Mean nocturnal baseline impedance in gastro-esophageal reflux disease diagnosis:Should we strictly follow the Lyon 2 Consensus?

Clinical practice guidelines drive clinical practice and clinicians rely to them when trying to answer their most common questions.One of the most important position papers in the field of gastro-esophageal reflux disease(GERD)is the one produced by the Lyon Consensus.Recently an updated second version has been released.Mean nocturnal baseline impedance(MNBI)was proposed by the first Consensus to act as supportive evidence for GERD diagnosis.Originally a cut-off of 2292 Ohms was proposed,a value revised in the second edition.The updated Consensus recommended that an MNBI<1500 Ohms strongly suggests GERD while a value>2500 Ohms can be used to refute GERD.The proposed cut-offs move in the correct direction by diminishing the original cut-off,nevertheless they arise from a study of normal subjects where cut-offs were provided by measuring the mean value±2SD and not in symptomatic patients.However,data exist that even symptomatic patients with inconclusive disease or reflux hypersensitivity(RH)show lower MNBI values in comparison to normal subjects or patients with functional heartburn(FH).Moreover,according to the data,MNBI,even among symptomatic patients,is affected by age and body mass index.Also,various studies have proposed different cut-offs by using receiver operating characteristic curve analysis even lower than the one proposed.Finally,no information is given for patients submitted to on-proton pump inhibitors pH-impedance studies even if new and extremely important data now exist.Therefore,even if MNBI is an extremely important tool when trying to approach patients with reflux symptoms and could distinguish conclusive GERD from RH or FH,its values should be interpreted with caution.

Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography

Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.

Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis:A comprehensive review

Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.

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.

Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion:A study from Poseidon,NW shelf Australia

Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.

Transverse mode-coupling instability with longitudinal impedance

Transverse mode-coupling instability(TMCI)is a dangerous transverse single-bunch instability that can lead to severe par-ticle loss.The mechanism of TMCI can be explained by the coupling of transverse coherent oscillation modes owing to the transverse short-range wakefield(i.e.,the transverse broadband impedance).Recent studies on future circular colliders,e.g.,FCC-ee,showed that the threshold of TMCI decreased significantly when both longitudinal and transverse impedances were included.We performed computations for the circular electron-positron collider(CEPC)and observed a similar phenom-enon.Systematic studies on the influence of longitudinal impedance on the TMCI threshold were conducted.We concluded that the imaginary part of the longitudinal impedance,which caused a reduction in the incoherent synchrotron tune,was the primary reason for the reduction in the TMCI threshold.Additionally,the real part of the longitudinal impedance assists in increasing the TMCI threshold.

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter(STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate(K) and the moment(t).Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scrless than 1.761.

Construction of a broadband impedance spectrum and synchronous DC voltammetry measurement system for solar cells

The current impedance spectroscopy measurement techniques face difficulties in diagnosing solar cell faults due to issues such as cost,complexity,and accuracy.Therefore,a novel system was developed for precise broadband impedance spectrum measurement of solar cells,which was composed of an oscilloscope,a signal generator,and a sampling resistor.The results demonstrate concurrent accurate measurement of the impedance spectrum(50 Hz-0.1 MHz)and direct current voltametric characteristics.Comparative analysis with Keithley 2450 data yields a global relative error of approximately 6.70%,affirming the accuracy.Among excitation signals(sine,square,triangle,pulse waves),sine wave input yields the most accurate data,with a root mean square error of approximately 13.3016 and a global relative error of approximately 4.25%compared to theoretical data.Elevating reference resistance expands the half circle in the impedance spectrum.Proximity of reference resistance to that of the solar cell enhances the accuracy by mitigating line resistance influence.Measurement error is lower in high-frequency regions due to a higher signal-to-noise ratio.

Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods

In cold regions,the dynamic compressive strength(DCS)of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering.Nevertheless,testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive.Therefore,this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques.Initially,a database of DCS for frozen-thawed rocks,comprising 216 rock specimens,was compiled.Three external load parameters(freeze-thaw cycle number,confining pressure,and impact pressure)and two rock parameters(characteristic impedance and porosity)were selected as input variables,with DCS as the predicted target.This research optimized the kernel scale,penalty factor,and insensitive loss coefficient of the support vector regression(SVR)model using five swarm intelligent optimization algorithms,leading to the development of five hybrid models.In addition,a statistical DCS prediction equation using multiple linear regression techniques was developed.The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes.A sensitivity analysis based on the cosine amplitude method has also been conducted.The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions.Among these models,the SVR model optimized with the chameleon swarm algorithm exhibited the best performance,with metrics indicating its effectiveness,including root mean square error(RMSE)﹦3.9675,mean absolute error(MAE)﹦2.9673,coefficient of determination(R^(2))﹦0.98631,and variance accounted for(VAF)﹦98.634.This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models.Notably,impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction.This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Solid-state impedance spectroscopy studies of dielectric properties and relaxation processes in Na_(2)O–V_(2)O_(5)–Nb_(2)O_(5)–P_(2)O_(5) glass

Solid-state impedance spectroscopy(SS-IS)was used to investigate the influence of structural modifications resulting from the addition of Nb2O5 on the dielectric properties and relaxation processes in the quaternary mixed glass former(MGF)system 35Na_(2)O–10V_(2)O_(5)–(55-x)P_(2)O_(5)–xNb_(2)O_(5)(x=0–40,mol%).The dielectric parameters,including the dielectric strength and dielectric loss,are determined from the frequency and temperature-dependent complex permittivity data,revealing a significant dependence on the Nb2O5 content.The transition from a predominantly phosphate glass network(x<10,region I)to a mixed niobate–phosphate glass net-work(10≤x≤20,region II)leads to an increase in the dielectric parameters,which correlates with the observed trend in the direct-cur-rent(DC)conductivity.In the predominantly niobate network(x≥25,region III),the highly polarizable nature of Nb5+ions leads to a fur-ther increase in the dielectric permittivity and dielectric strength.This is particularly evident in Nb-40 glass-ceramic,which contains Na_(13)Nb_(35)O_(94) crystalline phase with a tungsten bronze structure and exhibits the highest dielectric permittivity of 61.81 and the lowest loss factor of 0.032 at 303 K and 10 kHz.The relaxation studies,analyzed through modulus formalism and complex impedance data,show that DC conductivity and relaxation processes are governed by the same mechanism,attributed to ionic conductivity.In contrast to glasses with a single peak in frequency dependence of imaginary part of electrical modulus,M″(ω),Nb-40 glass-ceramic exhibits two distinct contributions with similar relaxation times.The high-frequency peak indicates bulk ionic conductivity,while the additional low-fre-quency peak is associated with the grain boundary effect,confirmed by the electrical equivalent circuit(EEC)modelling.The scaling characteristics of permittivity and conductivity spectra,along with the electrical modulus,validate time-temperature superposition and demonstrate a strong correlation with composition and modification of the glass structure upon Nb_(2)O_(5) incorporation.

Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field,Scotian Basin

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.

Impedance Model for Instability Analysis of LCC-HVDCs Considering Transformer Saturation

In line commutated converter based high-voltage direct current(LCC-HVDC)transmission systems,the trans-former saturation can induce harmonic instability,which poses a serious threat to the safe operation of the power system.How-ever,the nonlinear characteristics of the power grids introduced by the transformer saturation considerably limit the application of the conventional analysis methods.To address the issue,this paper derives a linear model for the transformer saturation caused by the DC current due to the converter modulation.Af-terwards,the nonlinear characteristics of power grids with the transformer saturation is described by a complex valued imped-ance matrix.Based on the derived impedance matrix,the sys-tem harmonic stability is analyzed and the mechanism of the transformer saturation induced harmonic instability is revealed.Finally,the sensitivity analysis is conducted to find the key fac-tors that influence the system core saturation instability.The proposed impedance model is verified by the electromagnetic transient simulation,and the simulation results corroborate the effectiveness of the proposed impedance model.Index TermsLine commutated converter based high voltage direct current(LCC-HVDC),transformer saturation,harmonic instability,impedance model.

Harmonic Transfer Function Based Single-input Single-output Impedance Modeling of LCC-HVDC Systems

This study presents a harmonic transfer function(HTF)based single-input single-output(SISO)impedance modeling method.The method converts an HTF from phase domain to sequence domain and then transforms it into an SISO impedance while preserving the frequency coupling information of different sequences and different harmonics.Applications of this method to a line-commutated converter based high-voltage direct current(LCC-HVDC)system are presented.The results demonstrate the accuracy of the derived SISO impedance,and a truncation-order selection is suggested.The case study shows that the proposed method facilitates simpler impedance measurements and associated stability analysis.

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

With the dramatic increase in electric vehicles(EVs)globally,the demand for lithium-ion batteries has grown dramatically,resulting in many batteries being retired in the future.Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery aging level on service and provide regroup strategy of the retied batteries in secondary use.There are still limitations on the current rapid battery capacity estimation methods,such as direct current internal resistance(DCIR)and electrochemical impedance spectroscopy(EIS),in terms of efficiency and robustness.To address the challenges,this paper proposes an improved version of DCIR,named pulse impedance technique(PIT),for rapid battery capacity estimation with more robustness.First,PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency,in which the coherence analysis is used to guide the selection of a reliable frequency band.The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method,which obtains more information on the battery capacity evaluation.Second,various statistical variables are used to extract aging features,and Pearson correlation analysis is applied to determine the highly correlated features.Then a linear regression model is developed to map the relationship between extracted features and battery capacity.To validate the performance of the proposed method,the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series.The results reveal that the proposed PIT can provide comparative indicators to EIS,which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

Nonlinear Electrical Impedance Tomography Method Using a Complete Electrode Model for the Characterization of Heterogeneous Domains

This paper presents an electrical impedance tomography(EIT)method using a partial-differential-equationconstrained optimization approach.The forward problem in the inversion framework is described by a complete electrodemodel(CEM),which seeks the electric potential within the domain and at surface electrodes considering the contact impedance between them.The finite element solution of the electric potential has been validated using a commercial code.The inverse medium problem for reconstructing the unknown electrical conductivity profile is formulated as an optimization problem constrained by the CEM.The method seeks the optimal solution of the domain’s electrical conductivity to minimize a Lagrangian functional consisting of a least-squares objective functional and a regularization term.Enforcing the stationarity of the Lagrangian leads to state,adjoint,and control problems,which constitute the Karush-Kuhn-Tucker(KKT)first-order optimality conditions.Subsequently,the electrical conductivity profile of the domain is iteratively updated by solving the KKT conditions in the reduced space of the control variable.Numerical results show that the relative error of the measured and calculated electric potentials after the inversion is less than 1%,demonstrating the successful reconstruction of heterogeneous electrical conductivity profiles using the proposed EIT method.This method thus represents an application framework for nondestructive evaluation of structures and geotechnical site characterization.

Prevalence of gastroesophageal reflux disease in children with extraesophageal manifestations using combined-video,multichannel intraluminal impedance-pH study

BACKGROUND Gastroesophageal reflux disease(GERD)might be either a cause or comorbidity in children with extraesophageal problems especially as refractory respiratory symptoms,without any best methods or criterion for diagnosing it in children.AIM To evaluate the prevalence of extraesophageal GERD using conventional and combined-video,multichannel intraluminal impedance-pH(MII-pH),and to propose novel diagnostic parameters.METHODS The study was conducted among children suspected of extraesophageal GERD at King Chulalongkorn Memorial Hospital between 2019 and 2022.The children underwent conventional and/or combined-video MII-pH.The potential parameters were assessed and receiver operating characteristic was used for the significant parameters.RESULTS Of 51 patients(52.9%males),aged 2.24 years were recruited.The common problems were cough,recurrent pneumonia,and hypersecretion.Using MII-pH,35.3%of the children were diagnosed with GERD by reflux index(31.4%),total reflux events(3.9%),and symptom indices(9.8%)with higher symptom recorded in the GERD group(94 vs 171,P=0.033).In the video monitoring group(n=17),there were more symptoms recorded(120 vs 220,P=0.062)and more GERD(11.8%vs 29.4%,P=0.398)by symptom indices.Longest reflux time and mean nocturnal baseline impedance were significant parameters for diagnosis with receiver operating characteristic areas of 0.907(P=0.001)and 0.726(P=0.014).CONCLUSION The prevalence of extraesophageal GERD in children was not high as expected.The diagnostic yield of symptom indices increased using video monitoring.Long reflux time and mean nocturnal baseline impedance are novel parameters that should be integrated into the GERD diagnostic criteria in children.

pH值调控方法对剩余污泥与柑橘废渣厌氧共发酵产酸影响

考察不同pH值调控方法对剩余污泥和柑橘废渣共发酵系统的产酸性能影响,结果表明:持续调节pH值为6可以提高共发酵系统的增溶过程,促进糖类物质水解,增强产酸和产甲烷过程,VFA产量和累积甲烷产量分别是空白组的1.36倍和1.25倍;提高共发酵系统中水解细菌和发酵产酸细菌的菌群丰度,促使甲烷生成途径从乙酰分解途径转向氢化营养途径.调节初始pH值为10可以有效促进共发酵系统中溶解性有机物的释放和达到快速水解的目的,促进产酸性能,但对产甲烷过程有抑制作用.

改进和优化黑果枸杞及其制品中花青素测定的pH示差法

建立一种基于美国官方分析化学师协会(Association of Official Analytical Chemists,AOAC)方法检测黑果枸杞及其制品中花青素含量的改进pH示差法。考察了黑果枸杞及其制品中花青素的最佳提取和检测条件,通过液相色谱-三重四级杆串联质谱法鉴别出黑果枸杞中花青素的具体化学结构,并计算出混合花青素的平均摩尔质量。通过分光光度法测得混合花青素的平均摩尔消光系数,对改进后的pH示差法进行方法学验证和花青素的含量测定。结果显示,最佳提取和检测条件如下:黑果枸杞花青素提取溶剂为盐酸-80%(体积分数)乙醇(3∶97,体积比),料液比为1∶100(g∶mL),提取温度为50℃,提取时间为30 min,缓冲溶液稀释5倍后静置平衡20 min。液相色谱-三重四级杆串联质谱法鉴别黑果枸杞中主要以矮牵牛素类花青素为主(占97.96%),黑果枸杞特有的混合花青素平均摩尔质量为912.7 g/mol,平均摩尔消光系数为29591 L/(mol·cm)。pH示差法改进后能够满足方法学验证要求,固体样品和液体样品最低检出限分别为28.2 mg/100 g、0.282 mg/100 mL。方法改进后花青素提取增长率均大于20%,静置平衡20 min后单次检测结果精密度小于0.3%。以矮牵牛素类花青素代替矢车菊素-3-O-葡萄糖苷计算花青素含量平均提高了2.41倍,能真实地反映黑果枸杞及其制品中花青素的含量。

两种调理剂对水稻土pH值和重金属吸收的影响

探明两种调理剂对近中性水稻土pH值、重金属有效性和水稻吸收的影响,及其与土壤性质的关系,为调理剂的科学开发及合理施用提供科学依据。选取水淬高炉渣源调理剂(T1)及其腐殖质改性调理剂(T2),分别设置2个施用量梯度(T1L、T1H和T2L、T2H),通过盆栽试验分析了两种调理剂及不同用量对土壤pH值、有效硅、镉、锌、铜含量,以及水稻籽粒和秸秆镉、锌、铜含量的影响。与对照(CK)相比,2个施用T1调理剂处理土壤pH值分别升高0.64和1.02,2个施用T2调理剂处理pH值分别升高0.27和0.56(P<0.05),土壤pH值随调理剂施用量的增加而增加。T1L和T1H处理土壤有效硅含量与CK相比分别提高5和22倍(P<0.05),且显著高于施用T2调理剂处理。与CK相比,T1H处理土壤有效镉、锌、铜含量分别降低37.21%、45.57%和95.30%(P<0.05);T2L和T2H处理土壤有效锌含量分别降低31.55%和30.67%,有效铜含量分别降低6.53%和19.32%(P<0.05)。与CK相比,T1L和T1H水稻籽粒锌含量分别降低23.44%和18.12%(P<0.05);施用T2调理剂后,籽粒和秸秆锌、铜含量无显著变化。土壤pH值、有效硅含量与土壤有效镉、锌、铜含量呈显著或极显著负相关,土壤有效硅含量与籽粒、秸秆镉含量呈显著负相关。研究表明,在近中性水稻土上施用富含硅的强碱性调理剂,既有利于提高土壤p H值和有效硅含量,又可有效降低土壤重金属有效性及其水稻吸收量。