Types Of Impurities Research Articles

A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.

Electrolysis of low-carbon methanol for point-of-use hydrogen generation: Opportunities and challenges for the direct use of unrefined feedstocks

Low-carbon methanol (LCM) is a promising hydrogen carrier that can reduce the overall transportation and storage costs associated with the industrial use of H2 as a chemical feedstock or fuel. Herein, LCM refers to methanol produced via processes that emit lower greenhouse gases (by 90% or more) relative to conventional methanol production derived from fossil fuels like pipelined natural gas or coal. LCM production pathways can valorize stranded methane as well as from sources such as biomass and biogas (e.g., landfill and digester gases). The distributed nature of these stranded feedstocks lends themselves well to methanol synthesis within a smaller-scale, decentralized infrastructure. While process intensification and relatively cheaper feedstock can support these distributed productions, forgoing economies of scale can significantly increase the cost of conventional upgrading steps such as distillation. Fortunately, these upgrading steps may be unnecessary by targeting specific applications, where high methanol purities are not required, thereby lowering the holistic cost and carbon intensity of an overall process.Here, we consider methanol as a hydrogen carrier and its conversion to H2 at the point of use through an electrolytic process by experimentally comparing the oxidation of methanol, of varying purity levels (e.g., grade (99.9+% purity) and crude forms) using a proton exchange membrane (PEM) electrolyzer. In this study, the supply of crude methanol derives from a distributed gas-to-liquid process, developed by M2X Energy Inc. The impurity content is speciated and quantified within the crude methanol sample, which is used directly with water co-reactants to evaluate the effect of impurity type and concentration on electrolytic performance on commercial Pt–Ru catalysts. Our experimental results show that about 1 wt% of C2–C5 alcohols in the starting crude methanol readily poison the catalyst, leading to an increased electricity consumption by 5.1–9.8 kWh per kg H2 produced when operating the electrolyzer at high current densities (>200 mA cm−2). A sensitivity analysis is developed according to the measured energy penalty, which can be used to decide whether bypassing methanol distillation provides economic benefit based on site-specific electricity and distillation costs.

Determination of Gas–Oil minimum miscibility pressure for impure CO2 through optimized machine learning models

Minimum miscibility pressure (MMP) is one of the most important parameters for designing CO2 enhanced oil recovery (EOR) and associated storage in depleted oil reservoirs. The injection gas stream often contains a certain concentration of impurities such as N2, H2S, and CH4 depending on the source of CO2. These impurities have different effects on CO2 MMP, but there is a lack of widely accepted approaches to account for these effects on MMP calculation. In this study, a series of activities were conducted to develop a machine learning (ML)-based methodology for determining MMP for CO2 with various impurities. A database containing 234 CO2 MMP test sets with around 5000 data points was built based on the reported experimental measurements in the public domain. The database was then subgrouped by three specific criteria: CO2 concentration in the injection gas, type of impurities in the injection gas, and heavier hydrocarbon content in the oil. This subgrouping was essential to capture the impact of different factors on CO2 MMP. An ensemble ML approach with seven ML models, including random forest, adaptive boosting, light gradient boosting machine, extreme gradient boosting (XGBoost), stacking, artificial neural network, and voting regressor, was employed to calculate MMP based on the subgrouped database. The hyperparameters of these ML models were optimized by the grid search technique to minimize the relative errors between calculated and measured MMP values. The performance of each algorithm was assessed using three regression metrics: average absolute relative error (AARE), R-squared score (R2), and root mean square error (RMSE). All of these metrics exhibited satisfactory values for the optimized ML models. The average values of R2, RMSE, and AARE were 0.962, 1.571, and 4.55%, respectively, for the three subgroups, indicating a high accuracy of MMP calculations using the optimized ML models. The XGBoost model emerged as the top performer across the three metrics, with an R2 of 0.979, an AARE of 2.835%, and an RMSE of 1.183 for a dataset with 190 cases. The overall high level of accuracy confirmed the reliability of these ML models in calculating MMP for CO2 with different impurities as well as the importance of optimization in the modeling process.

Effect of phosphorus impurities on sulfoaluminate cement-modified gypsum-based self-leveling mortar and improvement method

The preparation of gypsum-based self-leveling mortar (GSLM) from solid waste-derived sulfoaluminate cement-modified hemihydrate gypsum is an effective path to enhance its mechanical properties. However, the phosphorus impurities in phosphogypsum (PG) still pose a challenge to the hemihydrate gypsum & sulfoaluminate cement composite system. To evaluate the impact of phosphorus impurities and develop targeted removal strategies, this study first assessed the effects of different types of phosphorus impurities on the mechanical properties and hydration characteristics of the GSLM. Then, a method of pretreating PG with carbide slag (CS) was proposed, and the removal efficiency and mechanism of phosphorus were examined by ICP-OES and XPS testing. The results indicated that the soluble phosphorus impurities severely inhibit the hydration of both CŜH0.5 and C4A3Ŝ, leading to a most notable reduced mechanical performance. The sequence of inhibitory action was as follows: H3PO4 >> Ca(H2PO4)2·H2O > CaHPO4·2H2O > Ca3(PO4)2. CS could effectively immobilize the soluble phosphorus in PG. The pretreatment by CS notably enhanced the hydration degree of the GSLM by converting soluble phosphorus into Ca3(PO4)2 beforehand. In addition, the alkaline environment provided by CS promoted the ettringite formation. When the CS content approaches 3%, a PG-based GSLM with good performances meeting the G20 standard requirements of GSLM can be prepared.

SynONIM: A Comprehensive Database of Synthetic Oligonucleotide Modifications and Impurities to Aid in Their Characterization by Mass Spectrometry.

Given the resurgence of oligonucleotides in the biotherapeutic space, there is a profound focus on their characterization by mass spectrometry. These therapeutic moieties commonly employ synthetic modifications to aid in increasing efficacy and stability; however, these modifications can also increase the complexity of mass spectrometry data analysis. Additionally, various stress conditions can affect both the observed level and type of impurities stemming from the variety of utilized modifications. Within the oligonucleotide analytical development community, a clear desire exists for a unified database of synthetic oligonucleotide modifications and impurities where information regarding structure, mass, and shorthand nomenclature can be contained. To address this, the authors have prepared an online database and webtool of synthetic oligonucleotide impurities and modifications, SynONIM, to centrally locate information key to the mass spectrometry community. SynONIM can be queried by elemental composition lost or gained, mass shift, shorthand notation, nucleotide location, and species origin.

Sub-nanosecond magnetic vortex reversal induced by localized resonant strain field in doped nanomagnets

The strain-induced magnetic vortex dynamics in nanomagnets with ring-shaped impurities are investigated by means of micromagnetic simulations. It is found that the type and location of impurities can modulate the strain-stimulated spin wave spectrum of the magnetic vortex. Compared with pure nanomagnets without doping, the scattering impurities make the eigenfrequency of nanomagnets higher, while the pinning impurities lead to lower eigenfrequency. Moreover, the spin wave oscillation amplitude in a doped nanomagnet is strengthened by the gradient of exchange energy at the interface between the impurity ring and nanomagnet. The magnetic vortex polarity in a nanomagnet with specific doping schemes can be reversed in a sub-nanosecond scale by a localized resonant strain signal. Besides the switching efficiency improvement, the threshold stress of sub-nanosecond polarity reversal in nanomagnets with specific doping schemes is also reduced compared to the counterpart of nanomagnets without impurities. These results indicate that doping engineering of nanomagnets is a significant method to achieve straintronic devices with higher operating frequency and lower energy consumption.

Greening the supply chain: Sustainable approaches for rare earth element recovery from neodymium iron boron magnet waste

The increasing demand for modern technologies has led to a growing reliance on rare earth elements (REEs). To address this issue, recycling used products such as permanent magnet waste containing REEs. However, this approach necessitates the development of advanced extraction and separation techniques to ensure high yields and purity of the REEs extracted. This review provides an overview of the latest extraction and separation technologies, with a particular focus on the hydrometallurgical approach for extracting REEs from secondary sources, notably permanent magnets. Hydrometallurgy, which involves leaching followed by solvent extraction for purification, has gained widespread use for obtaining REEs from secondary sources, as evidenced by its reported high recovery efficiencies. We found that the recovery of REEs using chemical leaching varied between 80% and 99%, influenced by factors such as type of source material, leaching solvent, leaching conditions, impurities, reaction kinetics and solid-liquid ratio. However, effectively employing this process on a larger scale still faces certain challenges due to the excessive use of corrosive solvents for leaching magnet waste and the generation of toxic chemicals as end products in the form of leachate. Additionally, the current process exhibits deficiencies in the targeted recovery of REEs from magnet waste, specifically in achieving purity and selectivity by eliminating iron impurities. This article concludes that the future prospects for the selective recovery of REEs from neodymium iron boron magnet waste lie in green and environment-friendly solvents. One such approach revolves around the utilization of biodegradable organic acids and salt aqua regia for leaching. These solvents are less corrosive and have high dissolution efficiency, which results in less chemical consumption and an overall environment-friendly and cost-effective recovery process.

Raman peak shift and broadening in crystalline nanoparticles with lattice impurities

The effect of point-like lattice impurities on nanoparticle Raman spectra is studied using both numerical and analytical methods. Particular cases of replacement atoms of various masses, vacancies, and disorder in interatomic bonds are considered. It is shown that the disorder leads not only to the broadening of optical phonon lines but also to the shift of the corresponding Raman peak. The latter can be either positive (i.e., blueshift) or negative (redshift) depending on the type of impurities. Thus there is an additional contribution to the well-known redshift that occurs due to the size-quantization (confinement) effect. Considering nanometer-sized diamond particles as a representative example, we show that the broadening and the shift are, as a rule, of the same order of magnitude. The results are discussed in the framework of the self-consistent T-matrix approach. It is argued that both effects should be considered for accurate treatment of experimental Raman spectra. Simple recipes to do so are formulated for several important cases including NV centers in nanodiamonds.

Purification of Iranian bentonite for organoclay synthesis for use in clay–polymer composites

Abstract Polymer-based composites modified with organoclay are economically beneficial candidates for a variety of applications. Different types of impurities accompany montmorillonite phases in various bentonites, which impact the quality and cost of organoclays and final composites. To obtain organoclays for clay composite applications, eight Iranian raw bentonites from different geographical locations were selected as the candidates and characterized by X-ray diffraction (XRD). Sample IB was chosen due to its high purity and lower cristobalite content than the other samples. It was purified by centrifugation or sedimentation methods using a sodium hexametaphosphate (NaHMP) dispersant. The cation-exchange capacity (CEC) was measured for bentonite before and after purification by sedimentation, and it showed a significant increase from 6.944 to 12.128 eq g–1, confirming successful purification. Organoclays were prepared using purified bentonite (sedimentation method) with two surfactants (cetyltrimethylammonium bromide and octadecylamine), and the amount of octadecylamine was optimized. Purified bentonite and organoclay were characterized by XRD, scanning electron microscopy (SEM) and X-ray fluorescence. The results indicate that most of the impurities were removed after purification, and the interlayer space of organoclays increased to 35 Å in the optimized sample prepared with an amount of octadecylamine that was twice the CEC in purified bentonite. The prepared organoclay was used to improve low-density polyethylene (LDPE) polymer properties. The clay–polymer composite properties were studied by field emission SEM, thermogravimetric analysis and tensile strength tests. The organoclay was fully dispersed in the LDPE matrix and in the sample with 5 wt.% of organoclay, where Ti (the temperature at which 10% of the sample is decomposed) and T50% (the midpoint of degradation) were 17°C and 13°C greater than those of polyethylene, respectively. Additionally, the sample residue with 5 wt.% of organoclay at 600°C was 43.4%. The tensile strength of polyethylene increased from 8.67 to 9.03 MPa in the sample with 4 wt.% of organoclay.

Hole and positron interaction with vacancies and p-type dopants in epitaxially grown silicon

The concentration of vacancies and impurities in semiconductors plays a crucial role in determining their electrical, optical, and thermal properties. This study aims to clarify the nature of the interaction between positrons and ionized p-type impurities, emphasizing the similarities they share with the interaction between holes and this type of impurity. An overall strategy for investigating defects in semiconductor crystals that exhibit a combination of vacancies and p-type impurities is presented. By using positron annihilation spectroscopy, in particular, Doppler broadening of the annihilation radiation, we quantify the concentration of vacancies in epitaxial Si crystals grown by low-energy plasma-enhanced chemical vapor deposition. The vacancy number densities that we find are (1.2 ± 1.0) × 1017 cm−3 and (3.2 ± 1.5) × 1020 cm−3 for growth rates of 0.27 and 4.9 nm/s, respectively. Subsequent extended annealing of the Si samples effectively reduces the vacancy density below the sensitivity threshold of the positron technique. Secondary ion mass spectrometry indicates that the boron doping remains unaffected during the annealing treatment intended for vacancy removal. This study provides valuable insights into the intricate interplay between vacancies and ionized impurities with positrons in semiconductor crystals. The obtained results contribute to advance the control and understanding of material properties in heterostructures by emphasizing the significance of managing vacancy and dopant concentrations.

Electrochemical CO2 conversion in eutectic Li-Ba and Li-Ca carbonate mixtures

Here, the impact of electrolyte selection and electrolysis temperature on carbon morphology, energy consumption, and voltage efficiency in molten Li2CO3-BaCO3 and Li2CO3-CaCO3 electrolytes during 90 min 10 A constant current electrolysis are investigated. The scarcity of experimental investigations into these binary electrolytes, coupled with a lack of systematic investigation of temperature effects, necessitates further examination. The comprehensive analysis of the produced carbon reveals that both electrolyte composition and temperature influence carbon morphology. At lower temperatures the effect of electrolyte composition to the morphology is a more significant. The type and amount of metallic impurities mixed with the carbon vary depending on the electrolyte composition and electrolysis temperatures. The results from Li2CO3-CaCO3 electrolyte show more promising compared to results from Li2CO3-BaCO3 electrolyte in terms of product quality. Regarding electrolysis power and voltage efficiency, Li2CO3-CaCO3 proves equal or superior to other electrolytes, depending on temperature. Heat generation occurs in all the experiments, which emphasizes the importance maintaining a constant electrolysis temperature in industrial processes to manage heat generation effectively.

Central-cell corrections for hydrogenic, silicon (Si), selenium (Se), sulfur (S), and germanium (Ge) donor impurities and pressure–temperature effects on the optical properties of the GaAs/GaAlAs multi-layer quantum disk

GaAs/GaAlAs quantum dots can be doped with various impurities to modify their optoelectronic properties. Common impurities used for doping include: Hydrogenic impurity (HI), Silicon (Si), Selenium (Se), Germanium (Ge), and Sulfur (S) are common n-type dopant in GaAs-based materials. This work has investigated the effects of temperature and pressure on the refractive index changes (RICs) and optical absorption coefficients (OACs) in the multi-layer quantum disk, accounting for central cell correction. We have used the finite element method to calculate the eigenvalues and their corresponding wave-functions. These values are utilized in the computation of the OACs and RICs. Our outcomes can be summarized as follows: (i) The applied pressure induces a red-shift of the OACs peaks, and their magnitude is reduced. Similarly, temperature causes a blue-shift of the OACs peaks, and their magnitude is enhanced. (ii) The presence and type of impurity lead to significant changes in both OACs and RICs.

Botanical Impurities in the Supply Chain: A New Allergenic Risk Exacerbated by Geopolitical Challenges.

The supply chains of food raw materials have recently been heavily influenced by geopolitical events. Products that came from, or transited through, areas currently in conflict are now preferentially supplied from alternative areas. These changes may entail risks for food safety. We review the potential allergenicity of botanical impurities, specifically vegetable contaminants, with particular attention to the contamination of vegetable oils. We delve into the diverse types of botanical impurities, their sources, and the associated allergenic potential. Our analysis encompasses an evaluation of the regulatory framework governing botanical impurities in food labeling. Unintended plant-derived contaminants may manifest in raw materials during various stages of food production, processing, or storage, posing a risk of allergic reactions for individuals with established food allergies. Issues may arise from natural occurrence, cross-contamination in the supply chain, and contamination at during production. The food and food service industries are responsible for providing and preparing foods that are safe for people with food allergies: we address the challenges inherent in risk assessment of botanical impurities. The presence of botanical impurities emerges as a significant risk factor for food allergies in the 2020s. We advocate for regulatory authorities to fortify labeling requirements and develop robust risk assessment tools. These measures are necessary to enhance consumer awareness regarding the potential risks posed by these contaminants.

From sulfite liquor to vanillin: effect of crystallization process on the yield, purity, and polymorphic form of crystals

ABSTRACT Different approaches for vanillin crystallization were performed using an aqueous solution, with low concentrations of this potential food and pharmaceutical phenolic compound, obtained from adsorption/desorption experiments applied to an oxidized solution of lignin isolated from softwood sulfite liquor. The process yield and purity of the vanillin crystals were determined, and the thermal stability of the two resulting polymorphic forms was analyzed by DSC. Crystallization processes by swift cooling and evaporation with heat favor the nucleation of metastable vanillin crystals (Form II) with lower yields than slow evaporative and cooling processes that crystallize vanillin into the stable polymorph (Form I) with yields around 80%. Moreover, the polymorphic form of vanillin, yield, and purity were found to be highly dependent on the conditions of the crystallization process, mainly the temperature and the range of temperature variation. The melting points of polymorphic Form I and II were found to be 82.6 and 81.0°C, respectively, proving that Form I is the most thermodynamically stable vanillin crystal form. In conclusion, the process conditions for vanillin crystallization strongly affect the polymorphic form, the process yield, the contents, and the type of impurities in the final product, consequently, influencing its potential industrial applications.

Revision of the oxygen reduction reaction on N-doped graphenes by grand-canonical DFT.

Nitrogen-doped graphenes were among the first promising metal-free carbon-based catalysts for the oxygen reduction reaction (ORR). However, data on the most efficient catalytic centers and their catalytic mechanisms are still under debate. In this work, we study the associative mechanism of the ORR in an alkaline medium on graphene containing various types of nitrogen doping. The free energy profile of the reaction is constructed using grand-canonical DFT at a constant electrode potential in combination with an implicit electrolyte model. It is shown that the reaction mechanism differs from the generally accepted one and depends on the surface potential and doping type. In particular, as the potential decreases, coupled electron-proton transfer changes to sequential electron and proton transfer, and the potential at which this occurs depends on the doping type. It has been shown that oxygen chemisorption is the limiting step. The electrocatalytic mechanism of the nitrogen dopants involves reducing the oxygen chemisorption energy. Calculations predict that, at different potentials, different types of nitrogen impurities most effectively catalyze the ORR.

Releasing antiferromagnetic skyrmions from local magnetic-anisotropy defects

Lattice defects may work as a kind of apparatus for catching topological excitations, preventing their escape. So, the problem of removing skyrmions from eventual local defects in magnetic materials must be closely related to new technologies such as skyrmionic. Here, we examine the conditions for drawing a skyrmion from a magnetic impurity in a two-dimensional antiferromagnetic system by applying spin-polarized currents (SPC). Two types of impurities are investigated (local easy-axis and easy-plane anisotropy defects). Also, two methods to release the skyrmion with SPC are explored. In principle, our results could be qualitatively relevant to any other type of lattice defect.

Temperature Dependence of the Band Gap of Completely Fluorinated/Hydrogenated Carbon Nanotubes: The Role of One-Dimensional Chains

The temperature dependence of the band gap Eg(T) in zigzag single-walled carbon nanotubes at the maximum (50%) fluorination and hydrogenation has been theoretically investigated for three coating versions. It has been shown that the character of coating dramatically affects the dependence Eg(T), which may vary over a wide range from very weak (typical of pure carbon nanotubes) to strong (typical of bulk semiconductors). The character of the temperature behavior Eg(T) is directly related to the formation of one-dimensional alternating chains in nanotubes. The main factors determining this dependence are the diameter of carbon nanotube, impurity position, and impurity type.

The Kondo Effect in CexLaLuScY (x = 0.05-1.0) High-Entropy Alloys.

In the search for electronic phenomena in high-entropy alloys (HEAs) that go beyond the independent-electron description, we have synthesized a series of hexagonal rare earth (RE)-based HEAs: CexLaLuScY (x = 0.05-1.0). The measurements of electrical resistivity, magnetic susceptibility and specific heat have shown that the CexLaLuScY HEAs exhibit the Kondo effect, which is of a single impurity type in the entire range of employed Ce concentrations despite the alloys being classified as dense (concentrated) Kondo systems. A comparison to other known dense Kondo systems has revealed that the Kondo effect in the CexLaLuScY HEAs behaves quite differently from the chemically ordered Kondo lattices but quite similar to the RE-containing magnetic metallic glasses and randomly chemically disordered Kondo lattices of the chemical formula RE1xRE21-xM (with RE1 being magnetic and RE2 being nonmagnetic). The main reason for the similarity between HEAs and the metallic glasses and chemically disordered Kondo lattices appears to be the absence of a periodic 4f sublattice in these systems, which prevents the formation of a coherent state between the 4f-scattering sites in the T→ 0 limit. The crystal-glass duality of HEAs does not bring conceptually new features to the Kondo effect that would not be already present in other disordered dense Kondo systems. This study broadens the classification of HEAs to correlated electron systems.

Calculating Dielectric Constant at the Interface Between Polyethylene and Copper Using Microscopic Polarization Theory

The relative dielectric constant is the main electric property of polyethylene (PE) as a dielectric material. Based on experimental findings, the dielectric constant of bulk PE has been known to be constant. However, when interfaced with copper –as it is the case in most power applications- the dielectric constant may differ from that value at interface region in atomic scale, which in turn affects the charge injection process. This is further complicated by the inevitable presence of chemical impurities at the interface, where the dielectric constant becomes position-dependent and depends on the type of impurity. In this work, the dielectric constant is calculated for the cases of a pure slab of PE during the interface between PE and copper, and finally with chemical impurities present in the interface region. In those cases, the microscopic polarization theory is used in calculating the position-dependent relative dielectric constant. This theory makes use of the charge density distribution under an applied electric field, which is computed using the density functional theory (DFT). It is found that the calculated dielectric constant varies at the interface region within a range of a few Angstroms, beyond which it becomes constant well within the bulk polyethylene. The dielectric constant variation at the interface region is then used to study the impact on the injection process using the Schottky injection mechanism. The injection current density is computed from Schottky injection law as a function of applied field at different dielectric constant values. It is found that the injection current increased with decreasing the dielectric constant at the interface region. The paper concludes that the relative dielectric constant varies at the interface due to the morphological deformation between the polyethylene chains and the copper atoms. This variation is further enhanced in the presence of chemical impurities. It is further concluded that the injection current density does not only depend on the barrier height, but also on the varying relative dielectric constant.

Recent advances in the marketing, impurity characterization and purification of quartz

Due to its stable physical and chemical properties and its abundance in nature, quartz is widely employed in industrial and high-tech applications. However, the presence of diverse types and states of impurities in quartz ores from different geological formations poses a challenge in the process of purifying high-purity quartz, leading to wastage of raw materials and escalated costs. This study presents the socio-economic applications of quartz, scrutinizes the formation and separation mechanisms of impurities in quartz ores from a mineralogical perspective, examines the obstacles faced in quartz purification, explains the current state of development, and provides a technical summary of quartz purification. The analysis reveals that lattice impurity elements and various types of inclusion impurities are the principal factors affecting the purity of quartz. Various green separation techniques are applied based on the composition of the quartz minerals and the state of the impurities. Standard practices may involve physical pre-treatment such as scrubbing, ultrasonic crushing, and electromagnetic pulse cracking, followed by rough cleaning through color separation, superconducting high gradient magnetic separation, and flotation, and chemical pre-treatment (high-temperature or microwave roasting with chloride doping, and ammonium sulfate thermal crushing combined with water quenching to remove gas-liquid inclusions from quartz minerals). Finally, finishing processes such as fluorine-free and catalytic hot-pressure acid leaching or microbiological purification treatment with filamentous or Aspergillus fungi are used to obtain high-purity silica sand with an anticipated purity of about 99.99%.