Regulator binding and mutations alter protein dynamics. The transmission of the signal of these alterations to distant sites through protein motion results in changes in protein expression and cell function. The detection of residues involved in signal transmission contributes to an elucidation of the mechanisms underlying processes as vast as cellular function and disease pathogenesis. We developed an autoencoder (AE) based method that detects residues essential for signaling by comparing the fluctuation data, particularly the time fluctuation of the side-chain distances between residues, during molecular dynamics simulations between the ligand-bound and -unbound forms or wild-type and mutant forms of proteins. Here, the AE-based method was applied to the G protein-coupled receptor (GPCR) system, particularly a class A-type GPCR, CXCR4, to detect the essential residues involved in signaling. Among the residues involved in the signaling of the homolog CXCR2, which were extracted from the literature based on the complex structures of the ligand and G protein, our method could detect more than half of the essential residues involved in G protein signaling, including those spanning the fifth and sixth transmembrane helices in the intracellular region, despite the lack of information regarding the interaction with G protein in our CXCR4 models. A better understanding of this phenomenon, i.e., signal transmission, contributes to an elucidation of the mechanisms of cellular functions and the onset of disease. In our previous study, we developed a method for detecting the residues involved in signal transmission by analyzing the difference in patterns of fluctuations in a residue pair between ligand-bound (holo) and unbound (apo) forms8. With this method, the fluctuation data obtained through molecular dynamics (MD) simulations of the protein in apo and holo forms were compared in terms of the similarity of the fluctuation patterns using an unsupervised neural network, an autoencoder (AE), and the residues essential in signaling were detected based on the results of the inspection using the AE. In the previous study, we focused on the detection of essential residues involved in the transmission of the signal of ligand binding to allosteric sites without large conformational deformations in PDZ2 protein8. This event, which is mainly caused by changes in the side-chain conformations, is known as dynamic allostery7. The dynamic allostery in PDZ2 has been analyzed through several computational studies based on MD simulations of ligand-bound and -unbound PDZ2 proteins, where the analyses of the degree of conformational fluctuations and the correlation between the fluctuations of two residues revealed key residues for dynamic allostery in PDZ212. However, we used the AE to analyze the dynamic allostery by inputting the vector of the side-chain distances between the residues during the simulation, namely, the data of the time fluctuations of the side-chain distances in a pair of residues. This method can detect most of the essential residues involved in the signaling in PDZ2 that were detected through an NMR study13, and the number of detected residues was larger than that in other MD studies12, which focused on the changes in the degree of conformational fluctuation and correlation between fluctuations of two residues through ligand binding. Their ideas are completely different from ours, which focuses on the changes in protein fluctuations without large conformational changes owing to ligand binding.Ligand binding induces changes in the protein dynamics by altering the fluctuations in mainly side-chain atoms of the residues involved in ligand binding. The signal of this change, particularly the changes in side-chain fluctuations, is transmitted from the ligand-binding site to distant regions  through protein motions, and to downstream proteins through various interactions, which result in changes in protein expression and cell functionAs another important difference from previous MD studies, we did not perform the averaging of the structural coordinates. MD simulations were used to calculate the trajectories of the residues and atoms within a specified time frame. The mechanisms of dynamics changes and signal transduction can be analyzed by extracting the degree of conformational fluctuations and the correlation of the fluctuations between residues from the trajectories. In general, these analyses require averaging the structural coordinates in the trajectories through least-squares fitting. However, this may disregard subtle changes in the conformations, particularly side-chain conformations, between the apo and holo forms. To avoid this concern and acquire all of the important information, we used the AE in which the time fluctuations of the side-chain conformations in a pair of residues during the simulations were compared between the apo and holo forms using the distance data as the input vectors for the AE. As a result, averaging is unnecessary.14, ligand binding that occurs in the extracellular (EC) region leads to conformational changes in the fifth, sixth, and seventh transmembrane (TM) helices15. This signal is transmitted to the intracellular (IC) region through the TM helices, which leads to interactions with G proteins in a cell. In this study, we attempted to detect the essential residues involved in the transmission of ligand-binding signals to G proteins through TM regions. Herein, we focused on C-X-C chemokine receptor type 4 (CXCR4), a member of the class A-type GPCRs. Chemokine receptors are involved in the stimulation of cell migration16; in particular, CXCR4 is involved in tumor progression, angiogenesis, metastasis, and survival17. Therefore, an analysis of CXCR4 signaling is essential to elucidate the mechanism of tumor metastasis. We first conducted MD simulations of the CXCR4 structure with and without a ligand, using the crystal structure of CXCR4 in complex with the chemokine vMIP-II (Protein Data Bank [PDB] ID: 4rws)18 used as the initial model. In addition, using our AE-based method, we analyzed the signaling by detecting the residues essential for the transmission of the signal from the ligand to the G protein from the MD trajectories.In this paper, we introduce the application of the AE-based method to the analysis of G protein-coupled receptor (GPCR) signaling to confirm whether the method can detect essential residues involved in such a large system. In GPCRs, which are a large and diverse protein family encoded in the human genome19. We extracted 25 residues described in the literature as being essential for ligand binding, conformational changes of the TM helices induced through ligand binding, and the interaction and activation of the G protein. Our AE-based method was able to detect more than half of these essential residues involved in signaling in CXCR2; the method detected the essential residues involved in the change of fluctuations by ligand binding, containing the residues in the IC regions involved in signaling to the G protein, despite the lack of information on the interaction between CXCR4 and the G protein in our models, whereas the method did not detect the residues involved in conformational changes by ligand binding. It implies that our AE-based method evaluated the change in fluctuations, and not the conformations, induced through ligand binding. We also applied widely used methods for analyzing the correlative fluctuations from the MD trajectories, conducting a principal component analysis (PCA) and calculating the dynamic cross-correlation matrices (DCCMs), and then compared the correlative fluctuations between the apo and holo forms. The results of the PCA and DCCM analyses of our data showed the high sensitivity of these methods to conformational changes induced through ligand binding in GPCR protein, rather than to the signaling. These observations imply that our AE-based method can be utilized to elucidate signaling mechanisms in many different systems, particularly those of disease onset.To confirm the accuracy of the AE-based detection, we referred to the structure of C-X-C chemokine receptor 2 (CXCR2) in complex with the chemokine ligand; interleukin-8 (IL8), known as CXCL8; and the G protein determined through cryo-electron microscopy (EM) (PDB IDs: 6lfm and 6lfo)The architecture of our AE-based method is shown in Fig. Comparison of RMSFs in the apo and holo forms. In this study, we focused on the signal transmission initiated by ligand binding in GPCR CXCR4. We used the crystal structure of CXCR4 bound to the viral chemokine vMIP-II 18. Because a CXCR4 structure without a ligand (apo form) has yet to be determined experimentally, we constructed a model structure in the apo form from the CXCR4 structure in the holo form by executing MD simulations after eliminating the ligand structure, as described in the Methods section. To confirm whether the structural models in the apo form were “apo-like” and differ from those in the holo form, we calculated the RMSF for the trajectories, which is a standard measure of fluctuations in each residue during the simulation  was larger than that in holo form .To analyze these contacts more accurately, we classified them based on the helix numbers (from TM1 to TM7) and the regions on the helix (extracellular (EC), transmembrane (TM), and intercellular (IC) regions), such as “TM1_EC”, as written in gray in Fig. In the pairs between TM6_TM and TM7_TM, and between TM5_EC and TM6_EC, the number of contacts in apo forms was larger than that in holo forms. The EC regions of the helices were narrowed by the conformational changes of helices in apo form, as shown in Supplementary Fig. We speculate that the apo structure of GPCR is flexible and does not have a stable conformation. Therefore, we believe that the difference between intra-receptor protein contacts in apo and holo forms and the conformational changes by eliminating the ligand might be sufficient to explain the formation of apo structures. Based on the comparisons of the RMSFs and intra-receptor protein contacts between the apo and holo forms, we believe that the MD simulations of the crystal structure without the ligand could eliminate the effect of ligand binding and allow the construction of an apo(-like) structure of CXCR4.20. The vector of the side-chain distances in a pair of residues during the simulation was used as input data for the AE because we believed that the detection of subtle changes in the side-chain conformations and fluctuations was required in the analyses of the signal transmission from the ligand to the G protein. The distance vectors were constructed directly from the trajectory data. Thus, an averaging of the structural coordinates is not executed. A set of vectors in all residue pairs was used as the AE input data. We trained two AEs, apo1- and apo2-trained AEs, using a set of distance vectors in the apo1 and apo2 data, respectively. The apo1-trained AE was used to inspect the apo1, holo1, and holo2 data. Similarly, the apo2-trained AE was used to inspect the apo2, holo1, and holo2 data.To find clues for understanding the mechanism of signaling in CXCR4, we compared the MD trajectories to identify differences in the fluctuation patterns between the apo and holo forms using the characteristics of AE: reconstruction of the input data by extracting the features in the input through a dimensionality reductionThe output from the inspection of the apo data was almost the same as the input data because of the correct reconstruction using the AE Fig. . However8. Notably, we stopped at the ninth clustering because we cloud not obtain additional Lead and Accm receptor residues in the tenth clustering, and we did not focus on the Lead and Accm residues in the ninth cluster because this cluster included many remaining residue pairs. In addition, although we clustered all receptor–receptor, receptor–ligand, and ligand–ligand residue pairs, only the Lead and Accm receptor residues were the focus of this study.To extract the residues in holo form that were involved in the patterns of fluctuations different from those in apo form, that is, those involved in the transmission of the signal through ligand binding, we conducted a clustering of the residue pairs based on the vectors of DIO data, as described in the Methods section. The four combinations of apo and holo data were clustered based on the DIO vectors: combinations of apo1 and holo1 data, and apo1 and holo2 data, which were obtained through the inspections using apo1-trained AE, and those of apo2 and holo1 data, and apo2 and holo2 data, obtained through the inspections using apo2-trained AE. In this study, the clustering of the DIO vectors divided the receptor–receptor, receptor–ligand, and ligand-ligand residue pairs in the apo and holo forms simultaneously into nine groups. Among all combinations of the apo and holo forms, all residue pairs in apo form were clustered into one or two large groups during the early clustering steps. The residue pairs in the holo form were separated , the signaling from the ligand (EC region), the signaling to the G protein (IC region), or the transmission of the signal from the EC to IC regions (TM region), the Lead and Accm residues were involved in, we classified the Lead and Accm residues into three regions , as described in Fig. In all four combinations, 47, 19, and 36 residues in the EC, TM, and IC regions, respectively, were assigned as Lead and/or Accm residues  and the IC residues in TM5 and TM6 (TM5_IC and TM6_IC) were detected as Lead and/or Accm residues for all four combinations  and 6lfm (dimer ligand), respectively)From a different perspective, most of the 14 CXCR4 residues that were consistent with the essential CXCR2 residues were detected as Lead residues , and the other two residues were detected as only Accm residues, one of which is the only residue involved in the “TM5conf” interaction. Thus, our AE-based method could detect more than half of the essential residues identified from the experimental CXCR2 structure as Lead and Accm residues in CXCR4, most of which were involved in the signaling from the ligand to the G protein and identified as Lead residues. Both CXCR4 and CXCR2 are members of the C-X-C-chemokine receptor family, and their sequence identity is approximately 37%. Among the 11 essential residues that were extracted from the CXCR2 structures but inconsistent with any Lead or Accm residues in CXCR4, 3 (1 in the EC region of TM5 and 2 in the IC region of TM6) were not conserved in the CXCR family , discarding overlapping lipid molecules. The protein–membrane system was immersed in a rectangular box of water solvent. Subsequently, sodium and chloride ions were replaced with an adequate amount of water to an ion concentration of 150 mM, with the total charge being neutral. A CHARMM force field (version 27)24 was applied to the proteins and lipids. The TIP3P25 model was used for water. The system was treated under periodic boundary conditions. The x-, y-, and z-dimensions of the system were 8.6, 8.6, and 10.5 nm, respectively. The electrostatic energy and force were calculated using the particle mesh Ewald method26, wherein the cut-off lengths of the short electrostatic calculation and van der Waals interactions were set to 8 Å27. The chemical bonds associated with hydrogen atoms were constrained using the LINCS algorithm28, and water molecules were restrained through the SETTLE algorithm29. The total energy of the system was minimized using the steepest descent method. The energy-minimized system was equilibrated using an MD simulation with the Berendsen algorithm28 at 310 K. The thermally equilibrated system was also equilibrated under constant temperature and constant pressure conditions using the Parrinello–Rahman method30 at 310 K with a pressure of 1 atm. The time-step length of the MD simulations was set to 2 fs. To investigate the ligand allosteric effects on the chemokine receptors, we prepared holo (a receptor with a chemokine ligand) and apo (a receptor without a chemokine ligand) systems in the same manner. The 1-μs production runs of the systems were conducted twice for the holo and apo forms for confirmation with different initial velocities, and snapshots were collected every 2 ps for the analysis, where we designated the obtained data as apo1, apo2, holo1, and holo2 data. All MD simulations were conducted using GROMACS 4.5.523. The root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were calculated for each of the four trajectories 33. In this study, we used a multilayer AE comprising 13 fully connected layers  , was calculated from 50.5 to 1000 ns in 0.5-ns increments (1900 steps) within the trajectory obtained through the MD simulation. The distance data were reconstituted as a 1900-dimensional vector for each residue pair. For each of the trajectories in apo1, apo2, holo1, and holo2 data, a set of distance vectors for all residue pairs was prepared  in apo1 and apo2 and holo1 and holo2 data, respectively, wherein the holo1 and holo2 data contained the distance data of the pairs between the receptor and ligand residues and between the ligand and ligand residues.The AE was trained using 80% of the randomly selected distance vectors  in training and 20%  for validation of apo1 or apo2 data. Thus, we constructed two AEs: apo1-trained and apo2-trained AEs. In this study, the Adam optimizer with a learning rate of 0.00001 was used. The training data were divided into a set of vectors for every 100 residue pairs . The training was iterated for 100,000 epochs and terminated when the validation loss decreased. We decided to use the above-described hyperparameters because of the best performance in terms of loss and accuracy in the training and validation steps among their various combinations. Inspections were then conducted using the apo1-trained AE for apo1, holo1, and holo2 data and apo2-trained AE for apo2, holo1, and holo2 data  eight times with a cut-off value of 2 to 9 (Supplementary Fig. The residue pairs in apo and holo forms were then classified based on the similarity of the motion patterns during the simulation through the clustering of the DIO vectors. We used the “cosine” method of the “dist” function and the “ward.D2” linkage of the “hclust” hierarchical clustering function in the R programWe clustered the input and output vectors for confirmation (Supplementary Fig. Supplementary Information.


Larix sibirica Ledeb.—with sulfamic acid in 1,4-dioxane using different activators has been studied for the first time. The dynamics of the molecular weight of sulfated arabinogalactan upon variation in the temperature and time of sulfation of arabinogalactan with sulfamic acid in 1,4-dioxane has been investigated. It has been found that, as the sulfation time increases from 10 to 90 min, the molecular weights of the reaction products grow due to the introduction of sulfate groups without significant destruction of the initial polymer and sulfation products. Sulfation at 95 °C for 20 min yields the products with a higher molecular weight than in the case of sulfation at 85 °C, which is related to an increase in the sulfation rate; however, during the further process occurring under these conditions, sulfation is accompanied by the destruction and the molecular weight of the sulfated polymer decreases. The numerical optimization of arabinogalactan sulfation process has been performed. It has been shown that the optimal parameters for obtaining a product with a high sulfur content are a sulfamic acid amount of 20 mmol per 1 g of arabinogalactan, a process temperature of 85 °C, and a process time of 2.5 h.The process of sulfation of arabinogalactan—a natural polysaccharide from  The anticoagulant drug most widely used in modern medical practice is heparin, a natural hexosaminoglycan of animal origin. Heparin is used to improve the hemocompatibility of the surface of medical devices ,2,3. ThiL. sibirica Ledeb), Gmelina (L. gnielinii Rupr.), and Cajandera (L. cajanderi Mayr.) [A promising and available substance alternative to heparin is sulfated arabinogalactan ,24,25,26i Mayr.) . The quai Mayr.) . For gali Mayr.) ,28. The i Mayr.) .It was found that in AG macromolecules from western larch, arabinose units are located at the ends of lateral branches consisting of three or four monosaccharide residues . The conArabinogalactan exhibits a wide range of biological properties: the immunobiological, hepatoprotective, antimutagenic, mitogenic, gastroprotective, and membranotropic activity; the probiotic, mycogenic, hypolipidemic, and immunomodulatory characteristics; the dispersing effect; etc. ,29,30,313-dimethylformamide complex in dimethyl sulfoxide [At the laboratory of natural synthons and ligands of the Irkutsk Institute of Chemistry, Siberian Branch, of the Russian Academy of Sciences, sulfated arabinogalactan in the form of a potassium salt was obtained by sulfation with a SOulfoxide . The preulfoxide .As is known, the anticoagulant activity of sulfated polysaccharides directly depends on the sulfation method used, which affects the degree of sulfation, arrangement of sulfate groups, molecular weight, etc. ,19,20,21Current methods of AG sulfates producing are based on the use of aggressive and environmentally hazardous sulfating agents such as sulfuric anhydride and chlorosulfonic acid ,23,24,26In the continuation of the work , the aimAs is known, the process of sulfation with sulfamic acid is activated by organic bases ,36,37. SIn , the sulw value of 12,310 Da. An increase in the substituent chain length in the urea molecule leads to a decrease in the sulfur content in arabinogalactan sulfate; for ethyl urea, it becomes 8.0 wt %. It should be noted that the use of the ethyl urea and hydroxyethyl urea activators ensures the same sulfur content (8.0 wt %) and PD value (1.22) in the product, with slightly different Mw values . The use of biuret as an activator in the process of sulfation of arabinogalactan with sulfamic acid leads to the formation of a product with the lowest sulfur content (7.6 wt %).It was shown that, at the introduction of substituents into the urea molecule, their activating ability in the reaction of arabinogalactan sulfation with sulfamic acid decreases. In particular, the process activated by methylurea yields sulfated arabinogalactan with a sulfur content of 9.4 wt % and a MThe highest sulfur content (12.6 wt %) and molecular weight  in arabinogalactan sulfated with sulfamic acid were obtained with the urea activator. The results are consistent with the literature data .In this study, we exploited the effects of the process temperature and time and the amount of the sulfating complex on the sulfur content in the synthesized arabinogalactan sulfates.1 , sulfation time X2 , and sulfating complex amount X3 per 1 g of arabinogalactan . The output parameter of the sulfation process was sulfur content Y1 (wt %) in arabinogalactan sulfate. The Box‒Behnken experimental design (BBD) was used. Each experiment was carried out in duplicate. The designation of variables is given in The three factors included in the study as independent variables  were sulfation temperature XThe experimental results are shown in An increase in the process temperature should lead to an increase in the rate of sulfation, and the rate of depolymerization . It is oAccording to the data given in p < 0.05. For all independent variables in the area of the factor space, in our case, p < 0.0043 was observed . The straight shows the calculated Y1 values and dots correspond to the results of observations. The proximity of the dots to the line confirms the good predictive properties of Equation (1).The predictive properties of Equation (1) are illustrated in The dependence of the output parameter Y1 on the variable factors X1 and X2 is described by a slightly curved response surface and reaches a ‘plateau’ after the X2 value of 2.5 h . The depIn addition, the quality of the approximation is characterized by the coefficient of determination R2adj. In the problem under consideration, it is R2adj = 94.0%, which indicates the high quality of the approximation. Therefore, we can state that the data obtained using Equation (1) are consistent with the observation results and this equation can be used as a mathematical model of the investigated process.The calculated optimal parameters of the sulfation of arabinogalactan with sulfamic acid in dioxane in the presence of urea are a sulfamic acid amount of 15 mmol per 1 g of arabinogalactan, a temperature of 85 °C, and a time of 2.5 h.A classical method of purification of high molecular weight compounds from low molecular weight impurities is the dialysis process, in which low molecular weight components are removed through a semipermeable membrane. A membrane is conventionally a film based on cellulose derivatives with a pore size from 3 to 100 kDa. However, this polymer purification method is rather laborious. In view of this, we compared the molecular weight characteristics of the sulfated arabinogalactan samples obtained directly from the reaction medium and purified by dialysis. Chromatograms of the crude sulfated arabinogalactan samples are shown in It was found that the main arabinogalactan peak with a retention time of ~22 min shifts to the left with an increase in the sulfation time, which indicates an increase in the mass of polymer chains due to the included sulfate groups. The peaks with a retention time of more than 25 min correspond to the initial reagents  and to the low molecular weight reaction products.The peaks corresponding to the main product are somewhat broadened, which can be attributed to the inhomogeneity of the initial arabinogalactan molecules and the fact that the low- and high-molecular chains are sulfated differently.To remove the low molecular weight chains, the initial arabinogalactan was also dialyzed and then sulfated .At a sulfation process time of 60 min, the ‘low molecular weight’ shoulder appears at the main arabinogalactan peak. As the time increases to 120 min, arabinogalactan completely passes to the low molecular weight form. In this case, the product yield was lower than 30%. These changes may indicate that, during dialysis of the initial arabinogalactan, the stabilizing polysaccharide chains were removed, which intensified the glycosidic bond breaking and the formation of low molecular weight products during sulfation.The GPC data on the dialyzed sulfated arabinogalactan samples obtained at a temperature of 85 °C and the ratio of the amount of the complex to the amount of the polysaccharide corresponds to 1:15 (g:mmol) are presented in According to the GPC data, during the dialysis, low molecular weight impurities were removed almost completely, while the retention time and form of the main product remained unchanged, in contrast to the case of the crude samples. Thus, the GPC technique can be used to analyze the molecular weight characteristics of crude products of sulfation of arabinogalactan without the additional laborious dialysis process. The use of cellophane bags in dialysis, which have smaller pore sizes than those used, could lead to the appearance of the low molecular weight fraction peak on the chromatogram and a different molecular weight distribution.The GPC data on the MWD for the samples of the reaction mass of arabinogalactan sulfation at different process times and temperatures of 85 and 95 °C are given in The GPC data showed that, in contrast to the initial arabinogalactan, the number-average and weight-average molecular weights of sulfated arabinogalactan increase with the sulfation temperature already in the first 10–30 min of the process due to the introduction of sulfate groups; the degree of polydispersity changes insignificantly as compared with the value for the initial arabinogalactan and remains within 1.11–1.16 .Sulfation of arabinogalactan at a temperature of 85 °C and a process time of up to 90–120 min  proceedsThe chromatographic study of the molecular weight characteristics of the arabinogalactan sulfation products obtained at 95 °C yielded different results .p increases, most likely due to the introduction of sulfate groups into the macromolecule, while the Mw value decreases with an increase in polydispersity. Such a change in the molecular weight characteristics can result from the partial destruction of both the initial and sulfated arabinogalactan. To 120–180 min of the sulfation process, the molecular weights significantly decrease. A decrease in the molecular weight was previously observed during mechanochemical activation of arabinogalactan [Up to a 50-min process time, the maximum peak coordinate Mgalactan . As was galactan , during The obtained results of the study of the molecular weight characteristics of the samples of the reaction mass sulfated at 85 and the duration of the process 40–180 min show that under these conditions the formation of sulfated products with close values of the average molecular weight, molecular weight distribution, and sulfur content (~12%). In contrast, the results of the chromatographic study of the molecular weight characteristics of the samples of the reaction mass sulfated at 95 °C and a process duration of 20–60 min show the possibility of obtaining sulfated arabinogalactan of different average molecular weight, but close molecular weight distribution and sulfur content (~12%). The results obtained in the future can be useful in studying the effect of the preparation conditions and the value of the molecular weight of the sulfated biopolymer on its anticoagulant activity.Larix sibirica Ledeb.) produced by OOO “Khimiya drevesiny”  under the trade name “FibrolarS” was used as the initial material.AG from the wood of the Siberian larch , using the modified procedure, described in . 1,4-dioThe dynamics of the molecular weight characteristics of the reaction mass during the sulfation of arabinogalactan was studied by gel permeation chromatography (GPC). The reaction mass samples (100–200 mg) for the GPC investigations were taken after 10, 20, 30, 40, 50, 60, 90, 120, 150, and 180 min of the process, dissolved in 5 mL of distilled water, and neutralized with an aqueous solution of ammonia to pH 7.3 (pH = 7) as a mobile phase. The column was calibrated by the polyethylene glycol standards . The eluent flow rate was 1 mL/min and the sample volume was 100 μL. Before the analysis, the samples were dissolved in the mobile phase (1 mg/mL) and filtered through a 0.22 μm hydrophilic PTFE membrane filter (Agilent). Data collection and data processing were performed using the Agilent GPC/SEC MDS software.The molecular weight distribution (MWD) of the reaction mass samples was determined on an Agilent 1200 Infinity II multi-detector GPC/SEC system with triple detection: refractometer (PL), viscometer (VS) and light scattering (LS). The separation was made on two Aquagel-OH Mixed-M columns  using the aqueous solution of 0.1 M NaNOThe sulfur content was determined on a Thermo Quest Flash EA-1112 thermal analyzer . This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.The software Statgraphics Centurion XVI V16.1.09, DOE block (design of experiment), was used for numerical optimization of the AG sulfation process .In this study, the dynamics of the molecular weight characteristics of sulfated arabinogalactan upon variation in the temperature and time of sulfation of arabinogalactan with sulfamic acid in 1,4-dioxane was investigated.It was found that, during sulfation of arabinogalactan at 85 °C with an increase in the process time from 10 to 90 min, the molecular weights of the reaction products increase due to the introduction of sulfate groups without significant destruction of the initial polymer and sulfation products. Sulfation at 95 °C for 20 min leads to the formation of the products with a higher molecular weight than at 85 °C, since the sulfation rate increases; however, in the further course of the process under these conditions, sulfation is accompanied by destruction and the molecular weight of the sulfated polymer decreases.The conditions for obtaining sulfated arabinogalactan with the desired molecular weight characteristics were established.The effect of urea-based activators on sulfation of arabinogalactan with sulfamic acid was examined. It was shown that an increase in the substituent chain length leads to a decrease in the sulfur content in arabinogalactan sulfate. It was found that urea exhibits the best activating ability among the investigated activators.The Box‒Behnken numerical optimization of the process of sulfation of arabinogalactan with sulfamic acid in 1,4-dioxane was performed. It was shown that the optimal parameters for obtaining arabinogalactan sulfate with the maximum sulfur content (12.8 wt %) are a sulfamic acid amount of 20 mmol per 1 g of arabinogalactan, a process temperature of 85 °C, and a process time of 2.5 h.


Vision sensing is a key technology to realize on-line detection of welding groove sizes and welding torch relative position and posture parameters during the arc welding process of intelligent production. For the specially designed vision sensor based on combined laser structured lights, an integrated calibration method for its internal parameters is proposed firstly, which improves the efficiency, accuracy and comprehensiveness of internal parameter calibration for a line structured light vision sensor and provides a good foundation for industrial application of the vision sensor. Then, the high precision integrated detection algorithms are derived for the V-groove size parameters and the spatial position and posture (SPP) parameters of the welding torch relative to the welding groove based on a single modulated laser lines image. The algorithms make full use of the data in a single modulated laser lines image, adopting data segmentation and plane fitting to realize the 3D reconstruction of V-groove surfaces and its adjacent workpiece surfaces of planar workpiece, so solving the parameters with high precision. In the verification tests, the relative detection error of V-groove size parameters of planar workpiece is less than 1%, and the relative detection error of SPP parameters of welding torch relative to the welding groove is less than 5%, which separately shows the effectiveness and accuracy of the calibration method and the detection algorithms. This research work provides a good technical support for the practical application of the specially designed vision sensor in the intelligent welding production. For the welding of workpieces with determined groove shape and size located in specified spatial posture, the spatial position and posture (SPP) of the welding torch relative to the welding groove have great influence on weld formation quality that cannot be ignored. Thus, they should be reasonably set prior to implementing the arc welding process in addition to the basic process parameters such as welding current, arc voltage, welding speed and motion trajectory of the welding torch. However, machining and assembly errors for the welding groove sizes usually exist, and thermal deformation when workpieces are being welded inevitably occurs. This will often lead to defects and deficiencies with the weld formation if only relying on the preset SPP as well as the motion trajectory of the welding torch relative to the welding groove. Thus, it is necessary during the actual welding procedure to carry out high-precision welding groove sizes detection and weld tracking, that is, the motion trajectory control of the welding torch and synchronous control on the SPP of the welding torch relative to the welding groove. In other words, this important direction in the welding field is worthy of research and development to realize intelligent welding by on-line sensing detection and real-time feedback control, which can not only improve the efficiency of welding production but also effectively ensure the quality of the welded joint and product ,2.Vision sensing is a key technology to realize intelligent welding. Compared with other vision sensing technologies, the line structured light vision sensing based on the perspective projection principle has the comprehensive advantages of simple operation, high detection accuracy and low system cost and has been widely used in modern industrial production .The premise of a vision sensor based on line structured light successfully applied in realizing high precision detection is firstly to calibrate its internal parameters effectively, including camera calibration and structured light plane calibration . In the Meanwhile, the line structured light vision sensor has been widely used in the welding field, such as weld tracking, welding torch location determining and so on ,13,14. HIn terms of the welding groove size parameters detection based on line structured light vision sensing, some typical research works are as follows. He et al.  and Zhu In terms of the relative position and posture detection based on line structured light vision sensing, some research works have also been carried out. Xue et al.  and ZengFrom the above brief review and analysis, we can see that the current calibration method of the line structured light vision sensor is complex and inconvenient. Meanwhile, in the detection process, the mentioned detection methods for the welding groove sizes and welding torch relative position and posture parameters depend on sensor installation and welding equipment motion parameters at different degrees. There are few studies that have completed the integrated detection of welding groove sizes and welding torch relative position and posture parameters with high detection accuracy, robustness and adaptability at the same time.fx, fy, u0, v0, k1, k2) and the structured light plane equation parameters  in a camera coordinate system. Next, based on the processing of single modulated laser lines image captured by camera, the 3D point cloud data of the laser lines can be obtained. Then, the V-groove surfaces and its adjacent workpiece surfaces of the planar workpiece are reconstructed by data segmentation and plane fitting. Finally, the integrated detection of the V-groove size parameters  of the planar workpiece and the relative position and posture parameters  of the welding torch in any relative position and posture is realized. This research work effectively improves the robustness and applicability of detection for the vision sensor and has important application value in intelligent welding production.Aiming at the existing problems mentioned above, on the basis of the deduced detection mathematical model of the vision sensor based on combined laser structured lights, this paper firstly proposes an integrated calibration method using only an ordinary checkerboard calibration board for internal parameters of the vision sensor, including camera internal parameters  and o-uv is a 2D pixel coordinate system, which are used to characterize the 2D coordinates in the image plane; CO-CYCZCX is the 3D camera coordinate system  and WO-WYWZWX is the 3D world coordinate system , which are used to characterize the 3D coordinates of space points in the actual physical space. Point P is the sampling point in the modulated laser lines projected on the surfaces of the measured object and point p is the perspective imaging point of the sampling point P on the 2D image plane.In order to convert the 2D image data of the modulated laser lines captured by the camera into 3D space data through the perspective projection model, four rectangular coordinate systems are established, as shown in P from the 3D world coordinate system W-XWYWZWO to the 3D camera coordinate system C-XCYCZCO is a position and posture transformation of rigid body (including rotation and translation). This transformation matrix is called the external parameter matrix of the camera and it can be expressed as follows [CR is a 3 × 3 matrix and T is a 3 × 1 matrix, which represent the posture and position of the camera coordinate system C-XCYCZCO in the world coordinate system W-XWYWZWO, respectively.In  follows :(1)[XCYCP in the 3D camera coordinate system C-XCYCZCO and the coordinates of the imaging point p in the 2D image coordinate system O-xy is as follows:According to the perspective projection principle, the matrix relationship between the coordinates of the sampling point x and y directions are dx and dy (mm/pixel), respectively, and u0, v0 (pixel) are the position coordinates of the origin of the 2D image coordinate system O-xy in the 2D pixel coordinate system o-uv. Then, the matrix relationship of the imaging point p between its image coordinates  and pixel coordinates  is:On the image plane, the pixel widths along the P, the imaging point p and the camera optical center CO meet geometric collinear constraint. According to the conversion matrix of Equations (1)–(3), the conversion relationship between the 3D world coordinates of sampling point P and the 2D pixel coordinates of imaging point p can be obtained:fx = f/dx and fy = f/dy represent the dimensionless scale factor of the camera in the x direction and y direction, respectively.Ignoring the camera imaging distortion, the sampling point k1 and k2 represent the radial distortion coefficient of the camera, r represents the normalized distance between the distortion point and the main point of the image and cx and cy represent the image coordinates after radial distortion correction. So, k1, k2, xf, yf, u0 and v0 are called internal parameters of the camera system.However, the camera lens always has radial distortion and tangential distortion. In general, the tangential distortion is small and can be ignored, and only the radial distortion needs to be considered. The mathematical model used frequently for removing radial distortion of the camera is the Brown model , which iP on the surfaces of the measured object is also a point on the structured light plane projected by the line laser transmitter, a complete vision sensing detection mathematical model can be established by taking the structured light plane equation in the 3D camera coordinate system C-XCYCZCO as a supplementary condition. The general expression of the laser structured light plane equation in the 3D camera coordinate system C-XCYCZCO is:lA, lB, lC and lD are the coefficients of the laser structured light plane equation.Only relying on the constraint provided by Equation (4), the detection mathematical model of the vision sensor is incomplete. Considering that the sampling point XC, YC, ZC) of the sampling point P in the 3D camera coordinate system C-XCYCZCO can be solved by the pixel coordinates  of the imaging point p in the image plane.lA1, lB1, lC1, lD1 and lA2, lB2, lC2, lD2 are the plane equation parameters of the structured light planes projected by the line laser transmitters 1 and 2 in the 3D camera coordinate system C-XCYCZCO.Combining the above equations, the detection mathematical model of the vision sensor based on combined laser structured lights are obtained, as shown in Equation (7). Through this equation, the 3D coordinates  and the structured light plane equation parameters  of two line laser transmitters in the camera coordinate system. This method is only based on the ordinary planar checkerboard calibration board , and the images of the calibration board and the laser lines are collected before and after the two line laser transmitters project the laser lines onto the calibration board by using different exposure times, respectively, as shown in For the vision sensor based on combined laser structured lights, an integrated calibration method is proposed and applied for the calibration of vision sensor internal parameters, including the camera internal parameters , the caRC, T of the chessboard calibration board relative to the 3D camera coordinate system C-XCYCZCO under different positions and postures and Equation (4), the 20 sets of 2D coordinate data of the laser centerlines in the laser lines image are converted into the 3D coordinate data of their corresponding point in the laser lines projected on the calibration board in the 3D camera coordinate system C-XCYCZCO. Finally, according to the 20 sets of 3D coordinate data of laser lines projected on the calibration board under different positions and postures, the two laser structured light plane equation parameters can be obtained by fitting the two structured light planes separately, which are the actual structured light planes projected from the two line laser transmitters.For the calibration of laser structured light plane equation parameters, its detailed steps are as follows. Firstly, the laser lines image is preprocessed by median filtering, binary segmentation and morphological processing. Next, the laser centerlines in the image are extracted by skeleton thinning and Hough line transform. Then, according to the obtained external parameter matrices The calibration results of the internal parameters for the vision sensor are shown in The mathematical algorithms of vision detection are different for vision sensors with different arrangement structures between camera and laser structured light. For the designed vision sensor based on combined laser structured lights, the 3D reconstruction method for the V-groove surfaces and its adjacent workpiece surfaces of planar workpiece is primarily studied after the single modulated laser lines image processing effectively. Then, the detection algorithms of the V-groove size parameters and the SPP parameters of the welding torch relative to the welding groove are deduced. Finally, some experiments are carried out for the verification of the effectiveness and accuracy of the deduced detection algorithms.The laser lines projected on the V-groove surfaces and its adjacent workpiece surfaces are modulated, and then the CMOS camera captures the modulated laser lines image. The flow of processing and feature extraction of modulated laser lines image is shown in Firstly, the modulated laser lines image a is prepConsidering the requirements of real-time detection accuracy and anti-interference ability of image processing, the Zhang-Suen thinning algorithm  is selecρ and θ in polar coordinates for each line segment.Then, for the obtained single pixel laser centerline image, the probabilistic Hough transform method is applied to determine the lines in the image. In the actual detection, the detection results of multi-group points of line segment will appear. The final detected values of the laser centerline equations can be obtained by calculating the average values of Finally, the intersection points of the two adjacent lines detected can be calculated, which are the image feature points of the V-groove of the planar workpiece e. For thThe obtained intersections of the fitting lines after image processing and feature extraction, which are the characteristic points of the modulated laser lines image, will inevitably have pixel errors. If the size parameters of the V-groove are directly solved by using image coordinates of these points, it will inevitably produce large detection deviation, reduce the robustness of the detection algorithm and decrease the utilization of the laser lines image data.CO-CYCZCX, and then to realize the high precision calculation of the welding groove sizes and welding torch relative position and posture parameters. The flow of algorithm for solving the welding groove sizes and welding torch relative position and posture parameters is shown in In order to improve the accuracy and robustness of the detection results of welding groove size parameters, a plane fitting method is proposed, that is, to reconstruct the V-groove surfaces and its adjacent workpiece surfaces based on the 3D data points of the modulated laser lines in the camera coordinate system ix, iy; i = 1, 2,..., 6) according to their serial number. Each modulated laser line has three image feature points. Taking them as breakpoints, the data of each modulated laser line are divided into four segments, which are recorded as datai , as shown in The coordinates of the six feature points in o-xy are mapped into the camera coordinate system C-XCYCZCO based on the perspective projection principle. Then, the 3D point cloud segmented data of two single pixel modulated laser lines can be obtained, as shown in According to the detection mathematical model of Equation (7), the 2D segmented data of two single pixel modulated laser lines in the image coordinate system iS  can be obtained by fitting the datai  points with the same serial number in the two modulated laser lines, respectively.According to the structural characteristics of V-groove of planar workpiece, the data points with the same serial number in the two modulated laser lines are on the same welding groove surfaces or its adjacent workpiece surfaces. Theoretically, the welding groove surfaces and its adjacent workpiece surfaces are plane, so the 3D point cloud segmented data can be used for plane fitting. Four planes Considering that the obtained six image feature points may have pixel errors, their corresponding 3D feature points are not completely accurate segment interval points. Therefore, to ensure the accuracy of plane fitting data points and reduce the influence of the data error caused by segment interval points, the segment interval points and their neighborhood data points are removed during the process of data segmentation.M and column matrix X can be constructed as follows:C, C, C) is the average coordinates of one 3D segmented data point  which is used to fit corresponding plane, and column matrix X represents the normal vector of the plane.The singular value decomposition (SVD) is used to plane fitting for the different segmented 3D data points separately. The coefficient matrix The purpose of plane fitting is to minimize the sum of the distances between the fitting plane and all fitting points; thus, the objective function is established as follows:M is decomposed by the method of SVD. It can be obtained that the eigenvector corresponding to the minimum singular value of the coefficient matrix M is the optimal solution X of the objective function in Equation (9). According to D = −, all of the parameter values of the fitting plane are obtained. Taking the equation parameters of four fitting planes iS as Ai, iB, Ci and iD , the fitting plane equations of the four planes S1, S2, S3 and S4 associated with the V-groove are:m1 , m2 , m3 , m4 , respectively.The constraint condition is The welding groove surfaces and its adjacent workpiece surfaces obtained by fitting the segmented 3D point cloud data are shown in C-XCYCZCO, the welding groove size parameters and the SPP parameters of the welding torch relative to the welding groove can be detected and solved.Further, according to the reconstructed 3D welding groove and the position and posture of the welding torch in the camera coordinate system h, groove width b1 and b2. Their detection algorithms are as follows.The main size parameters of V-groove of planar workpiece include groove depth l1, which is the intersection line of the left groove surface S2 and the right groove surface S3, to the left workpiece surface S1 or the right workpiece surface S4 is called the groove depth.The distance from the groove bottom intersection line l1 is:The linear equation of the intersection line l1 to the left workpiece surface S1 and the right workpiece surface S4 are recorded as the groove depth h1 and the groove depth h2, respectively; The difference between h1 and h2 reflects the groove misalignment caused by planar workpiece assembly or thermal deformation. When the value of misalignment is small enough or negligible, take the average value of h1 and h2 as the groove depth h, as shown in The average distance from the points on the intersection line S1 and left groove surface S2, the intersection line l2 of the two planes can be obtained, and its linear equation is:According to the fitted left workpiece surface n2 of line l2 is:The direction vector n2 of the line l2 and the normal vector m1 of the fitting left workpiece surface S1, the normal vector m5 of the virtual vertical plane S5, which is perpendicular to the left workpiece surface S1 and passing through the line l2, can be obtained, expressed as:According to the direction vector l2, the plane equation of S5 can be obtained:m5 = , D5 = −(A5CXq + B5CYq + C5ZCq).  are the coordinates of any point on the line l2.Take any point of line S6 can also be obtained. Thus, the average distance from the points on the intersection line l1 to the virtual vertical planes S5 and S6 are the groove width b1 and b2, respectively, and the sum of b1 and b2 is the total groove width B, as shown in Similarly, the plane equation of the virtual vertical plane W-XWYWZWO is the world coordinate system fixedly connected with the planar workpiece, WX is the welding direction (groove direction) vector, WY is the welding groove width direction vector and WZ is the normal vector of the upper surface of the planar workpiece. The vector tor is the direction vector of the central axis of welding torch, which is used to characterize the posture of welding torch.e, angular deviation γ and welding torch height H.The relative position parameters of welding torch include transverse deviation S7, which is a plane passing through the groove bottom intersection line l1 and parallel to the upper surface of the planar welded workpiece. The point Pj0 and point Pj1 are the intersections of the central axis of welding torch and the optical axis of CMOS camera with the bottom plane S7, respectively. The transverse deviation e of welding torch is defined as the distance from point Pj0 to the groove bottom intersection line l1, and the angular deviation γ is the included angle between the line Pj0Pj1 and the intersection line l1.Construct the groove bottom plane H, camera height H1 are, respectively, the distances from the end of welding torch conductive nozzle PE, the camera focus along their respective axes to the upper surface of planar welded workpiece. The sensor installation height H0 is the distance from the camera focus along the central axis of welding torch to the end of welding torch conductive nozzle PE, which is a fixed value after the sensor being installed. When the central axis of welding torch is perpendicular to the upper surface of planar welded workpiece, the relationship among them is:The welding torch height α, left and right tilt angle β. The front and rear tilt angle α is the included angle between the welding torch direction vector tor and the normal vector WZ of planar workpiece upper surface along the welding direction. The left and right tilt angle β is the included angle between the welding torch direction vector tor and the normal vector WZ of planar workpiece upper surface in the plane of groove cross section.The relative posture parameters of welding torch include front and rear tilt angle γ. When welding process for different welding beads (layers) of V-groove are being implemented, the transverse deviation e and the height H of welding torch also need to be adjusted appropriately.The moving direction of welding torch during arc welding process may not always coincide with the direction of welding groove, which often leads to an angular deviation e, angular deviation γ and welding torch height H are solved as follows:1.Transverse deviation eAccording to the plane equations of the welding groove surfaces and its adjacent workpiece surfaces and the definition of position parameters of welding torch relative to the welding groove, the transverse deviation S7.(1). Solve the plane equation of the bottom plane S1 and S4 is taken as the value of the normal vector m7  of the bottom plane S7, which are given as follows:When the misalignment of the welding groove can be ignored, the average value of the normal vectors of planar workpiece upper surfaces P of the intersection line l1, the plane equation of the groove bottom plane S7 can be determined as:D7 = −(A7CXp + B7CYp + C7ZCp), and  are the coordinates of any point P on the intersection line l1.Take any point Pj0 of the central axis of welding torch and the plane S7.(2). Find the intersection point CX = D0, CY = 0. where D0 is the distance between the central axis of welding torch and the optical axis of camera, which are two parallel lines, and D0 is a fixed value after the sensor has been installed. The coordinates of the intersection point Pj0 of the welding torch central axis and plane S7 can be solved as /C7).The linear equation of the central axis of welding torch is: e.(3). Calculate the transverse deviation n1 of the groove bottom intersection line l1 is:The value of the direction vector Pj0 to intersection l1 is the transverse deviation e, which is:The distance from point 2.Angular deviation γPj1 between the camera optical axis and plane S7.(1). Find the intersection point XC = 0, YC = 0. The coordinates of the intersection point Pj1 of the camera optical axis and plane S7 can be obtained, which are .The linear equation of the camera optical axis is: γ.(2). Calculate the angular deviation Pj0Pj1 and the intersection line l1 are both in plane S7, their included angle is the angular deviation γ, which is:Since the straight line 3.HWelding torch height Pj of the central axis of welding torch and plane S1 can be solved by combining two corresponding equations, which are /C1). In the camera coordinate system C-XCYCZCO, the coordinates of the end of welding torch conductive nozzle PE are . where the sensor installation height H0 can be determined by calibration.The coordinates of the intersection point S1 of planar welded workpiece, the welding torch height H is the distance between point Pj and PE, which is:Thus, for the upper surface S4 of planar workpiece, the welding torch height H can also be calculated similarly. In practical application, the welding torch heights H relative to the two upper surfaces of S1 and S4, respectively, can be used alone or used with their mean value according to actual needs.For the upper surface α, left and right tilt angle β) affect the shape of weld pool and its fluidity, then will affect the quality of weld formation, including penetration, width and reinforcement. The values of relative posture parameters of welding torch are closely related to the absolute spatial posture of welding groove.During the arc welding process, the relative position parameters of welding torch affect the accuracy of the weld forming position and the relative posture parameters of welding torch . Take the value of the direction vector of the central axis of welding torch as: tor = . Therefore, the front and rear tilt angle α of welding torch can be solved, as follows:The direction vector of the line α is generally expressed as an acute angle during the actual welding process. A positive value of α indicates the welding torch tilts forward relatively along welding direction, and a negative value of α indicates the welding torch tilts backward relatively along welding direction.Considering the front and rear tilt angle 2.Left and right tilt angle β of the welding torchtor of welding torch and the vector q2, which is simultaneously perpendicular to the normal vector m7 and the vector q1, can be used to characterize the left and right tilt angle β of welding torch relative to the welding groove. The vector q2 is:The relationship between the direction vector β of the welding torch is:Therefore, the left and right tilt angle β is generally expressed as an acute angle during the actual welding process. A positive value of β indicates the welding torch slants to the left relatively in the welding groove cross section, and a negative value of β indicates the welding torch slants to the right relatively in the welding groove cross section.At the same, considering the left and right tilt angle H0 and D0  are used in the solutions of the relative SPP parameters of the welding torch, the solutions of other parameters only depend on the internal parameters of the designed vision sensor, which effectively improve its detection applicability.According to the above derived detection algorithm of welding groove sizes and relative position and posture parameters of welding torch, in the process of practical application of the designed vision sensor based on combined laser structured lights, there is no need to preset special or fixed relative posture between the welding torch (or the vision sensor) and the welding groove, that is, it can be arbitrary. The whole process only requires the modulated laser lines can be imaged completely, and the integrated detection of the welding groove sizes and the relative SPP parameters of the welding torch can be realized. At the same time, it can be seen that the derived detection algorithms have few application restrictions and do not depend on other devices. Except that the sensor installation parameters H0, the object distance of the camera is controlled at 140–160 mm in the verification tests, which can ensure that the magnification of the camera is similar in the verification tests, and the resolution of the camera is about 0.03–0.04 (mm/pixel) in the width direction and 0.07–0.10 (mm/pixel) in the depth direction in this case. These three tests were used to verify the detection accuracy of welding groove size parameters  and the spatial position parameters  and posture parameters  of the welding torch relative to the welding groove separately.In order to verify the correctness of the internal parameter calibration method for the designed vision sensor proposed in this paper and the accuracy of calibration results, as well as the effectiveness of the above derived detection algorithms of welding groove sizes and the SPP parameters of the welding torch relative to the welding groove, some verification tests under three different conditions were carried out on the test platform shown in h, groove width b1 and b2, respectively. T1: In test 1, symmetric and asymmetric V-grooves were used to verify the effectiveness and accuracy of detection algorithms of groove depth During the detection test of T1, on the premise of ensuring that the modulated laser lines are completely located in the field of vision of the camera, the workpiece was placed on the platform in any position and posture. The feature extraction diagram of the laser lines image modulated by typical V-groove and the 3D reconstruction diagram of the welding groove surfaces and its adjacent workpiece surfaces are shown in From e, γ, H1) relative to the welding groove. In order to facilitate the setting of position parameters and test verification, this test placed the planar workpiece with V-groove vertically under the camera, that is, the upper surface of planar workpiece was perpendicular to the optical axis of the camera. Under this condition, the camera height H1 can be expressed as: H1 = |D1/C1|, and the relationship between the torch height H and the camera height H1 is shown in Equation (14). As shown in e, the rotary precision displacement platform was used to set the angular deviation γ and the vertical precision displacement platform was used to set the relative height H1 of the camera.T2: Since the central axis of the welding torch is coplanar with the optical axis of the CMOS camera and the central axes of two line laser emitters, and the central axis of the welding torch is parallel to the optical axis of the camera, the SPP of the camera relative to the welding groove can be used to characterize the SPP of the welding torch relative to the welding groove. In test 2, a symmetric V-groove was used in the detection test of the camera (characterizing welding torch) position parameters  relative to the welding groove. The dual-axis tilt sensor was placed on the upper surface of the planar workpiece to real-time display the posture parameter values of the planar workpiece.T3: In test 3, a symmetric V-groove was used in the detection test of the camera (characterizing welding torch) posture parameters  relative to the welding groove is not an ideal vertical condition, which leads to the coupling among these parameters, and the measured values cannot fully represent the actual values of the parameters in the detection process of the relative position and posture parameters of the camera (characterizing welding torch).h, b1, b2) as well as the SPP parameters  of the welding torch relative to the welding groove. Their detection accuracy can fully meet the needs of actual welding production, which indicates the algorithms can provide strong support for on-line detection and feedback control of intelligent welding production.In short, based on the processing of a single modulated laser lines image, the derived detection algorithms can effectively realize the integrated detection of the welding groove size parameters ((1)fx, fy, u0, v0, k1, k2 and laser structured light plane equation parameters of Al1, Bl1, Cl1, Dl1 and Al2, Bl2, Cl2, Dl2 in the camera coordinate system) of the vision sensor can be integrated, calibrated effectively by the proposed calibration method. This reduces the requirement for high installation accuracy of two laser transmitters to a great extent, avoids the influence of non-parallel error in two laser structured light projection planes on detection results and eliminates the cumulative error in stepwise calibration. Thus, the proposed integrated calibration method improves the efficiency, accuracy and comprehensiveness of internal parameter calibration for a line structured light vision sensor and provides a good foundation for industrial application of the vision sensor.For the specially designed vision sensor based on combined laser structured lights, an integrated calibration method of vision sensor internal parameters is proposed, which uses only an ordinary planar checkerboard calibration board. The internal parameters b1, b2 and groove depth h) of the planar workpiece and the SPP parameters  of the welding torch relative to the welding groove can be applied under any SPP of the welding torch (or vision sensor). Based on the 3D data of modulated laser lines obtained by the processing of the single modulated laser lines image, the algorithms reconstruct the 3D surfaces of V-groove surfaces and its adjacent surfaces of planar workpiece by data segmentation and plane fitting. This improves the utilization of modulated laser lines image data and reduces the interference of image processing error on the parameter detection.The derived high precision integrated detection algorithms for the V-groove size parameters (groove width (3)According to the proposed integrated calibration method and derived high precision integrated detection algorithms, some verification tests were carried out. The experimental results show that the derived integrated detection algorithms can be applied under any position and posture of the welding torch (or vision sensor) and has good detection accuracy and robustness, which improves the applicability of the vision sensor and the integration of detection algorithms. This work has important value for the application of the vision sensor in intelligent welding production.There are three main research works accomplished in this paper. Some valuable conclusions could be obtained as follows:


Unrestricted DFT calculations were carried out for benzene (BZ) adsorption at the BP level of theory using triple-zeta basis sets. The adsorption of BZ rings (stepwise from one to four) was found to result in noticeable gain in energy and stabilization of resulting adsorption complexes. Moreover, the interaction of metal clusters with HPS micropores was also addressed. For the first time, the incorporation of small clusters in the HPS structure was shown to influences its geometry resulting in the stabilization of polymer due to its partial relaxation.Among different polymers nanostructured cross-linked aromatics have the greatest potential as catalytic supports due to their exceptional thermal and chemical stability and preservation of the active phase morphology. This work studies the ability of hyper-cross-linked polystyrene (HPS) to stabilize small Pd The development of new hybrid catalytic systems is of high importance, since it can lead to significant increases in the efficiency of catalytic reactions. Active centers formed in a polymeric environment combine the advantages of both homogeneous  and heterogeneous (easy separation) catalysts. Depending on the types of catalytically active metal and of polymeric support, the catalytic properties of such systems can be readily tuned.0 nanoparticles (NPs) [Highly porous hyper-cross-linked aromatic polymers (HAP) presenting high mechanical, thermal and chemical stability are commercially available . Such ames (NPs) ,4,5,6,7 es (NPs) ,10,11,12es (NPs) ,14,15. Ies (NPs) ,17.During the last decades the interest of both the industrial and scientific communities in hyper-cross-linked polystyrene (HPS) has greatly increased. This is because of its advantages over traditional macroporous resins and activated carbons in terms of higher sorption capacity and easy regeneration . Due to n (n ≤ 13) clusters can be also formed by the XPS method on the HPS surface along with Pd0 NPs [0 NPs, even used for complex reactions such as Suzuki cross-coupling, which involve homogeneous stages in the catalytic cycle [n clusters formed during the preliminarily catalyst activation with gaseous hydrogen is still unclear: either they possess catalytic activity, or they just serve as a depot for active metal; either they influence the polymer properties , or remain inert in the HPS micropores. To answer these questions, first the strength of interaction needs to be found, i.e., the energy of the adsorption of the small metal clusters (Pd and Pt) on the aromatic rings of HPS. It is important to determine also the mutual influence of metal clusters and polymeric chains.Recently we have found that small Pd Pd0 NPs . It is wic cycle . Howevern clusters [n (n ≤ 9) clusters using the CAM-B3LYP functional joined with the LANL2-DZ basis set and found that hosting HPS deforms when Pd atoms are added to growing cluster. The balance between the Pd-C and the Pd-Pd interactions was shown to be crucial, and Pd4 is on the borderline. At the same time it was proposed that the benzene (BZ) rings of HPS allow stabilization of Pdn clusters, and small cavities in HPS structure affect both the geometric and the electronic properties of Pd clusters [The dynamic nature of catalytically active species has become evident in heterogeneous catalysis ,25,26,27clusters ,34,35,36clusters ,38,39. Dclusters  have calclusters . Neverth7 cluster the energy of BZ adsorption was found to be about 26–29 kcal/mol. It is noteworthy that B3LYP is the most popular density functional [4 cluster in a triplet state while using several available exchange-correlation functionals  and Pt4 using BP functional. As a result the adsorption energies of BZ rings on Pd and Pt clusters were calculated, helping in the analysis of real catalytic experiments over HAP impregnated by noble metals. It is noteworthy that some works has studied small Pd clusters confined in the HPS [4 and Pd4 stabilized into the HPS micropores was shown.This work studies the interaction of immobilized small Pd and Pt clusters with the HPS micropores, and the degree of micropore deformation during the cluster adsorption. Thus, for the first time, in the framework of this work we carried out DFT modeling of BZ adsorption (both free BZ rings and BZ of HPS micropore) on small clusters of Pd the HPS , but the4, Pd9, Pt4, and Pt9) and the adsorption of one, two, three and four BZ rings on them, as well as for the adsorption of these clusters in the model HPS micropore to study the stabilization of palladium and platinum clusters in aromatic polymeric networks.Unrestricted DFT calculations were carried out at the BP level of theory for small metal clusters . At the same time, the average <Pd-Pd> bond length increased by 1.9%  and 5.9% (in Pd4*3C6H6).The adsorption of the second and third BZ rings on Pd Pd atom b and als Pd atom c. In the Pd atom b and 2.7 Pd atom c for Pd4The adsorption of fourth BZ molecule led to an even greater expansion of the cluster: the average <Pd-Pd> bond length increased up to 2.77 Å, which is by 7.0% higher than for the free cluster. In this case, the disproportion between the average <Pd-Pd> distance of the vertex atom and <Pd-Pd> distance of the base practically disappeared.4 cluster with BZ rings caused partial transfer of electron density from palladium to carbon , which is quite close to the adsorption energy of three BZ molecules on a Pd4 in the polymer micropores as compared to the case of BZ adsorption on Pd4 was likely due to the limitations of the conformational mobility of the polymer linkages, which did not allow optimizing the geometry of the adsorption complex the same way as in the case of individual BZ rings.The slight decrease of the adsorption energy of Pd4 cluster, the methylene groups, indicated in 4 itself is much smaller than the pore and cannot fill completely the entire micropore. Incorporation of Pd4 resulted in the increase in the pore diameter (“AB”) by 4.6%, which is associated with the adaptation of the pore geometry during the coordination of BZ rings with the cluster. At the same time, the valence angles of covalent bonds of the methylene groups “A” and “ B” decreased from 115° and 118° to 112° and 111°, respectively, which indicates the decrease in pore tension . Significant changes in other distances (“BD” and “AD” by 3.4 and 2.1%) are associated with conformational changes that do not affect the valence angles of methylene groups.To determine the changes in geometry of the polymer micropores during the incorporation of Pd9 .Incorporation of Pd9 see  into the9 see . The ads4 and Pd9, there was a shift in the electron density from palladium to BZ rings of the polymer . The average <Pt-Pt> bond length increased by 1.5%  and to 2.65 Å (Pt4*3C6H6), which corresponded to an increase of the average <Pt-Pt> bond length by 4.6% and 2.1% as compared to free Pt4 cluster. The average <Pt-Pt> distance between the atoms of base was greater than those for free Pt4 cluster by 6.6% (in Pt4*2C6H6) and 2.3% (in Pt4*3C6H6). Such slight changes in <Pt-Pt> bond length in the case of Pt4*3C6H6 are likely due to the observed rearrangement of the ensemble.The adsorption of the second and third BZ rings on Pt Pt atom b similar clusters were carried out at the BP level of theory, which allowed obtaining refined values of the adsorption energies serving as starting point for calculation of metal clusters interaction with the micropore of the aromatic polymer (HPS). It was shown that Pd4 and Pt4 clusters can be effectively stabilized due to the adsorption of BZ rings. Such adsorption leads to geometric distortions of the metal clusters as well as to charge redistribution between the clusters to their chemical environment. It was found for the first time that charge transfer in the case of palladium and platinum has contrary direction: in the case of Pd4 the partial transfer of electron density from the metal to carbon takes place, while in the case of Pt4, in contrary, the partial transfer of electron density occurs from carbon atoms of BZ rings to the metal. It is noteworthy that interaction of Pt4 cluster with BZ rings was stronger and caused higher gain in energy (the energy of adsorption complex Pt4*3C6H6 was −273.8 kJ/mol) as compared to Pd4 (the energy of adsorption complex Pd4*3C6H6 was −256.6 kJ/mol).DFT calculations of BZ adsorption (from one up to four rings) on the small Pd4 and Pt4) were found to be effectively stabilized in a model micropore of the polymer (HPS) calculated at the same level of theory. It was shown that the metal clusters can be captured by the HPS micropores and retained with a significant  adsorption energies. The adsorption energy of Pd4 in the HPS micropore was −237.3 kJ/mol, and the increase of Pdn cluster size up to n = 9 (Pd9) resulted in the energy gain (−341.2 kJ/mol), which confirms that the growth of small metal clusters in the aromatic polymeric environment is the energy beneficial process accompanied by the adaptation of clusters’ geometry to the micropore structure. At the same time, in the case of platinum the interaction of Pt atoms with the BZ rings of the HPS micropore is so strong that it causes noticeable distortion of clusters’ geometry even for Pt4.Clusters of both types  tension. Thus one can conclude that the noble metal clusters act as micropore stabilizers, which may prevent the polymer destruction during significant vibrations and pulsations of its chains. The latter can take place during high-temperature treatment of polymer-based composites. Such a treatment is commonly applied in heterogeneous catalysis as a part of the activation procedure, i.e., gas-phase reduction of catalysts in a hydrogen flow. Though it is not possible to directly assess small metal clusters buried in the pores of HAP, the results obtained in this study can be useful to explain behavior of real catalytic systems exposed to high-temperatures.4 and Pt4 clusters in the HPS polymeric network can lead to their deposition in micropores or small mesopores, which can have a strong impact on catalytic properties of such systems. While being stabilized in aromatic environment, small metal clusters can be inaccessible for reagents during heterogeneous catalytic reactions or alternatively they can play a role of buffer for catalytically active metal species. The interaction of metal clusters incorporated into micropores of HAPs with different catalytic substrates should be further studied and this work is in progress.Finally, it can be concluded that the high adsorption ability of Pd4 cluster in the triplet state. The adsorption energy was calculated by the unrestricted methods of Hartree-Fock, perturbation theory, as well as the DFT using B3LYP, PBE and BP functionals both with and without the ZORA formalism. The effect of the empirical van der Waals correction on the adsorption energy in the case of DFT was also tested. The results of calculations are presented in The search for the level of theory for calculations was performed on the basis of calculations of the energy of BZ adsorption on PdABnE is the energy of adsorption complex; AE is the metal cluster energy; BE is the energy of BZ.All the DFT calculations were unrestricted and were performed using Orca 4.2.1 ,49 packan and Ptn (n = 4 or 9) was calculated at the BP level of theory. The adsorption of arenes  was previously studied in many works, but we obtained the adsorption energies for the metal cluster interactions with the polymer (HPS) chains at the same level of theory. For the purpose of catalysis the calculation of adsorption energies of noble metal clusters is of great importance as it can serve as a starting point for further calculations of reactants’ adsorption and the migration of noble metals within polymeric networks. We have confirmed that the B3LYP functional, which was used in most previous works, not always provides accurate calculations in comparison with BP and PBE functionals. Thus, using the BP functional we compared for the first time the adsorption of BZ molecules on clusters of palladium and platinum, which allowed us to conclude that these metals behave differently in an aromatic environment, i.e., opposite charge transfer directions were found. Moreover, Pt4 clusters were found to undergo dramatic changes in geometry when encapsulated in the micropores of HPS. The stabilization of the polymer micropores by Pt and Pd clusters was also revealed for the first time, which represents a very important result for the preparation of heterogeneous polymer-based catalysts.In the framework of this study, BZ adsorption on small clusters of Pd