Bulk Grain Research Articles

Structural, magnetic and dielectric properties in double perovskite La2-xScxCoMnO6 (x = 0.0, 0.1, and 0.2)

The effect of Sc doping on structural, magnetic, and dielectric properties of double perovskite La2CoMnO6 synthesized by solid-state method was investigated. Crystal structure of La2-xScxCoMnO6 (x = 0.0, 0.1, and 0.2) compounds confirmed through X-ray diffraction patterns (XRD). Rietveld refinement of the XRD revealed the monoclinic phase with P21/n space group and the structural properties such as lattice parameter, bond lengths, and bond angles were obtained. Field emission scanning electron microscopy images revealed a uniform grain distribution, and the grain size was increased with increasing Sc content. The elemental mapping confirms the stoichiometry of the compounds. The room temperature Raman spectra confirm the monoclinic crystal structure and reveal two broad peaks viz., Ag, and Bg which were attributed to specific vibrational modes within the (Co/Mn)O6 octahedra. Magnetic measurements show that Curie temperature (Tc1) decreases with Sc substitution of 206 K for x = 0.0–193 for x = 0.2 due to the Co2+-Mn4+ FM coupling and it follows Curie–Weiss law in the paramagnetic region. Impedance spectroscopy studies show that the conductivity in these materials is predominantly due to the intrinsic bulk grains.

Correlating the microstructure of titanium hydride with its hydrogenation conditions via thermodynamics and kinetics

Titanium hydride is widely considered as an important hydrogen storage material due to its high capacity, stability and low cost. The microstructure of titanium hydride, which is usually induced by the hydrogenation of titanium, strongly influences its storage performance. However, the relationship between the hydrogenation conditions and the derived microstructure of titanium hydride is still unclear, limiting the development of its structure design strategy. In this work, the microstructure of titanium hydride is correlated with its hydrogenation conditions through the thermodynamics and kinetics. By evaluating the effect of the hydrogenation temperature, pressure and cooling rate, three classes of the hydrogenation processes were clarified as kinetic-limited, continuous and stepwise. Besides, according to the SEM and FIB-STEM results, these different processes are confirmed to greatly vary the bulk microstructure. It is concluded that a fast and continuous transition induces a broken bulk morphology, while a stepwise process leads to a larger bulk grain size. In summary, this study presents a framework for designing titanium hydride structures by modifying phase transition processes through careful adjustment of hydrogenation parameters, namely, a condition-process-structure relationship. This relationship offers crucial guidance in managing the grain refinement or coarsening of hydrides within hydrogen storage components and metallurgical applications.

Research on Fluidizing of Swirling Transport of Bulk Grain Based on Gas–Solid Coupled Bin Pump

Research on Fluidizing of Swirling Transport of Bulk Grain Based on Gas–Solid Coupled Bin Pump

Wheat grain classification using hyperspectral imaging: Concatenating Vis-NIR and SWIR Data for single and bulk grains

Wheat variety identification is an essential component of the official inspection of wheat grains to determine their quality and trade value. Traditionally, wheat class identification requires deep knowledge about the appearance of plants and kernels, their history and distribution. Therefore, there is an urgent need for an automatic technology to standardize the nomenclature of wheat varieties to enable both growers and producers to identify their grains practically. The present study investigated the feasibility of Visible-Near Infrared (Vis-NIR) and Short-Wave Infrared (SWIR) spectral imaging to identify wheat classes. Both sides of the wheat kernel as single kernel measurements and bulk grains were used to acquire hyperspectral data and vertical and horizontal data concatenation was implemented to explore performance differences. In addition, the classification of bulk kernels from single kernel data was investigated. Spectral data was pre-treated by standard normal variate (SNV), Savitzky-Golay first (SG-1) and second derivatives (SG-2) and linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN) were applied as the classification algorithms. Furthermore, feature selection was utilised using the minimum redundancy maximum relevance (mRMR) algorithm to investigate the performance difference between data concatenation and feature selection. The results showed that ventral (up) side data showed better classification performances than reverse (down) side data, while Vis-NIR data achieved higher classification accuracies than SWIR data. However, the best classification performance for single kernels was obtained by LDA-SNV using up and down data in the Vis-NIR and SWIR regions vertically and horizontally concatenated with an accuracy of 93.72% for 10-fold cross-validation and 94.93% for test sets. Models based on a hundred features did not achieve the accuracy of models based on concatenated data. Moreover, classification performances of bulk samples were higher than single kernels, which achieved 100% accuracy for both cross-validation and test sets. The study demonstrates that spectral imaging has a high potential to identify wheat classes non-destructively.

Investigation of structural, optical, dielectric, and electrical properties of NaMn4(PO4)3 (NMP) with fillowite-type structure

We synthesized sodium tetra manganese phosphate, NaMn4(PO4)3, by a solid-state method at a temperature of 900 °C for a duration of 10 h. The application of Rietveld analysis to the PXRD patterns revealed that the sample synthesized show the small amount of the secondary phase (Mn2P2O7). The NaMn4(PO4)3 compound belongs to the trigonal system, namely the R-3 space group. The powder's morphology was examined using scanning electron microscopy (SEM), which revealed an average grain size of approximately 7 μm. The powder underwent elemental analysis using energy dispersive X-ray spectroscopy (EDS), which confirmed the uniformity of the sample. Raman and Fourier transform infrared (FTIR) spectroscopies reveal the distinctive spectral peaks associated with P-O bonds in the (PO4)3- functional group. The optical bandgap energy of 1.57 eV was calculated using the diffuse reflectance technique with the Kubelka-Munk function and Tauc's relation. The chromatic coordinates of NaMn₄(PO₄)₃ suggest that the sample can generate blue-purple light, rendering it appropriate as a red phosphor in white light-emitting diodes (LEDs). Integrating this red emission with blue and green light from additional LED components might enhance the overall color quality and efficiency of white light generation. The dielectric properties of NMP exhibited characteristics that were consistent with ionic conductivity. The Nyquist plot displayed a conspicuous semicircle, indicating the presence of a dominant singular process, most probably linked to the bulk grain. The material demonstrates a significant degree of conductivity, with a measured value of around 3.72 × 10–4 S·cm-1 at a temperature of 300 °C.

RADIOCARBON MORTAR DATING INTERCOMPARISON MODIS2—APPROACH FROM THE ZAGREB RADIOCARBON LABORATORY, CROATIA

ABSTRACT The Second International Mortar Dating Intercomparison Study (MODIS2) took place in 2020. Three mortar samples from different sites and chronologies were distributed among various research groups in form of bulk mortar and grain fraction smaller than 150 µm. This is the first time the Zagreb Radiocarbon Laboratory, with support of the Center of Applied Isotope Studies, University of Georgia, took part in the international mortar intercomparison. The initial approach of the Laboratory to mortar dating was to separate 32–63 µm grain fraction and collect three CO2 gas portions by sequential dissolution with acid. After checking the 14C date trends of the gas portions, which should be ascending with later fractions, the one for the first and shortest gas portion was reported as the age of the mortar. However, the first gas portion might not be true age of the mortar, since it still might contain some “dead” carbon. Therefore, data extrapolation from the first two initial CO2 portions was also conducted on the results, but not reported to the intercomparison. Though in general, all the intercomparison reported dates fit the expected historical ages, for one sample, the extrapolated result showed a better match to the historical data.

The effectiveness of phosphine fumigation of wheat in a farm-scale thermosyphon silo

Fumigation of pests is essential for maintaining the quality of stored grain. Rapid and effective pest control is the key to minimise damage to the valuable grain. The current study investigated the efficiency of a farm-scale thermosyphon silo to passively circulate phosphine fumigant gas by measuring the phosphine concentrations at a series of points throughout the silo. A 50 tonne sealable grain silo with an external thermosyphon was fitted with gas sampling lines at three different levels in the grain body, and also in the headspace. The grain was fumigated with phosphine gas 12 times over a 12 month period to contrast the thermosyphon system efficiency under varied external environmental conditions. The minimum fumigation standards of 200 ppm for 10 days or 300 ppm for 7 days were reached and exceeded in all fumigation cycles throughout the year in 12.5–22 days, demonstrating the effectiveness of the thermosyphon system. In addition, the final fumigation was held until the phosphine concentration dropped to 200 ppm. The silo reached 200 ppm only four days after fumigation, reached a maximum of approximately 600 ppm after 30 days, and did not fall below 200 ppm until 106 days after fumigation. Linear air velocity in the was also measured in the thermosyphon pipe and showed that airflow could reach as high as 2.7 m/s, but flow was sporadic and not consistent, most likely due to pressure build up on the headspace of the silo due to resistance to airflow by the bulk grain body. Temperature and humidity results indicated that while both parameters are fundamental in the production of phosphine from aluminium phosphide tablets, they work against each other. This provides an effective control mechanism that prevents the rapid production of phosphine, thereby reducing the buildup of phosphine and reducing the risk of an explosion.

Interfacial modification by 2-fluoroisonicotinic acid enabling high-efficiency and stable n-i-p perovskite solar cells

The power conversion efficiency (PCE) of organic-inorganic halide perovskite solar cells (PSCs) developed rapidly in recent years. However, the defects at the bulk grain boundaries and heterojunction interfaces acting as non-radiative recombination centres and the ion-migration channels severely hinder the charge transport and stability of the PVKs, resulting in low PCE and poor stability. One of the principal impediments to the commercialization of PSCs resides in the challenge posed by this particular aspect. In this work, we employed 2-fluoroisonicotinic acid (2-FINA) as a passivation agent to improve the PCE and stability of n-i-p PSCs by interfacial modification strategy. Owing to the presence of carboxyl (COOH) functional groups and pyridine groups, 2-FINA can strongly interact with uncoordinated Pb2+ and regulate the growth of perovskite crystals, which in turn reduces ion migration and suppress non-radiative recombination along with improves optical properties. Thanks to these improvements, the champion n-i-p PSC solar cell, treated with 2-FINA, showed a PCE of 20.92 %, whereas the untreated one in the same batch exhibited a PCE of 17.13 %. In long-term stability tests, we demonstrated that 2-FINA treatment significantly increases the moisture resistance of non-encapsulated devices.

Research on flow field characteristics of curved pipe in bulk grain cyclone conveying based on gas solid coupling

AbstractIn the transportation of grain particles, elbow wear is a pressing concern. A novel approach to mitigate this involves introducing a lateral air supplement rotation mechanism that enables swirling transportation. We assessed the integration of this device at angles of 45°, 55°, and 65° to the central axis of the main pipeline. While the multiphase flow velocity within the main conduit was held constant at 20 m/s, the lateral device's airflow velocities were tested at 20, 30, and 40 m/s. Utilizing the CFD–DEM simulation for grain particle transportation around bends, our findings indicated that an optimal arrangement was with the lateral device at 55° relative to the main pipeline, with an airflow speed of 30 m/s. This setup fostered a forward particle spiral, drastically diminishing wear on the pipe wall. Experimental evaluations corroborated the simulation's outcomes.

Research on the Method for Recognizing Bulk Grain-Loading Status Based on LiDAR.

Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain's shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status.

Characterization of pore model and percolation simulation of bulk grain pile porous media

AbstractTo ensure the quality of grain storage, researchers have developed a variety of models for the morphological structure of bulk grain piles. However, traditional characterization methods suffer from issues such as inaccurate models, limited size ranges, and low precision. In this study, x‐ray computed tomography was employed for the first time to capture real three‐dimensional (3D) images, revealing the surface porosity distribution ranging from 30% to 38% along the slice direction and a fractal dimension primarily distributed between 1.45 and 1.47. Moreover, the box counting method was used to determine the representative elementary volume (REV) comprising 600 × 600 × 600 pixels, effectively characterizing pore structure using porosity as an index. Connectivity analysis of the REV was conducted by integrating the refined central axis method and the 3D watershed algorithm. Equivalent diameters of connected pores were mainly distributed between 0.5 and 3.5 mm, with an average pore diameter of 2.22 ± 0.02 mm, an average coordination number of 7.04 ± 0.07, and an average tortuosity of 1.58 ± 0.01. Based on the characteristic parameters of connected pores, an equivalent pore network model (EPNM) was reconstructed for numerical simulation of single‐phase percolation of bulk grain pile. In addition, the constructed experimental platform demonstrates that the constructed EPNM closely corresponds to the real pore structure of the seed body, accurately reflecting pore–throat size, connectivity, and morphological characteristics within the grain pile. Furthermore, this research model can be applied to the study of gas flow, heat transfer, and mass transfer within porous media.

Research on the Mechanism of the Skidding Device of Bulk Grain into Silo

In the field of handling, storage and transportation, chutes are used to transfer bulk solids between conveyors and warehouses. In these systems, traditional analytical methods based on the principles of continuum mechanics approximate an accelerated flow that contains the physical body solid properties obtained from standardized tests. Because it is difficult to physically observe the flow inside the transfer structure, there have been few studies to validate the method at full scale. In contrast, discrete element modeling (DEM) allows flow visualization through a transfer chute and qualitative and quantitative analysis if accurate simulation parameters are selected. In order to adapt to the needs of modern intelligent warehousing, we reduced the grain crushing and damage in the process of grain storage. To design and investigate the motion performance of grain particles in a sliding dustpan, this paper utilizes rocky simulation technology, combined with the corresponding bench experiments, to study the impact of the angle arrangement of the dustpan, and to verify the results of the simulation analysis based on the stress–strain analysis of the particle impact. It was found that when the angle of the dustpan arrangement was 40 degrees, the flow of all particles had a better performance in terms of pass ability and energy loss. In the continuous cycle obtained from the simulation, the particle group state at each moment is almost the same as the particle characteristics in the experiment, indicating that the angle of the bucket has an effect on the particle permeability. In this paper, the results of the study on the state of the grain group on the silo device will provide a useful reference for the design of a grain silo device.

Effect of thermal etching and oxidation on in situ characterisation of grain growth in carbon steel

This paper presents the results from a recent investigation into the impact of thermal etching and oxidation on grain growth across the surface and bulk of carbon steel during heat treatment. The study used in situ high temperature Scanning Electron Microscopy imaging, facilitated by a novel heat stage, to capture microstructural changes during heat treatments between 800 and 920 °C. The key observations made using this surface imaging included oxidation, the formation and development of thermal etching, and grain boundary movement. In situ data were further supported by ex situ compositional and optical microstructural data obtained by first sectioning the bulk material. Comparison between the results highlighted a significant discrepancy between the surface and bulk grain growth of the specimens during heat treatment at all temperatures. Further investigation concluded that the combination of thermal etching and oxidation lead to the retardation of grain growth on the surface of the carbon steel, but that grain growth in the bulk specimen appeared to be unaffected. The mechanism that causes the grain retardation is not dissimilar to that of Zenner pinning, where in this case oxide particles pin the newly exposed etched grain boundaries. Hence, oxidation formation within the boundaries decreases the energy of the overall system resulting in movement of the grain boundary becoming less energetically favourable. It is anticipated that these findings will be used to improve understanding of the surface effects that occur during the heat treatment of carbon steel in a vacuum environment.

Real‐time mildew detection and gradation in simulated containerized soybeans: Insights from GC‐IMS analysis of mVOCs and VOCs

AbstractIn the context of bulk grain container transportation, the complex logistics can lead to grain mildew and subsequent economic losses. Therefore, there is a pressing need to explore swift and real‐time mildew detection technology. Our investigation, simulating actual transportation conditions, revealed that Aspergillus, Penicillium, and Rhizopus were the primary molds responsible for soybean mildew during container transportation. Utilizing gas chromatography‐ion migration spectroscopy (GC‐IMS), we analyzed the correlation between the mVOCs (microbial volatile organic compounds) produced by dominant mold and the VOCs emitted during soybean mildew. Principal Component Analysis (PCA) and clustering results demonstrated the distinctive identification of VOCs in soybeans with varying degrees of mildew. The mildew degree significantly influenced the content variation of VOCs. As the mildew degree increased, the concentrations of nonanal, octanal, etc. progressively decreased, contrasting with the rising levels of phenylacetaldehyde, 3‐methyl‐2‐butenal, etc. Therefore, the combination of GC‐IMS with chemometrics proves to be a viable method for identifying the mildew degree of soybeans. Therefore, this study underscores the importance of implementing effective mildew detection techniques in the challenging context of bulk grain container transportation.

Enhancement the electrical and linear/nonlinear optical properties of ZnCo2O4 through Al3+doping

In this work, ZnCo2O4 samples doped with Al3+ were prepared by the hydrothermal method. Synchrotron x-ray diffraction, FTIR, SEM and EDX techniques were used to investigate the structure, microstructure, morphology, and composition of ZnCo2-xAlxO4 samples. Rietveld analysis revealed a nearly inverse structure for the undoped ZnCo2O4. The degree of inversion is increased upon increasing the Al3+ doping content. Al ions preferentially occupy the octahedral B-site. The optical band gaps of ZnCo2−xAlxO4 samples are 1.9, 1.8 and 1.91 eV for x = 0, 0.1 and 0.2, respectively. Different empirical models were used to obtain the refractive index of the different samples using their corresponding optical band gaps. The increase in non-linear optical values in the sample with x = 0.1 suggested that this sample could be used in a variety of photonic applications. The effects of frequency and temperature on the dielectric permittivity, impedance and electrical modulus of different samples were explored. All samples exhibited the non-Debye relaxation phenomenon. The relaxation time of each sample is affected with the frequency and temperature. The Cole-Cole curve is employed to reduce uncertainty regarding the electrical characteristics of these ZnCo2−xAlxO4 samples induced by grain boundaries or bulk grain. The substitution of Al3+ ions can regulate the conductivity of the ZnCo2O4 sample.

The pressure drop of fibrous surface filters for gas Filtration: Modeling and experimental studies

Surface filters are extensively utilized for the control of particulate matter and air pollution, and predicting the pressure drop can offer theoretical guidance in the selection of operational parameters for fibrous surface filters. However, there is presently a dearth of efficient and user-friendly methods for predicting the pressure drop. This paper focuses on the accurate and convenient prediction of the pressure drop of fibrous surface filters for gas filtration. Firstly, an analytical model for pressure drop, including the blocked fiber layer and the dust cake, was developed by considering the characteristics of the filter media, dust properties, and loading conditions. The model was validated through laboratory experiments, and the relative error between the model values and experimental data remains within a 15% error. Furthermore, based on the model, the quantitative analysis of the relationship between the pressure drop of the fiber layer and the pressure drop of the dust cake, and the filtration parameter importance were conducted. The influence of filtration parameters on the pressure drop follows the order of filtration velocity, dust concentration, and dust particle size, which provides guidance for choosing the gas filtration operating conditions in the efficient and low-resistance operation of surface filters. Additionally, the model was validated through field test data collected with large-scale cartridge filters in bulk grain loading. The predicted values fit well with the field test results. Overall, this study provides a promising tool for predicting the pressure drop of fibrous surface filters.

A Practicable Guideline for Predicting the Thermal Conductivity of Unconsolidated Soils

For large infrastructure projects, such as high-voltage underground cables or for evaluating the very shallow geothermal potential (vSGP) of small-scale horizontal geothermal systems, large-scale geothermal collector systems (LSCs), and fifth generation low temperature district heating and cooling networks (5GDHC), the thermal conductivity (λ) of the subsurface is a decisive soil parameter in terms of dimensioning and design. In the planning phase, when direct measurements of the thermal conductivity are not yet available or possible, λ must therefore often be estimated. Various empirical literature models can be used for this purpose, based on the knowledge of bulk density, moisture content, and grain size distribution. In this study, selected models were validated using 59 series of thermal conductivity measurements performed on soil samples taken from different sites in Germany. By considering different soil texture and moisture categories, a practicable guideline in the form of a decision tree, employed by empirical models to calculate the thermal conductivity of unconsolidated soils, was developed. The Hu et al. (2001) model showed the smallest deviations from the measured values for clayey and silty soils, with an RMSE value of 0.20 W/(m∙K). The Markert et al. (2017) model was determined to be the best-fitting model for sandy soils, with an RMSE value of 0.29 W/(m∙K).

Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment

The multi-field coupling of grain piles in grain silos is a focal point of research in the field of grain storage. The porosity of grain piles is a critical parameter that affects the heat and moisture transfer in grain piles. To investigate the distribution law of the bulk grain pile porosity in grain silos, machine learning algorithms were incorporated into the prediction model for grain porosity. Firstly, this study acquired the database by conducting compression experiments on grain specimens and collecting data from the literature. The back propagation neural network (BPNN) algorithm was optimized using three metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA)). Five machine learning models (GA–BPNN, PSO–BPNN, WOA–BPNN, BPNN, and random forest (RF)) were developed to predict the grain porosity using three input parameters (vertical pressure, grain type, and moisture content). The five models were assessed using four evaluation metrics: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), to determine the best porosity prediction model. Finally, the generalization ability of the best prediction model was verified using the results of the grain cell box experiment on wheat piles. The results indicated that the WOA–BPNN model was the best prediction model with an R2 value of 0.9542, an RMSE value of 0.0079, an MAE value of 0.0044, and an MAPE value of 1.1467%. The WOA–BPNN model demonstrated strong generalization ability, confirming the feasibility of using this model to predict grain porosity. It also established an expression for the relationship between wheat porosity and the vertical pressure of the grain pile. This study presents a machine learning prediction method for determining the porosity of grain piles. The obtained porosity distribution law serves as a crucial basis for conducting comprehensive multi-field coupling analysis of grain piles and offers theoretical support for safe grain storage.

MEASUREMENT OF RICE MOISTURE CONTENT BASED ON QUANTITATIVE ANALYSIS FROM RADIO TOMOGRAPHY IMAGES

Inefficient storage of paddy and rice grains can lead to grain deterioration, resulting in post-harvest losses ranging from 10% to 30%. The quality of grains cannot be improved throughout the storage period. Therefore, following the mechanisation of agricultural industries, air dryers have been developed to control the crops’ moisture level by blowing ambient or heated air into the silo to improve the aeration and allow the grains to be preserved with minimal loss of quality until the appropriate time for managing and marketing processes. However, the conventional sampling method used to measure the moisture level is inefficient because it is very localised and only represents part of the moisture distribution inside the bulk grains. Additionally, incorporating advanced technologies can be a significant cost limitation for small-scale industries. Thus, to address the issue, this research study developed a radio tomographic imaging (RTI) system in a silo-scale prototype using 20 sensor nodes operating at 2.4 GHz to localise and monitor the moisture level constructively. The RTI system reconstructs the cross-sectional images across the rice silo by measuring radio frequency attenuation, in terms of received signal strength (RSS) quality, caused by the rice moisture phantoms within the wireless sensor network (WSN) area. A total of five phantoms’ profiles having a percentage of moisture content (MC) of 15%, 20% and 25% were reconstructed using four image reconstruction algorithms, Linear Back Projection (LBP), Filtered Back Projection (FBP), Newton’s One-step Error Reconstruction (NOSER) and Tikhonov Regularisation.

The Role of Silica Nanofluid as a Plugging Agent for Enhanced Oil Recovery – Experimental Approach

Many enhanced oil recovery techniques have been adopted in oil production to recover as much oil as possible. However, enhanced oil recovery techniques are complex and costly. Therefore, nanofluids have been modified in the laboratory to be consistent with reservoir fluids and rocks while also being environmentally benign. This study aims to investigate the role of silica nanofluid in plugging the high permeable zones after oil is recovered from the reservoir to divert pressure to the low permeable zone for enhanced oil production from the low permeable zones. A digital gas permeameter (DGP-300-B) was used to determine the permeability of the core by injecting nitrogen gas. In addition, a DHP-100-M Digital Helium Porosimeter was used to determine bulk volume, grain volume, and grain density porosity. The experiment was conducted with different percentages of silicon dioxide nanoparticles to observe the effect of plugging in each stage. Results show that a 52% reduction in core permeability was observed when using 0.5wt.% of silica nanoparticles, a 72% reduction in core permeability when using 1wt.% of silica nanoparticles and a 94% reduction in core permeability when using 2wt.% of silica nanoparticles.