The corrosion behaviors of Mg-7Gd-5Y-1Nd-0.5Zr alloy under(NH4)_(2)SO_(4),NaCl and Ca(NO_(3))_(2)salts spray condition

The corrosion behaviors of Mg–7Gd–5Y–1Nd–0.5Zr alloys after T5 treatment under(NH4)_(2)SO_(4),NaCl and Ca(NO_(3))_(2)salt spray condition were investigated by weight loss rates,residual mechanical properties,scanning electron microscope(SEM),X-ray Diffraction(XRD)and potentiodynamic polarization tests.The corrosion degree of Mg–7Gd–5Y–1Nd–0.5Zr alloys in Ca(NO_(3))_(2)salt spray was very shallow by corrosion morphology and the corrosion route was extended along the surface in texture-like shape,while the alloy in NaCl and(NH4)_(2)SO_(4)salt spray were major local corrosion and there were serious corrosion pits on the surface.The weight loss rates in(NH4)_(2)SO_(4),NaCl and Ca(NO_(3))_(2)salt spray was respectively 0.4147,0.1618 and 0.0725 mg/(cm2 d−1).The results of residual mechanical properties indicated that the corrosion order in salts spray of Mg–7Gd–5Y–1Nd–0.5Zr alloys is NH4SO4>NaCl>Ca(NO_(3))_(2),which was consistent with the results of potentiodynamic polarization tests.The type of the salts will play a vital role in the initiation of the corrosion of EW75 alloy when they are used in the atmosphere environments.Inorganic salts with the smaller PH value after dissolution will have a stronger impact on the corrosion of EW75 magnesium alloys.

Dynamic precipitation during multi-axial forging of an Mg-7Gd-5Y-1Nd-0.5Zr alloy

The dynamic precipitation behavior during multi-axial forging in an Mg-7Gd-5Y-1Nd-0.5Zr alloy has been investigated and compared with that in static precipitation treatment. The results indicated that dynamic precipitation does occur during multi-axial forging. The dynamic precipitate can be deduced as β phase with face-centered cubic crystal structure (a = 2.22 nm). Most of the β precipitates locate at the dynamic recrystallization grain boundaries. The morphology and orientation relationship is different from that of the β phase formed in the static precipitation treated alloys, although the crystal structure is the same. The precipitation temperature of β phase during MAF is higher than that in the static precipitation treatment.

Biophysical-driven piezoelectric and aligned nanofibrous scaffold promotes bone regeneration by re-establishing physiological electrical microenvironment

The initial healing stages of bone fracture is a complex physiological process involving a series of spatially and temporally overlapping events,including pathogen clearance,immunological modulation,and osteogenesis.In this study,we have developed a piezoelectric and aligned nanofibrous scaffold composed of ZnO@PCL/PVDF with multiple antibacterial,immunomodulatory,and osteogenic effects using electrospinning technology.This scaffold’s piezoelectric signal output under ultrasound(US)control can be similar to the physiological electrical signals of healthy bone tissue,creating a truly biomimetic electrical microenvironment in the bone defect.In vitro studies have shown that ZnO@PCL/PVDF scaffold significantly enhances the proliferation,migration,and osteogenic differentiation of MC3T3-E1 cells under piezoelectric drive provided by ultrasound.Furthermore,the scaffold exhibits inhibitory effects on the growth of E.coli and S.aureus,as well as the ability to induce M2 macrophage polarization,indicating potent antibacterial and immunomodulatory properties.In vivo experiments demonstrated that the ZnO@PCL/PVDF scaffold can accelerate the repair of mandibular defects in rats,effectively inhibit bacterial colonization,and reduce inflammatory responses.Altogether,this study confirms that the newly developed ZnO@PCL/PVDF scaffold effectively promotes bone repair by truly mimicking the endogenous electrical microenvironment and precisely regulating the temporospatial disorders of initial bone healing,thus providing a simple and effective solution for bone defects.

Machine learning and theoretical analysis release the non-linear relationship among ozone,secondary organic aerosol and volatile organic compounds

Fine particulate matter(PM_(2.5))and ozone(O_(3))pollutions are prevalent air quality issues in China.Volatile organic compounds(VOCs)have significant impact on the formation of O_(3)and secondary organic aerosols(SOA)contributing PM_(2.5).Herein,we investigated 54 VOCs,O_(3)and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O_(3),SOA and VOCs.The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September,but the observed O_(3)was exactly the opposite.Machine learning methods resolved the importance of individual VOCs on O_(3)and SOA that alkenes(mainly ethylene,propylene,and isoprene)have the highest importance to O_(3)formation;alkanes(C_(n),n≥6)and aromatics were the main source of SOA formation.Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O_(3)and SOA formation.Ozone formation potential(OFP)and secondary organic aerosol formation potential(SOAFP)calculated by consumed VOCs quantitatively indicated that more than 80%of the consumed VOCs were alkenes which dominated the O_(3)formation,and the importance of consumed aromatics and alkenes to SOAFP were 40.84%and 56.65%,respectively.Therein,isoprene contributed the most to OFP at 41.45%regardless of the season,while aromatics(58.27%)contributed the most to SOAFP in winter.Collectively,our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O_(3).

Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory approaches.Our approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

Phase constitutions,growth pattern and mechanical properties of Mg-1.4Gd-1.2Y-xZn-0.15Zr(at%) alloys

The effects of minor Zn(0.2 at%,0.4 at%,0.6 at%) on the microstructures and mechanical properties of Mg-1.4 Gd-1.2 Y-0.15 Zr(at%) alloys were systematically explored.Results reveal that increasing Zn content leads to the increase of the intergranular phases and the change of their composition from Mg24(Gd,Y)5 phase and(Mg,Zn)3(Gd,Y) phase to 18 R-LPSO phase and(Mg,Zn)3(Gd,Y) phase.Mg24(Gd,Y)5 phase is body-centered cubic structure and shares the same lattice constant with Mg24Y5 while(Mg,Zn)3(Gd,Y)phase is face-centered cubic structure with lattice constant of 0.72 nm,slightly lower than Mg3Gd.18RLPSO structure is identified to be monoclinic with c-axis not strictly vertical to the bottom surface but93.5°.The growth patterns of intergranular phases change from the divorced growth to coupled growth as compositions change.Moreover,the mechanical performance improves with Zn rising,ascribed to the decrease of brittle phases at grain boundaries and the increase of LPSO structure phases.

Chemical characterization and source apportionment of PM_(1) and PM_(2.5) in Tianjin, China: Impacts of biomass burning and primary biogenic sources

The submicron particulate matter(PM_(1))and fine particulate matter(PM_(2.5))are very important due to their greater adverse impacts on the natural environment and human health.In this study,the daily PM_(1) and PM_(2.5) samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin,China.The collected samples were analyzed for the carbonaceous fractions,inorganic ions,elemental species,and specific marker sugar species.The chemical characterization of PM_(1) and PM_(2.5) was based on their concentrations,compositions,and characteristic ratios(PM_(1)/PM_(2.5),AE/CE,NO3^-/SO4^2-,OC/EC,SOC/OC,OM/TCA,K^+/EC,levoglucosan/K^+,V/Cu,and V/Ni).The average concentrations of PM_(1) and PM_(2.5) were 32.4μg/m and 53.3μg/m^3,and PM_(1) constituted 63%of PM_(2.5) on average.The source apportionment of PM_(1) and PM_(2.5) by positive matrix factorization(PMF)model indicated the main sources of secondary aerosols(25%and 34%),biomass burning(17%and 20%),traffic emission(20%and 14%),and coal combustion(17%and 14%).The biomass burning factor involved agricultural fertilization and waste incineration.The biomass burning and primary biogenic contributions were determined by specific marker sugar species.The anthropogenic sources(combustion,secondary particle formation,etc)contributed significantly to PM_(1) and PM_(2.5),and the natural sources were more evident in PM_(2.5).This work significantly contributes to the chemical characterization and source apportionment of PM_(1) and PM_(2.5) in near-port cities influenced by the diverse sources.

Hot deformation behavior and finite element simulation of Mg-8.3Gd-4.4Y-1.5Zn-0.8Mn alloy

To study the hot deformation behavior of Mg-8.3 Gd-4.4 Y-1.5 Zn-0.8 Mn(wt%) alloy,hot compression tests were conducted using a Gleeble-3500 thermal simulator at temperatures ranging from 653 to773 K,true strain rates of 0.001-1 s^(-1),and a deformation degree of 60%.Results of hot compression experiments show that the flow stress of the alloy increases with the strain rate.The true stress-true strain curves are corrected by correcting the effect of temperature rise in the deformation process.Activation energy,Q,equal to 287380 J/mol and material constant,n,equal to 4.59 were calculated by fitting the true stress-true strain curves.Then,the constitutive equation was established and verified via finite element simulation.Results of the hot processing map show that the probability of material instability increases with the degree of deformation,which indicates that the material is not suitable for large deformation in a single pass.On the whole,the alloy is appropriate for multipass processing with small deformation and a suitable processing temperature and strain rate are 733 K and 0.01 s-1,respectively.

Vertical characteristics and source identification of PM_(10) in Tianjin

Ambient PM 10 （particulate matter with a diameter less than 10μm） concentrations were measured on a 255 meter tower in Tianjin,China.The samples were collected at four vertical levels （10,40,120 and 220 m）.Vertical characteristics for PM 10 samples were studied.The results showed that the concentrations of PM 10 and constituent species had a negative correlation with the sampling height.The highest concentrations of PM 10 and species were obtained at the 10 m level,and the lowest concentrations were measured at the 220 m level.For the fractions of species to total mass,SO 4 2- and NO 3- had higher values （fraction） at greater height;while Ca had a higher fraction at lower height.Possible source categories for the PM 10 ambient dataset were identified by the principal component analysis method.The possible source categories included crustal dust,vehicles,cement dust,and incineration as well as secondary sulfate and nitrate sources.Analysis of meteorological factors on PM 10 concentrations indicated that wind speed and inversion may be the main factors contributing to different concentrations of PM 10 at different heights.

Microstructure evolution,mechanical properties and creep mechanisms of Mg-12Gd-1MM-0.6Zr(wt%)magnesium alloy

In this research,the microstructure evolution,mechanical properties,and creep mechanisms of Mg-12 Gd-1 MM-0.6 Zr(wt%)alloy under different conditions were systematically studied using scanning electron microscopy(SEM),transmission electron microscopy(TEM),X-ray diffraction(XRD),and tensile creep tests.Regarding the microstructure of the as-cast sample,the average grain size is about 42μm,and the eutectic compounds were determined to be Mg_(5)(Gd_(0.8)MM_(0.2)).During homogenization,these eutectic compounds gradually dissolve,and Mg_(12)MM particles are precipitated.During hot extrusion,complete dynamic recrystallization(DRX)occurs,resulting in equiaxial grains with an average grain size of about 12μm and the formation of streamlines consisting of Mg_(12)MM particles along the extrusion direction(ED).After T5 treatment(225℃for 7 h),a large number ofβ'(Mg_(7)Gd)phases are precipitated on the{11-20}αhabit plane and are interconnected,forming an interlaced network structure.The ultimate tensile strength(R_(m)=405 MPa)and yield strength(R_(P0.2)=288 MPa)of the T5 sample are significantly higher than those of the as-extruded sample(R_(m)=289 MPa,R_(P0.2)=185 MPa),but the elongation(A=4%)was remarkably lower than that of the as-extruded sample(A=18%).When crept at225℃under 100 MPa,the steady-state creep rates of the as-cast,as-extruded,and T5 samples are1.59×10^(-8),1.08×10^(-8),and 1.40×10^(-8)s^(-1),respectively,and their total strains within 100 h are respectively breaking,0.81%,and 0.92%,indicating that the as-extruded alloy exhibits the best creep resistance.TEM analysis reveals that,during the creep process of the T5 sample,theβ'particles coarsen and the precipitate-free zones(PFZs)widen,which increase the steady-state creep rate and the total strain within 100 h as compared with the as-extruded sample.

Drivers of PM2.5-O3 co-pollution:from the perspective of reactive nitrogen conversion pathways in atmospheric nitrogen cycling

Fine particulate matter(PM)and ozone(O),two globally signifcant air pollutants,have exerted substantial adverse impacts on climate and human health[1].From 2013 to 2020,China has achieved a signifcant decline of PMlevels,though O3pollution has deteriorated over time[2].PM-Oco-pollution includes not only both high levels of PMand O,but also high PMor Oeven when the other remain low.Therefore,the coordinated control of PMand Oshould not only focus on reducing high concentrations of PMand Osimultaneously.

Local and long-range transport influences on PM_(2.5) at a cities-cluster in northern China,during summer 2008

Hourly PM2.5 concentrations were observed simultaneously at a cities-cluster comprising 10 cities/towns in Hebei province in China from July 1 to 31, 2008. Among the 10 cities/towns, Baoding showed the high- est average concentration level （161.57μg/m3） and Yanjiao exhibited the lowest （99.35 μg/m3 ）. These observed data were also studied using the joint potential source contribution function with 24-h and 72-h backward trajectories, to identify more clearly the local and countrywide-scale long-range transport sources. For the local sources, three important influential areas were found, whereas five important influential areas were defined for long-range transport sources. Spatial characteristics of PM2.5 were determined by multivariate statistical analyses. Soil dust, coal combustion, and vehicle emissions might be the potential contributors in these areas. The results of a hierarchical cluster analysis for back trajectory endpoints and PM2.s concentrations datasets show that the spatial characteristics of PM2.5 in the cities-cluster were influenced not only by local sources, but also by long-range transport sources. Different cities in the cities-cluster obtained different weighted contributions from local or long-range transport sources. Cangzhou, Shijiazhuang, and Baoding are near the source areas in the south of Hebei province, whereas Zhuozhou, Yangfang, Yanjiao, Xianghe, and Langfang are close to the sources areas near Beijing and Tianjin.

Self-feedback LSTM regression model for real-time particle source apportionment

Atmospheric particulate matter pollution has attracted much wider attention globally.In recent years,the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques.Such demands are summarized,in this paper,as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances,such as the existence of secondary source and similar source.In this study,we firstly analyze the possible and potential restraints in single particle source apportionment,then propose a novel three-step self-feedback long short-term memory(SF-LSTM)network for approximating the source contribution.The proposed deep learning neural network includes three modules,as generation,scoring and refining,and regeneration modules.Benefited from the scoring modules,SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment,meanwhile,the regeneration module calculates the source contribution in a non-linear way.The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators(residual sum of squares,stability,sparsity,negativity)for the restraints.Additionally,in short time-resolution analyzing,SF-LSTM provides better results under the restraint of stability.

New insights into the formation of ammonium nitrate from a physical and chemical level perspective

High levels of fine particulate matter(PM_(2.5))is linked to poor air quality and premature deaths,so haze pollution deserves the attention of the world.As abundant inorganic components in PM_(2.5),ammonium nitrate(NH_(4)NO_(3))formation includes two processes,the diffusion process(molecule of ammonia and nitric acid move from gas phase to liquid phase)and the ionization process(subsequent dissociation to form ions).In this study,we discuss the impact of meteorological factors,emission sources,and gaseous precursors on NH4NO3 formation based on thermodynamic theory,and identify the dominant factors during clean periods and haze periods.Results show that aerosol liquid water content has a more significant effect on ammonium nitrate formation regardless of the severity of pollution.The dust source is dominant emission source in clean periods;while a combination of coal combustion and vehicle exhaust sources is more important in haze periods.And the control of ammonia emission is more effective in reducing the formation of ammonium nitrate.The findings of this work inform the design of effective strategies to control particulate matter pollution.

Dependence of solutions and eigenvalues of third order linear measure differential equations on measures

This paper deals with a complex third order linear measure differential equation id(y’)^(·)+ 2iq(x)y’dx + y(idq(x) + dp(x)) = λydx on a bounded interval with boundary conditions presenting a mixed aspect of the Dirichlet and the periodic problems. The dependence of eigenvalues on the coefficients p and q is investigated. We prove that the n-th eigenvalue is continuous in p and q when the norm topology of total variation and the weak*topology are considered. Moreover, the Fr′echet differentiability of the n-th eigenvalue in p and q with the norm topology of total variation is also considered. To deduce these conclusions, we investigate the dependence of solutions of the above equation on the coefficients p and q with different topologies and establish the counting lemma of eigenvalues according to the estimates of solutions.

Corrigendum to “Drivers of PM_(2.5)-O_(3) co-pollution: from the perspective of reactive nitrogen conversion pathways in atmospheric nitrogen cycling” [Sci. Bull. 67(18) (2022) 1833–1836]

The authors would like to correct Fig.1e,f.Due to our neglect when doing the picture layout of Fig.1,the abscissa in Fig.1e,f is error:the abscissa ranges from
80 to 0 in Fig.1e and ranges from
90 to
20 in Fig.1f.The image has been corrected:the abscissa ranges from
80 to 80 in Fig.1e and ranges from
80 to 80 in Fig.1f[1].We declare that this correction does not change the results or conclusions of this paper.