Investigation of the Silicon Content of Sands from River Wudil for the Development of Solar PV Panel

Investigation of the Silicon Content of Sands from River Wudil for the Development of Solar PV Panel

Zainulabideen Abbati (1), Ali Hussain Ali (2), Hindatu Idris Umar (3), Yusuf Muhammad Sani (4)

(1) Department of Welding and Fabrication Engineering Technology, Kano State Polytechnic, Nigeria

(2) Department of Mechanical Engineering, Aliko Dangote University of Science and Technology Wudil, Nigeria

(4) Department of Mechanical Engineering, Kano State Polytechnic, Nigeria

(3) Department of Textile and Polymer Technology, Kano State Polytechnic, Nigeria

Abstract – The increasing global demand for renewable energy has intensified the need for sustainable and locally available raw materials for photovoltaic (PV) technologies. Silicon, obtained primarily from silica (SiO), remains the principal semiconductor material used in the manufacture of crystalline solar photovoltaic cells due to its excellent electrical properties, abundance, and long-term stability. This study investigates the silicon content of River Wudil sand in Kano State, Nigeria, to evaluate its suitability as a potential raw material for silica extraction for the development of photovoltaic- related PVC solar panel components. Representative sand samples were collected from five different locations along River Wudil following standard sampling procedures. The samples were subjected to beneficiation processes, including washing, acid leaching, drying, magnetic separation, and sieve analysis before characterization using X-ray Fluorescence (XRF), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX). The sieve analysis revealed that Samples C and D possessed well-graded medium-sized particles, making them more suitable for silica beneficiation due to their larger surface area and lower grinding requirements. XRF analysis showed that silicon dioxide (SiO) was the predominant oxide in all samples, with concentrations ranging from 75.92 wt.% to 79.60 wt.%, confirming that River Wudil sand is naturally rich in silica. However, impurities such as AlO, FeO, KO, CaO, TiO, and trace metallic oxides were also detected, indicating that additional purification processes are required to achieve the high-purity silica needed for photovoltaic applications. SEM micrographs revealed predominantly sub-angular to sub-rounded quartz grains with relatively smooth surfaces, localized fractures, and limited impurity coatings, characteristics that favour efficient washing, acid leaching, and magnetic separation. Although the EDX results reported chromium and strontium as dominant detected elements, these findings were inconsistent with the XRF and SEM analyses due to the exclusion of oxygen during measurement, thereby limiting their reliability for quantitative elemental interpretation.

Overall, the combined characterization results demonstrate that River Wudil sand possesses significant potential as a local silica resource for industrial beneficiation. Among the investigated samples, Sample A exhibited the highest silica content (79.60 wt.%), while Sample D contained the lowest concentrations of iron and titanium impurities, making these samples the most promising candidates for high-purity silica production after beneficiation. The study concludes that appropriate processing techniques, including washing, magnetic separation, milling, and acid leaching, can substantially improve the silica purity to meet the stringent requirements for advanced polymer composites and photovoltaic applications. The successful utilization of River Wudil silica would contribute to the development of indigenous raw materials for renewable energy technologies, reduce dependence on imported silica resources, lower manufacturing costs, and promote sustainable industrial development in Nigeria.

KEY WORDS; Silica Sand, PVC Panel, Solar, Renewable Energy, River Sand.

1. INTRODUCTION

The rapid increase in global energy demand, coupled with growing environmental concerns associated with the extensive use of fossil fuels, has accelerated the transition towards renewable energy technologies. Conventional energy sources such as coal, crude oil, and natural gas remain the dominant contributors to global electricity generation; however, their continuous utilization has resulted in significant greenhouse gas emissions, climate change, environmental degradation, and depletion of finite natural resources. Consequently, governments, industries, and research institutions across the world are investing heavily in sustainable energy systems capable of providing clean, affordable, and reliable electricity while minimizing environmental impacts. Among the various renewable energy technologies currently available, solar photovoltaic (PV) systems have emerged as one of the most promising solutions because of their abundance, scalability, low operating costs, and ability to generate electricity directly from sunlight without producing harmful emissions during operation [1], [2].

The deployment of solar photovoltaic technology has experienced unprecedented growth over the past two decades owing to continuous improvements in conversion efficiency, reductions in manufacturing costs, supportive government policies, and increasing public awareness regarding environmental sustainability. According to the International Energy Agency (IEA), solar PV has become the fastest-growing renewable electricity technology worldwide, accounting for a significant proportion of newly installed renewable energy capacity in recent years. Global installed photovoltaic capacity surpassed one terawatt (TW), and projections indicate that the capacity may exceed 14 TW by 2050 to achieve international carbon neutrality targets and satisfy the rapidly growing electricity demand [1], [3]. Similarly, the International Renewable Energy Agency (IRENA) reports that renewable energy installations continue to expand annually, with solar PV contributing the largest share of newly installed renewable capacity because of its declining levelized cost of electricity and technological maturity [2].

The remarkable expansion of the photovoltaic industry has inevitably resulted in a corresponding increase in the demand for high-quality raw materials required for manufacturing solar cells and associated photovoltaic components. Silicon remains the dominant semiconductor material used in crystalline photovoltaic technologies, accounting for more than 95% of commercially manufactured solar cells worldwide [4]. This dominance is attributable to silicon’s excellent semiconductor properties, suitable electronic band gap, high thermal stability, mechanical strength, long operational lifetime, and the abundance of silicon-containing minerals within the Earth’s crust. Crystalline silicon photovoltaic cells also exhibit relatively high conversion efficiencies compared with alternative thin-film technologies while maintaining excellent durability under varying environmental conditions [5].

Silicon is the second most abundant element in the Earth’s crust after oxygen, constituting approximately 2728% of the crustal composition by mass. However, elemental silicon does not occur naturally because of its high chemical affinity for oxygen. Instead, it exists predominantly in the form of silicon dioxide (SiO) and various silicate minerals present in igneous, metamorphic, and sedimentary rocks [6]. Quartz, quartzite, sandstone, and silica-rich river sands represent the principal naturally occurring sources of silicon dioxide exploited for industrial applications. Industrial silicon production generally involves the carbothermic reduction of silica in electric arc furnaces operating at tmperatures exceeding 2,000°C to produce metallurgical-grade silicon. Subsequent purification processes such as chemical vapor deposition and zone refining are employed to obtain electronic- grade or solar-grade silicon suitable for photovoltaic cell fabrication [7].

The quality of silica feedstock plays a critical role in determining the efficiency, purity, and economic viability of silicon production. High-purity silica is an essential raw material not only for photovoltaic cells but also for glass manufacturing, semiconductor devices, optical fibres, ceramics, refractories, silicone chemicals, advanced composites, and numerous engineering applications [8]. For photovoltaic manufacturing, silica feedstock must possess exceptionally high silicon dioxide content while containing very low concentrations of metallic impurities such as iron, titanium, aluminium, calcium, sodium, potassium, and magnesium. These impurities can adversely affect the electrical, optical, and mechanical properties of silicon by introducing undesirable defects during crystal growth and semiconductor processing [9].

Natural silica sands vary considerably in mineralogical composition depending on their geological origin, transportation history, depositional environment, and weathering processes. River sands are particularly attractive as potential silica resources because continuous hydraulic transportation promotes natural washing, sorting, and concentration of resistant quartz minerals while removing softer and less stable mineral constituents. Nevertheless, even silica-rich river sands usually contain appreciable quantities of feldspars, clay minerals, iron oxides, heavy minerals, carbonates, and organic matter that must be removed through beneficiation processes before the silica becomes suitable for high-value industrial applications [10].

Beneficiation refers to a combination of physical and chemical treatment processes employed to improve the purity of mineral resources by removing undesirable impurities. For silica sand, commonly adopted beneficiation techniques include washing, attrition scrubbing, magnetic separation, gravity concentration, flotation, sieving, milling, and acid leaching. Physical separation techniques eliminate coarse particles, clay minerals, and magnetic contaminants, whereas chemical treatments dissolve metallic oxides and carbonate impurities that remain attached

to quartz grain surfaces [11]. Acid leaching using hydrochloric acid (HCl), sulfuric acid (HSO), oxalic acid, hydrofluoric acid (HF), or combinations thereof has been widely reported as one of the most effective methods for reducing iron, aluminium, titanium, and alkali impurities to levels acceptable for advanced industrial applications [12].

The characterization of silica-bearing materials before and after beneficiation is equally important because it provides detailed information regarding chemical composition, mineralogy, particle morphology, surface texture, elemental distribution, and impurity concentrations. Modern analytical techniques such as X-ray Fluorescence (XRF), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), Fourier Transform Infrared Spectroscopy (FTIR), and Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) are routinely employed to evaluate silica resources intended for industrial processing [13]. Among these methods, XRF provides reliable quantitative information on the bulk oxide composition of silica sand, SEM reveals particle morphology and surface characteristics, while EDX complements SEM observations by identifying localized elemental distributions on mineral surfaces [14].

Particle size distribution constitutes another important factor influencing the efficiency of silica beneficiation and extraction. Fine and uniformly graded particles generally provide larger specific surface areas that enhance acid penetration and impurity dissolution during chemical treatment. Conversely, excessively coarse particles require additional crushing and milling, thereby increasing energy consumption and processing costs. Consequently, sieve analysis is routinely performed before beneficiation to determine the optimum processing conditions required for maximizing silica recovery and minimizing operational expenses [15].

Growing industrialization and increasing demand for photovoltaic materials have intensified the search for alternative indigenous silica resources capable of reducing dependence on imported raw materials. Many developing countries possess abundant silica deposits that remain underutilized because of limited characterization and insufficient beneficiation research. Nigeria is endowed with significant deposits of silica-rich sands distributed across river channels, flood plains, coastal environments, and sedimentary basins. Previous investigations have identified commercially promising silica deposits in several states, including Ogun, Delta, Kaduna, Plateau, Kogi, Cross River, and Kano. However, many of these deposits have not been comprehensively evaluated regarding their suitability for photovoltaic and other high-technology applications [16], [17].

The utilization of locally available silica resources presents substantial economic and technological advantages for Nigeria. Domestic production of high-purity silica could stimulate local manufacturing of photovoltaic components, reduce foreign exchange expenditure associated with raw material importation, create employment opportunities, promote technology transfer, and support national renewable energy development policies. Furthermore, developing indigenous silica beneficiation technologies would contribute significantly to achieving the country’s industrialization objectives and sustainable development goals through increased value addition to locally available mineral resources [18].

The quality of silica required for photovoltaic applications is considerably higher than that required for conventional industrial uses such as construction, foundry moulds, and glass production. While ordinary industrial silica sand may contain between 90 and 98 wt.% SiO, the production of metallurgical-grade silicon and, ultimately, solar-grade silicon demands feedstock with exceptionally low concentrations of metallic impurities. Iron, aluminium, titanium, calcium, magnesium, potassium, sodium, and other trace elements must be reduced to very low levels because even minute concentrations can adversely affect the electrical characteristics of silicon wafers by introducing recombination centres, reducing carrier lifetime, and lowering photovoltaic conversion efficiency [19], [20]. Consequently, the successful production of high-quality photovoltaic materials depends not only on the abundance of silica but also on the effectiveness of purification and beneficiation processes capable of producing feedstock that satisfies stringent industrial specifications.

Recent advances in mineral processing have demonstrated that naturally occurring silica sands containing moderate impurity concentrations can be upgraded into high-purity silica suitable for advanced industrial applications through carefully designed beneficiation routes. Mechanical processing techniques such as washing, screening, attrition scrubbing, gravity concentration, and magnetic separation are frequently employed as

preliminary operations to remove clay minerals, organic matter, and iron-bearing heavy minerals from raw silica sand. These methods are often followed by chemical treatments, particularly acid leaching, which selectively dissolves metallic contaminants remaining on the quartz grain surfaces while preserving the chemically stable silica matrix [21]. The effectiveness of these treatments depends on several factors, including particle size distribution, mineralogical composition, acid concentration, reaction temperature, leaching duration, and the nature of impurity associations within the quartz grains [22].

Hydrochloric acid (HCl) is widely used for the removal of carbnates, iron oxides, and alkali-bearing minerals because of its strong dissolving ability and relatively low environmental impact compared with certain alternative mineral acids. Hydrofluoric acid (HF), although highly hazardous and requiring stringent safety precautions, has also been employed for the dissolution of silicate impurities and the removal of surface contamination from quartz particles. Other researchers have reported successful purification using sulfuric acid, nitric acid, oxalic acid, citric acid, and mixed-acid systems depending on the mineralogical characteristics of the silica deposit under investigation [23]. The selection of an appropriate beneficiation strategy therefore requires a comprehensive understanding of the chemical and morphological characteristics of the raw material before processing.

Characterization techniques have consequently become indispensable tools in silica beneficiation studies because they provide scientific evidence regarding the suitability of a deposit for industrial utilization. X-ray Fluorescence (XRF) spectroscopy is widely recognized as one of the most reliable techniques for determining the bulk chemical composition of silica-bearing materials. The technique provides quantitative information on the concentration of major and trace oxides, enabling researchers to assess silica purity and identify the principal contaminants requiring removal during beneficiation [24]. Since silica extraction is fundamentally influenced by the concentration of silicon dioxide and associated impurities, XRF analysis forms the basis for evaluating the industrial potential of natural silica deposits.

Scanning Electron Microscopy (SEM) complements chemical characterization by providing high-resolution images of particle morphology, grain shape, surface texture, fractures, and impurity coatings. The morphology of quartz grains influences washing efficiency, acid accessibility, and mechanical processing behaviour. Quartz particles possessing sub-angular to sub-rounded shapes with limited surface contamination generally exhibit improved beneficiation characteristics because impurities are concentrated primarily on grain surfaces rather than being incorporated into the crystal lattice [25]. Surface fractures and micro-cracks observed under SEM further enhance chemical purification by increasing the available surface area for acid penetration and impurity dissolution.

Energy Dispersive X-ray Spectroscopy (EDX), when integrated with SEM, provides localized elemental information from selected regions of the specimen. Unlike XRF, which analyses the bulk composition of the sample, EDX examines comparatively small areas and is therefore particularly useful for identifying surface contaminants, localized mineral inclusions, and the elemental distribution associated with individual grains. Nevertheless, the interpretation of EDX data requires careful consideration of instrument operating conditions, detector settings, and analytical limitations because the omission of critical elements or inappropriate calibration parameters may produce misleading quantitative results [26]. Consequently, meaningful mineralogical interpretation is generally achieved by integrating SEM observations with XRF chemical analysis and other complementary characterization techniques.

Particle size distribution also plays a significant role in determining beneficiation efficiency and overall silica recovery. Quartz particles with relatively uniform sizes promote effective washing, improve magnetic separation efficiency, and facilitate homogeneous acid penetration during chemical treatment. Conversely, highly heterogeneous particle size distributions may result in uneven purification, increased reagent consumption, and additional comminution requirements. Sieve analysis therefore provides valuable information for selecting suitable processing conditions while minimizing energy consumption and production costs [27]. In industrial mineral processing, optimized particle size not only enhances beneficiation performance but also improves the quality and consistency of the final silica product used in polymer composites, glass manufacturing, and photovoltaic applications.

Apart from its application in crystalline silicon solar cells, purified silica has gained increasing importance as a functional engineering material in polymer-based photovoltaic systems. Polyvinyl chloride (PVC) remains one of the most widely used thermoplastic polymers because of its low cost, ease of fabrication, chemical resistance, and excellent weathering characteristics. However, the incorporation of mineral fillers such as high-purity silica significantly enhances the mechanical strength, stiffness, abrasion resistance, thermal stability, dimensional stability, ultraviolet resistance, and long-term durability of PVC composites used in outdoor environments [28]. The performance improvements achieved through silica reinforcement have encouraged researchers to investigate locally available silica resources capable of supporting the manufacture of high-performance polymer composites for renewable energy applications.

The development of photovoltaic support structures, encapsulation materials, electrical insulation components, cable protection systems, mounting accessories, and protective polymeric housings increasingly relies on advanced composite materials incorporating finely dispersed silica fillers. Consequently, the availability of locally sourced high-purity silica presents substantial opportunities for reducing manufacturing costs while simultaneously promoting domestic industrial development. Countries possessing abundant silica resources can therefore establish integrated value chains linking mineral extraction, beneficiation, materials processing, and renewable energy manufacturing, thereby reducing dependence on imported industrial raw materials [29].

Nigeria possesses extensive geological formations containing quartz-rich sands distributed across river systems, sedimentary basins, coastal environments, and weathered granitic terrains. Numerous investigations have reported significant silica occurrences in states such as Ogun, Kaduna, Plateau, Kogi, Cross River, Delta, Edo, and Kano. Despite these abundant resources, the majority of silica utilized in high-value industrial applications continues to be imported because many local deposits have not undergone detailed physicochemical characterization or systematic beneficiation studies [30]. The limited availability of comprehensive scientific data concerning the purity, morphology, mineralogical composition, and beneficiation behaviour of many Nigerian silica deposits has consequently restricted their industrial exploitation.

River Wudil, located within Kano State in northwestern Nigeria, represents one such underexplored silica resource. Continuous fluvial transportation and sediment deposition have resulted in the accumulation of quartz- rich sands that may constitute an economically viable source of silica for advanced industrial applications. However, the suitability of these deposits for photovoltaic-related applications depends on several critical factors, including silica concentration, impurity levels, particle size distribution, grain morphology, and the effectiveness of beneficiation processes. Existing literature provides very limited information regarding the physicochemical characteristics of River Wudil sand, particularly with respect to its potential as a feedstock for high-purity silica production. This knowledge gap limits informed decision-making concerning its industrial utilization and underscores the need for comprehensive scientific investigation.

Accordingly, the present study investigates the silicon content and silica potential of River Wudil sand through an integrated experimental approach involving standardized sampling, beneficiation, particle size analysis, X-ray Fluorescence (XRF), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX). The combined use of these analytical techniques provides comprehensive information regarding the chemical composition, particle morphology, elemental distribution, and impurity characteristics of the investigated samples. Such information is essential for evaluating the feasibility of upgrading River Wudil sand into high-purity silica suitable for photovoltaic-related applications and advanced polymer composite manufacturing.

Unlike previous studies that have focused primarily on identifying silica deposits or reporting basic chemical compositions, this research integrates particle size characterization with complementary chemical and microstructural analyses to establish the beneficiation potential of River Wudil sand. Furthermore, the study evaluates the relationship between silica content, impurity distribution, and processing requirements, thereby providing practical guidance for future large-scale silica purification and industrial utilization. The findings are expected to contribute to the growing body of knowledge on indigenous mineral resource development while supporting Nigeria’s transition toward sustainable renewable energy technologies, local content development, and value-added mineral processing.

Ultimately, the successful utilization of River Wudil silica would provide multiple socioeconomic and technological benefits, including reduced dependence on imported silica feedstock, lower manufacturing costs for photovoltaic-related materials, enhanced utilization of indigenous mineral resources, employment generation within the mining and manufacturing sectors, and strengthened capacity for domestic renewable energy production. These anticipated outcomes align closely with national industrialization strategies, sustainable development objectives, and global efforts to accelerate the transition toward cleaner and more resilient energy systems. Against this background, the present investigation seeks to evaluate the suitability of River Wudil sand as a potential source of silica for the development of photovoltaic-related PVC composite materials through detailed physicochemical characterization and beneficiation assessment [31], [32].

RESEARCH GAP

Despite the increasing global demand for high-purity silica as a precursor for photovoltaic (PV) materials and advanced polymer composites, research on the characterization and beneficiation of indigenous silica resources in Nigeria remains limited. Numerous studies have focused on the extraction of silica from quartz, beach sands, and industrial silica deposits in different parts of the world; however, relatively few investigations have comprehensively evaluated river sand deposits in northern Nigeria for photovoltaic-related applications. Most existing studies have primarily reported the chemical composition of silica sand without integrating detailed particle size analysis, microstructural characterization, and elemental analysis required to assess the beneficiation potential of the material.

Previous investigations on Nigerian silica deposits have largely concentrated on glass manufacturing, foundry applications, ceramics, and construction materials, with limited attention given to their suitability as raw materials for photovoltaic technologies or polymer-based solar panel components. In addition, many published studies have relied solely on X-ray Fluorescence (XRF) analysis to determine silica content, while neglecting complementary characterization techniques such as Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX), which provide critical information on particle morphology, surface characteristics, impurity distribution, and elemental composition. Consequently, the relationships between particle morphology, impurity occurrence, beneficiation efficiency, and the potential utilization of silica in photovoltaic applications remain insufficiently understood.

Furthermore, although several silica-rich deposits have been identified across Nigeria, there is a paucity of scientific information regarding the physicochemical properties of River Wudil sand in Kano State. Specifically, there is limited published literature addressing its silicon dioxide content, impurity profile, grain size distribution, mineralogical characteristics, and suitability for producing high-purity silica required for photovoltaic-related applications. The absence of such information has hindered the development and industrial utilization of this potentially valuable indigenous mineral resource.

Another significant gap in the literature is the lack of integrated studies that combine beneficiation processes with comprehensive material characterization to determine the feasibility of upgrading naturally occurring river sand into silica suitable for renewable energy applications. Most available studies evaluate either beneficiation efficiency or material characterization independently, without establishing a clear relationship between particle size, chemical composition, surface morphology, impurity distribution, and the expected performance of the purified silica in photovoltaic materials. This fragmented approach limits the practical application of research findings for industrial-scale silica production.

Moreover, increasing global efforts to localize raw material supply chains for renewable energy technologies have highlighted the importance of developing indigenous mineral resources. However, there remains insufficient information on the potential contribution of locally sourced silica from River Wudil to Nigeria’s renewable energy sector, particularly regarding its use as a reinforcing filler in PVC composites and as a precursor for high-purity silica production. Addressing these gaps is essential for promoting local content development, reducing dependence on imported silica materials, and supporting sustainable industrialization within the country.

Therefore, this study seeks to bridge these knowledge gaps through a comprehensive investigation of River Wudil sand using standardized sampling procedures, beneficiation techniques, sieve analysis, XRF, SEM, and EDX characterization. The study provides an integrated evaluation of the chemical composition, particle morphology,

elemental distribution, and beneficiation potential of the sand, thereby generating scientific data required to determine its suitability as a local source of silica for photovoltaic-related PVC solar panel manufacturing.

NOVELTY OF THE STUDY

The novelty of this research lies in its comprehensive and integrated evaluation of River Wudil sand as a potential indigenous source of silica for photovoltaic-related applications, an area that has received little or no detailed scientific investigation. Unlike previous studies that primarily focused on the chemical composition of silica deposits for conventional industrial uses, this study combines particle size analysis, beneficiation processes, X- ray Fluorescence (XRF), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray Spectroscopy (EDX) to provide a holistic assessment of the suitability of River Wudil sand for high-purity silica production.

A major contribution of this work is the establishment of a direct relationship between particle size distribution, chemical composition, microstructural characteristics, and beneficiation potential. By integrating sieve analysis with advanced physicochemical characterization, the study demonstrates how particle morphology and impurity distribution influence silica purification efficiency and the overall suitability of the material for photovoltaic applications. This multidisciplinary approach provides a more comprehensive understanding of the material than studies relying solely on chemical analysis.

Another novel aspect of this research is the evaluation of River Wudil sand specifically for photovoltaic-related PVC solar panel applications rather than traditional applications such as glassmaking, ceramics, or foundry production. The study therefore extends the potential utilization of Nigerian silica resources into the rapidly expanding renewable energy sector, contributing to the growing body of knowledge on sustainable materials for solar energy technologies.

Furthermore, the study critically compares the results obtained from XRF, SEM, and EDX analyses to assess the reliability of each characterization technique. The identification and discussion of inconsistencies observed in the EDX results, together with their correlation to XRF and SEM findings, provide valuable methodological insights for future researchers undertaking similar investigations on silica-bearing materials.

The research also provides one of the first comprehensive datasets on the physicochemical characteristics of River Wudil sand, including its silicon dioxide concentration, impurity profile, grain morphology, particle size distribution, and beneficiation requirements. These baseline data constitute an important scientific resource for future investigations into silica purification, solar-grade silicon production, polymer composite development, and other advanced industrial applications.

Finally, this study contributes to the broader objective of promoting sustainable utilization of indigenous mineral resources by demonstrating the feasibility of converting an underutilized natural river sand deposit into a value- added industrial raw material. The findings have significant implications for reducing dependence on imported silica, supporting local manufacturing of photovoltaic materials, enhancing mineral resource utilization, and advancing Nigeria’s renewable energy and industrial development agenda. The integrated methodology and comprehensive characterization framework presented in this research may also serve as a reference model for evaluating similar silica deposits in other regions of Nigeria and beyond.

1. MATERIALS AND METHODS

2. Study Area Description

   Wudil is located in Kano State, Nigeria, within latitude 11.79°N11.81°N and longitude 8.83°E8.84°E. The area lies within the Sudan savannah zone and is influenced by semi-arid climatic conditions. River Wudil forms part of the Hadejia River basin and contributes to sediment deposition suitable for silica investigation.

3. Materials

   1. The materials used in this study include;

      • Wudil River sand samples, Hydrofluoric acid (HF), Hydrochloric acid (HCl), Distilled water and Tap water.

   2. Equipment

      The following equipment were used for the experimentation;

      • 250 mL conical flasks, 50 mL measuring cylinders, Mechanical stirrer, 4 L plastic, Specimen discs, washing container, Laboratory oven, Bar magnet, Standard sieve set, (XRF analyzer, SEM and EDX equipment located

   at Olanrewaju Gabriel Department of Mechanical engineering, Covenant University, Canaan land, sango OTA, Ogun state)

4. Methods

   1. Sample Collection

      Samples were collected from five different locations using ASTM D75/D75M.

      Sand samples were collected from five (5) different locations along River Wudil to ensure representativeness. Procedure:

      1. Sampling points were selected at intervals along the river (upstream, midstream, downstream, and two intermediate points).

      2. At each location, sand was collected from a depth of 1030 cm using a clean stainless-steel scoop.

      3. Approximately 2 kg of sand was collected per location.

      4. Samples were stored in labeled polyethylene bags.

      Plate 1. Deposit of Sand in River wudil as Selected Area.

   2. Pretreatment (Leaching)

      Samples were treated with HCl and HF following ASTM D3875.

      The sand samples were purified using acid leaching to remove impurities such as iron oxides and carbonates.

      Procedure:

      1. Samples were washed with distilled water to remove clay and organic matter.

      2. The cleaned sand was dried and then treated with:

         Hydrochloric acid (HCl) to remove carbonates and metal impurities. Hydrofluoric acid (HF) to dissolve silicate impurities.

      3. The mixture was stirred and allowed to react for 24 hours.

      4. The treated samples were thoroughly rinsed with distilled water until neutral pH was achieved.

   3. Drying

      Samples were dried at 105°C using ASTM C566.

      After leaching, samples were dried to remove moisture. Procedure:

      1. The washed samples were placed in a laboratory oven.

      2. Drying was carried out at 105°C ± 5°C for 24 hours.

      3. Samples were cooled in a desiccator.

      Plate 2. Sand sample under Oven dried

   4. Magnetic Separation

      Magnetic particles were removed using ASTM A342.

      Magnetic separation was carried out to remove iron-bearing minerals. Procedure:

      1. Dried samples were spread on a clean surface.

      2. A bar magnet was passed repeatedly over the sample.

      3. Magnetic particles (e.g., iron filings) were removed.

      4. The process was repeated until no further magnetic particles were detected.

      Plate 3. Sand sample Undergoing magnetization.

   5. Sieve Analysis

      Particle size distribution determined using ASTM C136. Particle size distribution was determined using sieve analysis. Procedure:

      1. A representative sample (~500 g) was obtained.

      2. The sample was placed in a stack of standard sieves arranged in descending order.

      3. The sieve shaker was operated for 1015 minutes.

      4. The mass retained on each sieve was recorded.

      Plate 4. Sand sample undergoing sieve analysis.

   6. XRF Analysis

      Sand sample Chemical analysis (XRF, EDX and SEM) was conducted at the chemical analysis laboratory located at Olanrewaju Gabriel Department of Mechanical engineering, Covenant University, Canaan land, sango OTA, Ogun state).

      Plate 5. Location for sand sample chemical analysis.

      Chemical composition determined using ASTM C114.

      XRF analysis was used to determine the elemental composition, especially silica (SiO). Procedure:

      1. The sample was ground into fine powder (<75 µm).

      2. Pellets were prepared using a hydraulic press.

      3. The sample was analyzed using an XRF spectrometer.

      4. The percentage composition of SiO and other oxides was recorded

   7. EDX Analysis

      Elemental analysis conducted using ASTM E1508.

      EDX analysis was used to confirm elemental composition. Procedure:

      1. A small quantity of the sample was mounted on a sample holder.

      2. The sample was coated with conductive material (if required).

      3. The EDX system attached to SEM was used to analyze elemental composition.

   8. SEM Analysis

Surface morphology studied using ASTM E986.

SEM was used to study the morphology and surface characteristics of the sand particles. Procedure:

1. Samples were mounted on SEM stubs.

2. The sample surface was coated with gold or carbon.

3. Imaging was carried out at magnifications.

4. Micrographs were obtained to observe particle shape and texture.

1. Characterizations.

   1. Sieving Analysis

      The grain size distribution analysis, performed using a set of standard sieves, revealed that the sieve analysis presents the particle size distribution of five sand samples (AE) with an initial weight of 1000 g each. The final recovered weights (992 g, 980 g, 872 g, 996 g, and 842 g) indicate minor to moderate material losses during sieving, with Samples C and E showing the greatest losses, possibly due to dust, handling losses, or the presence of very fine particles.

      Sample Initial weight = 1000g

      Sample No.	A	B	C	D	E
      Final	992	980	872	996	842
      600	76	40	26	34	80
      425	48	38	38	42	46
      300	16	26	32	32	16
      212	6	10	20	8	4
      150	2	24	26	26	2
      63	2	12	8	8	2

      1. Particle Size Distribution

         The sieve sizes are 600, 425, 300, 212, 150, and 63 µm. The results indicate the following trends:

         Sample	Dominant Particle Size	Interpretation
         A	600 µm (76%)	Very coarse sand
         B	600 µm (40%) and 425 µm (38%)	Moderately coarse with good grading
         C	425 µm (38%), 300 µm (32%), 150 µm (26%)	Well-distributed medium sand
         D	425 µm (42%) and 300 µm (32%)	Uniform medium-coarse sand
         E	600 µm (80%)	Extremely coarse sand

         The majority of particles in Samples A and E are retained on the 600 µm sieve, indicating coarse- grained sand. Samples C and D exhibit a more balanced distribution across the 425150 µm size range, suggesting better grading.

      2. Implications for Silica Extraction

         Particle size plays an important role in silica extraction because it influences:

         • Surface area available for chemical reactions.

         • Efficiency of washing and impurity removal.

         • Grinding energy required before purification.

         • Consistency during processing.

           Sample A

         • Predominantly coarse particles (600 µm).

         • Requires additional crushing or milling before chemical purification.

         • Lower surface area reduces reaction efficiency during leaching.

           Sample B

         • Good balance between coarse and medium particles.

         • Easier to process than Sample A.

         • Suitable for beneficiation with moderate grinding.

           Sample C

         • Well-graded distribution.

         • Higher proportion of medium-sized particles.

         • Offers larger surface area for acid leaching.

         • Likely to achieve higher silica recovery with lower energy consumption.

           Sample D

         • Similar to Sample C but slightly coarser.

         • Suitable feedstock after washing and classification.

         • Expected to produce high-purity silica efficiently.

           Sample E

         • Very coarse material.

         • Significant size reduction required.

         • Highest grinding cost before purification.

      3. Suitability for PVC Solar Panel Manufacturing

         For PVC solar panel production, silica is generally used as:

         • A reinforcing filler.

         • A functional additive to improve mechanical strength.

         • A UV-resistant additive after purification.

         • A raw material for producing high-purity silica powders. Manufacturers generally prefer silica with:

         • High SiO purity (typically above 9899%, depending on the application).

         • Uniform particle size.

         • Low iron content.

         • Low clay and organic impurities.

         • Fine particle size after milling (often below 75100 µm for filler applications).

           The sieve analysis only provides information about particle size, not chemical composition. Therefore, it cannot by itself confirm that the sand is suitable for PVC solar panel manufacturing. Chemical analyses such as:

         • X-ray fluorescence (XRF),

         • X-ray diffraction (XRD),

         • ICP-OES,

         • or wet chemical analysis are necessary to determine silica purity and impurity levels.

      4. Best Sample Based on the Sieving Results

         Considering particle size alone:

         1. Sample C Most suitable due to its well-graded medium-sized particles, which require less grinding and offer better processing efficiency.

         2. Sample D Also suitable with a relatively uniform distribution.

         3. Sample B Acceptable but contains more coarse particles.

         4. Sample A Requires significant milling before silica extraction.

         5. Sample E Least suitable because of its predominantly coarse particle size and higher expected processing cost.

   2. Chemical Composition

      The X-ray Fluorescence (XRF) analysis was conducted to determine the chemical composition of five Wudil River sand samples (AE). The results show that silicon dioxide (SiO) is the dominant oxide in all samples, confirming that the river sand is principally siliceous and has good potential as a raw material for silica extraction. However, the presence of impurities such as AlO, FeO, KO, CaO, TiO, and Cl indicates that beneficiation and purification processes will be necessary before the silica can be used in high-quality PVC solar panel applications.

      Analysis Report for Sand Sample A

      Thick Type Error Units Density Norm. Total

      1 0.00 Bulk 0.00 mg/cm2 0.00F On 100.00

      Sample Table

      Layer Component Type Concn. Error Units Mole% Error

      1	SiO2	Calc 79.602 1.917	wt.%	84.9552.046
      1	V2O5	Calc 0.126 0.019	wt.%	0.044 0.007
      1	Cr2O3	Calc 0.003 0.010	wt.%	0.001 0.004
      1	MnO	Calc 0.038 0.009	wt.%	0.034 0.008
      1	Fe2O3	Calc 1.405 0.027	wt.%	0.564 0.011
      1	CoO	Calc 0.013 0.009	wt.%	0.011 0.008
      1	NiO	Calc 0.002 0.006	wt.%	0.001 0.005
      1	CuO	Calc 0.067 0.006	wt.%	0.054 0.005
      1	Nb2O5	Calc 0.025 0.008	wt.%	0.006 0.002
      1	WO3	Calc 0.000 0.000	wt.%	0.000 0.000
      1	P2O5	Calc 0.000 0.000	wt.%	0.000 0.000
      1	SO3	Calc 0.780 0.153	wt.%	0.625 0.123
      1	CaO	Calc 1.025 0.073	wt.%	1.173 0.084
      1	MgO	Calc 0.000 0.000	wt.%	0.000 0.000
      1	K2O	Calc 6.902 0.177	wt.%	4.698 0.121
      1	BaO	Calc 0.158 0.118	wt.%	0.066 0.049
      1	Al2O3	Calc 6.807 3.621	wt.%	4.281 2.277
      1	Ta2O5	Calc 0.084 0.022	wt.%	0.012 0.003
      1	TiO2	Calc 1.229 0.046	wt.%	0.987 0.037
      1	ZnO	Calc 0.001 0.004	wt.%	0.001 0.003
      1	Ag2O	Calc 0.025 0.050	wt.%	0.007 0.014
      1	Cl	Calc 1.273 0.089	wt.%	2.302 0.162
      1	ZrO2	Calc 0.037 0.007	wt.%	0.019 0.004
      1	SnO2	Calc 0.241 0.539	wt.%	0.103 0.229
      1	PbO	Calc 0.025 0.012	wt.%	0.007 0.004
      1	Rb2O	Calc 0.031 0.005	wt.%	0.011 0.002
      1	Cs2O	Calc 0.069 0.123	wt.%	0.016 0.028
      1	SrO	Calc 0.031 0.006	wt.%	0.019 0.003

      Element Table

      Elmt Line Cond Ratio Intensity Error Intensity Conc. Conc Calibration

      Code Code Method (c/s) (c/s) Method Method Coefficient

      O	Ka	0 None 0.000 0.0000 Gaussian 48.640 None 0.000
      Mg	Ka	1 None 0.000 3.6320 Gaussian 0.000 FP	0.000
      Al	Ka	1 None 22.846 12.1536 Gaussian 3.603 FP	0.000
      Si	Ka	1 None 1210.444 29.1579 Gaussian 37.210 FP	0.000
      P	Ka	1 None 0.000 15.3952 Gaussian 0.000 FP	0.000
      S	Ka	1 None 34.974 6.8671 Gaussian 0.313 FP	0.000
      Cl	Ka	1 None 189.170 13.2756 Gaussian 1.273 FP	0.000
      K	Ka	1 None 1269.197 32.5814 Gaussian 5.729 FP	0.000
      Ca	Ka	1 None 217.929 15.5326 Gaussian 0.733 FP	0.000
      Ti	Ka	1 None 485.044 18.1273 Gaussian 0.737 FP	0.000
      V	Ka	1 None 63.339 9.6407 Gaussian 0.070 FP	0.000

      Cr	Ka	1 None 2.132 8.2322 Gaussian 0.002 FP	0.000
      Mn	Ka	1 None 42.798 9.7707 Gaussian 0.029 FP	0.000
      Fe	Ka	1 None 1749.874 34.1365 Gaussian 0.983 FP	0.000
      Co	Ka	1 None 22.511 14.9771 Gaussian 0.011 FP	0.000
      Ni	Ka	1 None 3.215 10.8060 Gaussian 0.001 FP	0.000
      Cu	Ka	1 None 148.741 13.4615 Gaussian 0.054 FP	0.000
      Zn	Ka	1 None 1.824 10.5404 Gaussian 0.001 FP	0.000
      Rb	Ka	1 None 103.725 15.4551 Gaussian 0.028 FP	0.000
      Sr	Ka	1 None 86.168 15.2190 Gaussian 0.027 FP	0.000
      Zr	Ka	1 None 76.360 14.8877 Gaussian 0.028 FP	0.000
      Nb	Ka	1 None 42.506 14.2832 Gaussian 0.017 FP	0.000
      Ag	Ka	1 None 5.474 10.9853 Gaussian 0.023 FP	0.000
      Sn	La	1 None 13.472 30.0814 Gaussian 0.190 FP	0.000
      Cs	La	1 None 9.144 16.3360 Gaussian 0.065 FP	0.000
      Ba	La	1 None 22.980 17.1771 Gaussian 0.141 FP	0.000
      Ta	La	1 None 52.350 14.0078 Gaussian 0.068 FP	0.000
      W	La	1 None 0.000 14.0273 Gaussian 0.000 FP	0.000
      Pb	La	1 None 29.480 14.7092 Gaussian 0.023 FP	0.000

      Analysis Report for Sand Sample B

      Thick Type Error Units Density Norm. Total 1 0.00 Bulk 0.00 mg/cm2 0.00F On 100.00

      Sample Table

      Layer Component Type Concn. Error Units Mole% Error

      1	SiO2	Calc 75.924 1.711	wt.%	78.2521.763
      1	V2O5	Calc 0.031 0.015	wt.%	0.010 0.005
      1	Cr2O3	Calc 0.093 0.011	wt.%	0.038 0.005
      1	MnO	Calc 0.043 0.007	wt.%	0.038 0.006
      1	Fe2O3	Calc 1.129 0.023	wt.%	0.438 0.009
      1	CoO	Calc 0.029 0.008	wt.%	0.024 0.006
      1	NiO	Calc 0.016 0.005	wt.%	0.013 0.004
      1	CuO	Calc 0.045 0.005	wt.%	0.035 0.004
      1	Nb2O5	Calc 0.012 0.007	wt.%	0.003 0.002
      1	WO3	Calc 0.008 0.019	wt.%	0.002 0.005
      1	P2O5	Calc 0.000 0.000	wt.%	0.000 0.000
      1	SO3	Calc 0.353 0.137	wt.%	0.273 0.106
      1	CaO	Calc 0.898 0.067	wt.%	0.991 0.074
      1	MgO	Calc 0.000 0.000	wt.%	0.000 0.000
      1	K2O	Calc 7.290 0.173	wt.%	4.793 0.114
      1	BaO	Calc 0.144 0.078	wt.%	0.058 0.032
      1	Al2O3	Calc 7.145 3.240	wt.%	4.340 1.968
      1	Ta2O5	Calc 0.071 0.019	wt.%	0.010 0.003
      1	TiO2	Calc 0.562 0.030	wt.%	0.436 0.024
      1	ZnO	Calc 0.000 0.000	wt.%	0.000 0.000
      1	Ag2O	Calc 0.023 0.041	wt.%	0.006 0.011
      1	Cl	Calc 5.761 0.164	wt.%	10.0630.286
      1	ZrO2	Calc 0.055 0.007	wt.%	0.028 0.003
      1	SnO2	Calc 0.264 0.520	wt.%	0.109 0.214
      1	PbO	Calc 0.010 0.011	wt.%	0.003 0.003
      1	Rb2O	Calc 0.026 0.004	wt.%	0.009 0.001
      1	Cs2O	Calc 0.030 0.085	wt.%	0.007 0.019
      1	SrO	Calc 0.036 0.005	wt.%	0.021 0.003

      Element Table

      Elmt Line Cond Ratio Intensity Error Intensity Conc. Conc Calibration Code Code Method (c/s) (c/s) Method Method Coefficient

      O	Ka	0 None 0.000	0.0000 Gaussian 46.255 None 0.000
      Mg	Ka	1 None 0.000	3.9328 Gaussian 0.000 FP	0.000
      Al	Ka	1 None 28.702	13.0152 Gaussian 3.782 FP	0.000
      Si	Ka	1 None 1375.670 30.9939 Gaussian 35.491 FP		0.000
      P	Ka	1 None 0.000	16.5741 Gaussian 0.000 FP	0.000
      S	Ka	1 None 19.206	7.4644 Gaussian 0.142 FP	0.000
      Cl	Ka	1 None 1017.357 28.8858 Gaussian 5.761 FP		0.000
      K	Ka	1 None 1465.393 34.7601 Gaussian 6.052 FP		0.000
      Ca	Ka	1 None 209.349 15.5379 Gaussian 0.642 FP		0.000
      Ti	Ka	1 None 244.952 13.2130 Gaussian 0.337 FP		0.000
      V	Ka	1 None 17.044	8.3092 Gaussian 0.017 FP	0.000
      Cr	Ka	1 None 82.177	9.8942 Gaussian 0.064 FP	0.000
      Mn	Ka	1 None 55.094	9.1878 Gaussian 0.034 FP	0.000
      Fe	Ka	1 None 1578.954 32.2998 Gaussian 0.790 FP		0.000
      Co	Ka	1 None 53.948	14.3711 Gaussian 0.023 FP	0.000
      Ni	Ka	1 None 34.441	11.1032 Gaussian 0.013 FP	0.000
      Cu	Ka	1 None 112.003 12.7402 Gaussian 0.036 FP		0.000
      Zn	Ka	1 None 0.000	10.6767 Gaussian 0.000 FP	0.000
      Rb	Ka	1 None 100.462 15.3333 Gaussian 0.024 FP		0.000
      Sr	Ka	1 None 110.712 15.4774 Gaussian 0.030 FP		0.000
      Zr	Ka	1 None 127.765 15.5872 Gaussian 0.041 FP		0.000
      Nb	Ka	1 None 23.483	13.4358 Gaussian 0.008 FP	0.000
      Ag	Ka	1 None 5.697	10.1871 Gaussian 0.021 FP	0.000
      Sn	La	1 None 16.136	31.7647 Gaussian 0.208 FP	0.000
      Cs	La	1 None 4.344	12.4801 Gaussian 0.028 FP	0.000
      Ba	La	1 None 23.045	12.5996 Gaussian 0.129 FP	0.000
      Ta	La	1 None 50.261	13.3052 Gaussian 0.058 FP	0.000
      W	La	1 None 6.031	13.6590 Gaussian 0.007 FP	0.000
      Pb	La	1 None 13.525	14.7337 Gaussian 0.009 FP	0.000

      Tar Filter Thick. kV uA Detector Thick. Atm Preset Actual get mg/cm2 Type Filter mg/cm2 Time(s) Time(s)

      1 Rh None 0.00 30.0 60.0 SDD None 0.00 Air 60.0 15.0

      Analysis Report for Sand Sample C

      Thick Type Error Units Density Norm. Total 1 0.00 Bulk 0.00 mg/cm2 0.00F On 100.00

      Sample Table

      Layer Component Type Concn. Error Units Mole% Error

      1	SiO2	Calc 78.531 1.753	wt.%	83.9041.873
      1	V2O5	Calc 0.056 0.014	wt.%	0.020 0.005
      1	Cr2O3	Calc 0.071 0.010	wt.%	0.030 0.004
      1	MnO	Calc 0.017 0.006	wt.%	0.015 0.005
      1	Fe2O3	Calc 1.316 0.024	wt.%	0.529 0.009
      1	CoO	Calc 0.006 0.007	wt.%	0.005 0.006
      1	NiO	Calc 0.002 0.005	wt.%	0.002 0.004
      1	CuO	Calc 0.071 0.005	wt.%	0.057 0.004
      1	Nb2O5	Calc 0.010 0.006	wt.%	0.002 0.002
      1	WO3	Calc 0.029 0.019	wt.%	0.008 0.005
      1	P2O5	Calc 0.083 0.549	wt.%	0.038 0.248
      1	SO3	Calc 0.326 0.113	wt.%	0.261 0.091
      1	CaO	Calc 1.437 0.072	wt.%	1.645 0.083

      1	MgO	Calc 0.000 0.000	wt.%	0.000 0.000
      1	K2O	Calc 6.136 0.152	wt.%	4.182 0.103
      1	BaO	Calc 0.199 0.070	wt.%	0.083 0.029
      1	Al2O3	Calc 9.083 3.269	wt.%	5.719 2.058
      1	Ta2O5	Calc 0.084 0.020	wt.%	0.012 0.003
      1	TiO2	Calc 0.482 0.027	wt.%	0.387 0.021
      1	ZnO	Calc 0.000 0.000	wt.%	0.000 0.000
      1	Ag2O	Calc 0.003 0.040	wt.%	0.001 0.011
      1	Cl	Calc 1.607 0.089	wt.%	2.909 0.160
      1	ZrO2	Calc 0.063 0.006	wt.%	0.033 0.003
      1	SnO2	Calc 0.247 0.471	wt.%	0.105 0.200
      1	PbO	Calc 0.028 0.010	wt.%	0.008 0.003
      1	Rb2O	Calc 0.032 0.004	wt.%	0.011 0.001
      1	Cs2O	Calc 0.049 0.079	wt.%	0.011 0.018
      1	SrO	Calc 0.032 0.005	wt.%	0.020 0.003

      Element Table Elmt Line Cond Ratio Intensity Error Intensity Conc. Conc Calibration

      Code Code Method (c/s) (c/s) Method Method Coefficient

      O	Ka	0 None 0.000	0.0000 Gaussian 48.574 None 0.000
      Mg	Ka	1 None 0.000	4.0310 Gaussian 0.000 FP	0.000
      Al	Ka	1 None 37.165	13.3769 Gaussian 4.807 FP	0.000
      Si	Ka	1 None 1413.531 31.5561 Gaussian 36.709 FP		0.000
      P	Ka	1 None 2.513	16.5557 Gaussian 0.036 FP	0.000
      S	Ka	1 None 17.608	6.1074 Gaussian 0.130 FP	0.000
      Cl	Ka	1 None 288.580 15.9034 Gaussian 1.607 FP		0.000
      K	Ka	1 None 1359.352 33.5937 Gaussian 5.094 FP		0.000
      Ca	Ka	1 None 373.639 18.7790 Gaussian 1.027 FP		0.000
      Ti	Ka	1 None 231.491 12.8072 Gaussian 0.289 FP		0.000
      V	Ka	1 None 34.564	8.7356 Gaussian 0.032 FP	0.000
      Cr	Ka	1 None 69.144	9.6509 Gaussian 0.049 FP	0.000
      Mn	Ka	1 None 23.785	8.2997 Gaussian 0.013 FP	0.000
      Fe	Ka	1 None 2024.789 36.3342 Gaussian 0.920 FP		0.000
      Co	Ka	1 None 13.292	15.2300 Gaussian 0.005 FP	0.000
      Ni	Ka	1 None 5.423	10.6578 Gaussian 0.002 FP	0.000
      Cu	Ka	1 None 194.328 14.5284 Gaussian 0.056 FP		0.000
      Zn	Ka	1 None 0.000	11.4029 Gaussian 0.000 FP	0.000
      Rb	Ka	1 None 131.676 15.9572 Gaussian 0.029 FP		0.000
      Sr	Ka	1 None 107.647 15.4855 Gaussian 0.027 FP		0.000
      Zr	Ka	1 None 157.870 16.2120 Gaussian 0.046 FP		0.000
      Nb	Ka	1 None 21.732	13.6131 Gaussian 0.007 FP	0.000
      Ag	Ka	1 None 0.748	10.8642 Gaussian 0.003 FP	0.000
      Sn	La	1 None 16.580	31.6055 Gaussian 0.194 FP	0.000
      Cs	La	1 None 7.852	12.7776 Gaussian 0.046 FP	0.000
      Ba	La	1 None 35.240	12.3696 Gaussian 0.178 FP	0.000
      Ta	La	1 None 64.887	15.1668 Gaussian 0.068 FP	0.000
      W	La	1 None 23.126	15.4580 Gaussian 0.023 FP	0.000
      Pb	La	1 None 41.343	14.9786 Gaussian 0.026 FP	0.000

      Tar Filter Thick. kV uA Detector Thick. Atm Preset Actual get mg/cm2 Type Filter mg/cm2 Time(s) Time(s)

      1 Rh None 0.00 30.0 60.0 SDD None 0.00 Air 60.0 15.0

      Analysis Report for Sand Sample D

      Thick Type Error Units Density Norm. Total 1 0.00 Bulk 0.00 mg/cm2 0.00F On 100.00

      Sample Table

      Layer Component Type Concn. Error Units Mole% Error

      1	SiO2	Calc 78.512 1.710	wt.%	83.1871.812
      1	V2O5	Calc 0.000 0.000	wt.%	0.000 0.000
      1	Cr2O3	Calc 0.027 0.008	wt.%	0.011 0.004
      1	MnO	Calc 0.017 0.006	wt.%	0.015 0.005
      1	Fe2O3	Calc 1.086 0.021	wt.%	0.433 0.008
      1	CoO	Calc 0.007 0.007	wt.%	0.006 0.006
      1	NiO	Calc 0.002 0.004	wt.%	0.002 0.003
      1	CuO	Calc 0.042 0.004	wt.%	0.034 0.004
      1	Nb2O5	Calc 0.008 0.006	wt.%	0.002 0.001
      1	WO3	Calc 0.006 0.017	wt.%	0.002 0.005
      1	P2O5	Calc 0.059 0.527	wt.%	0.027 0.236
      1	SO3	Calc 0.483 0.120	wt.%	0.384 0.095
      1	CaO	Calc 1.447 0.072	wt.%	1.643 0.081
      1	MgO	Calc 0.000 0.000	wt.%	0.000 0.000
      1	K2O	Calc 6.468 0.153	wt.%	4.371 0.103
      1	BaO	Calc 0.126 0.045	wt.%	0.052 0.018
      1	Al2O3	Calc 8.950 3.169	wt.%	5.588 1.979
      1	Ta2O5	Calc 0.032 0.016	wt.%	0.005 0.002
      1	TiO2	Calc 0.160 0.017	wt.%	0.127 0.014
      1	ZnO	Calc 0.006 0.004	wt.%	0.005 0.003
      1	Ag2O	Calc 0.024 0.039	wt.%	0.007 0.011
      1	Cl	Calc 2.202 0.100	wt.%	3.954 0.179
      1	ZrO2	Calc 0.027 0.005	wt.%	0.014 0.003
      1	SnO2	Calc 0.227 0.471	wt.%	0.096 0.199
      1	PbO	Calc 0.019 0.009	wt.%	0.005 0.003
      1	Rb2O	Calc 0.027 0.004	wt.%	0.009 0.001
      1	Cs2O	Calc 0.002 0.056	wt.%	0.000 0.013
      1	SrO	Calc 0.031 0.004	wt.%	0.019 0.003

      Element Table

      Elmt Line Cond Ratio Intensity Error Intensity Conc. Conc Calibration Code Code Method (c/s) (c/s) Method Method Coefficient

      O	Ka	0 None 0.000	0.0000 Gaussian 48.362 None 0.000
      Mg	Ka	1 None 0.000	4.1269 Gaussian 0.000 FP	0.000
      Al	Ka	1 None 38.706	13.7046 Gaussian 4.737 FP	0.000
      Si	Ka	1 None 1495.451 32.5742 Gaussian 36.700 FP		0.000
      P	Ka	1 None 1.888	16.7142 Gaussian 0.026 FP	0.000
      S	Ka	1 None 27.497	6.8332 Gaussian 0.193 FP	0.000
      Cl	Ka	1 None 415.293 18.8436 Gaussian 2.202 FP		0.000
      K	Ka	1 None 1486.324 35.0433 Gaussian 5.370 FP		0.000
      Ca	Ka	1 None 388.106 19.2109 Gaussian 1.034 FP		0.000
      Ti	Ka	1 None 79.365	8.5359 Gaussian 0.096 FP	0.000
      V	Ka	1 None 0.000	7.7860 Gaussian 0.000 FP	0.000
      Cr	Ka	1 None 27.479	8.6268 Gaussian 0.019 FP	0.000
      Mn	Ka	1 None 24.990	8.8351 Gaussian 0.013 FP	0.000
      Fe	Ka	1 None 1759.166 34.0586 Gaussian 0.759 FP		0.000
      Co	Ka	1 None 15.439	14.4994 Gaussian 0.006 FP	0.000
      Ni	Ka	1 None 5.295	10.1469 Gaussian 0.002 FP	0.000
      Cu	Ka	1 None 123.773 12.9646 Gaussian 0.034 FP		0.000
      Zn	Ka	1 None 21.127	11.6489 Gaussian 0.005 FP	0.000

      Rb	Ka	1 None 118.877 15.7250 Gaussian 0.024 FP	0.000
      Sr	Ka	1 None 113.735 15.5538 Gaussian 0.026 FP	0.000
      Zr	Ka	1 None 73.740 14.7091 Gaussian 0.020 FP	0.000
      Nb	Ka	1 None 18.442 13.5926 Gaussian 0.006 FP	0.000
      Ag	Ka	1 None 6.986 11.5029 Gaussian 0.022 FP	0.000
      Sn	La	1 None 15.840 32.8351 Gaussian 0.179 FP	0.000
      Cs	La	1 None 0.286 9.2897 Gaussian 0.002 FP	0.000
      Ba	La	1 None 23.102 8.1568 Gaussian 0.113 FP	0.000
      Ta	La	1 None 26.620 13.5077 Gaussian 0.026 FP	0.000
      W	La	1 None 4.958 14.0490 Gaussian 0.005 FP	0.000
      Pb	La	1 None 29.463 14.6241 Gaussian 0.017 FP	0.000

      Analysis Conditions

      Tar Filter Thick. kV uA —Detector— Thick. Atm Preset Actual get mg/cm2 Type Filter mg/cm2 Time(s) Time(s)

      1 Rh None 0.00 30.0 60.0 SDD None 0.00 Air 60.0 15.0

      Analysis Report for Sand Sample E

      Thick Type Error Units Density Norm. Total 1 0.00 Bulk 0.00 mg/cm2 0.00F On 100.00

      Sample Table

      Layer Component Type Concn. Error Units Mole% Error

      1	SiO2	Calc 76.438 1.735	wt.%	79.7881.811
      1	V2O5	Calc 0.000 0.000	wt.%	0.000 0.000
      1	Cr2O3	Calc 0.041 0.009	wt.%	0.017 0.004
      1	MnO	Calc 0.026 0.007	wt.%	0.023 0.006
      1	Fe2O3	Calc 1.295 0.024	wt.%	0.509 0.010
      1	CoO	Calc 0.026 0.008	wt.%	0.022 0.006
      1	NiO	Calc 0.015 0.005	wt.%	0.012 0.004
      1	CuO	Calc 0.060 0.005	wt.%	0.048 0.004
      1	Nb2O5	Calc 0.009 0.007	wt.%	0.002 0.002
      1	WO3	Calc 0.005 0.018	wt.%	0.001 0.005
      1	P2O5	Calc 0.101 0.543	wt.%	0.045 0.240
      1	SO3	Calc 0.602 0.144	wt.%	0.472 0.112
      1	CaO	Calc 1.981 0.086	wt.%	2.215 0.096
      1	MgO	Calc 0.000 0.000	wt.%	0.000 0.000
      1	K2O	Calc 6.499 0.162	wt.%	4.327 0.108
      1	BaO	Calc 0.144 0.056	wt.%	0.059 0.023
      1	Al2O3	Calc 7.776 3.265	wt.%	4.783 2.008
      1	Ta2O5	Calc 0.069 0.019	wt.%	0.010 0.003
      1	TiO2	Calc 0.258 0.021	wt.%	0.203 0.017
      1	ZnO	Calc 0.005 0.004	wt.%	0.004 0.003
      1	Ag2O	Calc 0.021 0.043	wt.%	0.006 0.012
      1	Cl	Calc 4.084 0.139	wt.%	7.225 0.246
      1	ZrO2	Calc 0.025 0.006	wt.%	0.013 0.003
      1	SnO2	Calc 0.454 0.512	wt.%	0.189 0.213
      1	PbO	Calc 0.016 0.011	wt.%	0.005 0.003
      1	Rb2O	Calc 0.026 0.004	wt.%	0.009 0.001
      1	Cs2O	Calc 0.000 0.000	wt.%	0.000 0.000
      1	SrO	Calc 0.024 0.005	wt.%	0.014 0.003

      Element Table

      Elmt Line Cond Ratio Intensity Error Intensity Conc. Conc CalibrationCode Code Method (c/s) (c/s) Method

      Method Coefficient

      O	Ka	0 None 0.000	0.0000 Gaussian 47.130 None 0.000
      Mg	Ka	1 None 0.000	3.9508 Gaussian 0.000 FP	0.000
      Al	Ka	1 None 31.086	13.0533 Gaussian 4.115 FP	0.000
      Si	Ka	1 None 1368.917 31.0732 Gaussian 35.731 FP		0.000
      P	Ka	1 None 3.035	16.3623 Gaussian 0.044 FP	0.000
      S	Ka	1 None 32.480	7.7462 Gaussian 0.241 FP	0.000
      Cl	Ka	1 None 721.094 24.5320 Gaussian 4.084 FP		0.000
      K	Ka	1 None 1348.048 33.5584 Gaussian 5.395 FP		0.000
      Ca	Ka	1 None 480.165 20.7951 Gaussian 1.416 FP		0.000
      Ti	Ka	1 None 114.361 9.4877 Gaussian 0.155 FP		0.000
      V	Ka	1 None 0.000	7.6245 Gaussian 0.000 FP	0.000
      Cr	Ka	1 None 36.868	8.4801 Gaussian 0.028 FP	0.000
      Mn	Ka	1 None 34.237	8.6241 Gaussian 0.020 FP	0.000
      Fe	Ka	1 None 1853.446 34.7956 Gaussian 0.906 FP		0.000
      Co	Ka	1 None 50.052	14.6829 Gaussian 0.020 FP	0.000
      Ni	Ka	1 None 31.515	10.4993 Gaussian 0.011 FP	0.000
      Cu	Ka	1 None 154.309 13.0030 Gaussian 0.048 FP		0.000
      Zn	Ka	1 None 13.935	10.3666 Gaussian 0.004 FP	0.000
      Rb	Ka	1 None 99.294	15.4030 Gaussian 0.023 FP	0.000
      Sr	Ka	1 None 74.562	14.9229 Gaussian 0.020 FP	0.000
      Zr	Ka	1 None 58.817	14.7152 Gaussian 0.018 FP	0.000
      Nb	Ka	1 None 18.515	13.4962 Gaussian 0.007 FP	0.000
      Ag	Ka	1 None 5.280	10.8347 Gaussian 0.019 FP	0.000
      Sn	La	1 None 28.526	32.1601 Gaussian 0.358 FP	0.000
      Cs	La	1 None 0.000	10.2085 Gaussian 0.000 FP	0.000
      Ba	La	1 None 23.401	9.0598 Gaussian 0.129 FP	0.000
      Ta	La	1 None 49.611	13.3760 Gaussian 0.056 FP	0.000
      W	La	1 None 4.024	13.7213 Gaussian 0.004 FP	0.000
      Pb	La	1 None 22.156	14.4471 Gaussian 0.015 FP	0.000

      Analysis Conditions

      Tar Filter Thick. kV uA —Detector— Thick. Atm Preset Actual get mg/cm2 Type Filter mg/cm2 Time(s) Time(s)

      1. Silicon Dioxide (SiO)

         Silicon dioxide is the primary constituent of all the samples, with the following concentrations:

         Sample	SiO (wt.%)
         A	79.60
         B	75.92
         C	78.53
         D	78.51
         E	76.44

         The SiO content ranges from 75.92% to 79.60%, with Sample A having the highest silica concentration. This demonstrates that Wudil River sand is naturally rich in silica and therefore represents a promising source for silica production. Nevertheless, the silica content is lower than the 9899.5% SiO generally required for high-purity industrial silica used in advanced polymer composites and photovoltaic applications. Consequently, the raw sand cannot be used directly and must undergo purification to remove mineral impurities.

      2. Iron Oxide (FeO)

         Iron oxide concentrations are:

         • Sample A 1.405%

         • Sample B 1.129%

         • Sample C 1.316%

         • Sample D 1.086%

         • Sample E 1.295%

           Iron oxide is one of the most significant impurities in silica sand because it reduces the whiteness of the silica and adversely affects optical and thermal properties. For silica intended for PVC solar panel components, iron should ideally be below 0.050.10% after purification. Although the measured FeO contents are relatively low for natural river sand, they remain too high for direct industrial application. Acid leaching using hydrochloric acid (HCl), sulfuric acid (HSO), or oxalic acid would be expected to substantially reduce the iron content. Among the samples, Sample D contains the lowest FeO concentration (1.086%), making it the most favorable with respect to iron impurity.

         • Aluminium Oxide (AlO)

           The aluminium oxide concentrations are:

           • Sample A 6.81%

           • Sample B 7.15%

           • Sample C 9.08%

           • Sample D 8.95%

           • Sample E 7.78%

             The relatively high AlO content suggests the presence of feldspar and clay minerals. These minerals reduce silica purity and should be removed through washing, classification, and acid treatment before silica extraction. Samples A and B contain slightly lower alumina levels and would therefore require comparatively less purification.

         • Potassium Oxide (KO)

           Potassium oxide concentrations range from 6.14% to 7.29%, indicating the presence of potassium- bearing feldspar minerals.

           Although KO does not prevent silica extraction, it lowers silica purity and must be removed during beneficiation. The high KO levels suggest that the Wudil River sand contains appreciable feldspathic material, which can be reduced by flotation or magnetic separation combined with chemical leaching.

         • Calcium Oxide (CaO)

           Calcium oxide concentrations vary between 0.90% and 1.98%.

           These values indicate the presence of carbonate or calcareous minerals. Since calcium compounds can interfere with high-purity silica production, they should be removed during acid washing. Sample B has the lowest CaO content, whereas Sample E has the highest.

         • Titanium Oxide (TiO)

           TiO concentrations range from 0.16% to 1.23%.

           Titanium-bearing minerals contribute to discoloration and reduce silica quality. These minerals can usually be reduced by gravity separation, magnetic separation, or acid leaching. Sample D contains the lowest TiO concentration, indicating better natural purity.

         • Chlorine (Cl)

           The chlorine contents range from 1.27% to 5.76%, with Sample B exhibiting the highest value. Elevated chlorine levels may result from soluble salts deposited during river transport. These salts

           should be effectively removed by repeated washing before silica purification to prevent contamination during processing.

         • Trace Elements

           Trace oxides such as CrO, MnO, CuO, NiO, CoO, ZnO, ZrO, NbO, PbO, and BaO are present only in very small quantities (generally below 0.3 wt.%). These concentrations are unlikely to significantly affect silica extraction but would be further reduced during beneficiation and purification.

           1. Suitability for Silica Extraction

              The XRF results demonstrate that Wudil River sand possesses good potential as a silica source because:

              • SiO is the dominant oxide (approximately 7680 wt.%).

              • Iron oxide is relatively low compared with many natural sands.

              • Most impurities occur at moderate or trace concentrations.

              • The chemical composition is suitable for beneficiation to produce higher-purity silica.

                However, the raw sand does not yet meet the purity requirements for PVC solar panel manufacturing. The silica content must be increased to above 98%, while iron, aluminium, alkali oxides, calcium, and titanium must be significantly reduced through appropriate processing.

                For PVC solar panel production, purified silica is commonly incorporated as a reinforcing filler to improve:

              • mechanical strength,

              • dimensional stability,

              • thermal resistance,

              • UV resistance, and

              • durability of the PVC composite.

                The XRF results indicate that Wudil River sand is a promising raw material for this purpose after purification. Samples with higher SiO and lower impurity levels would require less beneficiation and lower processing costs.

                Considering both silica content and impurity levels:

              • Sample A has the highest SiO content (79.60 wt.%) and is the best candidate from the standpoint of silica abundance.

              • Sample D has slightly lower SiO (78.51 wt.%) but the lowest FeO (1.086 wt.%) and the lowest TiO (0.16 wt.%), making it particularly attractive because iron and titanium are among the most problematic impurities.

              • Samples C and B are also suitable but would require somewhat greater impurity removal.

              • Sample E is the least favorable due to its lower SiO content and comparatively higher CaO and Cl contents.

   3. Scanning Electron Microscopy (SEM) analysis

      The Scanning Electron Microscopy (SEM) analysis of the Wudil River sand samples was carried out at magnifications of 250×, 500×, and 800× using a Backscattered Electron (BSE) detector operating

      at 5 kV. The SEM micrographs provide valuable information on the particle morphology, grain shape, surface texture, particle size, and distribution of impurities, all of which influence the efficiency of silica extraction and the suitability of the extracted silica as a reinforcing filler in PVC solar panel materials.

      1. Particle Shape and Morphology

         The SEM micrographs reveal that the Wudil River sand consists predominantly of sub-angular to sub- rounded grains, with a few angular particles observed throughout the samples. The majority of the grains exhibit irregular polygonal shapes with rounded edges, indicating that the particles have undergone moderate weathering and fluvial transport within the river system before deposition.

         Quartz grains generally retain their crystalline geometry despite partial abrasion during transportation. The observed sub-angular morphology is characteristic of river-derived silica sand and suggests that the grains have experienced sufficient mechanical erosion to remove sharp edges while maintaining the structural integrity of the quartz crystals.

         This particle morphology is advantageous during silica extraction because sub-angular grains possess a relatively large specific surface area, allowing acid solutions to readily penetrate and dissolve surface impurities such as iron oxides, aluminosilicates, and carbonate minerals.

      2. Surface Texture

         At 500× and 800× magnifications, most quartz particles exhibit relatively smooth and compact surfaces, although localized rough regions, shallow pits, scratches, and fractured edges are evident on several grains. The smooth surfaces indicate that many particles consist of well-crystallized quartz with minimal weathering. Conversely, the roughened areas are likely associated with:

         • adhered clay minerals,

         • iron oxide coatings,

         • feldspathic impurities,

         • weathered mineral inclusions, or

         • fractured quartz surfaces produced during sediment transport.

           These rough surfaces are particularly important because they provide additional reaction sites during acid leaching. Hydrochloric acid or sulfuric acid can penetrate these irregularities more effectively, facilitating the dissolution of impurity minerals while leaving the chemically resistant quartz largely unaffected.

      3. Grain Size Distribution

         The SEM images show particles ranging approximately from 100 m to 500 m, which agrees well with the sieve analysis where most particles were retained between the 300 m and 600 m sieve fractions.

         The relatively uniform particle size distribution ndicates effective natural sorting by river action. Uniform particle sizes are desirable during silica beneficiation because they:

         • improve washing efficiency,

         • enhance magnetic separation,

         • permit uniform acid penetration,

         • reduce processing time, and

         • improve silica recovery.

           The agreement between SEM observations and the sieve analysis confirms that the Wudil River sand possesses a consistent particle size suitable for industrial beneficiation.

      4. Surface Defects and Fractures

         Several particles exhibit:

         • fractured edges,

         • micro-cracks,

         • shallow depressions,

         • chipped corners, and

         • cleavage-like features.

           These structural defects are beneficial for silica purification because they increase the exposed surface area available for chemical reactions during acid leaching.

           The micro-cracks provide pathways through which acids can dissolve impurities trapped within the outer layers of the particles without significantly attacking the quartz itself.

           Consequently, the morphology observed by SEM suggests that high silica recovery can be achieved using conventional beneficiation processes.

      5. Presence of Surface Impurities

         Although most particles appear relatively clean, several grains exhibit rough coatings and bright patches in the BSE images. These brighter regions are likely associated with minerals having higher average atomic numbers than quartz, including:

         • iron oxides,

         • titanium-bearing minerals,

         • feldspars,

         • aluminosilicates, or

         • other heavy mineral impurities.

           This observation agrees closely with the XRF analysis, which detected measurable quantities of:

         • FeO,

         • AlO,

         • TiO,

         • CaO,

         • KO, and

         • trace metallic oxides.

           The occurrence of these impurities primarily on grain surfaces suggests that they can be effectively removed by:

         • washing,

         • attrition scrubbing,

         • magnetic separation, and

         • acid leaching.

           Therefore, the SEM results indicate that most impurities occur as surface coatings rather than being uniformly distributed within the quartz grains, making purification considerably easier.

      6. Particle Packing and Dispersion

         The lower magnification (250×) micrographs demonstrate that the particles are well dispersed with limited agglomeration. Individual grains remain largely separated from one another, indicating that the sand contains little cementing material.

         The absence of severe particle agglomeration is advantageous because:

         • washing becomes more effective,

         • acids have unrestricted access to particle surfaces,

         • beneficiation efficiency increases,

         • silica recovery improves.

      7. Quartz Grain Integrity

         Most particles retain compact interiors without evidence of extensive internal porosity.

         Quartz is naturally resistant to chemical weathering, and the SEM images demonstrate that the grains possess good structural integrity despite minor surface abrasion. The high mechanical stability of the quartz grains supports their use as a raw material for producing high-purity silica suitable for industrial applications.

      8. Correlation with XRF Results

         The SEM observations correlate well with the XRF chemical analysis. The XRF results indicated that the Wudil River sand contains approximately 7680 wt.% SiO, confirming quartz as the dominant mineral phase. The smooth quartz-rich particles observed in the SEM micrographs support this finding. Likewise, the rough surface coatings and brighter mineral patches visible on some grains correspond to the presence of FeO, AlO, TiO, KO, and CaO detected by XRF. Because these impurities are mainly concentrated on the grain surfaces, they are expected to be removed efficiently during beneficiation, thereby increasing the silica purity to levels suitable for industrial use.

         1. Implications for Silica Extraction

            The SEM morphology indicates that the Wudil River sand possesses several favourable characteristics for silica extraction:

            • quartz grains dominate the sample;

            • particles are mostly sub-angular to sub-rounded;

            • surfaces are largely clean with only localized impurity coatings;

            • fractures and micro-cracks facilitate acid penetration;

            • particle sizes are relatively uniform;

            • little agglomeration is observed.

              These characteristics suggest that beneficiation through washing, sieving, magnetic separation, and acid leaching should substantially increase the silica content while reducing iron, alumina, titanium, and alkali impurities.

         2. Relevance to PVC Solar Panel Manufacturing

            Silica extracted from Wudil River sand can be incorporated into PVC composites as a reinforcing mineral filler. The morphology observed by SEM indicates that the purified silica would possess characteristics favourable for composite manufacturing. The irregular yet relatively smooth particles are expected to disperse effectively within the PVC matrix after milling, promoting good fillerpolymer interfacial adhesion. Well-dispersed silica particles enhance the composite by:

            • increasing tensile and flexural strength,

            • improving hardness and abrasion resistance,

            • enhancing thermal stability,

            • improving dimensional stability,

            • increasing ultraviolet (UV) resistance,

            • reducing thermal expansion, and

            • extending the service life of PVC solar panel components exposed to outdoor conditions. Furthermore, the limited amount of surface contamination observed suggests that, after purification, the silica would exhibit improved compatibility with polymer matrices and contribute to higher- performance PVC composites.

   4. Energy Dispersive X-ray Spectroscopy (EDX)

      Energy Dispersive X-ray Spectroscopy (EDX) was employed alongside Scanning Electron Microscopy (SEM) to determine the elemental composition of the Wudil River sand samples (AE). The EDX analysis provides localized elemental information from selected regions of the sand grains and

      complements the XRF results by identifying the elements present on the particle surfaces. According to the EDX report, all five samples were analysed under identical operating conditions (5 kV accelerating voltage, BSD detector, and field of view of 671 m). Notably, the report indicates that oxygen (O) and boron (B) were disabled during the analysis, meaning oxygen was not included in the quantitative results.

      1. region

      Element Number	Element Symbol	Element ame	Atomic Conc.	Weight Conc.
      24	Cr	Chromium	95.99	93.42
      38	Sr	Strontium	4.01	6.58

      FOV: 671 µm, Mode: 5kV – Image, Detector: BSD Full, Time: FEB 12 2026 15:49

      Disabled elements: B, O

      1. region

      Element Number	Element Symbol	Element Name	Atomic Conc.	Weight Conc.
      24	Cr	Chromium	95.92	93.31
      38	Sr	Strontium	4.08	6.69

      FOV: 671 µm, Mode: 5kV – Image, Detector: BSD Full, Time: FEB 12 2026 15:59

      Disabled elements: B, O

      1. region

      Element Number	Element Symbol	Element Name	Atomic Conc.	Weight Conc.
      24	Cr	Chromium	95.56	92.73
      38	Sr	Strontium	4.44	7.27

      FOV: 671 µm, Mode: 5kV – Image, Detector: BSD Full, Time: FEB 12 2026 16:07

      Disabled elements: B, O

      1. region

      Element Number	Element Symbol	Element Name	Atomic Conc.	Weight Conc.
      24	Cr	Chromium	95.80	93.12
      38	Sr	Strontium	4.20	6.88

      FOV: 671 µm, Mode: 5kV – Image, Detector: BSD Full, Time: FEB 12 2026 16:16

      Disabled elements: B, O

      1. region

      Element Number	Element Symbol	Element Name	Atomic Conc.	Weight Conc.
      24	Cr	Chromium	95.48	94.60
      38	Sr	Strontium	3.03	5.06

      FOV: 671 µm, Mode: 5kV – Image, Detector: BSD Full, Time: FEB 12 2026 16:25

      Disabled elements: B, O

      1. Observed Elemental Composition

         The EDX results for Samples AE report the following elemental composition:

         Sample	Chromium (Cr) wt.%	Strontium (Sr) wt.%
         A	93.42	6.58
         B	93.31	6.69
         C	92.73	7.27
         D	93.12	6.88

         Sample	Chromium (Cr) wt.%	Strontium (Sr) wt.%
         E	94.60	5.06

      2. Interpretation of the EDX Results

         The quantitative EDX report shows chromium (Cr) and strontium (Sr) as the dominant detected elements. However, this result is not consistent with the SEM observations or the XRF analysis performed on the same Wudil River sand.

         The XRF analysis previously showed that the sand consists predominantly of silicon dioxide (SiO, approximately 7680 wt.%), with smaller amounts of AlO, FeO, KO, CaO, and TiO. Likewise, the SEM images clearly display quartz-rich sand grains that are characteristic of silica sand rather than chromium-bearing minerals.

         A critical note in the EDX report states that oxygen (O) was disabled during the measurement. Oxygen is one of the principal constituents of silica (SiO), and excluding it from the analysis prevents the instrument from correctly quantifying silica. Furthermore, silicon (Si) is absent from the reported elemental table, despite quartz being the dominant mineral identified by XRF and SEM. This strongly suggests that the EDX acquisition or processing settings were incorrect, or that the elemental library/calibration used during analysis did not include the appropriate elements.

         Consequently, the reported chromium- and strontium-rich composition should not be interpreted as representing the true bulk chemistry of the Wudil River sand.

      3. Correlation with SEM Morphology

         The SEM micrographs show predominantly quartz grains with sub-angular to sub-rounded morphology, smooth surfaces, and only localized roughness due to surface coatings and minor impurities. There is no evidence of extensive chromium-rich mineral phases that would justify chromium concentrations exceeding 90 wt.%. Similarly, only a few brighter particles were observed in the backscattered electron images, indicating the presence of small amounts of heavier minerals. If chromium- or strontium-rich minerals were truly dominant, the SEM images would be expected to show a much larger proportion of high-atomic-number phases with stronger backscattered electron contrast. Therefore, the SEM observations support the XRF results rather than the numerical EDX output.

      4. Relationship with the XRF Analysis

         The XRF analysis provides a more reliable representation of the overall chemical composition because it analyses the bulk sample rather than a very small localized region. The XRF results demonstrated that:

         • SiO is the major constituent (approximately 7680 wt.%),

         • FeO occurs at relatively low concentrations,

         • AlO, KO, CaO, and TiO are present as moderate impurities, and

         • trace oxides occur only in small quantities.

           These results agree well with the geological origin of river sand and with the observed SEM morphology. By contrast, the EDX results reporting chromium and strontium as the only major detected elements are inconsistent with both the mineralogical appearance of the particles and the XRF chemical composition. Accordingly, the XRF data should be regarded as the primary source for discussing silica extraction from the Wudil River sand.

      5. Implications for Silica Extraction

         Despite the limitations of the reported EDX dataset, the combined SEM and XRF results indicate that Wudil River sand remains a suitable source of silica. The SEM images show quartz grains with

         relatively clean surfaces, while the XRF analysis confirms silica as the dominant component. The localized surface impurities observed by SEM are likely associated with iron oxides, aluminosilicates, feldspars, and titanium-bearing minerals rather than chromium-rich phases. These impurities can be removed effectively through:

         • washing,

         • attrition scrubbing,

         • sieving,

         • magnetic separation, and

         • acid leaching.

           Following beneficiation, the silica purity can be significantly increased, making the material suitable for industrial applications.

      6. Relevance to PVC Solar Panel Development

         For the manufacture of PVC solar panels, silica is used as a reinforcing filler to improve the mechanical, thermal, and weathering performance of the polymer composite. The suitability of Wudil River sand for this application is supported primarily by the SEM morphology and XRF chemistry, which indicate quartz-rich particles that can be purified to high-grade silica. Although the submitted EDX report does not provide a reliable elementalcomposition for silica because oxygen was disabled and silicon was not reported, the overall characterization demonstrates that the sand contains abundant quartz with removable surface impurities. After purification, the extracted silica is expected to:

         • improve the tensile and flexural strength of PVC composites,

         • enhance thermal stability,

         • increase ultraviolet (UV) resistance,

         • improve dimensional stability,

         • reduce moisture uptake, and

         • extend the service life of PVC solar panel components exposed to outdoor environments.

CONCLUSION.

The sieve analysis indicates that Samples C and D possess the most favorable particle size distributions for silica extraction because their medium-sized particles provide greater surface area for washing, impurity removal, and chemical purification. Samples A and E are dominated by coarse particles retained on the 600 µm sieve and would require additional crushing and milling, increasing processing costs. Sample B shows intermediate characteristics and could also be processed effectively.

However, particle size alone does not determine suitability for PVC solar panel manufacturing. The sand must also exhibit high silica (SiO) content, low iron (FeO), and minimal clay, feldspar, and other contaminants. Consequently, the sieve analysis should be complemented by XRF/XRD and chemical purification studies before selecting the most appropriate sand source for manufacturing high-quality PVC solar panel materials.

The XRF analysis confirms that Wudil River sand is a viable source of silica for the production of PVC solar panel materials. Although the natural SiO content (75.9279.60 wt.%) is lower than the purity required for industrial-grade silica, the relatively low concentrations of iron and other trace impurities indicate that the sand can be upgraded through washing, magnetic separation, sieving, and acid leaching. Based on the chemical composition, Sample A is the most promising because of its highest silica content, while Sample D is also highly suitable due to its lower iron and titanium contents, which would reduce purification requirements and improve the quality of the extracted silica for PVC solar panel manufacturing.

The SEM analysis demonstrates that the Wudil River sand is composed predominantly of quartz grains with sub-angular to sub-rounded morphology, relatively smooth surfaces, minor surface roughness, and limited impurity coatings. The particles show favourable characteristics for beneficiation because impurities are mainly located on the grain surfaces rather than within the quartz matrix. These morphological features, together with the XRF results showing silica as the dominant oxide and the sieve analysis indicating suitable particle size distribution, confirm that the Wudil River sand is a promising raw material for the extraction of high-purity silica. Following appropriate beneficiation and purification processes, the extracted silica would be suitable as a reinforcing filler in PVC composites for solar panel applications, where it can improve the mechanical strength, thermal stability, weather resistance, and long-term durability of the final product.

The submitted EDX report identifies chromium and strontium as the detected elements in all five analysed regions; however, these results are inconsistent with the accompanying SEM observations and the XRF analysis of the same Wudil River sand. Because oxygen was disabled during the EDX acquisition and silicon is absent from the reported elemental tables, the quantitative EDX results should not be used as the principal evidence for evaluating silica extraction. Instead, the XRF and SEM analyses provide a more reliable characterization, demonstrating that the Wudil River sand is predominantly quartz with moderate surface impurities that can be removed through beneficiation and acid leaching. Therefore, the sand remains a promising raw material for producing high-purity silica suitable for use as a reinforcing filler in PVC solar panel manufacturing.

Recommendation:

It is recommended to verify the instrument settings and repeat the EDX analysis with silicon (Si) and oxygen (O) enabled. A correct EDX spectrum for silica sand should show strong Si and O peaks, with only minor peaks for impurities such as Fe, Al, Ti, Ca, K, and other trace elements. This would provide results that are consistent with both the SEM morphology and the XRF chemical composition.

Acknowledgement.

The author wishes to acknowledge to the management of Kano State Polytechnic, and Colloquies at Welding and Fabrication Department more especially Material Science Laboratory, Chemistry Laboratory Technologist for guidance and assistance during pre-treatment of the sand sample and general use of laboratory facilities.

Funding.

The research was funded by tertiary education trust fund (TETfund) with grand allocation number TETF/DR&D/CE/POLY/KANO/RG/2025/VOL.1

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