Static Process Research Articles

Application of supervised learning models for enhanced lead (II) removal from wastewater via modified cellulose nanocrystals (CNCs)

Heavy metal ions are acknowledged to impact the environment and human health adversely. CNCs are effective materials for removing heavy metal ions in industrial applications and process innovations since they can be used in static and dynamic adsorption processes. Cost-effective, uncomplicated water treatment technologies must be developed using biodegradable polymers, namely, modified cellulose nanocrystals. Adaptive neuro-fuzzy inference systems (ANFISs) and artificial neural networks (ANNs) were used to evaluate and examine the efficacy of modified cellulose nanocrystals in removing lead(II) from wastewater. The research indicated that the maximum adsorption capacity attained was 260 mg/g at a pH of 6, an initial concentration of 200 mg/L, a contact duration of 300 min, and a 5 g/200 mL dose. Influence of four input variables on the Pb(II) adsorption capacity: The experimental data were juxtaposed with the outcomes from ANN and ANFIS to ascertain the pH, contact time, starting concentration, and dose. The correlations of 0.9916 for the created artificial neural network (ANN) and 0.9953 for the adaptive neuro-fuzzy inference system ANFIS indicate that the study data may be predicted with precision. ANFIS had a Pearson’s chi-square value of 0.638, surpassing the ANN’s score of 0.979.

Thermal management broadband-emitting device based on VO2 applied in the mid-infrared band.

Mid-infrared thermal radiation has attracted attention due to its wide range of applications. Compared to the static process of thermal emission, if thermal radiation can be dynamically controlled, it would be more suitable for practical applications. Herein, we designed a controllable thermal emitter based on phase change materials. When the temperature changes from low to high, VO2 transitions from a dielectric state to a metallic state, and its imaginary part of the dielectric constant significantly increases, leading to differences in emission characteristics. At low temperatures, the device is in a low dielectric state and resonates weakly with incident light. The main emission comes from the bottom of the grating structure, with an emissivity of 0.21. At high temperatures, the structure is in a high dielectric state, and multiple resonance modes are excited within the structure, such as cavity resonance and surface plasmon resonance, which increases the emissivity to 0.95 and achieves effective heat dissipation. Given its superior thermal management capabilities and stability, this design holds promise for applications in thermal imaging, infrared communication, and energy-efficient devices.

Research on the Influence of Liquid Metal Embrittlement Cracks on the Strength and Fatigue Life of Spot-Welded Joints of Galvanized Q&P980 Steel.

Galvanized high-strength steel has emerged as a key focus in automotive lightweighting research. During resistance spot welding of galvanized steel, the phenomenon of liquid metal embrittlement (LME) can occur, which is characterized by the appearance of irregular cracks on the weld spot surface. However, the impact of LME cracks on the mechanical properties of joints remains unclear. This study investigates the LME phenomenon and its effects on the performance of spot-welded joints using galvanized QP980 steel as the subject. By combining theoretical analysis, experimental methods, and simulations, the formation and characteristics of LME cracks are explored through resistance spot welding experiments and elemental analysis. The influence of LME cracks on the static strength of joints is assessed through quasi-static tensile tests, fracture surface analysis, and theoretical calculations. Finite element simulations of the static tensile process reveal that LME cracks alter the stress-strain fields during joint failure. Additionally, the study examines how the location and size of LME cracks influence these effects. Finally, fatigue testing and fracture analysis of spot-welded joints demonstrate that LME cracks can negatively impact the fatigue life of joints.

Investigation of Hatay-Defne earthquake (20.02.2023) by using GNSS station

No studies have been conducted on the spatial changes related to the Hatay-Defne earthquake (20 February 2023) up to now. The reason for this situation is that the requested data cannot be accessed as a result of the power and internet interruptions caused by the earthquake. The southern Turkish city of Hatay had a 6.3 magnitude earthquake at 20:04 on February 20, 2023. Three minutes later, a 5.8 magnitude aftershock occurred, and 90 more aftershocks followed. These earthquakes, which were felt across the area, caused individuals who survived the horrific quakes on February 6, 2023, to experience new levels of dread. In the district of Hatay-Defne, the epicenter began. In its first assessment, the United States Geological Survey gave the earthquake a magnitude of 6.3 at a depth of 16 kilometers. In this study, firstly, GNSS data belonging to ARST station in 30 seconds recording interval were downloaded into CSRS-PPP. GNSS data was processed as static and then was processed as epoch to epoch by using kinematic method. The coordinate differences between the obtained coordinates by using the kinematic processing and the obtained coordinates by using the static processing were computed. According to the obtained results, a horizontal movement of approximately 8.30 cm in the south-west direction was observed at the ARST station, which is the closest to the earthquake center.

Trace Copilot: Automatically Locating Cryptographic Operations in Side-Channel Traces by Firmware Binary Instrumenting

A common assumption in side-channel analysis is that the attacker knows the cryptographic algorithm implementation of the victim. However, many labsetting studies implicitly extend this assumption to the knowledge of the source code, by inserting triggers to measure, locate or align the Cryptographic Operations (CO) in the trace. For real-world attacks, the source code is typically unavailable, which poses a challenge for locating the COs thus reducing the effectiveness of many methods. In contrast, obtaining the (partial) binary firmware is more prevalent in practical attacks on embedded devices. While binary code theoretically encapsulates necessary information for side-channel attacks on software-implemented cryptographic algorithms, there is no systematic study on leveraging this information to facilitate side-channel analysis. This paper introduces a novel and general framework that utilizes binary information for the automated locating of COs on side-channel traces. We first present a mechanism that maps the execution flow of binary instructions onto the corresponding side-channel trace through a tailored static binary instrumentation process, thereby transforming the challenge of locating COs into one of tracing cryptographic code execution within the binary. For the latter, we propose a method to retrieve binary instruction addresses that are equivalent to the segmenting boundaries of the COs within side-channel traces. By identifying the mapping points of these instructions on the trace, we can obtain accurate segmentation labeling for the sidechannel data. Further, by employing the well-labeled side-channel segments obtained on a profiling device, we can readily identify the locations of COs within traces collected from un-controllable target devices. We evaluate our approach on various devices and cryptographic software, including a real-world secure boot program. The results demonstrate the effectiveness of our method, which can automatically locate typical COs, such as AES or ECDSA, in raw traces using only the binary firmware and a profiling device. Comparison experiments indicate that our method outperforms existing techniques in handling noisy or jittery traces and scales better to complex COs. Performance evaluation confirms that the runtime and storage overheads of the proposed approach are practical for real-world deployment.

A Simulation study of the output characteristics of freestanding Triboelectric nanogenerators with interdigitated electrodes for self-powered sensing application

Diverse forms of mechanical energy are available in environmental routine activities. These energy forms can be harvested, measured and converted to produce electricity at nanogenerator (NG) and micro-scale levels based on the phenomenon of triboelectrification. These motivate this work to develop a self-powered sensing device to detect and monitor static and dynamic processes associated with mechanical activities. The significance is to study the output characteristics of freestanding mode triboelectric nanogenerator (TENG) with multiple units of metal and dielectric electrodes. The integrated modeling environment of COMSOL Multiphysics software was employed for the simulation study of the proposed TENG. The system output responses considered for analysis includes, open circuit electric potential and short circuit surface charge density. For the open circuit, the electric potential was achieved at maximum value of 10kV, while for short circuit surface charge density, the electric potential was achieved at maximum value of 27 Cm-2. The study results revealed that the input parameter of contact displacement of electrodes is proportional to the output electrical potential of the system. Hence, the efficiency of TENG can be deployed for energy harvesting and sensing of mechanical variables.

Recrystallization and spheroidization mechanisms in Ti6246 alloy under thermomechanical treatment

This paper provides a detailed analysis of the dynamic microstructure evolution of Ti-6Al-2Sn-4Zr-6Mo alloy (Ti6246) through a 4-pass hot rolling process. Additionally, it examines the effect of heat treatment on microstructure evolution during the post-annealing of the hot-rolled alloy. During the rolling process, dynamic recrystallization (DRX) predominantly takes place in the α-phase, leading to grain refinement and spheroidization as a consequence of DRX and lamellae spheroidization. Spheroidization of the lamellar structure is more pronounced in the 2-pass rolling process, while the level of DRX is greater in the 4-pass rolling process. The dislocations present within the α lamellae accumulate and entangle at the sub-grain boundary, leading to the formation of a high-angle grain boundary (HAGB). Meanwhile, the β phase infiltrates the grain boundaries of the α phase, leading to the formation of spheroidized equiaxed α grains. Schmid factors associated with the pyramidal slip systems are relatively high, facilitating the activation of slip systems during hot rolling processes. The evolution of the α-phase morphology during heat treatment processes in various rolling passes is primarily driven by static recrystallization (SRX) and grain boundary terminal migration mechanisms. The deformed microstructure volume fraction diminishes and transforms into substructures and recrystallized grains through post-annealing. Post-annealing facilitates the dissipation of stored deformation energy with increasing temperature, leading to a decrease in dislocation density and an enhancement in the extent of recrystallization. Accumulated deformation during rolling and the temperature of the heat treatment both play a role in affecting the static spheroidization process. Greater accumulated deformation and elevated post-annealing temperatures are advantageous for achieving grain spheroidization. The grain boundary splitting process is more pronounced, and the deformation energy is elevated during the 3,4-pass rolling.

Surface roughness prediction based on fusion of dynamic-static data

The surface roughness in mechanical milling is a critical indicator of machining quality. Due to the variability in the geometric structure of the workpieces and the complexity of the milling mechanisms, traditional roughness prediction methods often fail to meet the requirements of actual machining scenarios. To enhance the accuracy of surface roughness prediction during the milling process and to ensure its applicability in real-world production, this paper introduces a novel model that integrates Multi-scale Convolutional Neural Network, Stacked Bidirectional Long Short-Term Memory, and Self-Attention mechanisms (MC-SBiL-SA), which integrates dynamic current signals with static process data for roughness prediction. This model involves extracting features from dynamic signal data in various ways, then feeding these features into a BiLSTM network for training. To enable the model to learn more effective data features, an attention mechanism is introduced after the BiLSTM to assign different weights to the data features, thereby amplifying the impact of important features. Finally, the dynamic signal and static process data are combined in a shallow neural network for regression prediction. Comparative results with various baseline models demonstrate that the MC-SBiL-SA model, which fuses dynamic and static data, exhibits superior generalizability and effectiveness in predicting surface roughness.

Pectinase immobilized on magnetic nanoparticles coated with alginate for pectin hydrolysis in guava juice assisted by a stirred electromagnetic reactor

Aspergillus aculeatus pectinase was immobilized on magnetic nanoparticles coated with calcium alginate for pectin hydrolysis in guava juice by a stirred electromagnetic reactor (SER). The average crystallite size estimated by the Scherrer formula was 33.7 nm. The reaction rate in SER (701.7 U/mL) was almost twice that of the static process (362.5 U/mL). Both processes displayed a sigmoidal trend with positive cooperativity (n) of 5 and 4, respectively. Both free and immobilized pectinase showed great performance in the pH range of 4.0-7.0. After immobilization, pectinase acted optimally at 50 °C. Pectin hydrolysis was performed for over 10 successive cycles in SER losing only 30% of its initial activity. Thermodynamic activation parameters of the reaction revealed higher spontaneity and efficiency when hydrolysis was performed in SER. Pectin hydrolysis in guava juice displayed 41% turbidity and 85.5% viscosity reduction. The electromagnetic reactor displayed great potential for performing hydrolysis of pectin in guava juice. The biocatalyst showed good features for further applications in food industries.

Enhanced breast mass segmentation in mammograms using a hybrid transformer UNet model

Breast mass segmentation plays a crucial role in early breast cancer detection and diagnosis, and while Convolutional Neural Networks (CNN) have been widely used for this task, their reliance on local receptive fields limits ability to capture long-range dependencies. Vision Transformers (ViTs), on the other hand, excel in this area by leveraging multi-head self-attention mechanisms to generate attention maps that dynamically gather global spatial information, significantly outperforming CNN-based architectures in various tasks. However, traditional transformer-based models come with challenges, including high computational complexity due to the self-attention mechanism and inefficiency in the static MLP fusion process. To overcome these issues, the Hybrid Transformer U-Net (HTU-net) model is proposed for breast mass segmentation in mammography. Channel and spatial enhanced self-attention mechanisms are integrated with convolutions layers in HTU-Net, creating a hybrid architecture that combines the strengths of both CNNs and ViTs. The introduction of a multiscale attention mechanism further improves the model's ability to fuse information from different resolutions, enhancing the decoder's capacity to reconstruct fine details in the segmented output. By using both local texture-based features and global contextual information, HTU-Net excels in capturing essential features, thus improving segmentation performance. The experimental results across multiple datasets, including CBIS-DDSM and INbreast, demonstrate that HTU-Net outperforms several state-of-the-art methods, achieving superior accuracy, dice similarity coefficient, and intersection over union. This work highlights the potential of hybrid architectures in advancing computer-aided diagnosis systems, particularly in improving segmentation quality and reliability for breast cancer detection.

Splitting and Aggregation of Carbon Dots: Wavelength-Shifted and Ratiometric Fluorescence Sensing of Peroxynitrite.

Peroxynitrite (ONOO-) is a short-term reactive biological oxidant and plays an important role in cellular signal transduction and homeostatic regulation. However, excess ONOO- is associated with neurodegenerative and cardiovascular diseases. Therefore, rapid, sensitive, and accurate assays for ONOO- detection are essential for exploring its physiological and pathological function. In this work, a wavelength-shifted and ratiometric fluorescent sensing platform for ONOO- is constructed by splitting green fluorescent carbon dots (G-CDs) and aggregating orange fluorescent carbon dots (O-CDs). The mixed G-CDs and O-CDs (M-CDs) show a fast and precise response to ONOO- in the range of 0-250 μM, with a detection limit of 10 nM. In the linearity range within 3 μM ONOO-, an obvious wavelength shift of G-CDs from 495 to 475 nm is observed owing to the oxidation and nitration of ONOO- to the surface-state fluorescence of G-CDs, accompanied by the splitting of G-CDs. In the linearity range of 3-250 μM ONOO-, the fluorescence of G-CDs remains constant, while the molecular-state fluorescence of O-CDs gradually quenches by the oxidation and nitration of ONOO- through the fluorescence static process and induces their aggregation. Additionally, M-CDs show favorable intracellular imaging of endogenous and exogenous ONOO-. This study not only presents a new fluorescence wavelength shift mechanism for ONOO- sensing but also provides insights into CDs' fluorescence mechanism by exploring their morphology and structure via reacting with reactive oxygen species (ROS).

Investigation of In Situ Gelation Behavior and Enhanced Oil Recovery Ability of Polymer Gel Used for Controlling CO2 Channeling in Tight Fractured Reservoir.

Polymer gels are one of the most common plugging agents used for controlling CO2 channeling and improving sweep efficiency and oil recovery in tight fractured reservoirs. However, the in situ gelation behavior and enhanced oil recovery ability of polymer gel in fractured porous media is still unclear. Thus, in this study, the bulk and in situ gelation behavior of crosslinked phenolic resin gel in a long stainless microtube as the fractured porous media was investigated. The enhanced oil recovery ability of phenolic resin gel used for CO2 channeling was investigated by means of a fractured core model. Results show that, with the increase of polymer and crosslinker concentrations, the bulk gelation time shortens and gel strength improves during the static gelation process. With the increase of polymer concentration and temperature, the in situ static gelation time and dynamic gelation time of the gel system in the microtube are shortened, and the breakthrough pressure gradient increases after gelation. Compared with the in situ static gelation behavior, the in situ dynamic gelation time is prolonged and the breakthrough pressure gradient decreases after gelation. The in situ static gelation time in the microtube is 1.2 times that of bulk gelation time in an ampoule bottle, and the in situ dynamic gelation time is nearly 3 times that of ampoule bottles. When the injected slug volume was 1.0 FV (fracture volume), as the polymer concentration increased from 3000 mg·L-1 to 4000 mg·L-1, the incremental oil recovery increased from 3.53% to 4.73%.

Novel coumarin-thiazolidine-2,4‑dione hybrids as potential α-glucosidase inhibitors: Synthesis and bioactivity evaluation

To discover novel α-glucosidase inhibitors, a series of coumarin- thiazolidine-2,4‑dione hybrids (BJX1∼20) were synthesized and assessed their inhibitory activities against α-glucosidase. Compared to acarbose (IC50 = 655.01 ± 52.54 μM), coumarin-thiazolidinedione hybrids (BJX1∼20) displayed stronger α-glucosidase inhibitory activity, with IC50 values ranging from 5.12 ± 0.48 to 24.07 ± 1.96 μM. BJX3, BJX9, and BJX15 exhibited strongest inhibitory activities against α-glucosidase and acted as non-competitive inhibitors. The fluorescence quenching experiment revealed that the interaction between BJX3, BJX9, BJX15, and α-glucosidase followed a static binding process. CD spectra and 3D fluorescence spectra analysis revealed that the binding of BJX3, BJX9, and BJX15 to α-glucosidase induced a conformation change of α-glucosidase, thereby inhibiting its activity. Molecular docking simulation revealed the binding mechanism of BJX3, BJX9, and BJX15 with α-glucosidase. In vitro cytotoxicity assay indicated BJX15 (0∼64 μM) showed no obvious cytotoxicity against HEK 293 cells. In vivo experiments in mice demonstrated the ameliorative effect of orally administered BJX15 (50 mg kg-1) on postprandial hyperglycemia.

Dynamic uncertainty evaluation of cylindricity error based on Bayesian deep neural network propagation method

Most of the existing methods for measuring and evaluating product geometric tolerances are for static processes, which use lower dimensions, are more complex to calculate, and have poor traceability and dynamic manageability and control of the measurement process. In this paper, a dynamic evaluation model of cylindricity error uncertainty based on the Bayesian deep neural network propagation method is proposed. Firstly, the input random variable sampling sample containing error uncertainty is generated by proposing the improved Monte Carlo sampling method (IMCS), which has the characteristics of homogeneity and validity; then the principle and construction process of a Bayesian neural network model are introduced to generate the propagation model of multidimensional random error variables; further, the Gaussian mixture model is trained by the Monte Carlo method, and the probability density function of the target response is generated by using the Gaussian mixture model. Through numerical experiments, test analysis of bearing outer rings, and comparison with ISO standard methods, the results show that the model based on the dynamic Bayesian deep neural network propagation method can correctly and effectively assess the uncertainty dynamically, and the whole estimation process is stable.

Insights into inhibitory action and interaction of bisdemethoxycurcumin on tyrosinase: Spectroscopic and docking analysis

Bisdemethoxycurcumin (BDMC), a demethoxy derivative of curcumin, has garnered interest for its potential anti-tyrosinase properties, which are crucial for applications in food preservation and cosmetic industries. Despite its recognized bioactive effects, the detailed inhibitory action and interaction of BDMC on tyrosinase and its application in anti-browning processes remain unclear. This study aims to dissect the molecular interactions of BDMC with tyrosinase, elucidate its inhibition mechanism, and assess its efficacy in preventing browning, thereby underpinning its potential as a natural anti-browning agent. Employing a battery of spectroscopic techniques, we characterized the interaction of BDMC with tyrosinase. The anti-browning properties of BDMC were evaluated on fresh-cut apples, and its effect on enzymatic activities related to browning was also investigated. The results indicate that BDMC interacts with tyrosinase in a reversible mixed-type inhibition manner with an IC50 value of 12.5 ± 0.2 μM. Surface Plasmon Resonance (SPR) results indicated that BDMC presented good binding affinity toward tyrosinase. Fluorescence quenching results showed that BDMC could quench the fluorescence of tyrosinase in a static process. Synchronous fluorescence, circular dichroism spectra, and three-dimensional fluorescence results indicated that interaction of BDMC against tyrosinase resulted in changes of tyrosinase on microenvironment and conformation, thus causing decrease of tyrosinase activity. Copper-chelating results presented that BDMC could chelate to copper with stoichiometric ratio of 1: 2. Molecular docking provided intricate details of how BDMC interacted with tyrosinase. Moreover, BDMC exhibited anti-browning capability on fresh cut apples, and could regulate PPO, POD, and APX activities. The comprehensive analysis proposed in this study consolidated the theoretical basis of BDMC as a tyrosinase inhibitor and supported its potential anti-browning application.

Searching of optimal conditions and development of analysis of isocyanate groups in prepolymers

The purpose of the work was to study the options for volumetric analysis of isocyanate groups in prepolymers and to search for factors affecting the accuracy of the analysis in order to identify differences between the values specified by the manufacturer and those obtained in the laboratory.Metods. Intermediate polymerization products based on isocyanates from two or more monomeric units used for the production of polyurethanes, the quality of which is determined by the optimal ratio of isocyanate and hydroxyl groups, were selected as the object of research. The NCO index was determined by the volumetric method based on the interaction of isocyanate groups with diethylamine, with confirmation of the results by methods of mathematical statistics.Results. the work analyzes currently known methods for determining the content of isocyanate groups in prepolymers and identifies differences in the technique of execution. A volumetric method for determining the NCO index based on the interaction of isocyanate groups with diethylamine was used. The solubility of industrial prepolymer samples was evaluated and the dissolution rate was calculated, on the basis of which the optimal volume of the solution and prepolymer samples were determined, providing an accurate determination of the isocyanate number. The adjusted conditions allowed us to reach the required value of the NCO index of the SKU prepolymer, calculated on the basis of static processing of the results and the construction of the Gauss distribution curve. Changes in the methodology make it possible to reach a certified value of the indicator, which allows its use for further calculations in the synthesis of polyurethane.Conclusion. Recommendations for determining the isocyanate index for the analysis of prepolymer by the volumetric method will help to monitor incoming raw materials for compliance with certificates to perform an accurate calculation of raw materials in the synthesis of polyurethane.Key words: prepolymer, polyurethane, isocyanate index, volumetric analysis, diethylamine, solubility.

Neural Encoding of Bodies for Primate Social Perception.

Primates, as social beings, have evolved complex brain mechanisms to navigate intricate social environments. This review explores the neural bases of body perception in both human and nonhuman primates, emphasizing the processing of social signals conveyed by body postures, movements, and interactions. Early studies identified selective neural responses to body stimuli in macaques, particularly within and ventral to the superior temporal sulcus (STS). These regions, known as body patches, represent visual features that are present in bodies but do not appear to be semantic body detectors. They provide information about posture and viewpoint of the body. Recent research using dynamic stimuli has expanded the understanding of the body-selective network, highlighting its complexity and the interplay between static and dynamic processing. In humans, body-selective areas such as the extrastriate body area (EBA) and fusiform body area (FBA) have been implicated in the perception of bodies and their interactions. Moreover, studies on social interactions reveal that regions in the human STS are also tuned to the perception of dyadic interactions, suggesting a specialized social lateral pathway. Computational work developed models of body recognition and social interaction, providing insights into the underlying neural mechanisms. Despite advances, significant gaps remain in understanding the neural mechanisms of body perception and social interaction. Overall, this review underscores the importance of integrating findings across species to comprehensively understand the neural foundations of body perception and the interaction between computational modeling and neural recording.

Experimental study on residual compressive bearing capacity of cracked reinforced concrete columns by SCDA

Abstract The static demolition of structures via soundless chemical demolition agent (SCDA) is attracting increasing attention because of its environmental friendliness and safety, especially in the dense urban areas. However, the research on the application of SCDA in demolishing the reinforced concrete is still scarce, seriously limiting the development of static demolition. In this work, with the combined efforts of experiments and theoretical analysis, we investigate the critical role of borehole layouts in the static demolition of reinforced concrete (RC) by determining the occurrence and expansion of cracks during static demolition, and the resulting residual mechanical properties of RC. The results show that the growth of cracks is significantly dependent on the reaction of SCDA. Additionally, the initiation time of cracking exhibits minimal sensitivity to variations in the layouts pattern of boreholes, while the progression of cracking is predominantly governed by the layouts of boreholes. We demonstrate that cracks formed in static demolition process can significantly reduce the strength of RC with about the remaining bearing capacity is only 10% to 33% of the original and change its failure mode. Further, it is point out that the key factor controlling the failure of RC under axial compression (i.e., the integrity of structures), and the RC with larger crack size and less structural integrity is more prone to fail. The results and findings in this work further clarify the role of borehole layouts and are of great significance in improving the efficiency of static demolition.

The Flowback Proppant Scaling Mechanism in the Horizontal Shale Oil Wells of Qingcheng Oilfield

Abstract Proppant scaling in the horizontal shale oil wells is a common problem in the Qingcheng Oilfield, one of the largest shale oil producers in China. However, few studies have focused on the mechanism of the proppant scaling during the flowback stage of hydraulic fracturing. To elucidate the scaling mechanism, in this study we collected the samples of produced water and solid scales and analyzed their compositions. The experimental simulations of the static and dynamic scaling processes using fracturing fluids, formation water, and reservoir rocks were also performed. In these experiments, the scale was found to be mainly SiO2 combined with FeCO3 and/or CaCO3. FeCO3 was dominant at the initial stage of the flowback, and CaCO3 became dominant at later stages of the flowback and during the stable production stage. It is believed that the high percentage of ferrous minerals (e.g., 46.4% of ferrocalcite) in multiple formation rocks is an essential reason that the proppant scaling happened during the flowback stage. The Fe2+ and Ca2+ ions were believed to be released via reservoir rock reacting with the fracturing fluid under the reservoir temperature and pressure (e.g. 60 °C, 20MPa). In addition, the FeCO3 formed in the initial flowback stage of the fracturing has the greatest impact on production.

Preparation of diatomite-based porous ceramics and their adsorption properties for Cu2+

Diatomite-based porous ceramics were prepared by wet grinding and high temperature calcination with diatomite as the main raw material. The structure and properties of the ceramics were characterized by SEM, XRD, and FI-TR detection techniques. The results show that the diatomite-based porous ceramics are mainly quartz crystal phase and contain a large amount of Si-OH. Their pore size distribution is 500∼3500 nm, the specific surface area is 6.14 m2/g, and the porosity is 47.8 %. The static adsorption process of Cu2+ in water by ceramic particles conforms to the quasi-second-order kinetic model, and the adsorption equilibrium is reached after 6 h with the maximum adsorption capacity of 4.5 mg/g. The Thomas model can be applied to represent the dynamic adsorption of Cu2+ in water. In the adsorption of Cu2+, pH exerts a strong influence. In solution, Cu2+ forms a ligand with hydroxyl surface functional groups on the surface of ceramics and pores through electrostatic interaction, and the hydrogen on the hydroxyl group exchanges with Cu2+, so that Cu2+ can be removed.