Parsimonious Methodology for Synthesis of Silver and Copper Functionalized Cellulose

Parsimonious Methodology for Synthesis of Silver and Copper Functionalized Cellulose | 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 Parsimonious Methodology for Synthesis of Silver and Copper Functionalized Cellulose David Patch, Natalia O'Connor, Debora Meira, Jennifer Scott, Iris Koch, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1793366/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2023 Read the published version in Cellulose → Version 1 posted 4 You are reading this latest preprint version Abstract Metal nanomaterials, such as silver and copper, are often incorporated into commercial textiles to take advantage of their antibacterial and antiviral properties. In this study eight different methods were employed to synthesize silver, copper, and silver/copper functionalized cotton batting textiles. Using silver and copper nitrate as precursors, different reagents were used to initiate/catalyze the deposition of metal, including: (1) no additive, (2) sodium bicarbonate, (3) green tea, (4) sodium hydroxide, (5) ammonia, (6,7) sodium hydroxide/ammonia at a 1:2 and 1:4 ratio, and (8) sodium borohydride. The use of sodium bicarbonate as a reagent to reduce silver onto cotton has not been used previously in literature and was compared to established methods. All synthesis methods were performed at 80 ° C for one hour following textile addition to the solutions. The products were characterized by X-ray fluorescence (XRF) analysis for quantitative determination of the metal content and X-ray absorption near edge structure (XANES) analysis for silver and copper speciation on the textile. Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) and size distribution inductively coupled plasma mass spectrometry (ICP-MS) were used to further characterize the products of the sodium bicarbonate, sodium hydroxide, and sodium borohydride synthesis methods following ashing of the textile. For the silver treatment methods (1 mM Ag+), sodium bicarbonate and sodium hydroxide resulted in the highest amounts of silver on the textile (8900 mg Ag/kg textile and 7600 mg Ag/kg textile) and for copper treatment (1 mM Cu+) the sodium hydroxide and sodium hydroxide/ammonium hydroxide resulted in the highest amounts of copper on the textile (3800 mg Ag/kg textile and 2500 mg Ag/kg textile). Formation of copper oxide was dependent on the pH of the solution, with 4 mM ammonia and other high pH solutions resulting in majority of the copper on the textile existing as copper oxide, with smaller amounts of ionic-bound copper. Cellulose Silver nanomaterials Copper nanomaterials Green synthesis In-situ synthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Metals such as silver and copper have been used in medical, religious, and ornamental applications for thousands of years [ 1 , 2 ]. Many of these applications used bulk metals, such as silver containers for water purification, or ionic salts, such as silver nitrate as a caustic for wound treatment [ 3 ]. Advances in material sciences have seen the synthesis of smaller and smaller metal materials, seeking to taking advantage of the increased surface area to volume ratio and the unique properties these metals have compared to the larger bulk counterparts. These materials are often classified according to their size, with fine particles (2,500–100 nm), nanoparticles (100–1 nm), and atom clusters (< 1 nm) being the three smallest ranges. The definition of nanoparticles is sometimes expanded to include particles 500 nm and smaller, since these particles still exhibit some nanoscale properties [ 4 – 7 ]. Metal nanoparticles and nanocomposites are a growing focus of commercialization because of the beneficial qualities these nanomaterials can impart. One of the biggest areas of growth is in the development of nano-functionalized textiles with manufacturers seeking to take advantage of the antibacterial properties of the nanomaterials. Metal and metal oxide nanoparticles are often utilized as these have enhanced stability and antibacterial efficacy over other oxidation states. Silver is often selected for antibacterial applications, and copper is selected for antiviral and antifungal applications. Silver and copper are the most commonly studied metals used for creating high performance textiles (Figure S1). The COVID-19 pandemic has renewed interest in using nanotechnology for enhanced protectiveness against viral spread [ 8 ]. Other than antimicrobial activity, nanoparticles can impart unique properties to textiles including increased conductivity[ 9 , 10 ], self-cleaning[ 11 ], electromagnetic interference shielding [ 12 ] and ultraviolet shielding[ 13 ]. There are two common ways to prepare textiles with nanoparticles: ex-situ and in-situ. The ex-situ approach involves first forming metal nanoparticles separately from the textile, using a wet chemical method [ 14 – 16 ], a sacrificial anode method (Ditaranto et al., 2016), or a biological method[ 18 – 20 ]. The particles formed ex-situ are then applied to textiles through immersion or dry padding [ 21 ]. The in-situ approach consists of synthesizing nanoparticles directly onto the textiles, using an electroless plating or a chemical reduction method [ 22 – 25 ]. Some of these in-situ methods use an added reducing agent (e.g., glucose)[ 26 ] or use the textile polymer itself (e.g., cellulose in cotton textiles) as the reducing agent [ 27 – 29 ]. Cellulose is the most popular textile polymer used, due to its aesthetic qualities as well as it being renewable and biocompatible (Figure S1) [ 30 , 31 ]. The electron rich functional groups and polymer structure of cellulose allows for initial complexation of the ionic metal and stabilization of the resultant metal nanoparticle [ 32 ]. The extent of deposition, speciation, and morphology of the resultant metal nanoparticle is heavily influenced by the synthesis conditions, including heat, pH, and reagents used [ 13 , 25 , 33 – 36 ]. To the knowledge of the authors, there have been no previous studies quantitatively characterizing the concentration, speciation, and morphology of the resultant metal materials across different synthesis methods. The goal of the present study is to identify a parsimonious synthesis method for creating silver, copper, and bimetallic treated textiles by comparing eight different synthesis methods. A selection of novel and popular synthesis methods are examined through a stepwise approach. The parsimony of the synthesis method are evaluated based on three criteria: high metal content, measured by x-ray fluorescence spectroscopy (XRF); metallic or metal-oxide speciation, determined using x-ray absorption near-edge structure (XANES); and consistent particle morphology, determined using scanning electron microscopy (SEM). 2. Materials And Methods 2.1 Reagents Silver nitrate (ACS), copper (II) nitrate hemipentahydrate (ACS), sodium hydroxide pellets (ACS), ammonia solution (28–30%, ACS), sodium bicarbonate (ACS), Triton X100, and sodium borohydride (ACS) were all purchased from VWR. Cotton batting was purchased from a local supplier in Kingston, Ontario (Stitch by Stitch). Green tea was purchased from a local grocery store (Metro). Deionized water (DI water) was generated using an in-lab filtration system (Milli-Q Direct 8, 18 MΩ). 2.2 Preparation All glassware was cleaned three times with 2% nitric acid and then rinsed with three portions of DI water. 4x4 cm squares of cotton batting (106 g/m 2 ) were cut from the bulk material using a rotary cutter and prepared as follows: (1) washed with 1% Triton X100 solution at 60°C for 30 minutes with agitation (2) rinsed with DI water until no foaming was observed, (3) rinsed with DI water at 60°C for 15 minutes with agitation; and (4) dried overnight in an oven at 40°C. Textiles were trimmed with a rotary cutter after drying to remove any stray strands of batting. 2.3 Synthesis A summary of the methods and reagents used is provided in Table 1 , with a general procedure described in the following paragraphs. A final volume of 13.6 mL of solution and 0.17 ± 0.01 g of cotton batting was used to obtain a volume/textile mass ratio of 80:1, to allow for thorough wetting and mixing of the solutions. Erlenmeyer flasks were filled with DI water, sealed with aluminum foil, and placed into a water bath (digital general water bath, VWR) set to 60°C. Reagents were added as appropriate for each method, shown in Table 1 , and agitated for 10 minutes using an orbital shaker. Table 1 Overall synthesis steps performed, including reagents added at each step and analytical methods employed. Method # 1 2 3 4 5 6 7 8 Short name Control NaHCO 3 NaHCO 3 + green tea NH 3 NaOH NaOH/ NH 3Version1 NaOH/ NH 3Version2 NaBH 4 Step 1: Reagents added and mixed at 60 o C for 10 minutes Step 1 reagents DI H 2 O 4 mM NaHCO 3 4 mM NaHCO 3 4 mM NH 3 4 mM NaOH 2 mM NaOH + 4 mm NH 3 2 mM NaOH + 8 mm NH 3 DI H 2 O Step 2: Metals added and mixed at 60 o C for 10 minutes Step 2 metals 1 mM Ag + OR 1 mM Cu + OR [0.5 mM Ag + + 0.5 mM Cu + ] Step 3: Textile added, temperature set to 80°C Step 4: Reagents added Step 4 reagents none none 1% green tea none none none none 4 mM NaBH 4 Step 5: Mixed at 80 o C for 1 hour Step 6: Cooled, drained, textile rinsed with DI H 2 O at least 3x, and dried at 60 o C overnight Step 7: Textiles characterized using methods listed below XRF x x x x x x x x XANES x x x x x x x x ATR-FTIR x x x x SEM/EDX x x x ISE x (Ag) Silver and/or copper nitrate solutions were added to the flasks to reach the final concentrations listed in Table 1 and mixed for 10 minutes. One textile (4 x 4 cm, 0.17 ± 0.01 g) was added to each Erlenmeyer flask, the water bath temperature was raised to 80 ° C, and reagents were added for Methods 3 and 8 (see Table 1 ). Flasks were agitated on an orbital shaker at 30 RPM for one hour. The Erlenmeyer flasks were then removed from the shaker, and 12 mL of room temperature DI water was immediately added to cool the vessel and stop the reaction. The following steps were untaken to process the textiles: (1) textiles were removed from the solutions and rinsed with 20 mL DI water three times, (2) additional rinses were performed, if necessary, until the rinse solution was clear (e.g., for Method 2) (3) water was gently squeezed from the textiles, and (4) textiles were dried at 60 0 C in an oven overnight. Synthesis methods were performed in triplicate. A digital camera (Canon SL2) was used to image the textiles after the synthesis reactions to record color change. Additional experiments were performed to explore the effect of heat, silver/copper competition, and for cellulose analysis. These experiments were performed using synthesis method 6 as it is a well-established method in literature. 2.4 Metal Content Determination The metal concentration of the textiles following treatment was measured using x-ray fluorescence spectroscopy (Innov-X Systems α-2000 XRF). Textiles were analyzed whole after drying and the concentration determined by using an external calibration curve, since the XRF measurement parameters were set up for soil samples and not applicable to textiles. (See SI for method development of this calibration). Two calibration curve preparation methods were explored to determine the more accurate method. XRF detection limits were identified as ~ 375 mg Ag/kg and ~ 100 mg Cu/kg. Relative standard deviation for replicates was found to be 16 ± 11%. 2.6 Metal Speciation X-ray absorption near edge structure (XANES) analysis was used to perform silver and copper speciation analysis of the bulk textiles. XANES spectra were collected at the Sector 20 insertion device beamline (20ID-C) of the Advanced Photon Source (CLS@APS), within the X-Ray Science Division (XSD), Argonne National Laboratory. XANES spectra of the Ag Kα-edge and Cu Kα-edge were recorded in fluorescence mode by using a four-element silicon drift detector (Vortex®-ME4 with Xspress 3 pulse processor) while monitoring incident and transmitted intensities in straight ion chamber detectors filled with N 2 gas. Textiles were analyzed as 1cm x 1cm subsections rolled and packed in a 3D printed PETG sample holder, held between two layers of Kapton® tape. The Si (111) double crystal monochromator was calibrated using a silver metal foil at 25,514 eV, copper metal foil at 8989 eV, and the incident beam size was 800 µm. Fitting of XANES spectra was accomplished with Athena software. The silver standard spectra used for fitting had been measured as frozen aqueous dissolved species previously by our group[ 37 ], and included AgNP, AgNO 3 , AgO. The copper standards were synthesized in our lab using copper nitrate as a precursor and reacting it with the appropriate reagents to form the desired precipitate (where applicable). After synthesis, standards were washed with three portions of DI water and packed into the same 3D printed sample holder. The Ag (0) and Cu (0) standards used provided the metals in their zero oxidation state and could not distinguish between nanoparticulate or bulk metallic forms. 2.7 Metal Morphology Complete SEM sample preparation development is described in detail in the SI. Initial SEM analysis of the textiles failed to identify substantial metal materials on the textile surface, despite high concentrations present on the textiles (Figure S3). Cross-sectional analysis of the textiles identified the presence of nanomaterials within the cotton fiber core itself (Fig. 1 ). This led to the development of a textile ashing method that allowed for improved metal morphological determination. Separate square subsections of the textiles (approximately 0.1 g) were ashed in ceramic crucibles at 550°C for one hour [ 38 ]. The resulting grey ash was dispersed in 1 mL DI water, and diluted to 10 mL with DI water, ultrasonicating the solution at 30 kHz for one minute. A 0.1 mL subsample of the solution was dried onto double-sided carbon tape and analyzed. Surface analysis of the textile samples were analyzed (Quanta 250FED) operating under environmental mode at 100 kPa. EDX (EDAX Octane Elite) was performed for elemental determination. Analysis of the dried ash following reconstitution were analyzed on under high vacuum mode. Images were acquired first at 6000–7500 magnification, then taken at 18–21,000 magnification. ImageJ (NIH) was used to count and determine the spherical diameter for nanoparticles. For non-spherical or oval nanoparticles, the diameter was measured at the shortest dimension. 2.8 Fourier-Transform Infrared Spectroscopy (FTIR) Analysis The FTIR (Thermo Scientific Nicolet-IS10 Attenuated Total Reflection (ATR)-FTIR) spectra of cotton samples were acquired by folding samples twice for a total of four layers before being placed into the active element of the ATR-FTIR. 2.9 Ion Selective Electrode (ISE) Analysis Using a silver ion selective electrode (Fisher Scientific accumet), the kinetics of the NaHCO 3 synthesis method were explored at temperature profile 1 (60°C heated to 80°C) and temperature profile 2 (80 ° C from the start) by measuring the decrease in ionic silver present in the solution (assumed to correspond to formation of particulate silver on the textile). It is important to note that, as the ISE only measures ionic silver, any release of metallic silver from the textile during synthesis would not be identified. A no-textile control was analyzed using ISE at both temperature profiles to correct for changes to ISE response as a function of temperature. A six-point external calibration curve at the reaction temperature was used to quantify the ionic silver. 3. Results And Discussion 3.1 Metal Content and Speciation on Textile Visual inspection of the treated cellulose textiles (see Fig. 2 ) allowed for an immediate indication of the effectiveness of the different synthesis methods. Based on the extent of discoloration, the silver synthesis reactions with NaHCO 3 , NaOH, or NaOH/NH 3Version1 (Methods 2, 4 and 5) resulted in substantial silver present on the textile (darker brown), whereas copper synthesis methods 4, 5, 6, 7 and 8 appeared to have the most copper present (Fig. 2 ). With the combined silver/copper treatment methods, the color trend of the textiles is similar to either their silver or copper textile counterpart, indicating a likely dominance of silver (method 2) or copper (methods 4–8). XRF analysis confirms some of the observations made from the textile images: for the separate silver and copper textiles, the use of NaHCO 3 , NaOH and NaOH/NH 3Version1 (Methods 2, 4 and 5) resulted in the highest amounts of silver (Fig. 3 A, S5), and methods 4–8 resulted in comparably high amounts of copper on the textile (Fig. 3 B, S5). For silver/copper bimetallic treatment, the use of NaHCO 3 (Method 2) resulted in highest amount of silver, but methods 4–7 resulted in reduced silver concentrations and copper dominating (Fig. 3 C, S5). XANES analysis identified the metal speciation of silver and copper across the synthetic trials. For silver only synthetic trials, all the methods resulted in reduction of the ionic silver into metallic silver (Fig. 3 a). It is well accepted that ionic metals, such as silver, can be reduced by cellulose in cotton, and this reduction occurs more completely under alkaline conditions and at elevated temperatures [ 39 – 41 ]. The exact mechanism of this reduction is not often discussed in detail although some authors suggest that the hydroxyl groups on the cellulose polymer are oxidized into aldehydes, and then into carboxylates [ 42 ]. However, silver complexes like those formed using Tollen’s Reagent are not reduced in the presence of alcohol-containing compounds in classic chemical tests, whereas they are with aldehydes. Other authors identify that while cellulose itself is not considered a reducing sugar; hemiacetal groups are present at the termini of the polymer chain. These hemiacetals undergo ring-chain tautomerism under basic conditions and with heat (8), converting into aldehyde groups that, like typical reducing sugars such as glucose, can reduce metals [ 43 – 45 ]. There is also the potential for alkaline degradation of the cellulose polymer (i.e., the Lobry de Bruyn-Alberda van Ekenstein transformation[ 46 ]), resulting in the generation of glucose and other monosaccharides that will readily reduce ionic silver. It is also possible that the pectins and hemicelluloses present in the primary wall and winding layer of cotton fibers are reducing ionic silver, as these compounds have been found to reduce silver when isolated and used as primary reagents [ 47 , 48 ]. When no reagents are added, as is the case with the positive control synthesis method, some reduction of silver is observed to be occurring (as detected by XANES). Reduction of ionic silver onto cotton without the use of reagents has been observed previously, with the concentration of silver being related to reaction time and temperature [ 34 , 49 ]. The reactions for silver explored in this study are shown in equations 1 to 9 below. It is important to note that the silver hydroxide (3) immediately reacts to silver oxide (4) due to the favorable kinetics of the reaction (pK = 2.88) [ 50 ] (4). The silver complex formed following addition of NaOH and NH 3 (method 6, 7) is known as Tollen’s reagent, which is used to test for aldehydes and alpha-hydroxy ketones and is often used for synthesizing silver-treated textiles [ 41 , 42 ]. Reaction of the silver compounds with the aldehyde at the terminal end of the cellulose chain results in reduction of ionic silver to metallic silver, and oxidation of the aldehyde to the carboxylic acid. The resulting silver nanoparticles (8) are then stabilized by the cellulose inside the cotton fibers, similar to the stabilization effect that occurs with carboxymethylcellulose (CMC) coated nanoparticles [ 41 , 51 , 52 ]. While the speciation of silver on the textile was consistent across the methods, the amount of metal present on the textile varied substantially. This can be explained by the stability of the silver compounds. In the presence of green tea, reduction followed by stabilization via coating occurs, significantly inhibiting any silver deposition onto textile [ 53 – 58 ]. In the presence of ammonia (method 4), or high concentrations of ammonia (method 7) significant silver-ammonia complexation occurs, which stabilizes the silver and makes it a weaker oxidizing agent than the corresponding aquo complexes that result with Ag 2 O and Ag 2 CO 3 . When NaBH 4 was added reduction occurred in the solution (a process well understood in literature)[ 59 – 66 ], causing the majority of silver nanomaterials to aggregate and precipitate out of solution before they could be deposited and stabilized by the cellulose (10). For copper only synthetic trials, methods 1–3 resulted in the copper being deposited as ionic copper; likely intermolecularly bonded with the hydroxyl groups in the cellulose chain. For methods 4–7, which occurred at higher pH (pH > 10), the copper was deposited mainly as copper oxide. The reactions for copper explored in this study are shown in equations 11 to 17. When both NaOH and NH 3 are added, a copper-tetraammine-hydroxide complex is eventually formed, similar to the complex known as Schweizer’s reagent, which is copper ammonia complex used to dissolve cellulose (14,15). Despite the various complexes that are formed, all of the pH > 10 complexes are not stable at elevated temperatures and convert into copper oxide, supporting the speciation results observed [ 67 ]. The use of NaHCO 3 and NaHCO 3 /green tea did not have any appreciable effect on the total concentration or speciation of copper on the textile when compared to the reagent-free control, indicating these reagents are superfluous for copper textile synthesis. This was initially unexpected as the antioxidants in green tea have been shown capable of reducing other metals like iron and silver. It is likely the NaHCO 3 precipitated out the ionic copper as copper (II) carbonate before reduction with green tea could occur. When ionic copper and sodium borohydride react, the initial reduction follows a slightly different path when compared to the ionic silver reduction mechanism. In this reaction, ionic copper reacts with the sodium borohydride to form the reduced copper, hydrogen gas, and boric acid (17)[ 61 ]. The speciation of copper using NaBH 4 was expected to be metallic copper, based on NaBH 4 being a strong reducing agent, instead of the copper oxide that was found. It is hypothesized that copper treated onto the textile underwent oxidation during drying and storage in ambient atmosphere, resulting in the formation of copper oxide. XANES analysis for the bimetallic textiles identified a significant amount of ionic silver for methods 4–7, which was not present for the silver-only synthetic trials. Given the chemistry of copper oxide formation for methods 4–7, this incomplete reduction is likely due to copper partially outcompeting silver for binding onto the textile. Due to the novelty of the NaHCO 3 method and the unexpected results additional experiments were performed to investigate the kinetics of reaction as a function of temperature profiles (Fig. 4 ), and the final textile concentration as a function of pH (Figure S6). The reduction of silver by cellulose with NaHCO 3 is directly affected by the temperature of the reaction. As temperature is shifted from 60°C to 80°C, the rate of silver reduction increases (Fig. 4 , left) When the reaction temperature is held at 80°C for one hour the silver is reduced at a rate of 0.028 mM Ag + /minute, with complete reduction by 46 minutes (Fig. 4 , right). These findings indicate that the temperature of the reaction has an impact on kinetic rates. Unfortunately, while the same kinetic investigation was attempted with the copper reaction the amount of copper present in the solution disappeared immediately upon reaction with the sodium bicarbonate, forming an insoluble copper (II) carbonate complex. The kinetic investigation could not be attempted for other synthesis methods due to the high pH of the reactions damaging the ISE. While it was originally hypothesized that the effectiveness of NaHCO 3 for silver reduction was due to an optimal pH (pH = 8.24), it was identified that silver was still reduced at pH 6 (5400 ± 510 mg Ag/kg textile), pH 10 (8860 ± 1310 mg Ag/kg textile) and pH 12 (7500 ± 600 mg Ag/kg) (although reduction at pH 10 and 12 is likely due to the NaOH used to adjust the pH). The reduction at pH 6 indicates that the bicarbonate/carbonate ion itself has a key impact on the silver reduction. However, no reduction was found to occur at pH 3, indicating pH still plays a large role in the overall synthesis effectiveness (Figure S6). 3.2 Metal Particle Morphology SEM analysis was performed on silver, copper, and bimetallic treated textiles resulting from the three synthesis methods that gave the most distinct results (Method 2, 5, 8). Initial SEM method development identified that most of the metal particles were present inside the cellulose matrix, requiring ashing of the textiles before analysis. To the author’s knowledge this is the first study to identify metal materials present inside the cellulose matrix following in-situ synthesis. While classically defined nanoparticles (< 100 nm) were identified, the majority of the particles were found to be between 100 and 500 nm in diameter, which are considered nanomaterials depending on the application and field [ 7 ] (Fig. 5 , 6 ). For the bimetallic treated textiles, the combined presence of silver and copper particles precluded the measurement of the diameters, but visual examination of the SEM images and the corresponding EDX spectra revealed two findings. Use of NaHCO 3 (Method 3) resulted in significantly more silver present than copper, whereas use of NaOH or NaBH 4 resulted in more copper present (predominantly green (copper) shading), which aligns with the XRF concentration results presented previously (Figure 6 A). Secondly, the silver particles are significantly larger in the bimetallic textiles compared to silver only textiles, with many of them appearing to be well above 500 nm in diameter. Once again, this is likely caused by a lack of binding sites due to competition with the copper, causing the silver reduction to favor particle growth over new nanoparticle formation. The copper/ copper oxide nanoparticles are relatively unchanged in their diameters (based on visual inspection). 3.3 Role of Heat in Metal-Textile Synthesis Previous studies have identified that increased temperatures result in increased reaction rates and chemical pathways possible during treatment of cellulose with metal salts [ 34 ].The role of heat was qualitatively investigated for silver, copper, and silver/copper treated textiles following treatment with NaOH/NH 3Version1 . The synthesis was either performed at room temperature, with the solution heated after the textile was added, or the solution pre-heated (60°C) before adding the textile. For silver an increase in heat results in an increase to the amount of silver reduced into the textile. For copper the textile color is different based on the heating profile. When the textile is added to the synthesis solution at room temperature the blue copper complex (copper-tetraammine-hydroxide) immediately binds to the cellulose, stabilizing it against thermal conversion to copper oxide (Fig. 7 ). The dark textile color corresponding to copper oxide only occurs when the solution is heated at 60°C before the textile is added. At higher pH (pH > 10) the expected copper complexes are either unstable or are not formed at elevated temperatures in water, resulting in the formation or conversion to copper oxide (Cudennec and Lecerf, 2003). Interestingly for the bimetallic textile the color of the textile resembles copper oxide containing textiles, suggesting a possible silver-copper complex is formed that results in the deposition of copper oxide. This indicates that at pH > 10 the competition between silver and copper could be both physical competition for binding sites and chemical competition for reagents. 3.4 Silver Copper Competition The potential for competition between silver and copper was identified in the previous sections from XRF, XANES, and SEM analysis. To investigate this further, synthesis Method 6 (NaOH/NH 3Version1 ) was used to treat cotton textiles with different concentrations of silver and copper at a fixed (1:1) and variable ratio. The relationship between the concentration of metal in the textile and in solution was plotted for silver, copper, and silver/copper treated textiles. The slope for the silver only synthesis was found to be 5600 mg Ag/kg textile per mM of reagent (introduced) Ag in solution (R 2 0.98). The slope for the copper only synthesis was found to be 2200 mg Cu/kg textile per mM of Cu in solution (R 2 0.95). For the silver synthesis in the combined silver/copper treatment, the slope for silver decreased dramatically to 240 mg Ag/kg textile per mM of Ag in solution (R 2 0.99), whereas for copper the slope barely decreased (2000 mg Cu/kg textile per mM of Cu in solution, R 2 0.98) (Fig. 8 ). This confirms that, when silver and copper are in solution at equal concentrations, copper outcompetes silver for binding onto the textile. Additionally, by varying the ratio of silver and copper (1:0, 10:1, 5:1, 2:1, 1:1, 0:1) the amount of copper required to outcompete silver can be identified. While the amount of silver decreased slightly with the addition of 0.1 and 0.2 mM of copper, it was within the deviation of the silver with no copper added. The amount of silver present on the textile dropped dramatically with 0.5 mM of copper being added, or a 2:1 ratio. Adding 1 mM of copper (1:1) resulted in further decrease in the amount of silver in the textile, indicating that copper began to significantly outcompete silver between a ratio of 5:1 and 2:1 (Fig. 8 ). This explains the dominant amount of silver compared to copper in Ag/Cu synthesis Method 2 (NaHCO 3 ), as this method showed to result in a large amount of silver but a small amount of copper, resulting in a silver/copper ratio of ~ 5:1 on the textile. Without any silver present, the amount of copper on the textile increased, indicating that while silver is disproportionately outcompeted by copper, the presence of silver does lead to some inhibition of copper binding to the textile. It is hypothesized that the stabilization of the copper oxide precipitate onto the textile occurs more rapidly than the reduction of silver, leading to the competition phenomena observed. 3.5 Cellulose Analysis ATR-FTIR analysis was performed on samples treated with NaOH/NH 3Version1 (Method 6) with an increasing silver concentration (0.1–10 mM) to identify any changes to the cellulose structure following synthesis (Figure S12). Minor changes to various peak intensities were seen, thought to be due to differences in sample material thickness and homogeneity, and not actual changes to cellulose functional groups. The lack of identifiable functional group changes is expected when considering cellulose polymer chain length (degree of polymerization) for cotton is upwards of 10,000 units, and the reduction of silver only occurs at the termini of the polymer chain, leaving most of the cellulose untouched [ 31 ]. ATR-FTIR analysis of textiles resulting from Methods 2 (NaHCO 3 ),5 (NaOH), and 8 (NaBH 4 ) further confirm a lack of any functional group transformation (Fig. 9 ). 3.6 Parsimonious Assessment of the Synthesis Methods The goal of this investigation was to identify the most parsimonious – successful, simple, and ideally, efficacious – method for creating silver and copper treated textiles by comparing novel and previously identified synthesis methods in a stepwise fashion. The effectiveness of the methods can be identified following comprehensive characterization of the concentration (highest), speciation (non-ionic) and morphology of metal (nanoparticulate) in the textiles. For silver synthesis methods, treatment with NaHCO 3 or NaOH were assessed as the most parsimonious methods as they resulted in the highest concentrations of silver in the textile in metallic nanoparticulate form (100–500 nm). The use of ammonia, green tea, and NaBH 4 were all found to be ineffective as they reduce and/or stabilize the ionic silver in solution, inhibiting successful binding with the cellulose. For synthesis of copper-treated textiles, Methods 4–8 were found to be comparable in their effectiveness, as they resulted in the highest concentrations of copper in the textile, forming nanoparticles (50–500 nm, with non-ionic copper speciation (i.e., copper oxide). Of the four methods, use of NaOH was the most parsimonious method as it resulted in the highest total concentration of copper with minimal reagent input. For the bimetallic synthesis methods, the extent of metal on the textile was influenced by the competition between silver and copper for binding with the textile. The use of NaHCO 3 resulted in a parsimonious silver dominant synthesis method, resulting in a broad range of sizes in silver particles (greater than 1000 nm) interspersed with smaller (~ 50 to 300 nm) copper nanoparticles. The use of Methods 4–7 resulted in comparable amounts of copper oxide and metallic silver on the textile, with unreduced ionic silver also present. The use of either NH 3 , NaOH, or NaBH 4 were identified as being the most parsimonious balanced silver/copper synthesis method, with NaBH 4 being slightly advantageous due to complete reduction of silver. However, this slight advantage is countered by the toxicity of using NaBH 4 as a reducing agent. 4. Conclusion This study characterized silver, copper, and silver/copper containing cotton textiles resulting from eight different synthesis methods. The total metal content, metal speciation, and metal morphology of each synthesis reaction has been identified. Additionally, the roles of metal competition, temperature and pH (treatment with Ag/NaHCO 3 ) have also been investigated, providing additional mechanistic information. A NaHCO 3 synthesis method (Method 2) resulted in the highest concentration (8900 ± 500 mg Ag/kg textile) of elemental silver nanoparticles (356 ± 106 nm) in the cotton textile material, representing a successful method for the creation of silver nanoparticle treated textiles. The NaOH synthesis method (Method 5) was also found to result in high concentrations of metallic (silver) and metal-oxide (copper) nanoparticles, representing a successful method for the creation of silver, copper, and bimetallic silver/copper containing textiles. While the size of the nanoparticles identified in this study are larger than classically defined nanoparticles (< 100 nm in diameter), the larger size is potentially advantageous when considering their potential for antimicrobial textile applications. Larger-sized nanoparticles allow for sufficiently small particles to provide an enhanced ability (over sheets or coatings) to inhibit the growth of bacteria and viruses, while minimizing major nano-specific risk assessment concerns for the product [ 5 , 7 , 68 ]. The identification of the silver and copper materials being present inside the cellulose matrix suggests that the release of the metal materials during use and washing may be minimal. Future work is planned to evaluate the antimicrobial effectiveness of these silver, copper, and silver/copper treated textiles. Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE No ethics approval or consent to participate was required for this research. CONSENT FOR PUBLICATION All authors whose names appear on the submission have made substantial contributions to the publication, including but not limited to; conception, design, data acquisition, analysis, interpretation, writing, revisions, and have approved the version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. AVAILABILITY OF DATA AND MATERIALS The data that support the findings of this study are available from the corresponding author upon reasonable request. COMPETING INTERESTS The authors have no financial or non-financial interests that could impart bias on the work submitted for publication. FUNDING This research was supported by Natural Sciences and Engineering Research Council of Canada Discovery Grants held by Koch and Weber. AUTHORS’ CONTRIBUTIONS Conceptualization: David Patch, Kela Weber, Iris Koch; Methodology: David Patch; Formal analysis and investigation: David Patch, Natalia O’Connor, Debora Meira; Writing – original draft preparation: David Patch; Writing- review and editing: Natalia O’Connor, Iris Koch, Jennifer Scott, Kela Weber; Funding acquisition: Kela Weber; Resources: Iris Koch, Jennifer Scott, Kela Weber; Supervision: Iris Koch, Jennifer Scott, Kela Weber. ACKNOWLEDGEMENTS The authors would like to acknowledge Jacob Zachariah, Angela Richard and Francesca Body for their contributions to the initial literature review. The authors would like to acknowledge Brigitte Simmatis and Anbareen Farooq for their contributions with proofreading. The authors would also like to thank Dr. Jennifer Snelgrove for training and assistance with the SEM-EDX work, Dr. Fiona Kelly for providing her NexION 300D ICP-MS, as well as Dr. Zou Finfrock for performing XANES speciation analysis. This research used resources of the Advanced Photon Source, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory and was supported by the U.S. DOE under Contract No. DE- AC02-06CH11357, the Canadian Light Source and its funding partners (Sector 20 work), and DOE and MRCAT member institutions (Sector ID-B). Additional beam time was awarded for research related to COVID applications. References Nowack, B., Krug, H.F., and Height, M. (2011) 120 years of nanosilver history: Implications for policy makers. Environmental Science and Technology . Giannossa, L.C., Longano, D., Ditaranto, N., Nitti, M.A., Paladini, F., Pollini, M., Rai, M., Sannino, A., Valentini, A., and Cioffi, N. (2013) Metal nanoantimicrobials for textile applications. Nanotechnology Reviews , 2 (3), 307–331. Alexander, J.W. (2009) History of the medical use of silver. Surg Infect (Larchmt) , 10 (3), 289–292. 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Supplementary Files DavidPatchTextileSIKPW.docx GraphicaAbstarct.png Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2023 Read the published version in Cellulose → Version 1 posted Editorial decision: Major revision 28 Jun, 2022 Editor assigned by journal 28 Jun, 2022 Submission checks completed at journal 26 Jun, 2022 First submitted to journal 24 Jun, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-1793366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":116519768,"identity":"d23da92f-249b-42bc-9bda-978b06d6e270","order_by":0,"name":"David Patch","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCA0DEA2bxMD4gTQsPAw+zAdFaGKBa2CSI0sF3/IzhgTcMd+Tt2c8eq67MsWPgbz+AX4vkmRyDg3MYnhn28OSl3Ty7LZlB4kwCfi0GB9ISDvMwHGbskeAxu9m47QCDAQMhLeefgbXYg7QUgrXwPyCg5UbyAZCWRJAWRrAWCQK2SN54fODgHINnyT1ncowlG7cl80jcIGAL3/nE5g9vKu7YtrefMfzYuM1Ojr+fgC1Q5x2AM3mIUQ8CBwiqGAWjYBSMghEMALgtRxpLkx86AAAAAElFTkSuQmCC","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Patch","suffix":""},{"id":116519769,"identity":"a6be593f-5959-4231-8552-5be82abb92ae","order_by":1,"name":"Natalia O'Connor","email":"","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"O'Connor","suffix":""},{"id":116519770,"identity":"aea25206-7eb5-47c6-9d56-02ed4c308697","order_by":2,"name":"Debora Meira","email":"","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Debora","middleName":"","lastName":"Meira","suffix":""},{"id":116519771,"identity":"c68bbe1c-2f91-4cba-9681-8259b2a61c06","order_by":3,"name":"Jennifer Scott","email":"","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Scott","suffix":""},{"id":116519772,"identity":"8dc09348-4fe0-4011-9d4e-a1c3fb56b5a8","order_by":4,"name":"Iris Koch","email":"","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Iris","middleName":"","lastName":"Koch","suffix":""},{"id":116519773,"identity":"b6fdc08c-0614-4194-9459-295fd75c4ee8","order_by":5,"name":"Kela Weber","email":"","orcid":"","institution":"Royal Military College of Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kela","middleName":"","lastName":"Weber","suffix":""}],"badges":[],"createdAt":"2022-06-25 01:44:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1793366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1793366/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10570-023-05099-7","type":"published","date":"2023-02-25T19:01:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":23454420,"identity":"ad383705-363b-4eac-a272-27a3f519f3e0","added_by":"auto","created_at":"2022-07-05 14:25:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":407307,"visible":true,"origin":"","legend":"\u003cp\u003eSilver nanoparticles found in the cross-section of the cotton fiber following NaHCO\u003csub\u003e3\u003c/sub\u003e silver treatment method.\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/7deed282f4d3633cf3704c97.png"},{"id":23454418,"identity":"5b9803dd-c2e4-4e5e-99aa-2ba66bbeb287","added_by":"auto","created_at":"2022-07-05 14:25:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":493036,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographic\u003cstrong\u003e \u003c/strong\u003eimages taken of finished textiles using eight synthesis methods.\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/e66e02df8f817c129fb41870.png"},{"id":23453848,"identity":"ea0fcfc5-cfc3-4f3c-9c86-03f2ec87a338","added_by":"auto","created_at":"2022-07-05 14:20:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62764,"visible":true,"origin":"","legend":"\u003cp\u003eConcentration of metal present in the textile following treatment with (A) 1mM silver, (B) 1mM copper, and (C) combined 0.5 mM/0.5 mM silver/copper using XRF. Speciation information was obtained from XANES. CuX are samples that do not have speciation data available. D are samples that were not detected by XRF but have speciation data available. Error bars are the standard deviation of triplicate.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/268b6f5cfc936c53603d25c9.png"},{"id":23454422,"identity":"547c32ed-c20d-4efb-a0ab-f9a022126344","added_by":"auto","created_at":"2022-07-05 14:25:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42460,"visible":true,"origin":"","legend":"\u003cp\u003eIn-situ silver ion selective electrode measurements examining the rate of reaction at two different temperature profiles following NaHCO\u003csub\u003e3\u003c/sub\u003e synthesis method.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/54b685e4b584743c3d43f81f.png"},{"id":23455040,"identity":"e6ecaea7-7b50-4c79-a633-c3d827189c66","added_by":"auto","created_at":"2022-07-05 14:30:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":451702,"visible":true,"origin":"","legend":"\u003cp\u003eSEM analysis, EDX spectra, and ImageJ particle number/diameter determination of silver nanomaterials (A-C) and copper oxide nanomaterials (C-E) using NaHCO\u003csub\u003e3\u003c/sub\u003e (A/C), NaOH (B/D), and NaBH\u003csub\u003e4\u003c/sub\u003e (C/E) synthesis methods.\u0026nbsp;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/a1ac789664b659972c2b34a5.png"},{"id":23453854,"identity":"f265bee8-f027-4cb1-a955-80f6a55491c9","added_by":"auto","created_at":"2022-07-05 14:20:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1236851,"visible":true,"origin":"","legend":"\u003cp\u003eSEM analysis (top) and EDX elemental analysis (bottom) on silver (red) and copper (green) nanomaterials following NaHCO\u003csub\u003e3\u003c/sub\u003e (A), NaOH (B), and NaBH\u003csub\u003e4\u003c/sub\u003e (C) synthesis methods.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/ca175088fd4f1be93e347cd2.png"},{"id":23455041,"identity":"6921215d-4e88-4186-b930-a69817b7f628","added_by":"auto","created_at":"2022-07-05 14:30:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":421441,"visible":true,"origin":"","legend":"\u003cp\u003ePohotographic images of the synthesis of silver, copper, and silver/copper treated cotton textiles with three different water bath heat treatments using the NaOH/NH\u003csub\u003e3 Version 1\u003c/sub\u003e synthesis method and 10 mM of starting metal concentration. Starred values have one replicate under XRF detection limits.\u0026nbsp;\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/2ff7c74d06b640090e484342.png"},{"id":23453851,"identity":"d56c8a39-9741-4c6e-8f6e-dc30b64e5fb9","added_by":"auto","created_at":"2022-07-05 14:20:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":60962,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation into the effect of reagent concentration on resulting concentration in the textile using the NaOH/NH\u003csub\u003e3Version1\u003c/sub\u003e synthesis method by using a fixed ratio of silver/copper (left) and a variable ratio of silver:copper, with silver held constant at 1 mM (except for 0:1, which had no silver and 1 mM copper) (right).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/15a3f0e31b941984e38c9145.png"},{"id":44720580,"identity":"b5fc0829-a269-43cf-a308-b83b1392ea2e","added_by":"auto","created_at":"2023-10-16 19:12:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3629487,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/28e2c191-c22e-48dc-820b-b0bb37a91be2.pdf"},{"id":23453857,"identity":"c9a1ddd7-8931-441f-991e-5a8b4d5d1e1c","added_by":"auto","created_at":"2022-07-05 14:20:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9192335,"visible":true,"origin":"","legend":"","description":"","filename":"DavidPatchTextileSIKPW.docx","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/9a8f8a4692eb9050a0440c72.docx"},{"id":23455039,"identity":"b11809d9-b4a0-4ffd-a43b-ba3e7914351b","added_by":"auto","created_at":"2022-07-05 14:30:06","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":419940,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicaAbstarct.png","url":"https://assets-eu.researchsquare.com/files/rs-1793366/v1/a946d220bdf511549b92a6d9.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Parsimonious Methodology for Synthesis of Silver and Copper Functionalized Cellulose","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetals such as silver and copper have been used in medical, religious, and ornamental applications for thousands of years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Many of these applications used bulk metals, such as silver containers for water purification, or ionic salts, such as silver nitrate as a caustic for wound treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Advances in material sciences have seen the synthesis of smaller and smaller metal materials, seeking to taking advantage of the increased surface area to volume ratio and the unique properties these metals have compared to the larger bulk counterparts. These materials are often classified according to their size, with fine particles (2,500\u0026ndash;100 nm), nanoparticles (100\u0026ndash;1 nm), and atom clusters (\u0026lt;\u0026thinsp;1 nm) being the three smallest ranges. The definition of nanoparticles is sometimes expanded to include particles 500 nm and smaller, since these particles still exhibit some nanoscale properties [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetal nanoparticles and nanocomposites are a growing focus of commercialization because of the beneficial qualities these nanomaterials can impart. One of the biggest areas of growth is in the development of nano-functionalized textiles with manufacturers seeking to take advantage of the antibacterial properties of the nanomaterials. Metal and metal oxide nanoparticles are often utilized as these have enhanced stability and antibacterial efficacy over other oxidation states. Silver is often selected for antibacterial applications, and copper is selected for antiviral and antifungal applications. Silver and copper are the most commonly studied metals used for creating high performance textiles (Figure S1). The COVID-19 pandemic has renewed interest in using nanotechnology for enhanced protectiveness against viral spread [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Other than antimicrobial activity, nanoparticles can impart unique properties to textiles including increased conductivity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], self-cleaning[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], electromagnetic interference shielding [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and ultraviolet shielding[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are two common ways to prepare textiles with nanoparticles: ex-situ and in-situ. The ex-situ approach involves first forming metal nanoparticles separately from the textile, using a wet chemical method [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], a sacrificial anode method (Ditaranto et al., 2016), or a biological method[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The particles formed ex-situ are then applied to textiles through immersion or dry padding [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The in-situ approach consists of synthesizing nanoparticles directly onto the textiles, using an electroless plating or a chemical reduction method [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Some of these in-situ methods use an added reducing agent (e.g., glucose)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] or use the textile polymer itself (e.g., cellulose in cotton textiles) as the reducing agent [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Cellulose is the most popular textile polymer used, due to its aesthetic qualities as well as it being renewable and biocompatible (Figure S1) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe electron rich functional groups and polymer structure of cellulose allows for initial complexation of the ionic metal and stabilization of the resultant metal nanoparticle [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The extent of deposition, speciation, and morphology of the resultant metal nanoparticle is heavily influenced by the synthesis conditions, including heat, pH, and reagents used [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To the knowledge of the authors, there have been no previous studies quantitatively characterizing the concentration, speciation, and morphology of the resultant metal materials across different synthesis methods.\u003c/p\u003e \u003cp\u003eThe goal of the present study is to identify a parsimonious synthesis method for creating silver, copper, and bimetallic treated textiles by comparing eight different synthesis methods. A selection of novel and popular synthesis methods are examined through a stepwise approach. The parsimony of the synthesis method are evaluated based on three criteria: high metal content, measured by x-ray fluorescence spectroscopy (XRF); metallic or metal-oxide speciation, determined using x-ray absorption near-edge structure (XANES); and consistent particle morphology, determined using scanning electron microscopy (SEM).\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Reagents\u003c/h2\u003e \u003cp\u003eSilver nitrate (ACS), copper (II) nitrate hemipentahydrate (ACS), sodium hydroxide pellets (ACS), ammonia solution (28\u0026ndash;30%, ACS), sodium bicarbonate (ACS), Triton X100, and sodium borohydride (ACS) were all purchased from VWR. Cotton batting was purchased from a local supplier in Kingston, Ontario (Stitch by Stitch). Green tea was purchased from a local grocery store (Metro). Deionized water (DI water) was generated using an in-lab filtration system (Milli-Q Direct 8, 18 MΩ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Preparation\u003c/h2\u003e \u003cp\u003eAll glassware was cleaned three times with 2% nitric acid and then rinsed with three portions of DI water. 4x4 cm squares of cotton batting (106 g/m\u003csup\u003e2\u003c/sup\u003e) were cut from the bulk material using a rotary cutter and prepared as follows: (1) washed with 1% Triton X100 solution at 60\u0026deg;C for 30 minutes with agitation (2) rinsed with DI water until no foaming was observed, (3) rinsed with DI water at 60\u0026deg;C for 15 minutes with agitation; and (4) dried overnight in an oven at 40\u0026deg;C. Textiles were trimmed with a rotary cutter after drying to remove any stray strands of batting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Synthesis\u003c/h2\u003e \u003cp\u003eA summary of the methods and reagents used is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with a general procedure described in the following paragraphs. A final volume of 13.6 mL of solution and 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 g of cotton batting was used to obtain a volume/textile mass ratio of 80:1, to allow for thorough wetting and mixing of the solutions. Erlenmeyer flasks were filled with DI water, sealed with aluminum foil, and placed into a water bath (digital general water bath, VWR) set to 60\u0026deg;C. Reagents were added as appropriate for each method, shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and agitated for 10 minutes using an orbital shaker.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall synthesis steps performed, including reagents added at each step and analytical methods employed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort name\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;green tea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNaOH/ NH\u003csub\u003e3Version1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNaOH/ NH\u003csub\u003e3Version2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNaBH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eStep 1: Reagents added and mixed at 60\u003csup\u003eo\u003c/sup\u003eC for 10 minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 1 reagents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDI H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 mM NaHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 mM\u003c/p\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 mM NH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 mM NaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 mM NaOH\u0026thinsp;+\u0026thinsp;4 mm NH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 mM NaOH\u0026thinsp;+\u0026thinsp;8 mm NH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDI H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eStep 2: Metals added and mixed at 60\u003csup\u003eo\u003c/sup\u003eC for 10 minutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 2 metals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e1 mM Ag\u003csup\u003e+\u003c/sup\u003e OR 1 mM Cu\u003csup\u003e+\u003c/sup\u003e OR [0.5 mM Ag\u003csup\u003e+\u003c/sup\u003e + 0.5 mM Cu\u003csup\u003e+\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eStep 3: Textile added, temperature set to 80\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eStep 4: Reagents added\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 4 reagents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1% green tea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 mM NaBH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eStep 5: Mixed at 80\u003csup\u003eo\u003c/sup\u003eC for 1 hour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eStep 6: Cooled, drained, textile rinsed with DI H\u003csub\u003e2\u003c/sub\u003eO at least 3x, and dried at 60\u003csup\u003eo\u003c/sup\u003eC overnight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eStep 7: Textiles characterized using methods listed below\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXANES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATR-FTIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEM/EDX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex (Ag)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSilver and/or copper nitrate solutions were added to the flasks to reach the final concentrations listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and mixed for 10 minutes. One textile (4 x 4 cm, 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 g) was added to each Erlenmeyer flask, the water bath temperature was raised to 80\u003csup\u003e\u0026deg;\u003c/sup\u003eC, and reagents were added for Methods 3 and 8 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Flasks were agitated on an orbital shaker at 30 RPM for one hour. The Erlenmeyer flasks were then removed from the shaker, and 12 mL of room temperature DI water was immediately added to cool the vessel and stop the reaction. The following steps were untaken to process the textiles: (1) textiles were removed from the solutions and rinsed with 20 mL DI water three times, (2) additional rinses were performed, if necessary, until the rinse solution was clear (e.g., for Method 2) (3) water was gently squeezed from the textiles, and (4) textiles were dried at 60\u003csup\u003e0\u003c/sup\u003eC in an oven overnight. Synthesis methods were performed in triplicate. A digital camera (Canon SL2) was used to image the textiles after the synthesis reactions to record color change.\u003c/p\u003e \u003cp\u003eAdditional experiments were performed to explore the effect of heat, silver/copper competition, and for cellulose analysis. These experiments were performed using synthesis method 6 as it is a well-established method in literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Metal Content Determination\u003c/h2\u003e \u003cp\u003eThe metal concentration of the textiles following treatment was measured using x-ray fluorescence spectroscopy (Innov-X Systems α-2000 XRF). Textiles were analyzed whole after drying and the concentration determined by using an external calibration curve, since the XRF measurement parameters were set up for soil samples and not applicable to textiles. (See SI for method development of this calibration). Two calibration curve preparation methods were explored to determine the more accurate method. XRF detection limits were identified as ~\u0026thinsp;375 mg Ag/kg and ~\u0026thinsp;100 mg Cu/kg. Relative standard deviation for replicates was found to be 16\u0026thinsp;\u0026plusmn;\u0026thinsp;11%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Metal Speciation\u003c/h2\u003e \u003cp\u003eX-ray absorption near edge structure (XANES) analysis was used to perform silver and copper speciation analysis of the bulk textiles. XANES spectra were collected at the Sector 20 insertion device beamline (20ID-C) of the Advanced Photon Source (CLS@APS), within the X-Ray Science Division (XSD), Argonne National Laboratory. XANES spectra of the Ag Kα-edge and Cu Kα-edge were recorded in fluorescence mode by using a four-element silicon drift detector (Vortex\u0026reg;-ME4 with Xspress 3 pulse processor) while monitoring incident and transmitted intensities in straight ion chamber detectors filled with N\u003csub\u003e2\u003c/sub\u003e gas. Textiles were analyzed as 1cm x 1cm subsections rolled and packed in a 3D printed PETG sample holder, held between two layers of Kapton\u0026reg; tape. The Si (111) double crystal monochromator was calibrated using a silver metal foil at 25,514 eV, copper metal foil at 8989 eV, and the incident beam size was 800 \u0026micro;m. Fitting of XANES spectra was accomplished with Athena software. The silver standard spectra used for fitting had been measured as frozen aqueous dissolved species previously by our group[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and included AgNP, AgNO\u003csub\u003e3\u003c/sub\u003e, AgO. The copper standards were synthesized in our lab using copper nitrate as a precursor and reacting it with the appropriate reagents to form the desired precipitate (where applicable). After synthesis, standards were washed with three portions of DI water and packed into the same 3D printed sample holder. The Ag (0) and Cu (0) standards used provided the metals in their zero oxidation state and could not distinguish between nanoparticulate or bulk metallic forms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Metal Morphology\u003c/h2\u003e \u003cp\u003eComplete SEM sample preparation development is described in detail in the SI. Initial SEM analysis of the textiles failed to identify substantial metal materials on the textile surface, despite high concentrations present on the textiles (Figure S3). Cross-sectional analysis of the textiles identified the presence of nanomaterials within the cotton fiber core itself (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This led to the development of a textile ashing method that allowed for improved metal morphological determination.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeparate square subsections of the textiles (approximately 0.1 g) were ashed in ceramic crucibles at 550\u0026deg;C for one hour [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The resulting grey ash was dispersed in 1 mL DI water, and diluted to 10 mL with DI water, ultrasonicating the solution at 30 kHz for one minute. A 0.1 mL subsample of the solution was dried onto double-sided carbon tape and analyzed. Surface analysis of the textile samples were analyzed (Quanta 250FED) operating under environmental mode at 100 kPa. EDX (EDAX Octane Elite) was performed for elemental determination. Analysis of the dried ash following reconstitution were analyzed on under high vacuum mode. Images were acquired first at 6000\u0026ndash;7500 magnification, then taken at 18\u0026ndash;21,000 magnification. ImageJ (NIH) was used to count and determine the spherical diameter for nanoparticles. For non-spherical or oval nanoparticles, the diameter was measured at the shortest dimension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Fourier-Transform Infrared Spectroscopy (FTIR) Analysis\u003c/h2\u003e \u003cp\u003eThe FTIR (Thermo Scientific Nicolet-IS10 Attenuated Total Reflection (ATR)-FTIR) spectra of cotton samples were acquired by folding samples twice for a total of four layers before being placed into the active element of the ATR-FTIR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Ion Selective Electrode (ISE) Analysis\u003c/h2\u003e \u003cp\u003eUsing a silver ion selective electrode (Fisher Scientific accumet), the kinetics of the NaHCO\u003csub\u003e3\u003c/sub\u003e synthesis method were explored at temperature profile 1 (60\u0026deg;C heated to 80\u0026deg;C) and temperature profile 2 (80\u003csup\u003e\u0026deg;\u003c/sup\u003eC from the start) by measuring the decrease in ionic silver present in the solution (assumed to correspond to formation of particulate silver on the textile). It is important to note that, as the ISE only measures ionic silver, any release of metallic silver from the textile during synthesis would not be identified. A no-textile control was analyzed using ISE at both temperature profiles to correct for changes to ISE response as a function of temperature. A six-point external calibration curve at the reaction temperature was used to quantify the ionic silver.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003e3.1 Metal Content and Speciation on Textile\u003c/h2\u003e\n \u003cp\u003eVisual inspection of the treated cellulose textiles (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) allowed for an immediate indication of the effectiveness of the different synthesis methods. Based on the extent of discoloration, the silver synthesis reactions with NaHCO\u003csub\u003e3\u003c/sub\u003e, NaOH, or NaOH/NH\u003csub\u003e3Version1\u003c/sub\u003e (Methods 2, 4 and 5) resulted in substantial silver present on the textile (darker brown), whereas copper synthesis methods 4, 5, 6, 7 and 8 appeared to have the most copper present (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). With the combined silver/copper treatment methods, the color trend of the textiles is similar to either their silver or copper textile counterpart, indicating a likely dominance of silver (method 2) or copper (methods 4\u0026ndash;8).\u003c/p\u003e\n \u003cp\u003eXRF analysis confirms some of the observations made from the textile images: for the separate silver and copper textiles, the use of NaHCO\u003csub\u003e3\u003c/sub\u003e, NaOH and NaOH/NH\u003csub\u003e3Version1\u003c/sub\u003e (Methods 2, 4 and 5) resulted in the highest amounts of silver (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, S5), and methods 4\u0026ndash;8 resulted in comparably high amounts of copper on the textile (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, S5). For silver/copper bimetallic treatment, the use of NaHCO\u003csub\u003e3\u003c/sub\u003e (Method 2) resulted in highest amount of silver, but methods 4\u0026ndash;7 resulted in reduced silver concentrations and copper dominating (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, S5).\u003c/p\u003e\n \u003cp\u003eXANES analysis identified the metal speciation of silver and copper across the synthetic trials. For silver only synthetic trials, all the methods resulted in reduction of the ionic silver into metallic silver (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003eIt is well accepted that ionic metals, such as silver, can be reduced by cellulose in cotton, and this reduction occurs more completely under alkaline conditions and at elevated temperatures [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. The exact mechanism of this reduction is not often discussed in detail although some authors suggest that the hydroxyl groups on the cellulose polymer are oxidized into aldehydes, and then into carboxylates [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, silver complexes like those formed using Tollen\u0026rsquo;s Reagent are not reduced in the presence of alcohol-containing compounds in classic chemical tests, whereas they are with aldehydes. Other authors identify that while cellulose itself is not considered a reducing sugar; hemiacetal groups are present at the termini of the polymer chain. These hemiacetals undergo ring-chain tautomerism under basic conditions and with heat (8), converting into aldehyde groups that, like typical reducing sugars such as glucose, can reduce metals [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. There is also the potential for alkaline degradation of the cellulose polymer (i.e., the Lobry de Bruyn-Alberda van Ekenstein transformation[\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]), resulting in the generation of glucose and other monosaccharides that will readily reduce ionic silver. It is also possible that the pectins and hemicelluloses present in the primary wall and winding layer of cotton fibers are reducing ionic silver, as these compounds have been found to reduce silver when isolated and used as primary reagents [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eWhen no reagents are added, as is the case with the positive control synthesis method, some reduction of silver is observed to be occurring (as detected by XANES). Reduction of ionic silver onto cotton without the use of reagents has been observed previously, with the concentration of silver being related to reaction time and temperature [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe reactions for silver explored in this study are shown in equations 1 to 9 below. It is important to note that the silver hydroxide (3) immediately reacts to silver oxide (4) due to the favorable kinetics of the reaction (pK\u0026thinsp;=\u0026thinsp;2.88) [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e] (4). The silver complex formed following addition of NaOH and NH\u003csub\u003e3\u003c/sub\u003e (method 6, 7) is known as Tollen\u0026rsquo;s reagent, which is used to test for aldehydes and alpha-hydroxy ketones and is often used for synthesizing silver-treated textiles [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Reaction of the silver compounds with the aldehyde at the terminal end of the cellulose chain results in reduction of ionic silver to metallic silver, and oxidation of the aldehyde to the carboxylic acid.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eThe resulting silver nanoparticles (8) are then stabilized by the cellulose inside the cotton fibers, similar to the stabilization effect that occurs with carboxymethylcellulose (CMC) coated nanoparticles [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eWhile the speciation of silver on the textile was consistent across the methods, the amount of metal present on the textile varied substantially. This can be explained by the stability of the silver compounds. In the presence of green tea, reduction followed by stabilization via coating occurs, significantly inhibiting any silver deposition onto textile [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. In the presence of ammonia (method 4), or high concentrations of ammonia (method 7) significant silver-ammonia complexation occurs, which stabilizes the silver and makes it a weaker oxidizing agent than the corresponding aquo complexes that result with Ag\u003csub\u003e2\u003c/sub\u003eO and Ag\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e. When NaBH\u003csub\u003e4\u003c/sub\u003e was added reduction occurred in the solution (a process well understood in literature)[\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e], causing the majority of silver nanomaterials to aggregate and precipitate out of solution before they could be deposited and stabilized by the cellulose (10).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eFor copper only synthetic trials, methods 1\u0026ndash;3 resulted in the copper being deposited as ionic copper; likely intermolecularly bonded with the hydroxyl groups in the cellulose chain. For methods 4\u0026ndash;7, which occurred at higher pH (pH\u0026thinsp;\u0026gt;\u0026thinsp;10), the copper was deposited mainly as copper oxide. The reactions for copper explored in this study are shown in equations 11 to 17. When both NaOH and NH\u003csub\u003e3\u003c/sub\u003e are added, a copper-tetraammine-hydroxide complex is eventually formed, similar to the complex known as Schweizer\u0026rsquo;s reagent, which is copper ammonia complex used to dissolve cellulose (14,15). Despite the various complexes that are formed, all of the pH\u0026thinsp;\u0026gt;\u0026thinsp;10 complexes are not stable at elevated temperatures and convert into copper oxide, supporting the speciation results observed [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eThe use of NaHCO\u003csub\u003e3\u003c/sub\u003e and NaHCO\u003csub\u003e3\u003c/sub\u003e/green tea did not have any appreciable effect on the total concentration or speciation of copper on the textile when compared to the reagent-free control, indicating these reagents are superfluous for copper textile synthesis. This was initially unexpected as the antioxidants in green tea have been shown capable of reducing other metals like iron and silver. It is likely the NaHCO\u003csub\u003e3\u003c/sub\u003e precipitated out the ionic copper as copper (II) carbonate before reduction with green tea could occur.\u003c/p\u003e\n \u003cp\u003eWhen ionic copper and sodium borohydride react, the initial reduction follows a slightly different path when compared to the ionic silver reduction mechanism. In this reaction, ionic copper reacts with the sodium borohydride to form the reduced copper, hydrogen gas, and boric acid (17)[\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003eThe speciation of copper using NaBH\u003csub\u003e4\u003c/sub\u003e was expected to be metallic copper, based on NaBH\u003csub\u003e4\u003c/sub\u003e being a strong reducing agent, instead of the copper oxide that was found. It is hypothesized that copper treated onto the textile underwent oxidation during drying and storage in ambient atmosphere, resulting in the formation of copper oxide.\u003c/p\u003e\n \u003cp\u003eXANES analysis for the bimetallic textiles identified a significant amount of ionic silver for methods 4\u0026ndash;7, which was not present for the silver-only synthetic trials. Given the chemistry of copper oxide formation for methods 4\u0026ndash;7, this incomplete reduction is likely due to copper partially outcompeting silver for binding onto the textile.\u003c/p\u003e\n \u003cp\u003eDue to the novelty of the NaHCO\u003csub\u003e3\u003c/sub\u003e method and the unexpected results additional experiments were performed to investigate the kinetics of reaction as a function of temperature profiles (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and the final textile concentration as a function of pH (Figure S6).\u003c/p\u003e\n \u003cp\u003eThe reduction of silver by cellulose with NaHCO\u003csub\u003e3\u003c/sub\u003e is directly affected by the temperature of the reaction. As temperature is shifted from 60\u0026deg;C to 80\u0026deg;C, the rate of silver reduction increases (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, left) When the reaction temperature is held at 80\u0026deg;C for one hour the silver is reduced at a rate of 0.028 mM Ag\u003csup\u003e+\u003c/sup\u003e/minute, with complete reduction by 46 minutes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, right). These findings indicate that the temperature of the reaction has an impact on kinetic rates. Unfortunately, while the same kinetic investigation was attempted with the copper reaction the amount of copper present in the solution disappeared immediately upon reaction with the sodium bicarbonate, forming an insoluble copper (II) carbonate complex. The kinetic investigation could not be attempted for other synthesis methods due to the high pH of the reactions damaging the ISE.\u003c/p\u003e\n \u003cp\u003eWhile it was originally hypothesized that the effectiveness of NaHCO\u003csub\u003e3\u003c/sub\u003e for silver reduction was due to an optimal pH (pH\u0026thinsp;=\u0026thinsp;8.24), it was identified that silver was still reduced at pH 6 (5400\u0026thinsp;\u0026plusmn;\u0026thinsp;510 mg Ag/kg textile), pH 10 (8860\u0026thinsp;\u0026plusmn;\u0026thinsp;1310 mg Ag/kg textile) and pH 12 (7500\u0026thinsp;\u0026plusmn;\u0026thinsp;600 mg Ag/kg) (although reduction at pH 10 and 12 is likely due to the NaOH used to adjust the pH). The reduction at pH 6 indicates that the bicarbonate/carbonate ion itself has a key impact on the silver reduction. However, no reduction was found to occur at pH 3, indicating pH still plays a large role in the overall synthesis effectiveness (Figure S6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003e3.2 Metal Particle Morphology\u003c/h2\u003e\n \u003cp\u003eSEM analysis was performed on silver, copper, and bimetallic treated textiles resulting from the three synthesis methods that gave the most distinct results (Method 2, 5, 8). Initial SEM method development identified that most of the metal particles were present inside the cellulose matrix, requiring ashing of the textiles before analysis. To the author\u0026rsquo;s knowledge this is the first study to identify metal materials present inside the cellulose matrix following in-situ synthesis. While classically defined nanoparticles (\u0026lt;\u0026thinsp;100 nm) were identified, the majority of the particles were found to be between 100 and 500 nm in diameter, which are considered nanomaterials depending on the application and field [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e] (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor the bimetallic treated textiles, the combined presence of silver and copper particles precluded the measurement of the diameters, but visual examination of the SEM images and the corresponding EDX spectra revealed two findings. Use of NaHCO\u003csub\u003e3\u003c/sub\u003e (Method 3) resulted in significantly more silver present than copper, whereas use of NaOH or NaBH\u003csub\u003e4\u003c/sub\u003e resulted in more copper present (predominantly green (copper) shading), which aligns with the XRF concentration results presented previously (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). Secondly, the silver particles are significantly larger in the bimetallic textiles compared to silver only textiles, with many of them appearing to be well above 500 nm in diameter. Once again, this is likely caused by a lack of binding sites due to competition with the copper, causing the silver reduction to favor particle growth over new nanoparticle formation. The copper/ copper oxide nanoparticles are relatively unchanged in their diameters (based on visual inspection).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003e3.3 Role of Heat in Metal-Textile Synthesis\u003c/h2\u003e\n \u003cp\u003ePrevious studies have identified that increased temperatures result in increased reaction rates and chemical pathways possible during treatment of cellulose with metal salts [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].The role of heat was qualitatively investigated for silver, copper, and silver/copper treated textiles following treatment with NaOH/NH\u003csub\u003e3Version1\u003c/sub\u003e. The synthesis was either performed at room temperature, with the solution heated after the textile was added, or the solution pre-heated (60\u0026deg;C) before adding the textile.\u003c/p\u003e\n \u003cp\u003eFor silver an increase in heat results in an increase to the amount of silver reduced into the textile. For copper the textile color is different based on the heating profile. When the textile is added to the synthesis solution at room temperature the blue copper complex (copper-tetraammine-hydroxide) immediately binds to the cellulose, stabilizing it against thermal conversion to copper oxide (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The dark textile color corresponding to copper oxide only occurs when the solution is heated at 60\u0026deg;C before the textile is added. At higher pH (pH\u0026thinsp;\u0026gt;\u0026thinsp;10) the expected copper complexes are either unstable or are not formed at elevated temperatures in water, resulting in the formation or conversion to copper oxide (Cudennec and Lecerf, 2003). Interestingly for the bimetallic textile the color of the textile resembles copper oxide containing textiles, suggesting a possible silver-copper complex is formed that results in the deposition of copper oxide. This indicates that at pH\u0026thinsp;\u0026gt;\u0026thinsp;10 the competition between silver and copper could be both physical competition for binding sites and chemical competition for reagents.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003e3.4 Silver Copper Competition\u003c/h2\u003e\n \u003cp\u003eThe potential for competition between silver and copper was identified in the previous sections from XRF, XANES, and SEM analysis. To investigate this further, synthesis Method 6 (NaOH/NH\u003csub\u003e3Version1\u003c/sub\u003e) was used to treat cotton textiles with different concentrations of silver and copper at a fixed (1:1) and variable ratio.\u003c/p\u003e\n \u003cp\u003eThe relationship between the concentration of metal in the textile and in solution was plotted for silver, copper, and silver/copper treated textiles. The slope for the silver only synthesis was found to be 5600 mg Ag/kg textile per mM of reagent (introduced) Ag in solution (R\u003csup\u003e2\u003c/sup\u003e 0.98). The slope for the copper only synthesis was found to be 2200 mg Cu/kg textile per mM of Cu in solution (R\u003csup\u003e2\u003c/sup\u003e 0.95). For the silver synthesis in the combined silver/copper treatment, the slope for silver decreased dramatically to 240 mg Ag/kg textile per mM of Ag in solution (R\u003csup\u003e2\u003c/sup\u003e 0.99), whereas for copper the slope barely decreased (2000 mg Cu/kg textile per mM of Cu in solution, R\u003csup\u003e2\u003c/sup\u003e 0.98) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). This confirms that, when silver and copper are in solution at equal concentrations, copper outcompetes silver for binding onto the textile.\u003c/p\u003e\n \u003cp\u003eAdditionally, by varying the ratio of silver and copper (1:0, 10:1, 5:1, 2:1, 1:1, 0:1) the amount of copper required to outcompete silver can be identified. While the amount of silver decreased slightly with the addition of 0.1 and 0.2 mM of copper, it was within the deviation of the silver with no copper added. The amount of silver present on the textile dropped dramatically with 0.5 mM of copper being added, or a 2:1 ratio. Adding 1 mM of copper (1:1) resulted in further decrease in the amount of silver in the textile, indicating that copper began to significantly outcompete silver between a ratio of 5:1 and 2:1 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). This explains the dominant amount of silver compared to copper in Ag/Cu synthesis Method 2 (NaHCO\u003csub\u003e3\u003c/sub\u003e), as this method showed to result in a large amount of silver but a small amount of copper, resulting in a silver/copper ratio of ~\u0026thinsp;5:1 on the textile. Without any silver present, the amount of copper on the textile increased, indicating that while silver is disproportionately outcompeted by copper, the presence of silver does lead to some inhibition of copper binding to the textile. It is hypothesized that the stabilization of the copper oxide precipitate onto the textile occurs more rapidly than the reduction of silver, leading to the competition phenomena observed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003e3.5 Cellulose Analysis\u003c/h2\u003e\n \u003cp\u003eATR-FTIR analysis was performed on samples treated with NaOH/NH\u003csub\u003e3Version1\u003c/sub\u003e (Method 6) with an increasing silver concentration (0.1\u0026ndash;10 mM) to identify any changes to the cellulose structure following synthesis (Figure S12). Minor changes to various peak intensities were seen, thought to be due to differences in sample material thickness and homogeneity, and not actual changes to cellulose functional groups. The lack of identifiable functional group changes is expected when considering cellulose polymer chain length (degree of polymerization) for cotton is upwards of 10,000 units, and the reduction of silver only occurs at the termini of the polymer chain, leaving most of the cellulose untouched [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. ATR-FTIR analysis of textiles resulting from Methods 2 (NaHCO\u003csub\u003e3\u003c/sub\u003e),5 (NaOH), and 8 (NaBH\u003csub\u003e4\u003c/sub\u003e) further confirm a lack of any functional group transformation (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003e3.6 Parsimonious Assessment of the Synthesis Methods\u003c/h2\u003e\n \u003cp\u003eThe goal of this investigation was to identify the most parsimonious \u0026ndash; successful, simple, and ideally, efficacious \u0026ndash; method for creating silver and copper treated textiles by comparing novel and previously identified synthesis methods in a stepwise fashion. The effectiveness of the methods can be identified following comprehensive characterization of the concentration (highest), speciation (non-ionic) and morphology of metal (nanoparticulate) in the textiles.\u003c/p\u003e\n \u003cp\u003eFor silver synthesis methods, treatment with NaHCO\u003csub\u003e3\u003c/sub\u003e or NaOH were assessed as the most parsimonious methods as they resulted in the highest concentrations of silver in the textile in metallic nanoparticulate form (100\u0026ndash;500 nm). The use of ammonia, green tea, and NaBH\u003csub\u003e4\u003c/sub\u003e were all found to be ineffective as they reduce and/or stabilize the ionic silver in solution, inhibiting successful binding with the cellulose.\u003c/p\u003e\n \u003cp\u003eFor synthesis of copper-treated textiles, Methods 4\u0026ndash;8 were found to be comparable in their effectiveness, as they resulted in the highest concentrations of copper in the textile, forming nanoparticles (50\u0026ndash;500 nm, with non-ionic copper speciation (i.e., copper oxide). Of the four methods, use of NaOH was the most parsimonious method as it resulted in the highest total concentration of copper with minimal reagent input.\u003c/p\u003e\n \u003cp\u003eFor the bimetallic synthesis methods, the extent of metal on the textile was influenced by the competition between silver and copper for binding with the textile. The use of NaHCO\u003csub\u003e3\u003c/sub\u003e resulted in a parsimonious silver dominant synthesis method, resulting in a broad range of sizes in silver particles (greater than 1000 nm) interspersed with smaller (~\u0026thinsp;50 to 300 nm) copper nanoparticles. The use of Methods 4\u0026ndash;7 resulted in comparable amounts of copper oxide and metallic silver on the textile, with unreduced ionic silver also present. The use of either NH\u003csub\u003e3\u003c/sub\u003e, NaOH, or NaBH\u003csub\u003e4\u003c/sub\u003e were identified as being the most parsimonious balanced silver/copper synthesis method, with NaBH\u003csub\u003e4\u003c/sub\u003e being slightly advantageous due to complete reduction of silver. However, this slight advantage is countered by the toxicity of using NaBH\u003csub\u003e4\u003c/sub\u003e as a reducing agent.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study characterized silver, copper, and silver/copper containing cotton textiles resulting from eight different synthesis methods. The total metal content, metal speciation, and metal morphology of each synthesis reaction has been identified. Additionally, the roles of metal competition, temperature and pH (treatment with Ag/NaHCO\u003csub\u003e3\u003c/sub\u003e) have also been investigated, providing additional mechanistic information.\u003c/p\u003e \u003cp\u003eA NaHCO\u003csub\u003e3\u003c/sub\u003e synthesis method (Method 2) resulted in the highest concentration (8900\u0026thinsp;\u0026plusmn;\u0026thinsp;500 mg Ag/kg textile) of elemental silver nanoparticles (356\u0026thinsp;\u0026plusmn;\u0026thinsp;106 nm) in the cotton textile material, representing a successful method for the creation of silver nanoparticle treated textiles. The NaOH synthesis method (Method 5) was also found to result in high concentrations of metallic (silver) and metal-oxide (copper) nanoparticles, representing a successful method for the creation of silver, copper, and bimetallic silver/copper containing textiles.\u003c/p\u003e \u003cp\u003eWhile the size of the nanoparticles identified in this study are larger than classically defined nanoparticles (\u0026lt;\u0026thinsp;100 nm in diameter), the larger size is potentially advantageous when considering their potential for antimicrobial textile applications. Larger-sized nanoparticles allow for sufficiently small particles to provide an enhanced ability (over sheets or coatings) to inhibit the growth of bacteria and viruses, while minimizing major nano-specific risk assessment concerns for the product [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The identification of the silver and copper materials being present inside the cellulose matrix suggests that the release of the metal materials during use and washing may be minimal. Future work is planned to evaluate the antimicrobial effectiveness of these silver, copper, and silver/copper treated textiles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo ethics approval or consent to participate was required for this research. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors whose names appear on the submission have made substantial contributions to the publication, including but not limited to; conception, design, data acquisition, analysis, interpretation, writing, revisions, and have approved the version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or non-financial interests that could impart bias on the work submitted for publication. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Natural Sciences and Engineering Research Council of Canada Discovery Grants held by Koch and Weber. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026rsquo; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: David Patch, Kela Weber, Iris Koch; Methodology: David Patch; Formal analysis and investigation: David Patch, Natalia O\u0026rsquo;Connor, Debora Meira; Writing \u0026ndash; original draft preparation: David Patch; Writing- review and editing: Natalia O\u0026rsquo;Connor, Iris Koch, Jennifer Scott, Kela Weber; Funding acquisition: Kela Weber; Resources: Iris Koch, Jennifer Scott, Kela Weber; Supervision: Iris Koch, Jennifer Scott, Kela Weber. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Jacob Zachariah, Angela Richard and Francesca Body for their contributions to the initial literature review. The authors would like to acknowledge Brigitte Simmatis and Anbareen Farooq for their contributions with proofreading. The authors would also like to thank Dr. Jennifer Snelgrove for training and assistance with the SEM-EDX work, Dr. Fiona Kelly for providing her NexION 300D ICP-MS, as well as Dr. Zou Finfrock for performing XANES speciation analysis. This research used resources of the Advanced Photon Source, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Argonne National Laboratory and was supported by the U.S. DOE under Contract No. DE- AC02-06CH11357, the Canadian Light Source and its funding partners (Sector 20 work), and DOE and MRCAT member institutions (Sector ID-B). Additional beam time was awarded for research related to COVID applications.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNowack, B., Krug, H.F., and Height, M. 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(2009) Toxicity and environmental risks of nanomaterials: challenges and future needs. \u003cem\u003eJ Environ Sci Health C Environ Carcinog Ecotoxicol Rev\u003c/em\u003e, \u003cstrong\u003e27\u003c/strong\u003e (1), 1\u0026ndash;35.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cellulose","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cels","sideBox":"Learn more about [Cellulose](https://www.springer.com/journal/10570)","snPcode":"10570","submissionUrl":"https://submission.nature.com/new-submission/10570/3","title":"Cellulose","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cellulose ,Silver nanomaterials , Copper nanomaterials ,Green synthesis ,In-situ synthesis","lastPublishedDoi":"10.21203/rs.3.rs-1793366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1793366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetal nanomaterials, such as silver and copper, are often incorporated into commercial textiles to take advantage of their antibacterial and antiviral properties. In this study eight different methods were employed to synthesize silver, copper, and silver/copper functionalized cotton batting textiles. Using silver and copper nitrate as precursors, different reagents were used to initiate/catalyze the deposition of metal, including: (1) no additive, (2) sodium bicarbonate, (3) green tea, (4) sodium hydroxide, (5) ammonia, (6,7) sodium hydroxide/ammonia at a 1:2 and 1:4 ratio, and (8) sodium borohydride. The use of sodium bicarbonate as a reagent to reduce silver onto cotton has not been used previously in literature and was compared to established methods. All synthesis methods were performed at 80\u003csup\u003e\u0026deg;\u003c/sup\u003eC for one hour following textile addition to the solutions. The products were characterized by X-ray fluorescence (XRF) analysis for quantitative determination of the metal content and X-ray absorption near edge structure (XANES) analysis for silver and copper speciation on the textile. Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) and size distribution inductively coupled plasma mass spectrometry (ICP-MS) were used to further characterize the products of the sodium bicarbonate, sodium hydroxide, and sodium borohydride synthesis methods following ashing of the textile. For the silver treatment methods (1 mM Ag+), sodium bicarbonate and sodium hydroxide resulted in the highest amounts of silver on the textile (8900 mg Ag/kg textile and 7600 mg Ag/kg textile) and for copper treatment (1 mM Cu+) the sodium hydroxide and sodium hydroxide/ammonium hydroxide resulted in the highest amounts of copper on the textile (3800 mg Ag/kg textile and 2500 mg Ag/kg textile). Formation of copper oxide was dependent on the pH of the solution, with 4 mM ammonia and other high pH solutions resulting in majority of the copper on the textile existing as copper oxide, with smaller amounts of ionic-bound copper.\u003c/p\u003e","manuscriptTitle":"Parsimonious Methodology for Synthesis of Silver and Copper Functionalized Cellulose","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-05 14:20:03","doi":"10.21203/rs.3.rs-1793366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-06-28T19:41:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-06-28T19:40:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-06-27T02:52:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cellulose","date":"2022-06-25T01:35:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cellulose","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cels","sideBox":"Learn more about [Cellulose](https://www.springer.com/journal/10570)","snPcode":"10570","submissionUrl":"https://submission.nature.com/new-submission/10570/3","title":"Cellulose","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f5410c9b-13a8-4d6d-a102-e7642e0a5a03","owner":[],"postedDate":"July 5th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T19:10:19+00:00","versionOfRecord":{"articleIdentity":"rs-1793366","link":"https://doi.org/10.1007/s10570-023-05099-7","journal":{"identity":"cellulose","isVorOnly":false,"title":"Cellulose"},"publishedOn":"2023-02-25 19:01:48","publishedOnDateReadable":"February 25th, 2023"},"versionCreatedAt":"2022-07-05 14:20:03","video":"","vorDoi":"10.1007/s10570-023-05099-7","vorDoiUrl":"https://doi.org/10.1007/s10570-023-05099-7","workflowStages":[]},"version":"v1","identity":"rs-1793366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1793366","identity":"rs-1793366","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}