysis to establish a quantitative relationship between the chemical composition and experimentally observed phases of MPEAs.The originality of our work stems from performing instance-level (or local) variable attribution analysis of ML predictions based on the breakdown method,and then identifying similar instances based on k-means clustering analysis of the breakdown results.We also complement the breakdown analysis with Ceteris Paribus profiles that showcase how the model response changes as a function of a single variable,when the values of all other variables are fixed.Results from local model interpretability analysis uncover key insights into variables that govern the formation of each phase.Our developed approach is generic,model-agnostic,and valuable to explain the insights learned by the black-box models.An interactive web application is developed to facilitate model sharing and accelerate the design of MPEAs with targeted properties.

Geant4 applications for modeling molecular transport in complex vacuum geometries

We discuss a novel use of the Geant4 simulation toolkit to model molecular transport in a vacuum environment,in the molecular flow regime.The Geant4 toolkit was originally developed by the high energy physics community to simulate the interactions of elemen-tary particles within complex detector systems.Here its capabilities are utilized to model molecular vacuum transport in geometries where other techniques are impractical.The techniques are verified with an application representing a simple vacuum geometry that has been studied previously both analytically and by basic Monte Carlo simulation.We discuss the use of an application with a very complicated geometry,that of the Large Synoptic Survey Telescope camera cryostat,to determine probabilities of transport of contaminant molecules to optical surfaces where control of contamination is crucial.