Enhanced Heavy Metal Removal Using Mixed Micelle Systems

Enhanced Heavy Metal Removal Using Mixed Micelle Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhanced Heavy Metal Removal Using Mixed Micelle Systems Birendra Kumar, DEEPTI TIKARIHA JANGDE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8496143/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Through cloud point extraction, this study explores mixed micelle systems that combine CTPB, TTPB with Brij 30 and Brij 35 to improve the removal of heavy metals from water. Atomic absorption spectroscopy is used to identify which metals are present in water. Several physicochemical parameters were assessed. The results indicate that, in comparison to single surfactant systems, mixed surfactants exhibit better metal ion complexation and lower critical micelle concentrations (CMC), with removal efficiencies of up to 92%. Additionally, mixed micelle systems improved cationic metal selectivity in multi-metal solutions. These results demonstrate the potential of customised surfactant mixtures for heavy metal remediation that is both economical and ecologically sustainable. Mixed micelles Cationic–nonionic CMC Heavy meals Micellar-enhanced extraction Water remediation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1.0 Introduction The environment and human health are at serious risk because of the sharp rise in heavy metal concentrations day by day in aquatic ecosystems [1–3]. Metals like lead (Pb), iron (Fe), Magnese (Mn), zinc (Zn), nickel (Ni), chromium (Cr), and cadmium (Cd) are examples of persistent, non-biodegradable metals that can bio-accumulate in food chains [4–5]. Prolonged exposure to these metals has been associated with serious health effects, including neurological impairments, kidney dysfunction, and carcinogenic outcomes [6–7]. Since conventional water treatment techniques like chemical precipitation, ion exchange, and reverse osmosis usually fail to remove trace metal ions or achieve selective separation in multi-metal systems, innovative and sustainable remediation techniques are needed [8–9]. Heavy metal removal from water has drawn more attention to surfactant systems [10]. Micelles can enhance the removal of metal ions from aqueous media by encasing hydrophobic pollutants or complexing with them [11–12]. In past years, metal ions have been extracted from aqueous solutions using single surfactant systems [13–14]. Recently, mixed systems have been found to have a significantly lower critical micelle concentration (CMC) than single surfactant systems [15–19]. Thus, cationic and non-ionic mixed surfactant systems are employed to extract heavy metals from water. No research has been done on the use of mixed surfactant systems to remove heavy metals from water. Because of the improved ability to bind metal ions, a mixed surfactant system has been used. Thus, systems like cationic and non-ionic surfactants have high surface activity, enhanced stability, and strong metal-binding affinity [20–21]. Algheryani and Asweisi [22] studied the cloud point extraction (CPE) of trivalent chromium (Cr³⁺) from aqueous solutions using different nonionic surfactants. They optimized parameters such as surfactant type, concentration, and temperature to achieve high extraction efficiency. The study demonstrated that CPE is a simple, cost-effective, and eco-friendly method for removing Cr(III) from water samples, highlighting the influence of surfactant selection on extraction performance. Hazrina et al. [23] developed a cloud point extraction (CPE) method for removing methylphenol from water using a chelating agent combined with a surfactant. The study focused on optimizing extraction parameters such as surfactant concentration, temperature, and pH to enhance recovery efficiency. Results showed that the chelating–surfactant system significantly improved the extraction of methylphenol, demonstrating CPE as an efficient, green, and economical technique for organic pollutant removal from aqueous environments. Azizinezhad [24] conducted a comparative study on the removal of Pb²⁺ ions from aqueous solutions using different nonionic surfactants through cloud point extraction (CPE). The research examined factors such as surfactant type, concentration, temperature, and pH on extraction efficiency. Results revealed that the choice of surfactant significantly affected lead removal performance, with some surfactants exhibiting higher affinity for Pb²⁺ ions. The study confirmed CPE as a simple, efficient, and eco-friendly technique for heavy metal extraction from water systems. The benefits of mixed micelle systems which combine two or more surfactants to take advantage of synergistic effects have been highlighted in recent studies. Cationic-nonionic mixtures offer tunable selectivity, enhance solubilisation, and reduce CMC [25–26]. According to Varshney et al. [27], mixed micellisation systems outperform single-surfactant systems in terms of phase separation, metal-binding affinity, and surface activity. Our research group [28] has studied that single surfactant-mediated techniques for heavy metal removal from water, emphasizing eco-friendly approaches like cloud point extraction (CPE) and surfactant-assisted adsorption. The study highlighted the effectiveness of nonionic surfactants such as Triton X-100 and PEG derivatives in removing metals. They concluded that surfactant-based methods are cost-effective, efficient, and sustainable alternatives to conventional treatment processes. In the current study, mixed surfactant systems consisting of cetyltriphenylphosphonium bromide (CTPB) and tetradecyltriphenylphosphonium bromide (TTPB) with nonionic surfactants Brij-30 and Brij-35 were used to investigate the removal of heavy metal ions (mainly Fe) from water. Atomic absorption spectroscopy (AAS) was used to measure the concentrations of metal ions, and the surface tension method was used to analyse the physicochemical characteristics of both single and mixed surfactant systems. The cloud point extraction (CPE) technique was used to assess the effectiveness of heavy metal removal from water. 2.0 Materials and Methods 2.1 Materials Samples of wastewater were taken from a sugar factory in Kabirdham, Chhattisgarh, as well as the areas around it. They were kept in a freezer at 4°C to maintain the sample quality. Merck is the supplier of cetyltriphenyl phosphonium bromide (CTPB) and tetradecyltriphenyl phosphonium bromide (TTPB). High-purity water was used to prepare all solutions. Methyl alcohol, analytical-grade mineral acids, and additional reagents were purchased from Merck in Darmstadt, Germany. The standard calibration curve method was used to determine the concentrations of metal ions. A 1.0% (w/v) surfactant solution was purchased from Clariant. The method described in the literature [29] was used to prepare the ligand ammonium pyrrolidinedithiocarbamate (APDC). Various-sized beakers (100 and 200 ml), a micropipette, a volumetric flask, a test tube, a centrifuge tube (15 ml), a syringe, a spatula, vials, and reagents were among the equipment and glassware utilised. 2.2 Methods Aqueous solutions containing 10 mg/L of metal ions were treated with mixed micelle solutions in 100 mL volumes. Samples were incubated between 25 and 50°C for an hour while being gently stirred. The concentrations of the metals in the supernatant were determined using Atomic Absorption Spectroscopy (AAS). The concentrations of metals in the supernatant were measured at 420 nm using a Novaa 350 Atomic Absorption Spectrometer (AAS). The ring detachment method was used to measure surface tension using a digital surface tensiometer (Jencon, India) in order to determine the critical micelle concentration (CMC). All measurements were made using a pure, high-purity platinum ring to guarantee precision and repeatability. Repeated trials yielded consistent surface tension values, and the instrument's accuracy allowed for an accuracy of ± 0.1 mN m⁻¹. Phase separation was induced for the mixed micellisation system using cloud point extraction (CPE). Ten millilitres of metal ion solution, one millilitre of cationic + nonionic surfactant (different molar ratio), and one millilitre of ammonium pyrrolidinedithiocarbamate (APDC) were combined to create a 12-milliliter sample. The mixture was heated for 15 minutes at temperatures between 20°C and 100°C in a temperature-controlled water bath. Centrifugation was used to separate the phases for two minutes at 3000 rpm. The mixture was cooled in an ice bath for five minutes, which made the diluted aqueous phase and the surfactant-rich phase immiscible. This made it simple to separate the supernatant aqueous phase. The extraction yield (%) was computed using the following formula to assess the effectiveness of chromium extraction using the CPE. $$\:\%E=({C}_{o}-{C}_{t\:}{C}_{o})\times\:100$$ where C t is the final concentration of metal ions in the aqueous phase following extraction, C 0 is the initial concentration of metal ions in the feed phase, and %E is the extraction yield. 3.0 Results and Discussion 3.1 Some Important physicochemical parameters of water The techniques used to examine the different physicochemical characteristics of the water samples are compiled in Table 1. Water from two sugar factories and Kawardha was analysed, and the results showed differences in several parameters. Conditions were slightly acidic to nearly neutral, with pH values ranging from 6.7 to 6.9. Compared to Kawardha (376 µS/cm), conductivity was higher in sugar factory effluents (447 and 426 µS/cm), indicating higher levels of dissolved salts in industrial waters. A similar pattern was seen in total hardness, with higher levels in sugar factory samples (489 and 463 mg/L) than in Kawardha samples (381 mg/L), indicating a higher calcium and magnesium content. In line with conductivity and hardness observations, sugar factory waters had higher total dissolved solids (TDS) (746 and 678 mg/L) than Kawardha (647 mg/L). These results suggest that the higher mineral content and ionic strength caused by industrial effluents may have an impact on the water's suitability for residential or agricultural use [28, 30]. S.No. Parameters Kawardha Sugar Factory 1 Sugar Factory 2 1 pH 6.9 6.8 6.7 2 Conductivity (s/m) 376 447 426 3 Total Hardness (mg/l) 381 489 463 4 Total dissolved solid (TDS) (mg/l) 647 746 678 3.2 Metal Analysis of Water Sample Comprehensive findings of the metal analysis of water samples using atomic absorption spectroscopy are shown in Table 2 . Seasonal variations or localised contamination may be the cause of the Fe and Mn concentrations, which peaked in April 2025 and ranged from 0.831 to 2.703 mg/L and 0.192 to 1.781 mg/L, respectively, according to Atomic Absorption Spectroscopy. On the other hand, Zn (0.023–1.586 mg/L) and K (0.022–0.957 mg/L) were continuously lower. For the removal of heavy metals, single micelle systems are frequently employed, but mixed systems are still not well understood [31, 32]. Table 2 The sample analyzed from various source by atomic absorption spectroscopy January 2025 February 2025 March 2025 April 2025 May 2025 June 2025 July 2025 August 2025 September 2025 Metals in Water Sample 1 mg/L Sample 2 mg/L Sample 3 mg/L Sample 1 mg/L Sample 2 mg/L Sample 3 mg/L Sample 1 mg/L Sample 2 mg/L Sample 3 mg/L Fe 1.182 0.831 0.872 2.703 1.501 0.837 0.962 0.842 0.932 Mn 0.312 0.343 0.192 1.781 0.863 0.74 0.531 0.659 0.661 K 0.134 0.041 0.037 0.957 0.849 0.581 0.022 0.046 0.041 Zn 0.131 0.039 0.035 1.586 0.416 0.038 0.025 0.023 0.028 Because of their low toxicity and biodegradability, non-ionic surfactants—like Brij 30 and Brij 35—are favored because they improve adsorption through compact interfacial layers and modified microemulsion structures. By employing APDC as a complexing agent, Fe removal through cloud point extraction increased extraction efficiency from 36–100% (Brij 30) and 45–100% (Brij 35). The significance of surfactant selection for successful remediation is highlighted by the fact that higher metal concentrations decreased efficiency because of surfactant site saturation [28, 31]. Herein, it has been observed that the concentration of iron is higher than other metal ion concentration in water sample. Hence, we focused for removal of iron from water sample using mixed surfactant system by cloud point extraction technique. 3.3 Mixed Micelle Formation Surface tension measurements yielded the critical micelle concentration (CMC) values for both individual and mixed surfactant systems, which are shown in Table 3 . Brij 30 and Brij 35, two of the nonionic surfactants, showed extremely low CMCs (0.082 mM and 0.065 mM, respectively), which is indicative of their strong propensity to form micelles at low concentrations. The cationic surfactants CTPB and TTPB, on the other hand, demonstrated greater CMCs (0.400 mM and 0.800 mM), suggesting that micellisation calls for higher concentrations [15–17]. Table 3 Critical micelle concentration (CMC), Surface pressure at the CMC (cmc), the maximum surface excess ( max) and the minimum surface area per molecule (Amin) values of nonionic, cationic and cationic-nonionic mixed surfactant system (1:1) in aqueous solution at 300K by surface tension method. πΓ Surfactant CMC (mM) Г max 10 6 mol.m − 2 A min 10 20 m 2 π CMC mNm − 1 Brij 30 0.082 Brij 35 0.065 2.36 70.3 30.9 CTPB 0.400 2.65 62.6 25 TTPB 0.800 1.07 155.1 26.5 CTPB+Brij 30 0.112 1.35 122 32 CTPB+Brij 35 0.105 0.701 236.8 30 TTPB+Brij 30 0.128 2.94 56.5 28.5 TTPB+Brij 35 0.110 0.98 169.4 27.9 Compared to pure cationic surfactants, the mixed cationic–nonionic systems showed intermediate CMC values (0.105–0.128 mM), indicating synergistic interactions between cationic and nonionic components that promote micelle formation at lower concentrations. These findings demonstrate how mixed surfactant systems have improved micellisation efficiency. According to the surface parameter results, Brij 30 had the weakest adsorption, while CTPB had the most compact packing (Amin = 62.6 Ų) and the highest adsorption efficiency (Γmax = 2.65 × 10⁻⁶ mol.m⁻²). Strong synergistic effects were found in mixed systems, especially in TTPB+Brij 30, which displayed the lowest Amin (56.5 Ų) and the highest adsorption (Γmax = 2.94 × 10⁻⁶ mol.m⁻²), indicating tight molecular packing and increased surface activity. The ΠCMC values (25–32 mN.m⁻¹) indicate effective interfacial adsorption, and mixed systems exhibit superior surface-active behaviour that is advantageous for heavy metal removal and cloud point extraction. 3.4 Heavy Metal Removal Efficiency with APDC Figure 3 illustrates how the extraction efficiency of Fe(III) rose as the APDC concentration rose up to 7–10 mL, at which point it stayed relatively constant, signifying equilibrium. Due to insufficient Fe(III)–APDC complexation, extraction was subpar (30–40%) at low APDC levels (1–3 mL). From 5 mL onwards, there was a notable rise that reached 75–86%, indicating improved micellar solubilisation and Fe(III)–APDC complex formation. The systems with the highest extraction rates were CTPB + Brij 35 (85–86%), TTPB + Brij 35 (78–79%), CTPB + Brij 30 (77–78%), and TTPB + Brij 30 (68–72%). The synergistic interactions between cationic and nonionic surfactants, which result in the formation of more compact micelles with a higher solubilisation capacity, are what give CTPB + Brij 35 its superior performance. Therefore, 7–10 mL of APDC is the ideal concentration for Fe(III) extraction [28]. 3.5 Effect of Surfactant Concentration In mixed systems of cationic (CTPB, TTPB) and nonionic (Brij 30, Brij 35) surfactants, the extraction efficiency of Fe(III) was investigated at different surfactant concentrations. Up until the third addition, an increase in surfactant concentration resulted in a noticeable improvement in extraction efficiency; after that, the values either slightly decreased or levelled off [32]. The systems with the highest extraction efficiency (up to 90%) were CTPB + Brij 35, TTPB + Brij 35 (87%), CTPB + Brij 30 (83%), and TTPB + Brij 30 (78%). Strong synergistic interactions between cationic and nonionic surfactants result in compact mixed micelles with a higher solubilisation capacity for the Fe(III)–APDC complex, which is responsible for CTPB + Brij 35's superior performance. The extraction slightly decreased at higher surfactant levels (4–5 mL), which may have been caused by micellar saturation or dilution of the surfactant-rich phase, which lowers the effective concentration of solubilised Fe(III)–APDC complexes. Overall, the most effective mixed system for Fe(III) extraction was CTPB + Brij 35, with best results seen at a concentration of about 3 mL surfactant [33]. Several mixed surfactant systems were used to assess the impact of Fe(III) ion concentration on extraction efficiency. Throughout the tested Fe(III) concentrations (10–40 ppm), the extraction percentage stayed relatively constant, suggesting that the systems have enough micellar capacity to hold the Fe(III)–APDC complex even at higher metal ion levels. 3.6 Effect of metal ion concentration The systems that demonstrated the highest extraction efficiency among the ones that were examined were CTPB + Brij 35 (97–98%), TTPB + Brij 35 (95–96%), CTPB + Brij 30 (90–91%), and TTPB + Brij 30 (86–88%). Strong synergistic interactions and stable micelle formation that support effective solubilisation of the Fe(III)–APDC complex are suggested by the consistently high extraction by CTPB + Brij 35. 3.7 Cloud point extraction (CPE) method for heavy metal removal Analyte preconcentration and separation are common applications for Cloud Point Extraction (CPE), a low-cost, eco-friendly method with obvious advantages over conventional liquid-liquid extraction [34]. Due to their high water solubility and low ionisation, non-ionic surfactants are widely used. Here, we removed the heavy metals from the sample using a mixed system consisting of a cationic and non-ionic surfactant.CPE includes temperature-induced phase separation, and optimal extraction occurs within a specific range of surfactant concentrations [35]. A concentration that is too high reduces the preconcentration factor; while a concentration that is too low may result in poor analyte recovery.It is possible to use ionic and non-ionic surfactants separately or in combination [36]. Metal ion extraction using CPE requires complexation with suitable ligands, which is highly pH-dependent. The clouding phenomenon, which is also observed in systems containing ionic + non-ionic surfactants (CTPB+Brij 35, TTPB+Brij 35, CTPB+Brij 30, TTPB+Brij 30), is caused by the cloud point (CP), where the solution separates into surfactant-rich and surfactant-lean phases. Using mixed surfactant systems, the impact of temperature on the cloud point extraction efficiency of Fe(III) was examined between 20 and 100°C. Temperature was found to have a significant impact on the micellisation and phase separation behaviour of the surfactant systems, as evidenced by the notable variation in extraction efficiency [37]. The extraction efficiency was moderate at lower temperatures (20–40°C), indicating limited solubilisation of the Fe(III)–APDC complex and incomplete micelle formation. Because of improved micellisation and decreased hydration around surfactant head groups, which promoted phase separation and the transfer of the Fe(III)–APDC complex into the surfactant-rich phase, extraction efficiency rose as temperature rose. The highest extraction efficiency of all the systems under study was demonstrated by CTPB + Brij 35 (up to 90% at 60°C), which was followed by TTPB + Brij 35 (82%), CTPB + Brij 30 (80%), and TTPB + Brij 30 (70%). The synergistic interaction between cationic and nonionic surfactants, which encourages micellar compactness and stability during phase separation, is responsible for CTPB + Brij 35's superior performance. A slight decrease in extraction efficiency was noted at temperatures higher than 70–80°C, most likely as a result of micelle destabilisation or partial Fe(III)–APDC complex breakdown. As a result, 60 to 70°C is the ideal temperature range for maximum Fe(III) extraction; above this, efficiency declines [38]. Overall, the results show that CTPB + Brij 35 is the most efficient surfactant system and that temperature is a critical factor in regulating micellar phase behaviour and extraction performance in cloud point extraction of Fe(III). 3.8 Comparative Performance of Mixed Surfactant Systems In every experimental scenario, the CTPB + Brij 35 system continuously showed the highest extraction efficiency. The cationic head group of CTPB and the polyoxyethylene chain of Brij 35 work in concert to form compact, stable mixed micelles with a high solubilising capacity, which is responsible for this superior performance. Because of their shorter ethylene oxide chains, which offer less hydration and micellar stability, Brij 30-based systems demonstrated decreased efficiency, whereas the TTPB + Brij 35 system demonstrated slightly lower but still effective extraction. In the CPE process, a hydrophobic Fe(III)–APDC complex is formed during the extraction of Fe(III). When heated above the cloud point, this complex partitions into the surfactant-rich phase. The degree of complexation and the mixed micellar phase's capacity for solubilisation both affect extraction efficiency. Phase separation and solubilisation of the Fe(III)–APDC complex are improved in mixed systems due to the electrostatic and hydrophobic interactions between surfactants, which decrease the cloud point and raise micellar compactness. 3.9 Conclusion Temperature and surfactant combinations affected the removal efficiency of iron. While TTPB+Brij 35 peaked at 80% at 20°C, CTPB+Brij 35 demonstrated the highest efficiency (90%) at 60°C. While CTPB performance was improved by higher temperatures, TTPB systems performed better at lower temperatures. Overall, Fe was efficiently extracted by mixed surfactant systems, with temperature and surfactant type having an impact on efficiency.Fe was successfully extracted from water using cloud point extraction with mixed surfactant systems (CTPB/TTPB with Brij 30/35). Temperature and surfactant combination determined optimal removal, demonstrating the potential of customised surfactant systems for heavy metal extraction.With the potential for industrial-scale use, these systems provide a sustainable, effective, and adjustable approach to water remediation. Future research might examine multi-metal competition effects, mixed micelle recovery, and actual wastewater testing. Declarations 4.0 Funding The authors declare that no funding was received for this work. 4.1 Consent to Publish Declaration Consent to Publish declaration: not applicable. 4.2 Ethics Declaration Ethics declaration: not applicable. 4.3 Consent to Participate Declaration Consent to Participate declaration: not applicable. 4.4 Data Availability Statement All data generated or analysed during this study are included in this published article and its supplementary information files. 4.5 Author Contribution Birendra Kumar: Conceptualization, methodology, investigation, data curation, formal analysis, writing—original draft. Deepti Tikariha Jangde: Supervision, validation, resources, writing—review and editing. All authors read and approved the final manuscript. 4.6 Acknowledgement Authors grateful to Prof K.K. Ghosh School of Studies in Chemistry, Pt. Ravishankar Shukla University, Raipur (India) for providing valuable suggestions. References Fu F, Wang Q. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8496143","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591309284,"identity":"77f4aaf5-eafd-484d-86b0-c0214d2ca3e1","order_by":0,"name":"Birendra Kumar","email":"","orcid":"","institution":"Govt. Rajmata Vijiyaraje Sindhiya Kanya Mahavidyalaya Kawardha","correspondingAuthor":false,"prefix":"","firstName":"Birendra","middleName":"","lastName":"Kumar","suffix":""},{"id":591309285,"identity":"2c301dfb-9eb7-4006-a99d-18f90600f7da","order_by":1,"name":"DEEPTI TIKARIHA JANGDE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDCCAyDCgIFHAkgdZmCwAVKMjQcIajlgYADTkgbS0kCEFqA1IC3MYF0wq3EBvuPNxx5/KPgjI9l+9uHhgprzdmvbDwNtqbGJxqVF8syxdAOQw6R50g0Ozzh2O3nbmUSglmNpuQ04tBjcyDGTAGmRA3rjMA/b7WSzA0AtjA2HcWu5//4bRAv/M6CWf+eSzc4/JKDlBg8bWIu0BNAW3rYDdmY3CNgieSbN3OCMgTGP5AygLbx9yQlmN4C2JODxC9/xw88eVPyRs5c4n8b8meebnb3Z+fSHDz7U2ODUAgRsKLxEsMoE3MoxtdjjVzwKRsEoGAUjEQAAbqJlSgS5HKsAAAAASUVORK5CYII=","orcid":"","institution":"Acharya Panth Shri Grindh Muni Naam Saheb, Govt. P.G. College Kawardha","correspondingAuthor":true,"prefix":"","firstName":"DEEPTI","middleName":"TIKARIHA","lastName":"JANGDE","suffix":""}],"badges":[],"createdAt":"2026-01-01 16:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8496143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8496143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102985785,"identity":"7367c8b5-c54a-4525-8601-a78206e52774","added_by":"auto","created_at":"2026-02-19 10:14:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42786,"visible":true,"origin":"","legend":"\u003cp\u003eCritical micelle concentration (CMC) of single Brij 30 and Brij35 determined by surface tension method.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/184f4409c6590ab349f09f41.png"},{"id":104834875,"identity":"bfad9931-150d-46a9-bc92-aeba660b10a2","added_by":"auto","created_at":"2026-03-17 17:34:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47041,"visible":true,"origin":"","legend":"\u003cp\u003eCritical micelle concentration (CMC) of mixed system with CTPB + Brij 30 and Brij 35 determined by surface tension method.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/a8261a4987742d6beaf439fc.png"},{"id":102985778,"identity":"b716d51c-5fda-44df-8279-ed2927b2b3bc","added_by":"auto","created_at":"2026-02-19 10:14:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41619,"visible":true,"origin":"","legend":"\u003cp\u003eCritical micelle concentration (CMC) of mixed system with TTPB + Brij 30 and Brij 35 determined by surface tension method.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/8ecab7a6bc8d6ad7033320a8.png"},{"id":103049284,"identity":"b4c8dab0-f357-439a-9f0d-0bb0237b3ff6","added_by":"auto","created_at":"2026-02-20 07:39:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35642,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of APDC on CPE of Fe (III) ions with mixed surfactant system.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/f02501c9a38c7c0a47815eb0.png"},{"id":102985780,"identity":"587ec029-5975-40c7-99fe-4e0fa0f2b447","added_by":"auto","created_at":"2026-02-19 10:14:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28307,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of mixed surfactant volume on CPE of Fe (III) ions.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/1f4edd5d4893f646ffb11db9.png"},{"id":102985782,"identity":"84e3169c-8ae8-42b6-a6a8-fb9872e67d80","added_by":"auto","created_at":"2026-02-19 10:14:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":36689,"visible":true,"origin":"","legend":"\u003cp\u003eEffect Fe (III) ions on different mixed surfactant system.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/11b334a1f939bfd684bb3f48.png"},{"id":103049835,"identity":"f3cf7a19-7965-4f2d-8d70-036d81603dd3","added_by":"auto","created_at":"2026-02-20 07:46:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7133,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of temperature on CPE of Fe (III) ions with mixed surfactants.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/2758bcaea426ced6ff2c8777.png"},{"id":104835695,"identity":"52c5147e-0149-422c-9a33-d8453baf8d20","added_by":"auto","created_at":"2026-03-17 17:48:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1118092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8496143/v1/4e1576c2-cf3b-4689-8dad-414bdd20ae99.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Heavy Metal Removal Using Mixed Micelle Systems","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThe environment and human health are at serious risk because of the sharp rise in heavy metal concentrations day by day in aquatic ecosystems [1\u0026ndash;3]. Metals like lead (Pb), iron (Fe), Magnese (Mn), zinc (Zn), nickel (Ni), chromium (Cr), and cadmium (Cd) are examples of persistent, non-biodegradable metals that can bio-accumulate in food chains [4\u0026ndash;5]. Prolonged exposure to these metals has been associated with serious health effects, including neurological impairments, kidney dysfunction, and carcinogenic outcomes [6\u0026ndash;7]. Since conventional water treatment techniques like chemical precipitation, ion exchange, and reverse osmosis usually fail to remove trace metal ions or achieve selective separation in multi-metal systems, innovative and sustainable remediation techniques are needed [8\u0026ndash;9]. Heavy metal removal from water has drawn more attention to surfactant systems [10]. Micelles can enhance the removal of metal ions from aqueous media by encasing hydrophobic pollutants or complexing with them [11\u0026ndash;12]. In past years, metal ions have been extracted from aqueous solutions using single surfactant systems [13\u0026ndash;14]. Recently, mixed systems have been found to have a significantly lower critical micelle concentration (CMC) than single surfactant systems [15\u0026ndash;19]. Thus, cationic and non-ionic mixed surfactant systems are employed to extract heavy metals from water. No research has been done on the use of mixed surfactant systems to remove heavy metals from water. Because of the improved ability to bind metal ions, a mixed surfactant system has been used. Thus, systems like cationic and non-ionic surfactants have high surface activity, enhanced stability, and strong metal-binding affinity [20\u0026ndash;21].\u003c/p\u003e \u003cp\u003eAlgheryani and Asweisi [22] studied the cloud point extraction (CPE) of trivalent chromium (Cr\u0026sup3;⁺) from aqueous solutions using different nonionic surfactants. They optimized parameters such as surfactant type, concentration, and temperature to achieve high extraction efficiency. The study demonstrated that CPE is a simple, cost-effective, and eco-friendly method for removing Cr(III) from water samples, highlighting the influence of surfactant selection on extraction performance. Hazrina et al. [23] developed a cloud point extraction (CPE) method for removing methylphenol from water using a chelating agent combined with a surfactant. The study focused on optimizing extraction parameters such as surfactant concentration, temperature, and pH to enhance recovery efficiency. Results showed that the chelating\u0026ndash;surfactant system significantly improved the extraction of methylphenol, demonstrating CPE as an efficient, green, and economical technique for organic pollutant removal from aqueous environments. Azizinezhad [24] conducted a comparative study on the removal of Pb\u0026sup2;⁺ ions from aqueous solutions using different nonionic surfactants through cloud point extraction (CPE). The research examined factors such as surfactant type, concentration, temperature, and pH on extraction efficiency. Results revealed that the choice of surfactant significantly affected lead removal performance, with some surfactants exhibiting higher affinity for Pb\u0026sup2;⁺ ions. The study confirmed CPE as a simple, efficient, and eco-friendly technique for heavy metal extraction from water systems. The benefits of mixed micelle systems which combine two or more surfactants to take advantage of synergistic effects have been highlighted in recent studies. Cationic-nonionic mixtures offer tunable selectivity, enhance solubilisation, and reduce CMC [25\u0026ndash;26]. According to Varshney et al. [27], mixed micellisation systems outperform single-surfactant systems in terms of phase separation, metal-binding affinity, and surface activity. Our research group [28] has studied that single surfactant-mediated techniques for heavy metal removal from water, emphasizing eco-friendly approaches like cloud point extraction (CPE) and surfactant-assisted adsorption. The study highlighted the effectiveness of nonionic surfactants such as Triton X-100 and PEG derivatives in removing metals. They concluded that surfactant-based methods are cost-effective, efficient, and sustainable alternatives to conventional treatment processes.\u003c/p\u003e \u003cp\u003eIn the current study, mixed surfactant systems consisting of cetyltriphenylphosphonium bromide (CTPB) and tetradecyltriphenylphosphonium bromide (TTPB) with nonionic surfactants Brij-30 and Brij-35 were used to investigate the removal of heavy metal ions (mainly Fe) from water. Atomic absorption spectroscopy (AAS) was used to measure the concentrations of metal ions, and the surface tension method was used to analyse the physicochemical characteristics of both single and mixed surfactant systems. The cloud point extraction (CPE) technique was used to assess the effectiveness of heavy metal removal from water.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cp\u003eSamples of wastewater were taken from a sugar factory in Kabirdham, Chhattisgarh, as well as the areas around it. They were kept in a freezer at 4\u0026deg;C to maintain the sample quality. Merck is the supplier of cetyltriphenyl phosphonium bromide (CTPB) and tetradecyltriphenyl phosphonium bromide (TTPB). High-purity water was used to prepare all solutions. Methyl alcohol, analytical-grade mineral acids, and additional reagents were purchased from Merck in Darmstadt, Germany. The standard calibration curve method was used to determine the concentrations of metal ions. A 1.0% (w/v) surfactant solution was purchased from Clariant. The method described in the literature [29] was used to prepare the ligand ammonium pyrrolidinedithiocarbamate (APDC). Various-sized beakers (100 and 200 ml), a micropipette, a volumetric flask, a test tube, a centrifuge tube (15 ml), a syringe, a spatula, vials, and reagents were among the equipment and glassware utilised.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cp\u003eAqueous solutions containing 10 mg/L of metal ions were treated with mixed micelle solutions in 100 mL volumes. Samples were incubated between 25 and 50\u0026deg;C for an hour while being gently stirred. The concentrations of the metals in the supernatant were determined using Atomic Absorption Spectroscopy (AAS). The concentrations of metals in the supernatant were measured at 420 nm using a Novaa 350 Atomic Absorption Spectrometer (AAS). The ring detachment method was used to measure surface tension using a digital surface tensiometer (Jencon, India) in order to determine the critical micelle concentration (CMC). All measurements were made using a pure, high-purity platinum ring to guarantee precision and repeatability. Repeated trials yielded consistent surface tension values, and the instrument's accuracy allowed for an accuracy of \u0026plusmn;\u0026thinsp;0.1 mN m⁻\u0026sup1;. Phase separation was induced for the mixed micellisation system using cloud point extraction (CPE). Ten millilitres of metal ion solution, one millilitre of cationic\u0026thinsp;+\u0026thinsp;nonionic surfactant (different molar ratio), and one millilitre of ammonium pyrrolidinedithiocarbamate (APDC) were combined to create a 12-milliliter sample. The mixture was heated for 15 minutes at temperatures between 20\u0026deg;C and 100\u0026deg;C in a temperature-controlled water bath. Centrifugation was used to separate the phases for two minutes at 3000 rpm. The mixture was cooled in an ice bath for five minutes, which made the diluted aqueous phase and the surfactant-rich phase immiscible. This made it simple to separate the supernatant aqueous phase. The extraction yield (%) was computed using the following formula to assess the effectiveness of chromium extraction using the CPE.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\%E=({C}_{o}-{C}_{t\\:}{C}_{o})\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere C\u003csub\u003et\u003c/sub\u003e is the final concentration of metal ions in the aqueous phase following extraction, C\u003csub\u003e0\u003c/sub\u003e is the initial concentration of metal ions in the feed phase, and %E is the extraction yield.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Some Important physicochemical parameters of water\u003c/h2\u003e \u003cp\u003eThe techniques used to examine the different physicochemical characteristics of the water samples are compiled in Table\u0026nbsp;1. Water from two sugar factories and Kawardha was analysed, and the results showed differences in several parameters. Conditions were slightly acidic to nearly neutral, with pH values ranging from 6.7 to 6.9. Compared to Kawardha (376 \u0026micro;S/cm), conductivity was higher in sugar factory effluents (447 and 426 \u0026micro;S/cm), indicating higher levels of dissolved salts in industrial waters. A similar pattern was seen in total hardness, with higher levels in sugar factory samples (489 and 463 mg/L) than in Kawardha samples (381 mg/L), indicating a higher calcium and magnesium content. In line with conductivity and hardness observations, sugar factory waters had higher total dissolved solids (TDS) (746 and 678 mg/L) than Kawardha (647 mg/L). These results suggest that the higher mineral content and ionic strength caused by industrial effluents may have an impact on the water's suitability for residential or agricultural use [28, 30].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKawardha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSugar Factory 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSugar Factory 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity (s/m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Hardness (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal dissolved solid (TDS) (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Metal Analysis of Water Sample\u003c/h2\u003e \u003cp\u003eComprehensive findings of the metal analysis of water samples using atomic absorption spectroscopy are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Seasonal variations or localised contamination may be the cause of the Fe and Mn concentrations, which peaked in April 2025 and ranged from 0.831 to 2.703 mg/L and 0.192 to 1.781 mg/L, respectively, according to Atomic Absorption Spectroscopy. On the other hand, Zn (0.023\u0026ndash;1.586 mg/L) and K (0.022\u0026ndash;0.957 mg/L) were continuously lower. For the removal of heavy metals, single micelle systems are frequently employed, but mixed systems are still not well understood [31, 32].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe sample analyzed from various source by atomic absorption spectroscopy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJanuary 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFebruary 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarch 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApril 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMay 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJune 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJuly 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAugust 2025\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSeptember 2025\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetals in Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample 3\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSample 3\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSample 1\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSample 2\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSample 3\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBecause of their low toxicity and biodegradability, non-ionic surfactants\u0026mdash;like Brij 30 and Brij 35\u0026mdash;are favored because they improve adsorption through compact interfacial layers and modified microemulsion structures. By employing APDC as a complexing agent, Fe removal through cloud point extraction increased extraction efficiency from 36\u0026ndash;100% (Brij 30) and 45\u0026ndash;100% (Brij 35). The significance of surfactant selection for successful remediation is highlighted by the fact that higher metal concentrations decreased efficiency because of surfactant site saturation [28, 31]. Herein, it has been observed that the concentration of iron is higher than other metal ion concentration in water sample. Hence, we focused for removal of iron from water sample using mixed surfactant system by cloud point extraction technique.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Mixed Micelle Formation\u003c/h2\u003e \u003cp\u003eSurface tension measurements yielded the critical micelle concentration (CMC) values for both individual and mixed surfactant systems, which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Brij 30 and Brij 35, two of the nonionic surfactants, showed extremely low CMCs (0.082 mM and 0.065 mM, respectively), which is indicative of their strong propensity to form micelles at low concentrations. The cationic surfactants CTPB and TTPB, on the other hand, demonstrated greater CMCs (0.400 mM and 0.800 mM), suggesting that micellisation calls for higher concentrations [15\u0026ndash;17].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCritical micelle concentration (CMC), Surface pressure at the CMC (cmc), the maximum surface excess ( max) and the minimum surface area per molecule (Amin) values of nonionic, cationic and cationic-nonionic mixed surfactant system (1:1) in aqueous solution at 300K by surface tension method.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eπΓ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurfactant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCMC (mM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eГ\u003c/b\u003e\u003csub\u003e\u003cb\u003emax\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emol.m\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003csub\u003e\u003cb\u003emin\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003csup\u003e\u003cb\u003e20\u003c/b\u003e\u003c/sup\u003e \u003cb\u003em\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eπ\u003c/b\u003e\u003csub\u003e\u003cb\u003eCMC\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emNm\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrij 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrij 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTPB+Brij 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTPB+Brij 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTPB+Brij 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTPB+Brij 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared to pure cationic surfactants, the mixed cationic\u0026ndash;nonionic systems showed intermediate CMC values (0.105\u0026ndash;0.128 mM), indicating synergistic interactions between cationic and nonionic components that promote micelle formation at lower concentrations. These findings demonstrate how mixed surfactant systems have improved micellisation efficiency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the surface parameter results, Brij 30 had the weakest adsorption, while CTPB had the most compact packing (Amin\u0026thinsp;=\u0026thinsp;62.6 \u0026Aring;\u0026sup2;) and the highest adsorption efficiency (Γmax\u0026thinsp;=\u0026thinsp;2.65 \u0026times; 10⁻⁶ mol.m⁻\u0026sup2;). Strong synergistic effects were found in mixed systems, especially in TTPB+Brij 30, which displayed the lowest Amin (56.5 \u0026Aring;\u0026sup2;) and the highest adsorption (Γmax\u0026thinsp;=\u0026thinsp;2.94 \u0026times; 10⁻⁶ mol.m⁻\u0026sup2;), indicating tight molecular packing and increased surface activity. The ΠCMC values (25\u0026ndash;32 mN.m⁻\u0026sup1;) indicate effective interfacial adsorption, and mixed systems exhibit superior surface-active behaviour that is advantageous for heavy metal removal and cloud point extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Heavy Metal Removal Efficiency with APDC\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates how the extraction efficiency of Fe(III) rose as the APDC concentration rose up to 7\u0026ndash;10 mL, at which point it stayed relatively constant, signifying equilibrium. Due to insufficient Fe(III)\u0026ndash;APDC complexation, extraction was subpar (30\u0026ndash;40%) at low APDC levels (1\u0026ndash;3 mL). From 5 mL onwards, there was a notable rise that reached 75\u0026ndash;86%, indicating improved micellar solubilisation and Fe(III)\u0026ndash;APDC complex formation. The systems with the highest extraction rates were CTPB\u0026thinsp;+\u0026thinsp;Brij 35 (85\u0026ndash;86%), TTPB\u0026thinsp;+\u0026thinsp;Brij 35 (78\u0026ndash;79%), CTPB\u0026thinsp;+\u0026thinsp;Brij 30 (77\u0026ndash;78%), and TTPB\u0026thinsp;+\u0026thinsp;Brij 30 (68\u0026ndash;72%). The synergistic interactions between cationic and nonionic surfactants, which result in the formation of more compact micelles with a higher solubilisation capacity, are what give CTPB\u0026thinsp;+\u0026thinsp;Brij 35 its superior performance. Therefore, 7\u0026ndash;10 mL of APDC is the ideal concentration for Fe(III) extraction [28].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Effect of Surfactant Concentration\u003c/h2\u003e \u003cp\u003eIn mixed systems of cationic (CTPB, TTPB) and nonionic (Brij 30, Brij 35) surfactants, the extraction efficiency of Fe(III) was investigated at different surfactant concentrations. Up until the third addition, an increase in surfactant concentration resulted in a noticeable improvement in extraction efficiency; after that, the values either slightly decreased or levelled off [32]. The systems with the highest extraction efficiency (up to 90%) were CTPB\u0026thinsp;+\u0026thinsp;Brij 35, TTPB\u0026thinsp;+\u0026thinsp;Brij 35 (87%), CTPB\u0026thinsp;+\u0026thinsp;Brij 30 (83%), and TTPB\u0026thinsp;+\u0026thinsp;Brij 30 (78%). Strong synergistic interactions between cationic and nonionic surfactants result in compact mixed micelles with a higher solubilisation capacity for the Fe(III)\u0026ndash;APDC complex, which is responsible for CTPB\u0026thinsp;+\u0026thinsp;Brij 35's superior performance. The extraction slightly decreased at higher surfactant levels (4\u0026ndash;5 mL), which may have been caused by micellar saturation or dilution of the surfactant-rich phase, which lowers the effective concentration of solubilised Fe(III)\u0026ndash;APDC complexes. Overall, the most effective mixed system for Fe(III) extraction was CTPB\u0026thinsp;+\u0026thinsp;Brij 35, with best results seen at a concentration of about 3 mL surfactant [33].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeveral mixed surfactant systems were used to assess the impact of Fe(III) ion concentration on extraction efficiency. Throughout the tested Fe(III) concentrations (10\u0026ndash;40 ppm), the extraction percentage stayed relatively constant, suggesting that the systems have enough micellar capacity to hold the Fe(III)\u0026ndash;APDC complex even at higher metal ion levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Effect of metal ion concentration\u003c/h2\u003e \u003cp\u003eThe systems that demonstrated the highest extraction efficiency among the ones that were examined were CTPB\u0026thinsp;+\u0026thinsp;Brij 35 (97\u0026ndash;98%), TTPB\u0026thinsp;+\u0026thinsp;Brij 35 (95\u0026ndash;96%), CTPB\u0026thinsp;+\u0026thinsp;Brij 30 (90\u0026ndash;91%), and TTPB\u0026thinsp;+\u0026thinsp;Brij 30 (86\u0026ndash;88%). Strong synergistic interactions and stable micelle formation that support effective solubilisation of the Fe(III)\u0026ndash;APDC complex are suggested by the consistently high extraction by CTPB\u0026thinsp;+\u0026thinsp;Brij 35.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Cloud point extraction (CPE) method for heavy metal removal\u003c/h2\u003e \u003cp\u003eAnalyte preconcentration and separation are common applications for Cloud Point Extraction (CPE), a low-cost, eco-friendly method with obvious advantages over conventional liquid-liquid extraction [34]. Due to their high water solubility and low ionisation, non-ionic surfactants are widely used. Here, we removed the heavy metals from the sample using a mixed system consisting of a cationic and non-ionic surfactant.CPE includes temperature-induced phase separation, and optimal extraction occurs within a specific range of surfactant concentrations [35]. A concentration that is too high reduces the preconcentration factor; while a concentration that is too low may result in poor analyte recovery.It is possible to use ionic and non-ionic surfactants separately or in combination [36]. Metal ion extraction using CPE requires complexation with suitable ligands, which is highly pH-dependent. The clouding phenomenon, which is also observed in systems containing ionic\u0026thinsp;+\u0026thinsp;non-ionic surfactants (CTPB+Brij 35, TTPB+Brij 35, CTPB+Brij 30, TTPB+Brij 30), is caused by the cloud point (CP), where the solution separates into surfactant-rich and surfactant-lean phases. Using mixed surfactant systems, the impact of temperature on the cloud point extraction efficiency of Fe(III) was examined between 20 and 100\u0026deg;C. Temperature was found to have a significant impact on the micellisation and phase separation behaviour of the surfactant systems, as evidenced by the notable variation in extraction efficiency [37].\u003c/p\u003e \u003cp\u003eThe extraction efficiency was moderate at lower temperatures (20\u0026ndash;40\u0026deg;C), indicating limited solubilisation of the Fe(III)\u0026ndash;APDC complex and incomplete micelle formation. Because of improved micellisation and decreased hydration around surfactant head groups, which promoted phase separation and the transfer of the Fe(III)\u0026ndash;APDC complex into the surfactant-rich phase, extraction efficiency rose as temperature rose. The highest extraction efficiency of all the systems under study was demonstrated by CTPB\u0026thinsp;+\u0026thinsp;Brij 35 (up to 90% at 60\u0026deg;C), which was followed by TTPB\u0026thinsp;+\u0026thinsp;Brij 35 (82%), CTPB\u0026thinsp;+\u0026thinsp;Brij 30 (80%), and TTPB\u0026thinsp;+\u0026thinsp;Brij 30 (70%). The synergistic interaction between cationic and nonionic surfactants, which encourages micellar compactness and stability during phase separation, is responsible for CTPB\u0026thinsp;+\u0026thinsp;Brij 35's superior performance. A slight decrease in extraction efficiency was noted at temperatures higher than 70\u0026ndash;80\u0026deg;C, most likely as a result of micelle destabilisation or partial Fe(III)\u0026ndash;APDC complex breakdown. As a result, 60 to 70\u0026deg;C is the ideal temperature range for maximum Fe(III) extraction; above this, efficiency declines [38]. Overall, the results show that CTPB\u0026thinsp;+\u0026thinsp;Brij 35 is the most efficient surfactant system and that temperature is a critical factor in regulating micellar phase behaviour and extraction performance in cloud point extraction of Fe(III).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Comparative Performance of Mixed Surfactant Systems\u003c/h2\u003e \u003cp\u003eIn every experimental scenario, the CTPB\u0026thinsp;+\u0026thinsp;Brij 35 system continuously showed the highest extraction efficiency. The cationic head group of CTPB and the polyoxyethylene chain of Brij 35 work in concert to form compact, stable mixed micelles with a high solubilising capacity, which is responsible for this superior performance. Because of their shorter ethylene oxide chains, which offer less hydration and micellar stability, Brij 30-based systems demonstrated decreased efficiency, whereas the TTPB\u0026thinsp;+\u0026thinsp;Brij 35 system demonstrated slightly lower but still effective extraction. In the CPE process, a hydrophobic Fe(III)\u0026ndash;APDC complex is formed during the extraction of Fe(III). When heated above the cloud point, this complex partitions into the surfactant-rich phase. The degree of complexation and the mixed micellar phase's capacity for solubilisation both affect extraction efficiency. Phase separation and solubilisation of the Fe(III)\u0026ndash;APDC complex are improved in mixed systems due to the electrostatic and hydrophobic interactions between surfactants, which decrease the cloud point and raise micellar compactness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Conclusion\u003c/h2\u003e \u003cp\u003eTemperature and surfactant combinations affected the removal efficiency of iron. While TTPB+Brij 35 peaked at 80% at 20\u0026deg;C, CTPB+Brij 35 demonstrated the highest efficiency (90%) at 60\u0026deg;C. While CTPB performance was improved by higher temperatures, TTPB systems performed better at lower temperatures. Overall, Fe was efficiently extracted by mixed surfactant systems, with temperature and surfactant type having an impact on efficiency.Fe was successfully extracted from water using cloud point extraction with mixed surfactant systems (CTPB/TTPB with Brij 30/35). Temperature and surfactant combination determined optimal removal, demonstrating the potential of customised surfactant systems for heavy metal extraction.With the potential for industrial-scale use, these systems provide a sustainable, effective, and adjustable approach to water remediation. Future research might examine multi-metal competition effects, mixed micelle recovery, and actual wastewater testing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e4.0 Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Consent to Publish Declaration\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Consent to Publish declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Ethics Declaration\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Ethics declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Consent to Participate Declaration\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Consent to Participate declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4\u003c/strong\u003e \u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Author Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBirendra Kumar:\u003c/strong\u003e Conceptualization, methodology, investigation, data curation, formal analysis, writing—original draft.\u003cbr\u003e\u003cstrong\u003eDeepti Tikariha Jangde:\u003c/strong\u003e Supervision, validation, resources, writing—review and editing.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors grateful to Prof K.K. Ghosh School of Studies in Chemistry, Pt. Ravishankar Shukla University, Raipur (India) for providing valuable suggestions. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFu F, Wang Q. Removal of heavy metal ions from wastewaters: A review. J Environ Manage. 2011;92(3):407\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEzzat A, Ahmed, Abdalla M, El-Ayaat AT, Fathy MA, Moneim, Fatma M. Dardir. Effective removal of heavy metal ions (Pb, Cu, and Cd) from contaminated water by limestone mine wastes Scientific Reports | (2025) 15:1680.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernalte E, Kamieniak J, Randviir EP, Bernalte-Garc\u0026iacute;a \u0026Aacute;, Banks CE. The preparation of hydroxyapatite from unrefined calcite residues and its application for lead removal from aqueous solutions. 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AfzalShah, Musharaf Khan, Shahan ZebKhan,Zia-ur-Rehma, Amin Badshah, Synthesisand Spectrophotometric Study of Toxic Metals Extraction by Novel Thio-Based Non-Ionic Surfactan, Tenside Surf. Det. 2015;52(5):406\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhn KH, Kim J, Lee H. Efficiency of mixed surfactant systems for heavy metal removal. J Hazard Mater. 2008;150(2):385\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmbaby MA, Ghafar HHA, Shakdofa MME, Khalil NM, Radwan EK. Removal of iron and manganese from aqueous solution using some clay minerals collected from Saudi Arabia. Desalin Water Treat. 2017;65:259\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenderrag A, Haddou B, Daaou M, Benkhedja H, Bounaceur B, Kameche M. Experimental and modeling studies on Cd (II) ions extraction by emulsion liquid membrane using Triton X-100 as biodegradable surfactant. J Environ Chem Eng. 2019;7(3):103166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jece.2019.103166\u003c/span\u003e\u003cspan address=\"10.1016/j.jece.2019.103166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mixed micelles, Cationic–nonionic, CMC, Heavy meals, Micellar-enhanced extraction, Water remediation","lastPublishedDoi":"10.21203/rs.3.rs-8496143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8496143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThrough cloud point extraction, this study explores mixed micelle systems that combine CTPB, TTPB with Brij 30 and Brij 35 to improve the removal of heavy metals from water. Atomic absorption spectroscopy is used to identify which metals are present in water. Several physicochemical parameters were assessed. The results indicate that, in comparison to single surfactant systems, mixed surfactants exhibit better metal ion complexation and lower critical micelle concentrations (CMC), with removal efficiencies of up to 92%. Additionally, mixed micelle systems improved cationic metal selectivity in multi-metal solutions. These results demonstrate the potential of customised surfactant mixtures for heavy metal remediation that is both economical and ecologically sustainable.\u003c/p\u003e","manuscriptTitle":"Enhanced Heavy Metal Removal Using Mixed Micelle Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 10:14:48","doi":"10.21203/rs.3.rs-8496143/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-28T13:35:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T04:24:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T16:31:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T04:23:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67778222144316518869295692100304250973","date":"2026-03-08T11:45:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58349369347816221469747825011681042862","date":"2026-03-08T11:32:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51101249988549126960173931013595694318","date":"2026-03-06T21:03:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T13:26:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312272495676925282122936681866187262776","date":"2026-03-06T04:13:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300329997417440005802887845935915376349","date":"2026-03-05T16:14:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T18:11:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T03:38:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212037376296156299383032973750622990738","date":"2026-02-13T14:03:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93767655032214655573655424259622491657","date":"2026-02-12T14:52:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156048253708759224534611414319682771507","date":"2026-02-12T10:21:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T09:41:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T16:25:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-23T03:57:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Chemistry","date":"2026-01-23T03:48:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea7cb6fa-f770-4af9-9bd3-2303bda61bf8","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T14:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 10:14:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8496143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8496143","identity":"rs-8496143","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}