This study investigates the relationships between the hydration kinetics of waterglass-activated slag and the development of its physical-mechanical properties, as well as its color change. To modify the calorimetric response of alkali-activated slag, hexylene glycol was selected from various alcohols for in-depth experiments. In presence of hexylene glycol, the formation of initial reaction products was restricted to the slag surface, which drastically slowed down the further consumption of dissolved species and slag dissolution and consequently delayed the bulk hydration of the waterglass-activated slag by several days. This allowed to show that the corresponding calorimetric peak is directly related to the rapid evolution of the microstructure and physical-mechanical parameters and to the onset of a blue/green color change recorded as a time-lapse video. Workability loss was correlated with the first half of the second calorimetric peak, while the most rapid increase in strengths and autogenous shrinkage was related to the third calorimetric peak. Ultrasonic pulse velocity increased considerably during both the second and third calorimetric peak. Despite the modified morphology of the initial reaction products, the prolonged induction period, and the slightly reduced degree of hydration induced by hexylene glycol, the overall mechanism of alkaline activation remained unchanged in the long-term perspective. It was hypothesized that the main issue of the use of organic admixtures in alkali-activated systems is the destabilizing effect of these admixtures on soluble silicates introduced into the system with an activator. Alkali-activated slag (AAS) belongs to the wide and interesting group of alkali-activated materials (AAMs) and has a considerable potential to contribute to the sustainability of construction materials . For the2+ and Mg2+ are preferentially released into the solution followed by aluminate species and silicates [In general, the course of alkaline activation consists of several stages, of which the starting stage is the dissolution of the glassy phase. Song and Jennings  stated tilicates . After silicates . C-A-S-Hilicates ,5,6.2CO3 and liquid sodium silicates (waterglasses), a new additional initial peak commonly emerges in the pre-induction period due to the reactions of anionic groups of these activators. After this peak, both of these activators exhibit a significant induction period, after which the third peak of acceleration/deceleration hydration occurs, showing the formation of a C-A-S-H gel [The type and quantity of hydration products depend on several factors, such as slag and activator composition, activator dose or concentration, and temperature. Powerful tools for monitoring the hydration kinetics are isothermal calorimeters. In the 1990s, Shi and Day  describe-S-H gel ,10,11.In addition to the composition of the activator and precursor or temperature ,12, the However, a comprehensive overview of the relationships between the AAS hydration rate and the evolution of its properties from the fresh to well-hardened state supported by investigations of phase assemblage and microstructure, and its color change as well, is missing. Consequently, this is the main objective of this paper. Because different stages of AAS hydration usually more or less overlap, it is necessary to separate them to clearly observe the associated effects. Unlike other papers, in which SRAs are applied to reduce AAS shrinkage ,26,27, w2/kg was used. According to the X-ray diffraction method with Rietveld analysis using Empyrean diffractometer , it consisted mainly of an amorphous phase (~87 wt. %) and small amounts of calcite, merwinite, and akermanite. Calcium fluoride was used as an internal standard. Its chemical composition determined using X-ray fluorescence is given in 2O and 30.7 wt. % SiO2 (silicate modulus of approximately 2.0) diluted with water to prepare the stock solution prior to mixing.Ground granulated blast furnace slag with a Blaine fineness of about 400 m2O with respect to the weight of the slag and the water-to-slag ratio  was 0.35. Such plain AAS paste was modified by hexylene glycol  at the doses of 0.5, 1.0 and 2.0% with respect to the slag weight. The hexylene glycol dose was used for designation of the pastes, i.e., Hex-0 for the plain AAS paste without hexylene glycol, while Hex-0.5, Hex-1.0, and Hex-2.0 were used for pastes containing hexylene glycol. Hexylene glycol was added to the activating solution and briefly homogenized just before mixing the AAS paste.The dose of stock waterglass solution was adjusted to 4% NaThe mixing of the pastes was carried out using a common hand mixer and a beaker in an air-conditioned laboratory at 25 °C. The time of adding the slag to the activating solution was taken as time zero, after which the mixing process started. The total mixing time was three minutes and consisted of four intervals: First 60 s of slow mixing, then 30 s at maximum mixing rate, 30 s rest for scraping the paste down into the batch, and finally 60 s at maximum mixing rate. The pastes prepared in this manner were then used for the tests described in the following sections.Hydration of AAS was monitored using a TAM Air 8-channel calorimeter (TA Instruments) at 25 °C for 28 days. For the measurement, the amount of paste corresponding to 4.0 g of slag was placed in a 20 mL PE vial, tightly sealed with a screw cap, and inserted into the calorimeter. Recording of heat flow started five minutes after the start of the mixing. The vials with water were used as a reference.2O with respect to the slag weight.The initial and final setting time was tested using a common Vicat needle with a diameter of 1.13 mm. Measurement was carried out on the basis of EN 196-3  but it wMonitoring of changes in ultrasonic pulse velocity (UPV) during the ongoing hydration was carried out using a Vikasonic device  working with a frequency pulse of 54 kHz. The measuring cell had the same shape as the ring for the setting time determination using a Vicat needle (EN 196-3) with probes mounted in the lower and upper base of the path length of 40 mm. UPV was automatically calculated from the path length and transit time. The surfaces of the probes were coated with couplant grease and separation foil to prevent direct contact of the probes with the measured material. UPV values were recorded in one-minute intervals from the start of the measurement in the fresh state for as long as possible or until the signal was disturbed.V (in volume percent) was calculated using the Equation (1), where t(ini)m and tm are the weight of the specimen immersed in oil at the initial time to which shrinkage is related  and the weight at a given time of measurement, respectively, and airm is the weight of the sample before immersion into the oil.Autogenous dimensional changes were determined using the hydrostatic weighing method at 25 °C controlled by a circulating thermostat. The paste was filled into a polyurethane condom and then approximately 90 g of the paste was tied with a fishing line and hung on an analytical balance with readability of 0.1 mg. First, the specimen weight in air was recorded, then the specimen was immersed in paraffin oil and its weight was automatically recorded in one-minute intervals for at least 28 days. The relative volume change ΔThe compressive strength was determined on cubic specimens with nominal edge size of 25 mm using the DESTTEST 4310 Compact A . After mixing, the pastes were cast into the molds and sealed with polyethylene plastic bags for 23 h, after which they were demolded and tested to obtain 24-h compressive strength or sealed with stretch foil and stored in a chamber at 25 °C until the time of testing, i.e., 2, 3, 7, 10, 14, 21, and 28 days. At each age, 2 to 3 specimens of each paste’s composition were used.For the isolation of the pore solution, fresh AAS pastes were cast into the cylindrical plastic molds with a diameter of 52 mm and a height of 60 mm with inserted stretch foil to prevent them from drying and carbonation as well as to facilitate its demolding. The pore solution of the fresh pastes was obtained by the vacuum filtration using a Büchner funnel. Later, when the paste stiffened and set, the foil was removed from the samples and they were placed into a steel mold to press the pore solution out using the hydraulic device DESTTEST 4310 Compact A . The obtained pore solution was place in a syringe equipped with a nylon filter with a pore diameter of 0.45 μm and immediately diluted in a plastic volumetric flask using demineralized water. The concentration of the selected elements was determined by an optical emission spectrometer with inductively coupled plasma (ICP-OES) Horriba Jobin Yvone, type Ultima 2. The sample was transported using a peristaltic pump into a nebulizer and further carried by argon into a torch. The elemental concentration of the pore solution was automatically calculated on the basis of the calibration curves for standard solutions and the background represented by demineralized water.To record a well-known but still not fully understood blue/green color change in AAS pastes during hydration, samples of the pastes containing 0, 0.5, 1, and 2% of hexylene glycol were filled into the plastic cuvettes with a square cross-section of 10 mm × 10 mm and a height of 50 mm. To prevent drying and air reactions, the surfaces were covered with a paraffin oil. Photos of these AAS paste-containing cuvettes were continuously captured for 28 days every 30 min using a digital camera equipped with a macro lens and connected to a computer. This experiment was carried out in an air-conditioned room at 25 °C. The obtained photographs were then automatically processed in MATLAB software to separate out only the areas of the samples as illustrated in The microstructure and formation of the hydration products were assessed using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray powder diffraction (XRD). Before these analyses, the hydration reactions were stopped using the solvent exchange method by immersing the broken parts (SEM) and crushed paste  in the isopropyl alcohol and then drying at 40 °C. SEM was performed on both fracture surfaces and ionic polished samples embedded in the epoxy resin. Fracture surfaces were observed using EVO LS 10  with an accelerating voltage of 10 kV in the mode of secondary electrons, while polished surfaces were observed using JSM-7600F  with an accelerating voltage of 5 kV in the mode of backscattered electrons. The TGA was carried out in air atmosphere with a flow rate of 100 mL/min with a temperature ramp of 10 °C/min from 40 to 990 °C using a Q600 SDT analyzer . XRD was carried out using Empyrean diffractometer  in the Bragg–Brentano configuration at the range of 5–90° 2θ with step of 0.013° 2θ. An X-ray tube with copper anode, a voltage of 40 kV, and a current of 30 mA was used. Data were evaluated using HighScore Plus software.The effect of hexylene glycol dose on the AAS hydration is given in It is worth noting that similar effect can be reached by other various organic additives, as clearly illustrated in It should be noted that although hexylene glycol greatly retarded the hydration of AAS, it did not dramatically change its overall mechanism from the long-term perspective. This is suggested by the evolution in cumulative heat and further supported by XRD after 28 days  and by tThe only notable difference in the XRD patters is a different intensity below 8° 2θ, i.e., in the region corresponding to the presence of C-A-S-H ,38. For Thermogravimetry , Table 2Weight loss in the range of 550–990 °C was not significantly affected by hexylene glycol , Table 22O) with a somewhat lower silicate modulus (1.2) was used to activate the mixture of slag/fly (50/50).Despite its great effect on AAS hydration, hexylene glycol did not have significant effects on initial and final setting times determined using the Vicat needle, as it remained around 35 and 55 min, respectively, regardless of the dose of hexylene glycol. At the same time, the onset of UPV was not affected by the presence of hexylene glycol, while its subsequent increase slowed slightly. All these phenomena correlated well with the pore solution composition, which was roughly the same for the Hex-0 and Hex-2 pastes during the first 30 min: The concentrations of Si and Na introduced into the system mainly within the activator remained around 3 M, while the concentrations of other elements originating from the dissolving slag, such as Ca, Mg, and Al, gradually increased. The maximum concentration of Ca was around 0.4 M irrespective of the presence of hexylene glycol, while the maximum concentrations of Mg and Al were lower by approximately one order of magnitude compared to those of Ca. After the first 30 to 45 min, the concentrations of all elements started to drop quite rapidly, particularly for the reference Hex-0 paste; and until the 118th minute, they decreased as much as by an order of magnitude compared to their maximum values. The determined values as well as the observed trend of the evolution of the pore solution composition over time correlate well with a recently published study , in whicIn the case of the reference paste, the simultaneous sharp increase in mechanical properties and high concentrations of elements originating from slag in the pore solution after 30 to 45 min point to the concurrent formation of new solid products and the continuing dissolution of slag. At later stages, the dissolved species are consumed rapidly. Considerably slowed down consumption of dissolved species was observed for Hex-2 paste, which correlates with less heat generated at this stage and with a slightly slower increase in the UPV velocity.This behavior is likely related to the effect of hexylene glycol on microstructural evolution in these very early stages of reaction, as shown in In contrast to the reference, no bulk matrix can be found in the paste with 2% hexlyene glycol B,D. InstThe darker gray shade of the formed layer compared to that of the slag particle means that the former is composed of lighter elements. The more detailed image given in In-depth investigations of the role of hexylene glycol during the gelation stages of AAS hydration are beyond the scope of this paper. Nevertheless, we propose that it is closely related to the action of the organic substances in the activating solution. In our previous article , the decThe third calorimetric peak is mainly associated with the acceleration/deceleration stage of hydration ,8, the mThe presented results of autogenous shrinkage in context with the bulk hydration peak on the calorimetric curves are in agreement with those of recent studies of Li et al. ,22. A siThe quality of curing can be partially indicated by the level of fascinating blue/green coloration of the slag-containing binders. However, it can be an aesthetic issue in many applications . The pheThe exact origin of the coloration has not yet been fully understood. It is most often explained by the progressive oxidation of sulfide-based compounds present in slag under anoxic conditions to di-, tri-, and tetrasulfur radical anions ,47. SulfThe presented findings clearly show that the timing of the onset of the color change can be tailored by the nature and dose of an organic additive, which could be exploited for further investigations of this interesting phenomenon. In practical applications, the coloration indicates that the products are at least partially mature, able to be safely demolded, transported, etc. It should be emphasized that the absolute values of the strengths can be very different for different activators despite the similar color of the specimens due to the different nature of the reaction products .2O with respect to the slag weight), modified by varying the dose and type of organic admixture, especially hexylene glycol. Based on the results presented in this paper and their discussion with the literature, the following conclusions can be drawn:Hexylene glycol and many other organic admixtures significantly affected the hydration process of AAS by means of decreased intensity of the gelation peak and simultaneously prolonged induction period, i.e., retardation of the bulk hydration peak.The position of the second peak is crucial for the timing of setting or loss of workability, while the intensity of this peak is related to the initial compressive strengths that reach a few to several MPa. After that, the hydration goes into the induction period of minimized heat flow and can last several days or even weeks depending on the type and dose of the organic admixture.The bulk hydration peak perfectly correlates with a great increase in strength, autogenous shrinkage, and ultrasonic pulse velocity, as well as with the onset of a fascinating color change from off-white to green/blue, characteristic for AAS. Therefore, the color change is a very simple and practical indicator of the bulk hydration of the slag and thus tells something about the maturity of the material, particularly under anoxic conditions.The influence of organic additives lies in their action during the gelation stages of hydration. Hexylene glycol caused the formation of a few hundred nanometer thick layer of silicate-based gel on the slag surface instead of the formation of the continuous matrix in the bulk of the interstitial space, typical for waterglass-activated slag without additives. This seems to be the main reason for both the decreased rate of slag dissolution and the decrease in the formation of reaction products, resulting in a prolonged induction period.Despite the strong retardation effect of hexylene glycol on AAS hydration, the overall reaction process did not change distinctively. No new crystalline phase nor the significant changes observed by XRD and thermogravimetry, respectively. The latter only confirmed the retardation effect by very low and almost the same weight losses of the paste with hexylene glycol after 24 h and 7 days.This paper summarizes the relationships between the evolution of various properties and calorimetric responses of slag activated with waterglass with a relatively high silicate modulus (2.0) and an intermediate dose (4% NaThe physical and chemical changes over the first weeks of AAS hydration were comprehensively summarized. This allows estimations of the behavior of from the very early setting stage to long-term hardening stage based on its calorimetric response or potentially based on the evolution internal temperature. Although the changes in hydration rate were intentionally carried out using intentional addition of hexylene glycol, further research on the interactions between organic substances and AAS is necessary, especially during the gelation stage, to fully understand the observed phenomena. This would promote the tailoring of organic admixtures for alkali-activated systems. At the same time, this paper can serve as a starting point for an in-depth study of the origin of color change and other not fully understood phenomena related to the hydration of AAS.


This study aimed to establish a learning system using an artificial neural network (ANN) to predict the effects of vitamin D supplementation on the serum levels of vitamin D, inflammatory factors, and total antioxidant capacity (TAC) in women with breast cancer.3 supplement therapy. A prediction ANN model was designed to detect the effects of vitamin D3 supplementation on the serum level changes of vitamin D, inflammatory factors and TAC.The data set of the current project was created from women with breast cancer who were referred to the Shafa State Hospital of Patients with Cancers in Ahvaz city, Iran. Modeling was implemented using the data set at the serum levels of vitamin D, tumor necrosis factor-α (TNF-α), transforming growth factor β (TGF-β), and TAC, before and after vitamin D3 supplementation on the serum level changes of vitamin D, TNF-α, TGF-β1, and TAC with an accuracy average of 85%, 40%, 89.5%, and 88.1%, respectively.The results showed that the ANN model could predict the effect of vitamin D3 supplementation on the serum levels of vitamin D, TNF-α, TGF-β1, and TAC. The results showed that the proposed ANN method can help specialists to improve the treatment process more confidently in terms of time and accuracy of predicting the influence of vitamin D supplementation on the factors affecting the progression of breast cancer (https://www.irct.ir/ identifier: IRCT2015090623924N1).According to the findings of the study, the ANN method could accurately predict the effect of vitamin D Breast cancer is a common disease and depends on several factors such as female gender, aging, family history of breast cancer, premature menstruation, belatedly menopause, advanced age at first live birth, genetic mutation, poor diet, obesity, smoking, alcohol consumption, and sedentary lifestyle, removal of the uterus, the experience of fibrocystic diseases and cancer of the uterus or ovaries and radiation exposure to the chest area , 2. ReceOxidative stress and inflammation can cause and advance cancers , 7. In cversus cancer by various mechanisms, such as anti-inflammatory effects and reducing the production of inflammatory cytokines [Many breast cancer patients are deficient in serum vitamin D, and vitamin D deficiency is associated with the progression of malignancies and increased mortality , 12, 13.ytokines , 15. Stuytokines . Vitaminytokines . The antytokines . The VDRytokines . Numerouytokines , 20, 21.ytokines  examinedBreast cancer is a costly disease for patients, and the faster diagnosis of factors affecting the improvement of the treatment process helps to reduce mortality and recuperative patients’ quality of life , 22. TheRecently, with the advancement of machine learning systems, the use of these techniques in medical predictions and diagnoses related to breast cancer has been enhanced . The artAn artificial neural network (ANN) is a common method of machine learning systems. ANNs are simulated based on the human brain, which uses the connection of artificial neurons through artificial synapses to understand input information . The ANNANN models showed suitable performance in predicting the effect of supplement therapy whereas in those studies, the experimental information and treatment intervention results were obtained from the clinical trial studies , 30, 31.3 supplementation in the recovery of breast cancer patients based on machine learning algorithms. Therefore, the main purpose of this study was to predict the effect of vitamin D3 supplementation on the serum level changes of vitamin D, inflammatory factors , and TAC using an ANN.As far as we know, no study has focused on predicting the effect of vitamin D3 supplements received 50,000 IU/week , for a period of 8 weeks, on the serum levels of vitamin D, TNF-α, TGF-β1, and TAC. In this study, research factors were obtained from women with breast cancer who were referred to the Shafa Specialty Cancer Diseases Hospital in Ahvaz, Khouzestan, the center of the southwest state of Iran [This analytical study was conducted to predict the effect of vitamin D of Iran . Blood sThe baseline characteristics of the clinical trial participants in this study are shown in 3 supplementation are indicated in The mean serum levels of vitamin D, TNF-α, TGF-β1 and TAC before and after vitamin D3 supplementation. Moreover, patients’ demographic, anthropometric, and dietary information as the main features for supplement therapy in patients with breast cancer were selected [The prediction model was developed using the breast cancer serum biomarkers including serum levels of vitamin D, TNF-α, TGF-β1, and TAC before and after vitamin Dselected . GeneralAn ANN framework is composed of several layers, i.e., the input layers, the hidden layers, and the output layers. ANNs have various kinds, such as multilayer perceptron (MLP), radial basis function, Hopfield, etc. In this study, the MLP neural network, which is a common type of neural network, was used to discover the relationship between input and output variables. To implement the proposed model, Matrix Laboratory (MATLAB) software version 2015b was used. The proposed model simulated the process of supplement therapy to predict the behavior of vitamin D in terms of its impact on the mentioned factors among women with breast cancer.Modeling was performed with the Neural Net Fitting Section of MATLAB environment. The data were entered consisting of four input data related to before the intervention and one output data related to after the intervention in four-stage separately. The structure of the neural network used is presented in The different architectures of the ANN were evaluated with diverse segmentation of data, disparate number of neurons and various training algorithms to achieve an optimal structure of the predictor model.The data was divided into three forms: training, validation, and testing to adjust the network, measure the progress of the general network and evaluate the independent performance of the network, respectively. The disparate numbers of neurons were optimized by trial and error, including 5, 8, 10, 12, and 15 in various layers size. Moreover, the various training algorithms such as Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient were also investigated to optimize the model performance.Due to the limited number of available samples, the method of rotational placement was used with repetition in experiments of the proposed model to validate the model. Every designed model was evaluated 10 times using the training dataset. Moreover, the accuracy averages of all 10 runs in each pattern were calculated separately to specify the highest accuracy.The performance of the ANNs models was evaluated using the mean squared error (MSE) through Equation 1. The MSE function was used to determine the error values in which the computing of three factors, including system output error, the square of the system error, and the mean of all the squared errors are possible.The function formula is given below (Equation 1):i is equivalent to the observed value, yi is the corresponding predicted value for fi, and N is the number of observations [Where frvations .In total, all experiments of the proposed models used 188 data (including 47 subjects in four experiments) to train, validate, and test phases using the random repetitive spin process to evaluate the models’ performance for serum levels of output factors in four predictors. Moreover, every predictor was run 10 times using the training dataset to evaluate the reliability of the model. After implementing and examining different models in a computer simulation, the optimal model with 10 neurons in the hidden layers with the Bayesian Regularization training algorithm was determined to predict the effect of the vitamin D supplementation.The average accuracy rates obtained from 10 repetitions in the proposed model to predict the effects of vitamin D on four variables are presented in The performance of neural network in the training procedure to reduce gradually the estimated error level is shown in 3 supplementation on the serum level changes of vitamin D, TNF-α, TGF-β1, and TAC in patients with breast cancer by ANN method in MATLAB environment. The results of this research have confirmed the precision of the ANN method to predict the effect of vitamin D supplementation on the variables. Nowadays, to improve the accuracy and speed of the patient’s treatment process, attention through the use of ANN techniques has increased. This project has been carried out in order to predict the effect of vitamin D supplementation on factors affecting breast cancer progression using the MLP neural network method, the most common and simplest type of neural network.This study was conducted to predict the effect of vitamin DIn our study, finding the best model for predicting the effect of vitamin D supplementation on the studied factors was a challenge. For this reason, the rotational placement and repetition methods were used in experiments related to modeling training, and different modes were brought together. The results of the study showed that the model can appropriately predict the effect of vitamin D supplementation on variables except for TNF-α.3 [Oxidative stress means an imbalance in the systems of oxidants and antioxidants, and inflammation can be the result of oxidative stress in the body. Oxidative stress and inflammation suppress the immune system and cause the formation and growth of tumors and thus the progression of cancer , 34. Vit3 , 36. Vit3 . Vitamin3 . VDRs ha3 , 40. Vit3 .An inverse relationship between the serum level of vitamin D and the development and progression of breast cancer has been shown; indeed, the reduction of the serum vitamin D level leads to an increase in the risk of relapse and premature death of these patients . SeveralIn accordance with the above studies on the anti-inflammatory and antioxidant effects of vitamin D on the serum levels of vitamin D, inflammatory factors, and TAC in the treatment of cancer and due to the effect of increasing TNF-α and TGF-β1 , 44 and 3 supplementation on serum levels of vitamin D, TGF-β1, and TAC with high accuracy including 85.4%, 89.5%, and 88.1%, respectively. The average accuracy of the developed model for the TNF-α variable was 40%, which showed that there was not a significant correlation between the input variables and the TNF-α changes in the patient with vitamin D supplementation therapy.The application of artificial intelligence methods and clinical decision-making algorithms can accelerate the process of analyzing information related to patients, interpreting data, and predicting treatment results , 48, whiRecently, studies have also been conducted to predict the effects of supplements using the ANN methods , 30, 31.Hosseini et al.  also conIn another study, Allahyari et al.  examinedIn agreement with our project, studies have confirmed that machine learning methods such as ANN can predict the effect of different supplements on diseases. The application of these methods can help patients by saving time and economizing the costs during the periods of treatment. In general, studies have shown that we can use artificial intelligence algorithms instead of implementing old computational methods, to improve the performance of models in data analysis and understand patterns in them . In the In the present study, the ANN was used to predict the effect of vitamin D supplementation on serum levels of vitamin D, inflammatory markers, and TAC in women with breast cancer. Given that the considered factors are effective in the progression of breast cancer, the results of this research can help to meliorate the treatment process of patients.These techniques can help specialists improve the treatment procedure by predicting the effect of vitamin D supplementation on the factors affecting the progression of breast cancer in different people to prescribe vitamin D supplementation with greater confidence.As study limitations, an exploration of more alternative machine learning approaches warrants consideration. Incorporating more data from diverse centers could augment result quality and real-world relevance. Furthermore, independent evaluation of treatment methodologies and their impacts on patients with cancer, alongside a deeper probe into drug effects through machine learning, beckon as promising avenues for future research in this domain.


The fourth year of the COVID-19 pandemic without decreasing trends in the global numbers of new daily cases, high numbers of circulating SARS-CoV-2 variants and re-infections together with pessimistic predictions for the Omicron wave duration force studies about the endemic stage of the disease. The global trends were illustrated with the use the accumulated numbers of laboratory-confirmed COVID-19 cases and deaths, the percentages of fully vaccinated people and boosters , and the results of calculation of the effective reproduction number provided by Johns Hopkins University. A new modified SIR model with re-infections was proposed and analyzed. The estimated parameters of equilibrium show that the global numbers of new daily cases will range between 300 thousand and one million, daily deaths—between one and 3.3 thousand. To illustrate the global trends, we will use the accumulated numbers of laboratory-confirmed COVID-19 cases Vj and deaths Dj, the percentages of fully vaccinated people VCj and boosters BCj, and the results of calculation of the effective reproduction number Rj10. Information corresponding to day tj  is available in COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)8. We will use the version the JHU file updated on December 7, 2022 and the smoothed daily and annual characteristics11 , infectious I(t) and removed persons R(t) over time t and can be successfully used for simulations of the first COVID-19 waves in individual countries and worldwide20. Nevertheless, this model yields only trivial equilibrium point 24 do not take into account re-infections. In this paper we will modify the classical SIR model by adding the re-infection terms, calculate a non-trivial equilibrium point, and estimate its stability and endemic characteristics of SARS-CoV-2 infection.The classical SIR-modelVCj and BCj , Rj values (magenta) and results of calculations of smoothed numbers of new daily cases DVi, deaths DDi, and the case fatality risk CFRi = DDi/DVi listed in Table 10 was close to the critical value 1.028. In 2022 we see the a sufficient drop in CFI figures.The solid lines in Fig. DV, DD, CFI for every year of the pandemic with the use of accumulated characteristics on January 22 and December 31, 2020; January 1 and December 31, 2021; and January 1 and December 6, 2022. The results are listed in Table To estimate the long term trends we calculated average values of DV values approximately doubled every year, the DD figure in 2021 was 1.8 times higher than in 2020. But in 2022 we see a sufficient decrease in mortality and much lower CFR values. This can be explained as a positive effect of vaccinations , lower pathogenicity of the Omicron strain (which began to spread widely at the end of 202130), as well as the influence of natural immunity24.The 17, which relates the numbers of susceptible S(t), infectious I(t) and removed persons R(t) over time t:To simulate the re-infections, we add terms S(t), which includes the persons who are sensitive to the pathogen, is increasing with rate Now compartment s in Eq. .31 to simulate and predict different waves of COVID-19 pandemic in different countries and worldwide.Parameters 34The set of differential Eqs. –3) has  has 3) hDV* must be equal to the daily number of new infections; to the sum of immunized, isolated and dead persons; and to the number of persons who have lost their immunity and moved from compartment R to S, i.e.:At the equilibrium, the daily number of new cases 34 of set (The Jacobian matrix4 of set –3) can  can 34 oTaking into account , the eigEigenvalues DV value for 2022 (around 1 million), presented in Table DVi values illustrated by the blue solid line and non-linear correlation approach35, which yields the best fitting curveCFR value from the Table 7. Calculating the average daily characteristics, we obtain approximately 400,000–464,000 new daily cases and 1857–1893 daily deaths. These figures are consistent with the given theoretical estimates of SARS-CoV-2 endemic characteristics.To estimate the endemic global number of new daily cases we can use the average 2 around  million,2 around  million,37. To estimate the global visibility coefficient let us take the number of cases per million 72,524 registered worldwide as of August 1, 20228. On the same day, the average value was 460,834 for European countries with high enough testing level (more than 3 tests per capita)35. It means that the real numbers of cases can be approximately 6 times higher than registered figures. In the future, reducing the testing level can lead to lower numbers of detected cases and deaths.It is necessary to emphasize that the presented estimates refer to the laboratory confirmed cases only. Due to the high numbers of asymptomatic patients and insufficient testing level, the real number of SARS-CoV-2 cases is much higher38 began to be lifted only after November 30, 202239. Very high numbers of new daily cases in December 2022 reported by the media40 do not look reliable41, nevertheless it is better to use the presented estimations excluding the Chinese datasets.Other restrictions on the accuracy of the given estimates may be related to the situation in mainland China, where the Zero-COVID policy42). According to the presented estimations, the annual mortality caused by SARS-CoV-2 will range from 365 thousand to 1.2 million. Unfortunately, these figures are higher than in the case of seasonal influenza  global daily numbers of SARS-CoV-2 cases and related deaths. In different countries and regions, these characteristics are different and need special investigations. To take into account the influence of other factors , more complicated mathematical models can be used.11:Since numbers of new cases and deaths are random and characterized by some weekly periodicity, the use of the smoothed characteristics is recommended8 based on the values corresponding to the fixed and previous 6 days. To estimate the smoothed numbers of new daily cases DVi, deaths DDi, and the case fatality risk CFRi =DDi/DVi, the numerical derivatives of the smoothed values (11:Some different smoothing procedure is used inThe results of calculations are listed in Table No humans or human data was used during this study.


Two types of membranes have been developed: dense membranes for pervaporation and porous membranes for ultrafiltration. The optimal content of nanoparticles in the PVDF matrix was selected: 0.3 wt% for porous membranes and 0.5 wt% for dense ones. The structural and physicochemical properties of the developed membranes were studied using FTIR spectroscopy, thermogravimetric analysis, scanning electron and atomic force microscopies, and measuring of contact angles. In addition, the molecular dynamics simulation of PVDF and the TiO2 system was applied. The transport properties and cleaning ability under ultraviolet irradiation of porous membranes were studied by ultrafiltration of a bovine serum albumin solution. The transport properties of dense membranes were tested in pervaporation separation of a water/isopropanol mixture. It was found that membranes with the optimal transport properties are as follows: the dense membrane modified with 0.5 wt% GO-TiO2 and the porous membrane modified with 0.3 wt% MWCNT/TiO2 and Ag-TiO2.Membrane technology is an actively developing area of modern societies; with the help of high-performance membranes, it is possible to separate various mixtures for many industrial tasks. The objective of this study was to develop novel effective membranes based on poly(vinylidene fluoride) (PVDF) by its modification with various nanoparticles (TiO The concentration of [TiO2] in the polymer was 4.2 wt%.To study the composite PVDF- nanoparticle, water molecules are assembled into clusters (up to 10–50 molecules). This is due to the fact that water molecules do not form hydrogen bonds with the polymer but interact with each other and with isopropanol (IPA) molecules. To illustrate this fact, the number of hydrogen bonds H2O–H2O and IPA–H2O in From the simulation data, it was found that in a system without a [TiO2O   .Thus, it can be concluded that the addition of a nanoparticle to the polymer matrix will lead to an increase in the hydrophilicity of the system, and, consequently, it will increase the permeability of water through the polymer membrane. This result is in qualitative agreement with the results of transport characteristics in the pervaporation  and ultr2, Ag-TiO2, GO-TiO2, and MWCNT/TiO2), and the method of membrane preparation (non-solvent induced phase separation (NIPS) for porous membranes and the evaporation induced phase inversion method (EIPS) for dense membranes) on the transport, structural, and physicochemical characteristics of membranes were studied.In the present study, the influence of various modifiers possessed photocatalytic properties  mixture. Additionally, molecular dynamics simulation of PVDF and the TiO2 system demonstrated that the addition of a modifier to the polymer matrix led to an increase in the hydrophilicity of the system and, as a consequence, to the increase in the permeability of water through the polymer membrane that was confirmed by data obtained in pervaporation in ultrafiltration.Porous membranes were developed for purification of pollutants from aqua solutions in ultrafiltration using the BSA solution as an example. The introduction of 0.3 wt% of modifiers led to an improvement in the transport characteristics of porous membranes compared to the pristine membrane, namely, to enhanced water and BSA fluxes, the BSA rejection coefficient, the flux recovery ratio, and the cleaning ability under UV illumination. Dense membranes were developed for pervaporation separation of a water/isopropanol mixture. The introduction of 0.5 wt% of modifiers led to an increase in permeation flux maintaining high selectivity with respect to water (99.99 wt% water in the permeate). The transport performance changes were attributed to the surface hydrophilization of porous modified membranes (proven with contact angle measurement) and changed cross-sectional structure, increased surface roughness, and decreased contact angles for dense modified membranes . Among porous membranes, optimal transport properties have been obtained for membranes modified with 0.3 wt% MWCNT/TiO


The influence of 3, 10 and 50 washing cycles on the properties of cotton fabric and cotton-polyester blend in plain weave, was investigated in this study. In addition to the analysis of tensile properties in weft and warp directions and thickness, the number of particles produced in the dry state was also measured after 3, 10 and 50 washes. After washing, the entire effluent was analysed by determining the total suspended solids (TSS), the total solids (TS), the pH value and the conductivity. To determine the similarity of the observed wash cycles and properties of all processed samples, hierarchical cluster analysis (HCA) was performed. The fabric changes indicated by total wear in the warp direction after 50 washing cycles compared to unwashed ones amounting to 41.2% for cotton and 30.9% for cotton-polyester blend, may be attributed to the synergy of washing factors and raw material composition. Cotton fabric produced significantly more particles than cotton-polyester fabric in the dry state after the examined washing cycles in all size categories. A smaller number of released particles are in the larger size category >25 μm. The obtained TSS values confirm the degree of loading of the effluent with particulate matter from the analysed fabrics, since the detergent consists of water-soluble components. The HCA dendrograms confirmed that the release of particles during the first washing cycles is mainly determined by the structural properties of fabrics, while in the subsequent cycles the synergistic effect of chemical, mechanical and thermal effects in the interaction with the material prevailed. Environmental protection and methods to analyse the impact of textiles on quality and human health have been mandatory for many years and are now legal norms that need to be clarified through extensive product monitoring, such as circular economy principles. The choice of fibre is significant for the appearance of garments  and the During the washing process, textiles are exposed to chemicals and mechanical agitation, which can create changes after repeated cycles. The extent of changes depends on textile characteristics  as well as Sinner factors  .Detergent formulations are often alkaline and contain bleach as oxidising agents, which can trigger the chemical damage of cellulose when combined with other elements. The loss of tensile properties in cotton textiles is strongly linked to the depolymerisation of cellulose ,5. LowerPolyester fibres are characterised by high strength, crease resistance and fast drying, while cotton fibres are characterised by high strength, hydrophilicity and comfort . These tCotton fibres in the alkaline medium can swell radially and longitudinally during finishing, causing shrinkage and increasing the take-up and yarn cross-section. In fibres where the orientation of the cellulose chains is in the direction of the fibres, swelling is greater in the transverse direction than in the longitudinal direction . IncreasPolyester materials may shrink or become damaged if exposed to a temperature higher than the temperature of thermal transition. It was found that recycled polyester sheds almost 2.3 times more microplastics in washing compared to virgin polyester, as the strength of the fibre is reduced due to thermal exposure and shear degradation during the recycling process .Cotton and polyester blends can meet special requirements for functional products, such as feel, appearance, dimensional stability, easy care and sufficient comfort . The bleOn the other hand, the presence of microplastic particles (MP) in natural and wastewater, sediment, soil, aquatic organisms, and air has been linked to textile sources ,22,23,24w/w of particles have all dimensions 0.1 nm–5 mm, or a length of 0.3 mm–15 mm and length to diameter ratio of >3”.According to , “micropThe fragmentation , degradaThe qualitative and quantitative determination of MP emissions and other released substances is difficult due to the variability and complexity of sources, as well as the fact that particulate matter is a vector for the dispersion of other emissions with varying degrees of risk in a real system. Numerous methods for determining particles were applied, either directly for the characterisation of dispersed systems  or afterThe purpose of this research was to examine cotton fabrics and cotton-polyester blends after three, ten, and fifty cycles by measuring the released particles in the dry state and in washes. Particles in the dry state were released by cyclic bending, so the particle sizes of the washed fabrics were measured separately. The distribution of particles released from cotton fabrics and fabrics made of a blend of cotton and polyester was observed both individually and as a combined system. The effluents of the washing process of the aforementioned fabrics were analysed by determining the physico-chemical parameters, TSS, TS , pH and conductivity to determine the degree of particle load.−1, which allows for the determination of low concentrations of individual groups of compounds [At the same time, the effect of 3, 10 and 50 washing cycles on the thickness and strength, which are the structural properties of the investigated fabrics, were monitored. It is well known that Fourier transform infrared spectroscopy (FTIR) provides information about changes in the chemical structure and environment of polymeric materials, such as: the presence or absence of certain functional groups: shifts in the frequency of absorption bands and changes in the relative intensity of the bands; the appearance of new peaks due to modifications; and the monitoring of changes during the life cycle of the material . In addiompounds . For thiThe originality of the research carried out can be seen in the connection between the amount of particles released from the fabrics in the dry state and and after the washing process.−2, while the cotton fabric has a mass per unit area of 160.8 gm−2. Cotton and cotton-polyester fabrics have a warp and weft density of 20 picks per cm, and the fineness of two-ply yarn 14.2 tex.Fabrics of 100% cotton and cotton-polyester (50:50) fabric in plain weave were produced on a Picanol OMNIplus 800 loom  at the Čateks d.o.o. textile mill, Čakovec, Croatia. The fabrics prepared in this way were scoured and, bleached according to the factory’s recipes and purchased for research purposes.The mass per unit area of cotton-polyester fabric is 158.6 gmThe washing of the 3.6 kg fabrics, which contain 2.7 kg of cotton and 0.9 kg of polyester (3:1), was carried out in accordance with HRN EN ISO 15797:2002 using a standard detergent with fluorescent whitening agent, the composition of which is shown in The cotton and cotton-polyester blend fabrics were washed in the laboratory washing machine Wascator FOM71 CLS by Electrolux at 75 °C using programme 2 with 3, 10, and 50 washing cycles. All process parameters comply with the standard, but due to the importance of the influence of the process conditions, a detailed description of programme 2 is included in After washing, the samples were air-dried. 2 circular plate that presses down on the material at a specific pressure (preload of 0.5 kPa). Tensile properties were measured on the samples in the weft direction before and after the washing cycles according to EN ISO 13934-1:1999 Textiles—Tensile properties of fabrics—Part 1: Determination of maximum force and elongation at maximum force using the strip method on a TensoLab Strength Tester , distance between clamps 100 mm, bursting speed 100 mm/min and pretension 2 N.In order to monitor the influence of washing on the structural properties of fabrics before and after the washing cycle, the thickness was tested according to HRN EN ISO 5084:2003 Textiles -- Determination of thickness of textiles and textile products at ten different places using a thickness gauge DM 2000—Wolf, Germany with a precision of 0.001 mm. The thickness gauge is made up of two parts: a support to hold the material in place and a device called a pressure plate, which is a 25 cmDetermination of overall decrease in breaking strength  was calculated according to ISO 4312:1989: Surface active agents—Evaluation of certain effects of laundering—Methods of analysis and test for unsoiled cotton control cloth.Ut of the fabrics was calculated according to Equation (1):Ut is total wear (%), F0 is breaking force of unwashed fabric (N), and F is breaking force of washed fabric (N).The total wear, −1 between 4000 cm−1 and 380 cm−1Cotton and cotton-polyester fabric before and after 3, 10 and 50 washing cycles were analysed using Fourier transform infrared spectroscopy  with the attenuated total reflection (ATR) measurement technique obtained spectral curves were processed in Spectrum 100. Four scans were performed for each sample with a resolution of 4 cmThe generation lint and other particles was measured on LasAir III (Particle Measuring Systems) laser particle counter connected to a particle generator in a laminar airflow booth. Samples were prepared according to EN ISO 9073-10 and mounted on the particle generator and subjected to controlled bending. The number of particles released during the test was measured in the following size categories: 0.3 µm for particle sizes of 0.3–0.5 µm; 0.5 µm for particle sizes of 0.5–1 µm; 1 µm for particle sizes of 1–5 µm; 5 µm for particle sizes of 5–10 µm; 10 µm for particle sizes of 10–25 µm; 25 µm for particle sizes larger than 25 µm. For each sample, the measurements were performed on 5 test tubes. This method was adopted from EN ISO 9073-10 Textiles—Test methods for nonwovens—Part 10: Lint and other particles generation in the dry state, with the test time adjusted to 30 min [The entire effluent (hence referred to as effluent) collected from the washing procedure and three rinse cycles was analysed as part of the research. Methods for characterising effluent after 3, 10 and 50 washing cycles include selected physicochemical parameters such as TSS, TS, pH, and conductivity.The total suspended solids (TSS) of effluents were determined by the standard gravimetric method. After membrane filtration of effluent using 0.7 µm fibre glass filter (GF), mass of GF with filter cake as residue after drying at 100 °C was determined.The total solids (TS) of effluents were determined by the standard gravimetric method in evaporating dish at 105 °C until constant mass was achieved.pH and conductivity of effluents were determined using pH meter, Schott, ProLab 3000 and conductometer, CG 853, Schott, respectively.To determine the similarity of the observed wash cycles and similar properties of all processed samples, hierarchical cluster analysis (HCA) was performed using a Minitab software, and graphs in the form of Ward’s dendrograms showed the homogeneous groups or clusters whose variables are connected by a certain similarity .The effect of the cyclic washing procedure on the qualities of cotton fabrics and cotton-polyester blends was studied by monitoring structural features, thickness, and tensile properties. These fabrics were washed under the same conditions, and it is predicted that the percentage of cotton component will have an effect on thickness variations during washing cycles.The average values and coefficients of variation of the measured fabric thicknesses before and after 3, 10, and 50 washing cycles are presented in The data in The breaking forces of unwashed cotton fabrics and cotton-polyester blend fabrics differed in the warp and weft directions, and the value in the warp direction was twice that of the weft direction. The breaking force of the examined fabrics  in the wTotal wear (Ut) was calculated in the direction of the warp and weft throughout the washing process, which included tensile changes caused by mechanical and chemical degradation of the washed sample in relation to the unwashed sample, The results for the breaking forces and calculated Ut  show thaStandard detergent does not include the enzyme cellulase, which eliminates cellulose fibrils during the washing process . When teThe spectral curves of the cotton and cotton-polyester fabrics before and after 3, 10, and 50 wash cycles are showen in −1, where the peak is due to the vibration of the C-OH bond of primary alcohols, and in the ranges of wavenumbers 996 cm−1 and 983 cm−1, where the peaks indicate vibrations within the—CH—bond, indicating that minor changes occurred within the polymer as a result of the washing process under the influence of chemistry as described. Minor changes can also be seen in the CO/PES-50w sample, specifically by the increase in the intensity of the peak at wavenumber 1014 cm−1, indicating an enhancement of the vibrations in the ester group (O=C-O-C) of the polyester component, which also indicates a higher surface wear of the cotton component within the CO/PES blend, which was also confirmed by the analytical methods in the previous research [From research ,47.The number of particles thus produced in the dry state was determined by applying cyclic bending. In view of the material differences mentioned above, the test was carried out for cotton and cotton-polyester fabrics. The obtained results refer to the release of lint and other particles from the material due to the deposits formed during the washing process.Based on the number of particles released in The relative values of the number of particles for each material were determined in proportion to the largest number of released particles in order to compare the results based on the washing cycles. The results are shown in According to the graph shown in The effluent after washing can be contaminated by soil from textiles, lint, dyes, finishing agents, and detergents . Some deSince cotton and cotton-polyester fabrics in the study have not been finished, dyed and soiled, the effluent can be contaminated with particles of textile origin and detergent. The components of the used standard detergent are soluble, so contamination by particles originating from the detergent is not expected. It can therefore be assumed that the contamination of the wash effluent contains particles from cotton and cotton-polyester fabrics. Effluent characteristics after 3, 10 and 50 washes of cotton and cotton-polyester fabrics are presented in The high pH value of the studied effluents is nearly consistent during all washing cycles and is caused by the alkaline components in the detergent. This characteristic is not expected to change despite the dilution that occurs during the three-step washing process. In this procedure for washing textiles without soiling , the alkThe conductivity of the effluents under consideration given in Based on this, it is clear that the composition of the reference detergent with the addition of PAA and other parameters of the Sinner cycle  influence the polymeric structures of cotton cellulose and cotton-polyester during the washing cycles. The TSS values obtained, as reported in The different systems (wash-wet and dry) to which the cotton and cotton-polyester fabrics studied were exposed and the different physicochemical properties in each system required the use of advanced statistical methods. Multivariant analysis was used to determine system similarities in terms of the number of particles released from the examined fabrics in procedures with varying numbers of washing cycles. The results using the Ward dendrogram are shown in The different distribution of clustering with regard to the number of washing cycles indicates that cotton contributes to the increasing release of particles with a higher number of washing cycles, while the contribution of polyester is greater in a lower number of washing cycles.The results include the formation of clusters, where a data set was selected for particle release from cotton and cotton-polyester fabrics in washes, The dendrograms of clustering also show a high degree of similarity, but the cluster distribution is different with respect to the number of washing cycles. The washing conditions, especially the high alkali content, affect the damage of the textiles, which contributes to the release of particles.Tensile properties, thickness, and total wear in the weft and warp directions were examined to establish the importance of textile composition and washing cycles to particle release, In the initial washing cycles, the structural properties of the material play a significant role in the release of particulate matter; however, in subsequent cycles, the interaction between the chemical, mechanical and thermal factors and of the fabrics becomes increasingly prominent.This research examines the chemical, mechanical, and thermal impacts of 3 washing cycles, 10 washing cycles, and 50 washing cycles on 100% cotton and a 50/50 cotton-polyester blend in plain weave with no soiling in the dry state and after washes utilising fabric and effluent characteristics. Tensile properties, thickness and generated particles in the dry state depend on the number of washing cycles. Increased strength of cotton-polyesteru fabric after 3 and 10 washing cycles in the weft direction is the result of fibrillation and shrinkage. Changes in fabric properties, expressed as total wear in the warp direction after 50 washing cycles compared to unwashed amounting to 41.2% for cotton and 30.9% for cotton-polyester blend, can be attributed to the synergy of process parameters, fabric structure and raw material composition.The number of particles released in the dry state > 25 µm is significantly lower than the number of particles released in the size of 0.3 to 5 µm. In all size categories, the quantity of particles released in the dry state is much larger from washed cotton fabric than from washed cotton-polyester fabric. Because the detergent contains water-soluble components, the TSS values obtained confirm the degree of contamination of the effluent with particles from the tested textile materials.From the spectral bands of CO and CO /PES samples before and after 3, 10 and 50 washing cycles, no significant changes at the physicochemical level within the polymers of the tested samples were identified.According to HCA dendrograms, particle release in early wash cycles is mostly regulated by material structure. Future wash cycles are influenced by chemical, mechanical, and thermal interactions. The findings of the research highlight the need for the use of an analytical approach to categorise and quantify particles released from textiles in both the dry state and washes in order to decrease the potential for detrimental impacts on humans and the environment.


MOF  materials have been used as functional materials in a number of fields due to their diverse spatial tunability, which produces rich porous structures with stable and continuous pores and a high specific surface area. A triboelectric nanogenerator can convert trace mechanical energy into electrical energy, and the application of MOF materials to triboelectric nanogenerators has been intensively studied. In this work, we report on two MOFs with similar spatial structures, and the modulation of the end microstructures was achieved using the difference in F content. The output performance of friction power generation increases with the increase in F content, and the obtained polyacidic ligand materials can be used to construct self-powered corrosion protection systems, which can effectively protect metallic materials from corrosion. With the development of society, the gradual depletion of fossil energy sources and the increase in environmental pollution have forced us to accelerate the search for green and sustainable energy sources. Nature provides ample sustainable energy sources, such as solar, thermal, wind, water, and mechanical energy, which, if utilized, can partially mitigate the depletion of fossil fuels and the energy crisis ,2,3,4,5.Metal organic frameworks (MOFs), consisting of metal ions/clusters and organic linkers, are a class of highly crystalline porous materials with a large specific surface area, high porosity, tunable structure, and a rich variety of crystalline materials and other characteristics ,19. WithThe influence of the overall negative electric effect of the negative electronegative groups on the output performance of friction power generation is investigated while keeping the spatial structure approximately the same. Exploring the conformational relationship, the effect of terminal microstructure on TENGs was thoroughly investigated to provide a theoretical basis for improving the output performance of TENGs based on a metal–organic skeleton.1, and the MOF material synthesized from 2,5-bis(trifluoromethyl)terephthalic acid ligand is called compound 2. The Co-TENG devices prepared from the corresponding electrode materials were named 1- and 2-TENG. The effect of the number of F groups on the output properties of TENGs was investigated by controlling the body structure to change the terminal structure. The electrochemical characterization test results show that compound 2-TENG has the best output performance, followed by 1-TENG; compound 2 has more electronegative groups, and electronegative groups can improve the output performance of TENGs. In order to protect metal materials from corrosion, we introduce it into the self-powered cathodic protection system, and the TENG-based cathodic protection system realizes the ideal model of utilizing nature’s energy to protect metals from corrosion. The self-powered cathodic protection system has the potential to be used in daily life, industrial production, and marine development due to its advantages of low cost, no external energy consumption, and ease of preparation.In this paper, we choose to synthesize MOF materials from the same Co metal center and different ligands to use as friction power generation materials. The MOF material synthesized from 2,3,5,6-tetrafluoroterephthalic acid ligand is called compound 2 was synthesized using a hydrothermal method centered on the metal Co and 2,5-bis(trifluoromethyl)terephthalic acid. The crystal structure of the compound is shown in 2, the central metal Co ion is in the geometrical configuration of a six-coordinated octahedron, in which the four oxygen atoms are derived from the carboxyl groups in each of the two ligands, and the two oxygen ligands come from water molecules, and the organic ligand uses all of its oxygen atoms to form a ligand bond with the metal-centered cobalt  of compounds 1 and 2 can be seen to be about 1.61 eV and 3.98 eV, respectively , and the HOMO orbitals of 1 and 2 are 1.04 eV and 3.34 eV, respectively. From the results, it can be seen that the HOMO orbitals of compound 2 are slightly higher than those of compound 1, which implies that the internal electron jumping ability of compound 2 is stronger than that of compound 1, and the output performance will be higher, so we speculate that the output performance of 2-TENG will be slightly better than that of 1-TENG. In addition to the electrode materials that have a greater influence on the output performance of the TENG, the contact tightness between the electrode materials also has an influence on the output performance. Therefore, we tested the two compounds under the same experimental conditions by taking the same amount of compounds 1 and 2 in the same mortar for the same amount of time and grinding them into powder and then uniformly coating them on copper tape to minimize the influence of other errors on the output performance of the TENG.Prior to the TENG output performance test, we performed the Mott—Schottky test and the UV-2600 spectroscopic test for compounds 1 and 2 . The tesectively b,d. Compl curves e,f. The 1 and 2, and the negative electrode material is polyvinylidene difluoride (PVDF), named 1-TENG and 2-TENG.The working principle of TENGs based on MOF materials is based on the coupling of frictional charging and electrostatic induction. The simplest vertical contact–separation model was used in this experiment . The pos1-TENG, the Isc and Voc of 2-TENG are larger. At 5 Hz, the values obtained with 1- and 2-TENG through the rectifier bridge are 39.0 μA and 76.8 μA, respectively  and the elemental distributions of compounds 1 and 2 with characteristic K spectral lines (EDS)  . The SEMMetal corrosion protection has always been a hot topic of research and application because it can cause huge economic losses and serious accidents ,34. The 2-TENG, it can be used for self-powered cathodic protection. The schematic diagram of the anti-corrosion mechanism is shown in 2-TENG output signal is converted from DC to be rectified by connecting a rectifier bridge, the positive pole of which is connected to a platinum electrode, and the negative pole of which is connected to carbon steel. Rust can allow a visualization of the degree of metal corrosion, and the degree of metal corrosion is usually judged by observing rust spots. In our experiments, we used 3.5 wt% NaCl in aqueous solution to simulate a seawater electrolyte and determined the effectiveness of protection by observing and comparing the surface morphology of the carbon steel with and without 2-TENG connected. By comparing the rust spots on the surface of carbon steel after 0 h, 1 h, 3 h, 5 h, and 7 h, it was found that the surface of the carbon steel with 2-TENG attached was almost free of rust spots in 0–7 h, but the surface of the carbon steel without 2-TENG attached became more and more rusty with the extension of time  of the protected metal carbon steel was stabilized at 0.20 V when no 2-TENG was connected, whereas when 2-TENG was connected, the OCP of the protected metal carbon steel decreased rapidly to −0.10 V. This was due to the negative migration of the OCP due to the charge transfer between the sample and the solution, and the negative migration indicated that the metal carbon steel was in the protected stage. When 2-TENG is removed, the OCP value returns to the original position again , EIS, and Tafel with and without on again b.2-TENG attached shows a smaller semicircle than does the carbon steel without 2-TENG attached, indicating that the internal resistance of the connection is smaller. This is due to the fact that the charge transfer between the sample and the solution makes the carbon steel with 2-TENG connected more susceptible to electrochemical reactions than the carbon steel without a TENG connected. The equivalent circuit diagram in the corrosion study is clearer and more concise in explaining and illustrating the EIS plot, which is located in the upper-left corner of the EIS plot. .The size of the radius of the EIS circle in the impedance spectrum usually indicates the magnitude of the electrical resistance in the electrochemical reaction; the larger the radius of the circle, the more difficult it is for the electrochemical reaction to take place, which leaves the carbon steel unprotected and corroded c. The ca2-TENG carbon steel connected. From the experimental data, it can be seen that the polarization potential (Ecorr) of the carbon steel with 2-TENG connected was negatively shifted compared with that of the carbon steel without 2-TENG connected  data were collected on a Netzsch STA 449C thermal analyzer. Compounds were subjected to the Mott—Shottky test with a three-electrode system on an electrochemical workstation . The chemical bond determination of the compounds was tested via FT-IR; purity was characterized via powder X-ray diffraction mapping (PXRD) under Cu-kα irradiation using an instrument manufactured by Bruker D8 Advance. For testing the elemental composition and valence states, XPS was used to obtain spectral data plots of the compounds using Al-Kα as the ray source. The morphology, elemental composition, and distribution of the compounds were tested with a German-made FE-SEM and its accompanying EDS. The Isc and Voc of the friction nanogenerators were tested on an SR570 low-noise current amplifier (Stanford Research Systems) and a 2657A high-power system source meter. The bandgap size of the samples was tested with a UV-2600 UV-Vis (Manufacturer Perkin Elmer Instruments (Shanghai) Co., Shanghai, China, Model Lambda 950) diffuse reflectance spectrometer.All the chemicals and reagents used in this experiment were obtained by purchasing them through Aladdin’s Reagent platforms and were used directly without further purification. Compound terature  and comp3)2-6H2O  and 2,3,5,6-tetrafluoroterephthalic acid  was weighed and dissolved in a mixture of DMF (2 mL), water (2 mL), and ethanol (6 mL), the resultant solution was transferred to a small glass vial with a pipette gun, and the vial was opened and allowed to evaporate naturally at room temperature; light-purple crystals were precipitated after three weeks, washed three times with ethanol, and dried at room temperature.A solid mixture of Co(NO3)2-6H2O  and 2,5-bis(trifluoromethyl)terephthalic acid  was weighed and dissolved in a mixture of DMF (6 mL), water (2 mL), and ethanol (2 mL), the resulting solution was transferred to the reactor using a pipette gun, and then the reactor was placed in an oven preheated to 80 °C for 24 h. The solution was cooled to room temperature, washed three times with DMF and placed in a vacuum drying oven for drying.A solid mixture of Co preparation, we weighed the appropriate amount of PVDF powder, dissolved in a mixture of DMA and acetone, stirring at 60 °C until the PVDF powder was completely dissolved to stop the heating. It was cooled to room temperature, and the desktop homogenizer was uniformly coated with the PVDF mixture on the Kapton membrane. The membrane was spin-coated, placed in the oven for drying at 80 °C for 4 h, and then removed to be cool to room temperature. A copper tape measuring 5 cm × 6 cm was applied to the reverse side of the PVDF film, and the negative electrode sheet was prepared in the same way as the positive electrode sheet was.


The design of a low-pass-frequency filter with the electronic change of the approximation characteristics of resulting responses is presented. The filter also offers the reconnection-less reconfiguration of the order . Furthermore, the filter offers the electronic control of the cut-off frequency of the output response. The feature of the electronic change in the approximation characteristics is investigated for the Butterworth, Bessel, Elliptic, Chebyshev and Inverse Chebyshev approximations. The design is verified by PSpice simulations and experimental measurements. The results are also supported by the transient domain response (response to the square waveform), comparison of the group delay, sensitivity analysis and implementation feasibility based on given approximation. The benefit of the proposed electronic change in the approximation characteristics feature  is presented and discussed for an exemplary scenario. Active analog frequency filters represent an important part in sensor applications  .The electronic control of the pole (cut-off) frequency is available (considered) only in papers ,22,23.None of the designs offer the electronic control (reconnection-less reconfiguration) of the order. Reference  offers tOnly research discussed in ,11,26 isTo the best of the authors’ knowledge, there has been no report of a low-frequency current-mode filter offering the electronic change in the approximation characteristics. The designed filter offers the feature of the electronic change in the used approximation  and reconnection-less reconfiguration of the order . The electronic adjustment of the cut-off frequency is also possible. The proposal is verified by PSpice simulations together with the experimental measurements of the implemented filter. The further analysis is focused on the comparison of the features of the filter depending on the selected approximation.The filtering topology, originally proposed in  for purpIOUT± = ±gm (VIN+ − VIN–), where gm denotes the transconductance of the OTA. The OTA in the filter structure requires two outputs (of the opposite polarity). Since the commercially available devices, working as the OTA, typically offer only one output, the implemented solution was created by one LT1228 device [gm tunable by DC control current ISET_gm) and one EL4083 device [The schematic symbol of the OTA and its used implementation is shown in 8 device  function3 device  (it copiIOUT = −Bi(IIN), where Bi stands for the current gain of the individual output of this element; thus, i = {1, 2, 3, 4}. The IOGC–CA provides the feature of the reconnection-less reconfiguration of the order of the filter based on the setting of the current gains Bi. It is implemented by one Universal Current Conveyor (UCC) [VSET_B). There are also two additional OPA860 devices [or (UCC) , which ior (UCC)  used to  devices  (added iK(s) = N(s)/D(s), whereThe transfer function of the filter  is expreB1 to B4 cancelling corresponding terms of the numerator if a specific current gain is set to zero. For instance, the 4th-order LP function will be available when B1 = 1, B2 = B3 = B4 = 0. Also, the resulting filtering response can be amplified if particular current gain B is higher than 1. Furthermore, the obtainment of the selected approximation characteristics is achieved through the change in the values of the transconductances, dependent on particular coefficients of the transfer function (regarding the chosen approximation). Moreover, if the particular implementation of the current amplifiers offers the analog (continuous) control of their current gain, this feature can be used to adjust the pass-band gain of the output response (fine tuning) if the pass band does not have the unity gain .Equation (1) indicates clear dependence of the resulting order of the function on the setting of current gains The design was verified using PSpice simulations and experimental measurements. The simulations were performed using models in TSMC 0.18 µm CMOS technology. Used models of OTA, the current amplifier and current follower can be found in ,47,48. TThe practical implementation of individual active elements was performed by the UCC, EL2082, EL4083, LT1228 and OPA860, as introduced in the previous section. The UCC, made by Brno University of Technology, and the ON Semiconductor design center in I3T 0.35 µm CMOS technology, used a supply voltage of ±1.65 V. The remaining used active elements used a supply voltage of ±5 V. The measurement itself was performed by a network analyzer Agilent 4395A utilizing voltage-to-current and current-to-voltage converters constructed by OPA860 and OPA861  devices.b), depending on the chosen order and approximation, have to be calculated [b can then be obtained based on chosen parameters such as the angular frequency, approximation, steepness of the transition, etc., (the tolerance field) from design tables or NAF.The design of the filters of a higher order (>2) is usually performed by the cascade combination of individual 1st- and 2nd-order filters. For the direct design, coefficients of the transfer function  (this frequency was chosen considering the bandwidth limitations of the used active elements (5–10 MHz) so there were at least two decades before the response was affected by parasitic characteristics); ripple in the pass band (if any)—KP = 3 dB, stop-band frequency fS—3,000,000 rad/s (470 kHz); transfer in stop band (KS)—depending on used approximation in order to create the transfer function of a given order (4th-order) −84 dB (Elliptic), −77 dB (Butterworth), −73 dB (Chebyshev), −65 dB (Bessel), and −73 dB (Inverse Chebyshev). Based on the selected approximation, the coefficients of the transfer function were calculated, as stated in The following specification of the tolerance field for the calculation of the coefficients, was used: approximation— Elliptic, Butterworth, Chebyshev, Bessel and Inverse Chebyshev, operational angular frequency, 300,000 rad/s  and the general transfer function of the 4th-order (3):The relations for the transconductances C1 = C2 = C3 = C4 = 1 nF, the resulting values of the transconductances  can be compared in The magnitude and phase characteristics  of the 4th-order  output response are shown in The simulation and experimental results provided in this section were carried out using the same setup as described in the previous section (unless stated otherwise).mg1 to mg4, as long as the ratio between them remained unchanged so it did not affect the approximation features and the value of the quality factor. This was tested in case of the 4th-order function with Butterworth characteristics for three settings of the desired  f0 . The resulting values of the transconductances in relation to the selected value of f0 are summarized in The electronic adjustment of the cut-off frequency was achieved by the change in the values of transconductances The behavior of the filter in the time domain depending on the used approximation was evaluated for the square waveform input signal in order to investigate the overshot of the step response. The results were provided only in the case of the simulations, as the current responses (for the measurements) would be rather difficult to acquire without proper equipment. The input (square) signal had its amplitude set to 15 µA with a frequency of 1 kHz. Since the group delay characteristics are associated with the linearity of the phase responses and analogically with the step response results, the response with Bessel approximation was the one with a minimum deviation from the constant group delay time in the pass-band area. Furthermore, Elliptic and Chebyshev approximation showed the worst group delay properties from used approximations, and Butterworth and Inverse Chebyshev approximations offered relatively small ripples in the group delay responses to the pass-band area, as demonstrated by C1, C2, C3, C4, mg11, mg12, mg21, mg22, mg31, mg31, mg41, mg42, B1, B2, B3 and B4). Parameters mg11, mg12, mg21, mg22, mg31, mg31, mg41, mg42 represent individual outputs of OTAs and parameters B1, B2, B3 and B4 then stand for the outputs of the IOGC–CA element. Considering all these parameters, the real transfer function takes form of Kreal(s) = Nreal(s)/Dreal(s), whereA certain inaccuracy  of individual outputs of used active elements, together with the tolerance of used passive parts, can cause a significant deviation in the resulting transfer function. Since each approximation will lead to different values of used transconductances and thus, it might lead (depending on the implementation) to different values of output impedances of used active elements interacting with each other; the sensitivity of individual parameters of the filter will vary depending on the currently used approximation. Therefore, an analysis of the subsequent sensitivity functions in relation to the used approximation can prove useful. Based on the above-mentioned, the real transfer function of the filter consists of 16 individual parameters (K(jω) is the transfer of the filter and qi represents ith parameter of the filter. The mathematical expression of individual sensitivities was calculated using Maple software.The relative sensitivity function (magnitude) of the filter in relation to the variation of the individual parameters can be described as (10)Sqi|B were set to one) is performed so it includes the results for all parameters. It can be seen that the sensitivities reached typical values (sensitivity around 1), with the highest values around the designated cut-off frequency (f0 = 47 kHz). Elliptic and Chebyshev approximations showed higher sensitivities . It is evident that the sensitivities for Butterworth, Bessel and Inverse Chebyshev approximations are slightly lower.f0 = 47 kHz and selected values of capacitors) for Elliptic approximation was 342.6 µs and was 283.6 µs in the case of Chebyshev approximation. Butterworth and Inverse Chebyshev characteristics required similar values of transconductances . Bessel approximation was characterized by the highest required values of the transconductances . Considering typical implementation limits and the related fabrication issues of CMOS structures and commercially available solutions of the OTA element, the maximal  obtainable value of the transconductance was around 1 ms for modern integrated CMOS designs and 10–50 ms for BJT commercially available devices. Therefore, from the implementation aspects, Elliptic and Chebyshev approximations came out as more advantageous ones. Moreover, we need to keep in mind that, in case of the adjustment of the cut-off frequency, the value of transconductances is twice as much if the value of cut-off frequency is doubled  are summarized in bled see . This faThe filter has to be able to process different types of signals from simple shapes to more complex ones. In this case, various approximations can be found more suitable for specific types of signals. Therefore, in order to point out a beneficial feature of the electronic change in the approximation, the following situation is considered: the proposed filter will be tested for two types of processed input signal. The first signal is of a simple shape ), in comparison to the second signal, having more complex shape . Furthermore, the processed (useful) signal is affected by noise. f0 = 47 kHz) was used with Bessel, Butterworth and Elliptic approximations chosen as representative examples. The transfer (attenuation) of the used V/I, I/V converters was approximately –12 dB. For the purposes of the demonstration, the 4th-order filtering function ;The ability to electronically change approximation characteristics;The ability of fine tuning (adjusting the pass-band area);The electronic adjustment of the pole frequency.The advantages of the presented filter are the following:The above-mentioned features provide additional degrees of freedom for the filter and its adjustment based on the application. With these features, the filter can adapt to a changing situation and requirements such as sensors, wireless communication and cognitive radio environments, where a change in the filtering function might be necessary. Standard filtering approaches and multifunctional filters do not allow these features.The electronic change in the approximation characteristics can be useful in order to influence the resulting behavior of the filter when searching for the optimal characteristics for intended application or based on the processed signal. Based on this fact, the proposed filter offers the possibility to choose the most fitting characteristics when focusing on a particular feature . For instance, the steepness of the transition between the pass-band area and stop-band area can be adjusted without having to add another integrator in a cascade, as topology modification might not be possible (in the case of on-chip implementation). As another example, the approximation with smaller overshots of the step response can be used for applications which are more sensitive when it comes to their stability. All this can be achieved from one topology that offers the electronic adjustment of its approximation characteristics, which can be more freely adjusted for particular needs of a given application, or that can be adjusted anytime during the lifespan if the parameters of the application change or deteriorate. Another possible use of this ability to change the approximation characteristics of the filter could be served in case of sensors, where the conditions for the signal change often and the ability to change the approximation characteristics can be used depending on the current situation in order to decrease the noise in the processed signal. Particular benefits of the electronic change in the approximation characteristics were discussed and an example was presented see .