Online Analysis Method Research Articles

Application of 13CH4 double model online analysis method based on Fourier Transform Infrared Spectroscopy in energy exploration

In the last decades, the substantial demands for 13CH4 detection in oil, natural gas, and geological prospecting have been increasing. Among the commonly used gas detection methods, Fourier Transform Infrared Spectroscopy has the advantages of a short detection cycle and simultaneous detection of many gas components, but it has not been realized for the online analysis of 13CH4. In this paper, we propose a double model analysis method for 13CH4 concentration based on Fourier Transform Infrared Spectroscopy. This method uses a Fourier Transform Infrared Spectrometer to obtain infrared absorption spectra of gases containing 13CH4. It corrects the drift of the spectral baseline using the adaptive smoothness parameter penalized least square method. In addition, aiming to have a more fine-grained result, we categorize the volume concentration into two intervals (1 ×10−6-0.5 % and 0.5 %–100 %) for quantitative analysis, and combine a Novel Frequency control and Regression Coefficients method for characteristic variable extraction and quantitative analysis modeling to exclude the effect of other alkane gases on 13CH4 detection. We conducted a series of experiments and the results demonstrate that our proposed double model analysis method is capable of fast 13CH4 detection from mixed gas with a detection error lower than 2 % and a detection cycle shorter than 12 s for the concentration ranges from 1 × 10−6 (1ppmv) up to 100 %. Finally, we have accurately analyzed the 13CH4 content in the gas-producing formation from oilfield logging experiments.

In Situ Raman Methodology for Online Analysis of CO2 and H2O Loadings in a Water-Lean Solvent for CO2 Capture.

Carbon capture represents a key pathway to meeting climate change mitigation goals. Powerful next-generation solvent-based capture processes are under development by many researchers, but optimization and testing would be significantly aided by integrating in situ monitoring capability. Further, real-time water analysis in water-lean solvents offers the potential to maintain their water balance in operation. To explore data acquisition techniques in depth for this purpose, Raman spectra of CO2, H2O, and a single-component water-lean solvent, N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (2-EEMPA) were collected at different CO2 and H2O concentrations using an in situ Raman cell. The quantification of CO2 and H2O loadings in 2-EEMPA was done by principal component regression and partial least squares methods with analysis of uncertainties. We conclude with discussions on how this simultaneous online analysis method to quantify CO2 and H2O loadings can be an important tool to enable the optimal efficiency of water-lean CO2 solvents while also maintaining the critical water balance under operating conditions relevant to post-combustion CO2 capture.

In-situ preparation of β-cyclodextrin-crosslinked polymer coated glass fiber for in-tube solid-phase microextraction of polycyclic aromatic hydrocarbons from water samples

Governments and organizations are paying increasing attention to the monitoring of polycyclic aromatic hydrocarbons (PAHs) in the environment. In this study, a β-cyclodextrin-based hyper-crosslinked polymer (β-CD-HCP) was prepared via the Friedel–Crafts reaction and used as a coating for an in-tube solid-phase microextraction (IT-SPME) glass fiber based on an in-situ preparation method. Subsequently, it was used for the analysis of six PAHs in water samples. The β-CD-HCP showed an excellent adsorption performance for the PAHs owing to its unique structural characteristics and multiple bonding forces (hydrophobic interaction, π–π, and electrostatic interactions) between the β-CD-HCP and PAHs. The adsorption mechanism of the β-CD to PAHs was investigated using density functional theory calculation experiments. By coupling high-performance liquid chromatography with a diode array detector, an online analysis method was established for the PAHs with a wide linear range (0.01–20 µg L–1), high enrichment factor (2130–2670), and low limits of detection (0.004–0.008 µg L–1). The PAHs in the water samples were successfully analyzed using this proposed online method with satisfactory recoveries in the range of 79.9–106.7% and relative standard deviations under 4.0%. The above results demonstrate that the prepared β-CD-HCP-based IT-SPME glass fiber has good application prospects for the pretreatment of PAHs in various aqueous environmental samples.

PGNAA Spectral Classification of Metal With Density Estimations

For environmental, sustainable economic and political reasons, recycling processes are becoming increasingly important, aiming at a much higher use of secondary raw materials. Currently, for the copper and aluminium industries, no method for the non-destructive online analysis of heterogeneous materials are available. The Prompt Gamma Neutron Activation Analysis (PGNAA) has the potential to overcome this challenge. A difficulty when using PGNAA for online classification arises from the small amount of noisy data, due to short-term measurements. In this case, classical evaluation methods using detailed peak by peak analysis fail. Therefore, we propose to view spectral data as probability distributions. Then, we can classify material using maximum log-likelihood with respect to kernel density estimation and use discrete sampling to optimize hyperparameters. For measurements of pure aluminium alloys we achieve near perfect classification of aluminium alloys under 0.25 second.

Technological advances for analyzing the content of organ-on-a-chip by mass spectrometry.

Three-dimensional (3D) cell cultures, including organ-on-a-chip (OOC) devices, offer the possibility to mimic human physiology conditions better than 2D models. The organ-on-a-chip devices have a wide range of applications, including mechanical studies, functional validation, and toxicology investigations. Despite many advances in this field, the major challenge with the use of organ-on-a-chips relies on the lack of online analysis methods preventing the real-time observation of cultured cells. Mass spectrometry is a promising analytical technique for real-time analysis of cell excretes from organ-on-a-chip models. This is due to its high sensitivity, selectivity, and ability to tentatively identify a large variety of unknown compounds, ranging from metabolites, lipids, and peptides to proteins. However, the hyphenation of organ-on-a-chip with MS is largely hampered by the nature of the media used, and the presence of nonvolatile buffers. This in turn stalls the straightforward and online connection of organ-on-a-chip outlet to MS. To overcome this challenge, multiple advances have been made to pre-treat samples right after organ-on-a-chip and just before MS. In this review, we summarised these technological advances and exhaustively evaluated their benefits and shortcomings for successful hyphenation of organ-on-a-chip with MS.

Online analysis method for pyrolysis products with large volatility difference at high temperature and pressure: Pyrolysis kinetics of supercritical pressure n-decane

Pyrolysis is widely applied in industrial fields including waste-to-energy conversion and hypersonic-vehicle cooling technology. Traditional offline analysis methods for fluid products based on cooling, depressurization, and separation processes often impart measurement uncertainties. Here, we propose a novel online analysis method for fluid pyrolysis products with significant volatility differences. High-frequency sampling based on instant cooling–flashing was used to avoid gas–liquid separation, where the fluid mixture is directly transported into an online-GC system as a vapor through a preheated vacuum tube. The method reliability was evaluated experimentally. Pyrolysis of supercritical-pressure n-decane (3–7 MPa) in a mini-tubular reactor was analyzed using both the proposed online method and an offline method. The gas yield measured using the offline method is significantly lower than that measured online due to the dissolution and volatilization loss of gaseous products during gas–liquid separation. The mass fraction of C3–C4 products measured using the offline method was 40–80% less than the online results. The maximum deviation in the mass fraction of the main products obtained offline was reduced to 20–25% after dissolution-loss correction. The kinetic constants of n-decane pyrolysis were determined based on product-yield data, and the calculated residence time was experimentally verified using particle image statistics. The proposed model was implemented in a computational fluid dynamics model. The deviation between the predicted conversion and experimental results was below 11%, while the deviations of the predicted mass fraction of typical products was significantly higher based on the offline model than the online model.

Methacrylate bonded covalent organic framework monolithic column online coupling with high-performance liquid chromatography for analysis of trace estrogens in food

Covalent organic frameworks (COFs) are a burgeoning class of crystalline porous materials with unique properties and have been considered as a promising functional extraction medium in sample pretreatment. In this study, a new methacrylate-bonded COF (TpTh-MA) was well designed and synthesized via the aldehyde-amine condensation reaction, and the TpTh-MA was incorporated into poly (ethylene dimethacrylate) porous monolith by a facile polymerization reaction inside capillary to prepare a novel TpTh-MA monolithic column. The fabricated TpTh-MA monolithic column was characterized with scanning electron microscope, Fourier transform infrared spectrometer, X-ray diffraction, and N2 adsorption–desorption experiments. Then, the homogeneous porous structure, good permeability and high mechanical stability of TpTh-MA monolithic column was used as separation and enrichment media of capillary microextraction, which was coupled with high-performance liquid chromatography fluorescence detection for online enrichment and analysis of trace estrogens. The main experimental parameters influencing the extraction efficiency were systematically investigated. The adsorption mechanism for three estrogens was also explored and discussed based on hydrophobic effect, π-π affinity and hydrogen bonding interaction, which contributed to its strong recognition affinity to target compounds. The enrichment factors of the TpTh-MA monolithic column micro extraction method for the three estrogens were 107–114, indicating a significant preconcentration ability. Under optimal conditions, a new online analysis method was developed and exhibited good sensitivity and wide linearity range of 0.25–100.0 µg·L-1 with a coefficient of determination (R2) higher than 0.9990 and a low limit of detection with 0.05–0.07 µg·L-1. The method was successfully applied for online analysis of three estrogens of milk and shrimp samples and the recoveries obtained from spiking experiments were in range of 81.4–113% and 77.9–111%, with the relative standard deviations of 2.6–7.9% and 2.1–8.3% (n = 5), respectively. The results revealed the great potential for the application of the COFs-bonded monolithic column in the field of sample pretreatment.

Validation of on-line crack compliance data analysis methods for the residual stress intensity factor

Residual stress can significantly impact the fatigue crack growth rate (FCGR) observed in standard tests. In the present study, implementation of the on-line crack compliance (OLCC) method for determining the residual stress intensity factor, Kres, in FCGR tests is supported by validation data, details of novel data analysis methods, and strategies for data reduction. Slitting measurements on C(T) specimens of quenched (but not stress relieved) and aged aluminum alloy 7050-T74 provide reference profiles of Kres as a function of crack size. The OLCC method quantifies Kres profiles from stress intensity factor control and load control FCGR tests over a range of residual stress states. Novel OLCC data reduction techniques are developed, and data analysis attributes and limitations explored, contributing to an improved methodology for standardization. The results demonstrate that the on-line crack compliance method is a valid means of quantifying Kres during fatigue crack growth rate testing.

A new on-line SPE LC-HRMS method for simultaneous analysis of selected emerging contaminants in surface waters.

In recent years emerging contaminants (ECs) have received significant attention due to their widespread detection in surface waters and concerns that these compounds can cause adverse ecological and/or human health effects. Therefore, accurate methods for determining and quantifying ECs in surface water are essential for estimating their environmental impact. This work describes the development, validation and application of a sensitive multiclass method for simultaneous determination of 22 per and polyfluorinated alkyl substances (PFASs), 3 pharmaceuticals, 15 pesticides, and 2 bisphenols in surface water using on-line solid phase extraction (SPE) coupled with ultra-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). The method allows simultaneous sample clean-up from interfering matrices and lower limits of detection (LODs) by injecting a large sample volume into the LC system without compromising chromatographic efficiency and resolution. Linearity of response over several orders of magnitude was demonstrated for all tested compounds (R2 > 0.99), with the LODs ranging from 0.8 and 33.7 pg mL-1, allowing detection of ECs at trace levels in surface water. The method showed acceptable accuracy and precision (CV, % and RE below 20%) for all tested ECs. It also provided recoveries between 60% and 130% for all tested ECs. The validated method was successfully applied for analysis of surface water samples from three rivers (Cam, Ouse and Thames) in England. Several ECs, including perfluorooctanesulfonic acid (PFOS), perfluorobutanesulfonic acid (PFBS), perfluorohexanoic acid (PFHxA), perfluorohexane sulfonic acid (PFHxS), dimethyl-metatoluamide (DEET) and ibuprofen were observed in analysed surface water above the method's limit of quantitation (LOQ), with concentrations ranging between 3.5 and 460 pg mL-1.

Online Prediction Method for Power System Frequency Response Analysis Based on Swarm Intelligence Fusion Model

Instability at transient frequency caused by faults in complex power systems is one of the greatest threats to operational safety. By analyzing the frequency response of power system in real-time and adopting control strategies promptly, power system accidents can be efficiently prevented. While existing online analysis methods integrate physical-driven and data-driven methodologies, they do not effectively utilize frequency timing characteristics. Consequently, a swarm intelligence fusion model, which integrates physical-driven and data-driven methods, is proposed as an improved frequency response analysis method. The transient frequency affecting components are separated into primary state variables and system time series data based on the properties of the time sequence. To preserve the actual relationship of the electrical mechanism model, the system frequency response (SFR) model is used as the physical-driven method for the primary state variables of the system. The Long Short Term Memory (LSTM) network was used as the data-driven method to extract timing features and correct the SFR model’s prediction using the system time series data as input. The two methods are combined using the bootstrap mode to form the fusion model, and the structure of the model is optimized using an improved sparrow search algorithm (ISSA), a swarm intelligence optimization algorithm. The model structure is adapted autonomously, implementing a method for online frequency response analysis. The simulation on the New England 39-bus system has verified that the method can quickly and accurately calculate the dynamic process of frequency response after a large-scale disturbance.

Online analysis method to correlate the mode shape for forced vibration in milling thin-walled workpieces

Online analysis method to correlate the mode shape for forced vibration in milling thin-walled workpieces

Linking scientific instruments and computation: Patterns, technologies, and experiences

SummaryPowerful detectors at modern experimental facilities routinely collect data at multiple GB/s. Online analysis methods are needed to enable the collection of only interesting subsets of such massive data streams, such as by explicitly discarding some data elements or by directing instruments to relevant areas of experimental space. Thus, methods are required for configuring and running distributed computing pipelines—what we call flows—that link instruments, computers (e.g., for analysis, simulation, artificial intelligence [AI] model training), edge computing (e.g., for analysis), data stores, metadata catalogs, and high-speed networks. We review common patterns associated with such flows and describe methods for instantiating these patterns. We present experiences with the application of these methods to the processing of data from five different scientific instruments, each of which engages powerful computers for data inversion,model training, or other purposes. We also discuss implications of such methods for operators and users of scientific facilities.

A novel framework for online decision-making and feedback optimization of complex products process parameter based on edge-cloud collaboration

Background: Intelligent manufacturing is perceived as a manufacturing mode with powerful learning and cognitive capabilities empowered by information technologies such as the internet of things, edge computing, and cloud computing. The mode can be used to address the problems of low intelligence and poor timeliness of traditional process planning. Methods: The framework includes the multi-objective process planning method based on real-time data, and the process closed-loop optimization mechanism of “cloud-based theoretical process planning plus edge MEC (multi-access edge computing) side online simulation verification and real-time feedback adjustment”, which realizes online process planning and iterative optimization in mass customization. Results: The feasibility of the online analysis method for thin-walled part milling deformation is verified by taking the finishing process of aerospace thin-walled parts as an example. The experimental results show that the simulation time on the single analysis step is reduced from 6s to 1s, and the accuracy rate is 86.9%. Conclusions: A new intelligent process planning theoretical framework integrating with online process planning and autonomous collaborative control, namely, digital twin and multi-access edge computing process planning (DT-MEC-PP) is proposed in this paper.

Real-time measuring energy characteristics of cane bagasse using NIR spectroscopy

Online measurement of moisture content (MC) and higher heating value (HHV) is desired for supporting the thermal conversion process in power plants. The main objective of this study was to develop a reliable and accurate method for online measuring of the HHV and MC of dried and as received sugarcane bagasse, respectively, using near-infrared (NIR) spectroscopy. Simulation of the conveyor belt in power plants and online energy quality analysis of bagasse for combustion process were designed. One hundred samples of bagasse were collected from different sugar mills at various locations of large piles of bagasse stored in both indoor and outdoor buildings. A long-wave spectrometer with a wavelength range of 860–1755 nm was used for scanning using diffuse reflection mode. The models were developed by partial least squares (PLS) regression using spectral sets obtained from the raw spectra, the preprocessed full spectra from the traditional approach, and the preprocessed spectra from the multi-block technique. The multi-blocks of the spectral pre-treatment technique provided effective models for both MC and HHV prediction. The best model for MC had a coefficient of determination of prediction (R2p) and the root mean square error of prediction (RMSEP) of 0.90 and 3.9% wb respectively. In the meantime, the best model for HHV had R2p and the RMSEP of 0.71 and 188.7 J g−1. This research demonstrated that NIR spectroscopy provides a feasible and reliable method for online analysis of MC and could be used for screening of HHV of bagasse.

State Evaluation Method of Distribution Equipment Based on Health Index in Big Data Environment

Aiming at the problems of time-consuming and low accuracy in the existing state evaluation methods of distribution equipment, a state evaluation method of distribution equipment based on health index in big data environment is proposed. Firstly, in order to optimize the time-consuming of big data analysis on large-scale and distributed clusters, a distribution equipment condition monitoring data platform in big data environment is designed, and a hive based relational online analysis method (ROLAP) is proposed. Secondly, the health index (HI) is introduced as the evaluation index to evaluate the health status of distribution equipment. According to the different influence degree of different fault factors on the equipment status, a comprehensive multifactor fault rate correction model is obtained, and the method based on success flow is used to solve the model to improve the accuracy of state evaluation. Finally, experiments show that when the data volume of distribution equipment is 60 GB, the time of the proposed method is only 30.0 s, which is far lower than 73.6 s and 82.5 s of the comparison method. The evaluation accuracy of the proposed method is 95.1%, while the evaluation accuracy of the comparison method is only 82.4% and 73.1%, respectively. Therefore, the proposed method can effectively improve the efficiency of distribution equipment condition evaluation.

Method Development and Qualification of pH-Based CEX UPLC Method for Monoclonal Antibodies

Post-translational modifications (“PTMs”) in monoclonal antibodies (mAbs) contribute to charge variant distribution, which will affect biological efficacy and safety. For the characterization of mAbs, charge variants are used as a critical quality attributes for product quality, stability consistency and effectiveness. Charge variants in mAbs are characterized by a time-consuming and a multistep process starting from cation/anion exchange chromatography, acidic/basic fractions collection and subsequent reverse phase (RP) liquid chromatography, coupled with mass spectrometry (MS) analysis. Hence, an alternative characterization approach that would be highly selective for ion exchange chromatography-based charge variant analysis, which is compatible with on-line MS detection, is needed in the biopharma industry. Against this backdrop, multiple studies are being conducted to develop a simple straight on-line charge variant analysis method. In this regard, we apply the current study, which aims to develop a charge variant analytical method, based on volatile buffers with low ionic strength that can be used for on-line MS detection of charge variants of mAbs. This would enable the detection on “PTMs” using low ionic strength mobile phase compatible with MS. Hence, fruitful data can be obtained with a single chromatography run without any test sample preparation, eliminating the need for multiple steps of analysis, time-consuming process and multiple sample preparation steps. Thus, Charge Variant Analysis-MS technique will allow the characterization of charge-related PTMs on the intact protein stage. In this regard, this study is about development of a method having combination of chromatography and volatile mobile phase for mass spectrometry detection of mAbs being analyzed in native form. The method is qualified considering pharmacopeia guidelines because the ultimate aim is to transfer this method for Quality Control (QC) release testing of a monoclonal antibody, which is critical for batch release and the regulatory point of view. Acidic and basic variants have been separated with high resolution peak profile. Furthermore, there was no matrix interference and good separation selectivity in terms of specificity was obtained using this method. The experimental data suggested for the linearity of the method are 2.4 mg/mL to 3.6 mg/mL with % RSD below 2.0%. Additionally, Limit of Quantitation is found to be 0.15 mg/mL, which is 5% of loading amount. Consistently, the data show that the method is precise under the same operating conditions with a short time interval. Overall a simple, accurate, robust and precise pH gradient cation exchange chromatography method was developed and qualified for the characterization of a therapeutic native mAb. Additionally, this method can be used to claim a biosimilar product profile of an in-house product compare to an innovator.

Microvibration-based orderly redistribution of wear particles in lubricating oil

On-line monitoring of wear particles in lubricating oil has become an important requirement for engine health management. This paper proposes a new online analysis method for oil wear particles. Exciting with micro-vibration, wear particles in the oil are redistributed on the surface of the vibrating film according to their size such that the collection and analysis of wear particles is realized in an orderly manner. A monolayer horizontal vibrational particle separation model was developed. The distribution law of wear particles in multi-physics field is theoretically deduced. Both a numerical simulation model and a simplified experimental system are established to illustrate the redistribution process of wear particles with different sizes. The numerical and experimental results show that the micro vibration-based redistribution method can separate the aggregation bands of different wear particles under the excitation of a certain frequency.

NMR Analysis Method of Gas Flow Pattern in the Process of Shale Gas Depletion Development

Shale gas reservoirs have pores of various sizes, in which gas flows in different patterns. The coexistence of multiple gas flow patterns is common. In order to quantitatively characterize the flow pattern in the process of shale gas depletion development, a physical simulation experiment of shale gas depletion development was designed, and a high-pressure on-line NMR analysis method of gas flow pattern in this process was proposed. The signal amplitudes of methane in pores of various sizes at different pressure levels were calculated according to the conversion relationship between the NMR T 2 relaxation time and pore radius, and then, the flow patterns of methane in pores of various sizes under different pore pressure conditions were analyzed as per the flow pattern determination criteria. It is found that there are three flow patterns in the process of shale gas depletion development, i.e., continuous medium flow, slip flow, and transitional flow, which account for 73.5%, 25.8%, and 0.7% of total gas flow, respectively. When the pore pressure is high, the continuous medium flow is dominant. With the gas production in shale reservoir, the pore pressure decreases, the Knudsen number increases, and the pore size range of slip flow zone and transitional flow zone expands. When the reservoir pressure is higher than the critical desorption pressure, the adsorbed gas is not desorbed intensively, and the produced gas is mainly free gas. When the reservoir pressure is lower than the critical desorption pressure, the adsorbed gas is gradually desorbed, and the proportion of desorbed gas in the produced gas gradually increases.

The Content Analysis of Amino Acids in Auricularia auricula from Heilongjiang and Jilin

The content of amino acids in Auricularia auricula was analyzed by the method of acid hydrolysis and automatic online analysis. The content of total amino acids in A. auricula produced in Heilongjiang was between 68.287 and 110.949 mg/g, and the average was 90.848 mg/g. The content of essential amino acids in A. auricula from Heilongjiang was between 28.847 and 45.757 mg/g, and the average was 37.987 mg/g. The proportion of essential amino acids (EAA) to total amino acids (TAA) in A. auricula from Heilongjiang was between 41.24% and 42.26%. However, the content of total amino acids in A. auricula from Jilin was between 71.716 and 124.143 mg/g, and the average was 94.318 mg/g. The content of essential amino acids in A. auricula from Jilin was between 29.775 and 52.063 mg/g, and the average was 38.498 mg/g. The ratio of essential amino acids (EAA) to total amino acids (TAA) in A. auricula from Jilin was between 39.75% and 41.94%. The content of total amino acids and essential amino acids in A. auricula from Jilin was higher than that from Heilongjiang. However, EAA/TAA in A. auricula from Heilongjiang was higher than that of Jilin. The content of total amino acids in different batches of A. auricula in the same production area was quite different, but the ratio of essential amino acids content to total amino acids content was basically the same.

Initial-Stage Performance Evolution of Solid Oxide Fuel Cells Based on Polarization Analysis

Solid oxide fuel cell (SOFC) has attracted more and more attention in recent years due to its high efficiency and low pollution. However, lifetime and stability are still limitations that affect further commercial applications of the SOFC technology. Therefore, it is critical to establish an online monitoring and analysis method to study the mechanism of SOFC performance evolution. The initial performance evolution of SOFCs is more apparent and non-linear than the subsequent stages, affecting the performance calibration and trading, especially for the industrial large-size cells and stacks. In this study, a single cell with an effective area of 100 cm2 was tested in Xuzhou HuaTsing Jingkun Energy Co. Ltd. under the constant current condition with humidified H2 for 80 hours after the NiO-YSZ anode was reduced for 4 hours. During the operation, the EIS data under different DC electrical current biases (0, 20, 50, 100, 300 mA cm-2) and j-V curve were recorded at 24-hour intervals. The EIS data were decomposed by the distribution of relaxation time (DRT) method, and the evolution of each electrode process under different polarization conditions was obtained. For the first 22 hours, the voltage under galvanostatic test and the power density at 0.7 V increased. The performance improvement is mainly due to the increase of the OCV and the decrease of the anode gas diffusion as well as gas conversion resistance, which can be attributed to the further anode reduction. The long reduction time is related to the low porosity and fine granularity of the Ni-YSZ anode, which was preliminarily verified by FIB-SEM. During the next 60 hours, the voltage dropped rapidly, and the power density at 0.7 V decreased by 27%. This nonlinear degradation was mainly caused by the weakening of the charge transfer reactions at the anode and the O2 surface exchange kinetics at the cathode. With the increase of current density, the deterioration of anode reaction gradually became the dominant factor for the increase of polarization impedance.