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Modulating Physicochemical Features of Mesoporous Strontium Silicate Nanostructures: A Design of Experiments Approach

A peer-reviewed version of this preprint was published in:
Journal of Functional Biomaterials 2026, 17(10), 490. https://doi.org/10.3390/jfb17100490

Submitted:

31 August 2026

Posted:

03 September 2026

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Abstract
Mesoporous silicate nanoparticles are known for pore size, pore volume, high surface area, and tunable surface properties and as a result, have found application in medicine, healthcare, advanced materials, and devices. Furthermore, tailoring the silicate domain with certain metals has advanced the tissue engineering properties by moderating the cellular function at the molecular level for therapeutic response. However, scarce efforts have been made to explore systemic methods that could change the structure and shape of these particles and their effects on biological properties. Here, we have focused on, using Design of Experiments (DoE), optimizing reaction conditions for the synthesis of mesoporous strontium silicate nanostructures (NS) (nanoparticles – NPs and nanorods – NRs) with different physico-chemical properties. The conditions allowed change in shape from NPs to NRs by tailoring the concentration of liquid ammonia (NH4OH, 82.7 mM to 248.2 mM) during the pre-synthesis incubation state. In addition, the optimized reaction conditions allowed modulating the physico-chemical properties such as surface area, surface charge, amount of metal, pore diameter, pore volume, tailored degradation profile and release profile of the loaded drug (gentamicin). Finally, the protocol has the advantage of allowing incorporation of other metals such as cerium, rhodium, and ruthenium independently and/or in combination.
Keywords: 
metal silicates
;  
mesoporous metal silicates
;  
nanoparticles
;  
design of experiment
;  
nanorods
;  
strontium
;  
cerium
;  
rhodium
;  
ruthenium
Subject: 
Chemistry and Materials Science  -   Nanotechnology

1. Introduction

Since the discovery of colloidal and porous silica in 1983, there have been tremendous research done to develop strategies for the synthesis of silica nanoparticles with different physicochemical properties which include solid nanoparticles (NPs), porous nanostructures (NSs), tunable size, shape, and surface chemistries [1]. These properties and approaches have played a critical role exploring these materials for various biomedical applications and advanced the field of nanomedicine, tissue engineering and regenerative medicine, as numerous NPs-based formulations are in preclinical and clinical trials [1,2,3,4]. The current novel strategies focusing on managing and treating various bone-associated disease conditions rely on properties such as biomacromolecule delivery, bone regeneration, modulating cell function, and bioactivity which are linked to silica nanostructures as a delivery vector or active component [5]. Such nanostructures composed of mesoporous (MP) silica (SiO2) nanoparticles (NPs) when doped with metals like calcium, magnesium, and strontium are gaining attention because of their physico-chemical properties and tendency to modulate biological responses [6,7,8,9,10,11,12,13,14,15]. The common physico-chemical properties such as tunable size, biocompatibility, bioactivity, high surface-to-volume ratio, and high mechanical strength are of great significance for any biomedical or biological application [9]. The mesoporous silica nanoparticles (MPSiO2 NPs) offer additional advance over metal, metal oxide, and/or polymeric nanoparticles. These NPs can be synthesized to have a high surface area, a large pore diameter, and uniform pore size, can be functionalized with specific ligands; and due to their large surface area, these NSs can be loaded with model drug as nanoadditives within the pores.
By combining and collectively harvesting these physical and chemical properties of MPSiO2 NPs, the innate bone regeneration has been recovered from clinically traumatic, infectious, and cancerous conditions [16,17]. In addition, silica-based nanostructures are known to possess the characteristics to improve cell-cell communication, cell adhesion, growth, and proliferation for bone cell/tissue regeneration both in vitro and in vivo [18,19,20,21]. However, the shape (particle or rod) of nanomaterials can play a critical role in tailoring the biological response locally as they can optimize the particle’s physicochemical properties, such as total surface area, loading capacity, release profile, and degradation profile [22]. On the one side, there have been reports that focused on metal-based mesoporous silicate composites scaffolds eg. cobalt-chromium, steel, and titanium; to enhance the load-bearing capability. In addition, the modification or loading such scaffolds with active drug tend to augment osteogenic differentiation and induction of mesenchymal stem cells (MSCs) with localized delivery [16]. The potential of MPSiO2 NPs in remineralization led to investigating the application of these particles in bone-related diseases or defects [23]. Based on the size and or surface-active functional group of nanoparticulate system, the NPs have the potency to target and deliver the payload at different sites within the human body, including craniofacial structures [24,25,26,27]. In addition, there have been efforts to design injectable hydrogels and bioinks constructs for harvesting osteogenic capabilities either by releasing loaded molecules likes microRNA or doped metals with MPSiO2 NPs [28,29]. Furthermore, gold nanoparticles loaded with mSiO2 NPs not only stimulated the anti-inflammatory response but also accelerated the bone formation in coculture defect in an in vivo model [30].
On the other side, similarly, MPSiO2 nanorods (NRs) with cone shape pores and yolk-shell nanostructures (NSs) composed of NiCuFe2O4@mSiO2) have found the application in protein delivery and nano-catalysis [22,31]. The doping of MPSiO2 NSs with metal types such as calcium (Ca), magnesium (Mg), silver (Ag), and strontium (Sr) have shown additional benefits in osteogenic and regenerative applications [9,22,29,32,33,34,35,36,37,38]. The synthesis conditions that guide metal-based MPSiO2 nanostructures towards nanoparticles (NPs, spherical) vs nanorods (NRs) morphologies, and their effective porosity remained unexplored.
The objective of our study was to perform a systematic exploration of the effects of the different synthesis parameters in the structural features of these particles. For that, we used a Design Experiments (DoE) approach and investigated the potential reaction conditions and factors that influence the transition from particles to rods in a colloidal suspension. The different shapes of NSs are potentially known to provide microenvironment signals to cells and may alter the degradation profile or rates as well as may affect strontium and loaded nanoadditive release profile. We also examined the impact of shape on the physico-chemical properties of these Sr-doped MPSiO2 nanostructures, including size, surface charge (zeta potential), surface area, pore diameter, and degradation profile. Furthermore, we examined how both NP and NS affect the loading and release of a potential nanoadditive, gentamicin, which could be added to MP-SiO2 to mitigate infection and improve osteoinduction.

2. Materials and Methods

2.1. Materials

The required chemical reagents such as tetraethyl orthosilicate (TEOS, Si(OC2H5)4, 86578), Hexadecyl trimethyl ammonium bromide (CTAB, CH3(CH2)15N(Br)(CH3)3, H6269), Strontium nitrate (Sr(NO3)2, 243426), cerium nitrate hexahydrate (Ce(NO3)3·6H2O), ruthenium chloride hydrate (RuCl3·xH2O), and rhodium chloride hydrate (RhCl3·3H2O) were purchased from Sigma Aldrich USA. Ammonium hydroxide 28-30% (liq. NH3, NH4OH, BDH3014) ACS grade was purchased from VWR chemicals USA. The stock solutions for each reagent were prepared in DI water.

2.2. Methods

2.2.1. Synthesis of Mesoporous Strontium Silicate Nanostructures (Design of Experiments, DoE Approach)

The strontium (Sr) dopped MPSiO2 nanostructures were synthesized using a modified protocol based on previous publication [9,39]. In brief, the known amount of CTAB was mixed with predetermined volume of H2O and liq. ammonia (NH4OH). The solution was stirred for 30 minutes followed by addition of TEOS (150µL aliquots every 30 second, final volume as per table by DoE) and powdered strontium nitrate salt was successively added over the period of 30 minutes. The reaction was allowed to complete at room temperature for the next four hours. Afterward the nanostructures (NSs) were centrifuged at 3500 rpm in 50 ml centrifuge tubes (about 35 ml at a time), dried at 60 °C overnight. The dried NSs pellet was powdered with mortar-pestle and then calcined at 550 °C for 5 h in an oven. The NSs thus obtained were stored at room temperature prior to any characterization and analysis. Based on the previous publication and preliminary data [9,39], we selected the reaction conditions to design experiments using a full 24 factorial (1st repeat) and a fractional 24-1 factorial (2nd and 3rd repeats) model. As per the model, the DoE software (version 13, by Stat-Ease Inc., Minneapolis, Minnesota, USA) suggested 16 (full 24) and 8 (fractional 24-1) experimental conditions to investigate the effect of conditions on response factors such as surface area, pore diameter, pore volume, nanostructure size and shape. The conditions investigated have been included in table S1 (supplemental information, SI) for repeat 1, a full 24 factorial model - 16 reaction conditions. The final reaction mixture volume was 200.0 ml in a 500.0 ml round bottom flask. The consecutive repeats 2 and 3 were a fractional 24-1 factorial with 8 experiments (Table 1) with common reaction conditions with repeat 1. Additional samples characterization has been included in SI.

2.2.2. Gentamicin Loading and Release Study

For gentamicin loading and release study, 20mg/ml gentamicin solution in phosphate-buffered saline (PBS, 1x, pH 7.4) was prepared. Then, 3mg of each sample type was weighed in 5.0ml clear tubes and mixed with 3.0ml of gentamicin solution for overnight incubation (37 °C, 24h loading). After loading, the samples were centrifuged at 5000rpm for 10 minute and supernatant was carefully removed. The MPSrSiO2 NSs loaded with gentamicin were dried at 37 °C and used within 24h for release study in PBS (1x). At each collection time (up to day 5), the tubes were centrifuged and supernatant (3.0ml) was carefully collected for UV-visible spectroscopy analysis; and refreshed with PBS (1x, 3.0ml) – left at 37 °C with gentle shaking. Gentamicin (absorbance collected at lambda max: 256 nm as per literature) standard curve (included in SI as Figure S1) was used for the calculation of drug released at each time point [40].

2.2.3. Transmission Electron Microscopy (TEM) for Size

For transmission electron microscopy (TEM) imaging the NSs solution was prepared in ethanol and 6 µl suspension was dried on copper grid. The images were collected using a FEI Tecnai Spirit Bio or a FEI Tecnai G2 F30 field emission gun TEM at the Characterization facility (Charfac) University of Minnesota Twin Cities Campus. The TEM images for additional reaction with different amounts of Strontium nitrate (4.75 g and 9.5 g) have been included in supplementary information (SI) file as Figure S2 and Figure S3 respectively.

2.2.4. Surface Charge and Brunauer-Emmett-Teller (BET) Analysis

For surface charge analysis, the particles were dispersed in distilled water (1 mg/ml) and analyzed with a Malvern Nano ZetaSizer ZS (University of Minnesota Nano Center, Physics and Nanotechnology Building) using a disposable U-shaped cuvette with gold electrodes. For surface area, pore diameter and pore volume characterization for prepared nanostructures at different reaction condition, Nitrogen (N2) physisorption isotherm analysis was conducted on a Quantachrome Autosorb-i instrument (Chemical Engineering department, University of Minnesota Twin Cities Campus). For analysis, 25-30 mg powdered samples were degassed in quartz tubes in automated mode (heating at 300 °C for 4 h at 5 °C per minute temperature ramp rate) under vacuum followed by running the set conditions for N2 isotherm using ASiQwin software for data collection and analysis [9].

2.2.5. Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES) Analysis

The different sample types were weighed in transparent plastic tubes (5.0 ml) and redispersed in tris-HCl 3.0 ml pH 7.4, also used as blank for ICP-OES analysis. At the time of collection, the tubes were vortexed and NPs suspension was centrifuged at 5000 rpm for 10 min. The 3.0 ml supernatant was carefully pipetted out and refreshed with 3.0 ml tris-HCl buffer. For ICP-MS analysis, 100 µl stock HNO3 (12 M) was added to each sample tube, vortexed to dissolve the solute in the buffer. The samples were transferred to 15.0 ml centrifuge tubes and the volume of the solution was spiked to 10.0 ml using 2% HNO3 solution in tris-HCl buffer. The samples were analyzed using the iCap 7600 Duo Inductively Coupled Plasma - Optical Emission Spectrometers (ICP-OES) system (Research Analytical Laboratory, Crops Research building, University of Minnesota, St. Paul).

2.2.6. Design of Experiments (DoE) and Statistical Analysis

The data collected through different analyses (total 3 repeats, R1 – a full factorial 24 model - 16 reactions, while R2 and R3, each a fractional 24-1 model with 8 reactions) was fed into the table generated by DoE software and analyzed. The DoE software also ran the statistical analysis using ANOVA model and data has been included in SI. The raw data was also statistically analyzed using Graphpad prism v10 by Graphpad software LLC. The data was analyzed first to confirm parametric distribution and comparison amongst the groups was performed using ANOVA, with Tukey’s multiple comparison tests. The 95% confidence intervals have been used as error bars in all the graphs/data sets. The asterisk representation is as follows: non-significant (ns) for p > 0.05, * for p ≤ 0.05 (a),** for p ≤ 0.01 (b), *** for p ≤ 0.001 (c), and **** for p ≤ 0.0001 (d).

3. Results and Discussion

3.1. Synthesis and Characterization

The strontium (Sr)-dopped MPSiO2 NSs) were synthesized using a modified protocol as described in literature [9]. The CTAB (1.1 or 2.2 g) above the critical micelle concentration with liquid ammonia NH4OH (2.0 ml (82.7mM) or 6.0 ml (248.2mM)) were used in aqueous suspension to form expected templates followed by serial addition of other components such as tetraethyl orthosilicate (TEOS, 5.0 or 10.0 ml with injection of 150 µL every 30 second) and solid powdered strontium nitrate (4.5 or 9.5 g using disposable spatula every 20 seconds). The nanostructures were collected by centrifugation in 50.0 ml tubes, dried at 60 °C overnight, and then calcined at 550 °C for 5h. The synthesized and calcined mesoporous strontium silicate (MPSrSiO2) nanostructures (NSs) were characterized by transmission electron microscopy for size and shape, shown in Figure 1. The TEM imaging suggested that irrespective of shape and size all the nanostructures were mesoporous with different pore diameter, pore volume, and surface area (discussed in next section). However, the shapes from this approach resulted in nanoparticles (spherical, perpendicular axes/diameters, d1 = d2) and nanorods (perpendicular axes/diameters, d1 < d2) irrespective of the amount of strontium salt used. The shape of NSs predominantly decided by the concentration of liquid ammonia (NH4OH) as shown in Figure 1. The self-assembly of CTAB like surfactant in aqueous medium in entropy driven and the transition from spherical shape at critical micelle concentration to worm or rod shape can be driven by change in ionic strength (addition of liq. ammonia) or incorporation of additives such as TEOS or metal salts [41]. The extended TEM characterization of full factorial (16 experiments) has been included in supplementary information as Figure S1 and Figure S2. The TEM imaging analysis showed that the shape of mesoporous strontium silicate was dependent on concentration of liquid ammonia.

3.2. Surface Charge and Brunauer-Emmett-Teller (BET) Nitrogen Isotherm

Zeta potential analysis of Sr-dopped MPSiO2 NSs (mesoporous (MP) strontium silicate nanostructures, MPSrSiO2) suggested that the surface charge on the nanostructures remained between -21.85 mV and -31.80 mV, as shown in Table 2. However, when we compared the surface charge of MPSrSiO2 NPs (average Zeta = -25.43mV) with NRs (average Zeta = -30.50mV), the NRs had a significantly lower surface charge (two-tailed t-test, p = 0.0061). Interestingly, his difference in surface charge was significant despite both NPs and NRs having the same average amount of strontium salt and TEOS. In general, the zeta potential or surface charge corelates to the stability of nanostructures. With increasing surface charge (towards positive scale), the nanostructures tend to be unstable [42]. The nitrogen physisorption isotherm was used to characterize the surface area, pore volume, and pore size. A typical adsorption and desorption curve for nitrogen for each run has been included as Figure 2. The shape of curves in Figure 2A–H suggest the mesoporous pore diameter. Typically, the structures/materials with pore diameter less than 2nm are known to be microporous, while an average pore diameter between 2nm to 50nm constitutes mesoporous; and macroporous materials are known to have pores with diameter larger than 50nm [43,44]. The pore size distribution graphs are included in Figure 2I,J. Figure 2I,J clearly demonstrate that both NPs and NRs exhibit a reduced number of pores within the microporous range, predominantly displaying pores in the mesoporous range. Based on the data collected via BET nitrogen isotherm, NPs (run: 5, 3, 7, and 6) had, on an average, significantly higher surface area, pore diameter, and pore volume compared to NRs (run: 2, 4, 1, and 8) as shown in Figure 3. Design of Experiment software data analysis has been discussed in section 3.4, investigating the effect of reaction parameters on various response-factors.

3.3. Degradation Profile via ICP-OES Analysis

The different nanostructures synthesized at respective conditions were exposed to Tris-HCl buffer pH 7.04 for degradation analysis. The samples were divided into two groups as NPs and NRs to make the analysis simple. The degradation profile, based on the amount of Strontium and silicon quantified by ICP-OES analysis, suggested a logarithmic fit to the data. Such a logarithmic fit analysis is known to couple influence of multiple stress factors [45]. Based on this, we can speculate a initial accelerated degradation of nanostructures, which could be related to the surface effect either because of degradation itself or release of chemisorbed metal on the surface, followed by a more controlled or slower release of both elements (strontium (Sr) and silicon (Si)) up to 120 days. According to the analysis, NPs exhibited a slower and relatively smaller release of Sr and Si compared to NRs as shown in Figure 3. The amount of Si released from NPs varied from 23.13ppm to 115.1ppm for Day 1 to Day 112, respectively, while the in the case of NRs, the amount increased to 29.11ppm and 138.4ppm. However, the released amount of silicon was not statistically different until day 84 (p = 0.0393) and day 112 (p = 0.0316) between NPs and NRs (data included in SI as Figure S4). Contrary to that ICP-OES data suggested statistically significant changes in the released concentration of Sr from Day 3 (p = 0.0402, NPs (274.2ppm) vs NRs (256.2ppm)). Furthermore, the amount of Sr released from NPs increased to 518.1ppm while for NRs it was 695.9ppm on day 112 (p = 0.0002). The Sr and Si release profile from NPs and NRs showed the opposite trend relative to surface area, pore diameter, and pore volume, which indicates that the synthesized Sr-dopped MPSiO2 NPs are more stable than the NRs. Even though the NPs reported slightly lower negative surface charge, and higher surface area, pore diameter, and pore volume as conveyed in Figure 3.

3.4. Design of Experiments (DoE) Analysis

The effect of CTAB (A, g) liquid ammonia (NH4OH, B, ml), tetraethyl orthosilicate (TEOS, C, ml), and Strontium nitrate (Sr(NO3)2, D, g) on response factor such as surface charge (mV), surface area (m2/g), pore diameter (nm), pore volume (cc/g), Gentamicin loading (µg/mg), and Gentamicin release (%) were investigated using DoE software. The study was conducted using the 2-level factorial design with Reduced 2FI model. For the simplicity of the manuscript, the data for only the factors significantly affecting the responses have been included in Figure 4 (Surface charge, surface area, Pore diameter, and pore volume), and 5 (Gentamicin loading and release). The additional conditions have been included in the SI file.
Figure 4i and 4ii show changes in surface charge and surface area as a function of the amount of liq. NH3 (B) and TEOS (C) added relative to the amount of CTAB (A) and Sr (D). The ‘Reduced 2FI’ model suggested that AD combination had a significant effect on surface charge and surface area, with p-values of <0.0001 and 0.0237, respectively. The model used an ANOVA linear regression with no transformation applied to the model. The model itself was statistically significant with F-values of 30.18 and 27.31, respectively. The data suggested that as the concentration of TEOS increased, the charge reduced, which is associated with an increasing number of hydroxyl (-OH) groups, as the polycondensation reaction proceeds during either nanoparticles or nanorods formation. As shown in Figure 4i, the surface charge remained between -21.3 and -31.1 mV, with an average value of -25.43 mV (SD: 3.77) for MPSrSiO2 NPs, whereas for the NRs, it further reduced to -30.5 mV (SD: 1.54), which was statistically different (as shown in Figure 3A). In terms of surface area, NPs showed little variability across CTAB concentration. In contrast, NRs at lower CTAB (1.1g) had lower surface area (~150-350 m2/g) irrespective of Sr amount (4.75 or 9.5 g), which increased to approximately 500 m2/g for CATB 2.2g (Figure 4ii). This increase in surface area suggests that incorporating CATB molecules into the template would lead to more and large mesoporous structures or canals within the nanostructures, which has been reported previously [46,47,48,49]. The model also suggested CTAB (A, p = 0.8012), independently and in combination with liq. NH3 (B, AB: p = 0.0673), and TEOS (C, AC: p = 0.9866) had some effect on charge of nanostructures, but the effect was not statistically significant. While in the case of surface area, TEOS (C) and CTAB (A or AC) did not significantly affect it, independently (p = 0.2333) or in conjunction (p =0.4922).
Similar to surface area, pore diameter and pore volume followed the similar trend. Figure 4 shows the variability in pore diameter (4iii) and pore volume (4iv) as a function of CTAB (A) and liq. NH3 (B), in relation to the amount of TEOS (C) and Sr-Nitrate (D). The increase in CTAB (A) concentration (1.1 g to 2.2g) at a higher concentration of liq. NH3 (B), 6.0ml did not significantly change the pore diameter (4.48 nm, SD: 1.59), independent of increasing concentration of TEOS (C) and or Sr-nitrate (D). The reaction condition resulted in the synthesis of mesoporous nanorods. However, when concentration of liq. NH3 (B) was reduced to 2.0ml, this led to the synthesis of mesoporous nanoparticles. There was a significant increase in pore diameter with increasing concentration of CTAB, TESO and Sr-nitrate, reaching 26.48 nm (SD: 14.51) as shown in Figure 4iii. While the pore volume was lowest for nanorods when synthesized using 6.0ml liq. NH3 but increased with an increasing amount of TEOS. This further increased significantly for nanoparticles (synthesized using 2.0ml liq. NH3), with the highest being at highest concentration of CTAB and TEOS as shown in Figure 4iv.
The surface area, pore diameter, and pore volume can sufficiently affect drug or molecular loading within the nanostructure and furthermore can tailor the release of loaded molecules. According to Christoffer G. Bavnhoj and et.al., molecular loading increases with increasing surface area and decreasing pore volume because of decreasing pore diameter in mesoporous silica; thereby influencing the drug-loading capacity dependent on the molecular size of the drug [50]. Contrary to that in our study, gentamicin loading in both types of nanostructures, nanoparticles or nanorods followed similar trends as evident in Figure 5i. For Sr-dopped MPSiO2 NPs, there was a small variability in surface area, which translated to smaller changes in gentamicin loading. Contrary to that MPSrSiO2 NRs showed large variability in surface area from ~150 m2/g to ~650 m2/g, which translated to gentamicin loading of ~26 to 135 µg/mg nanorods with an average loading of ~89.51 µg /mg (SD: 44.26). In addition, the MPSrSiO2 NPs showed on an average, higher drug loading (136.93 µg/mg (SD: 14.15)), which was tailored based on the concentration of TEOS, Sr-nitrate and CTAB. Higher TEOS, lower Sr-nitrate and lower CATB led to higher drug loading (143.27 µg/mg) in our study. Contrary to drug loading, MPSrSiO2 NRs nanorods showed faster and more drug release compared to NPs. Based on the composition, NRs released 65-90% drug; on the other hand, MPSrSiO2 NPs showed ~48 to 90% drug release, based on the composition, as can be seen in Figure 5ii. Amongst NRs, increasing the amount of CTAB from 1.1g to 2.2g resulted in only faster release (71.36% to 88.12%). While in the case of NPs, increasing the amount of CTAB led to slower drug release (73.05% to 51.08%). All the F values and P values for each factor and respective model are included in tabular form as Figure S5 in SI.

3.5. Tunning the Shape

The potential path to NR studied via electron microscopy has been included as Figure 6. The actual mechanism from particle to rod shape might be more critical and complicated to investigate which needs further investigation. However, based on all the experimental approaches via DoE study, at the same concentration of all the reagents but liquid ammonia (increased from 2.0ml (82.7mM) to 6.0ml (248.2mM)), we observed a change in shape from NP to NR. Figure 6i shows a schematic of the speculated formation of spherical and rod-shaped CTAB micelles as the liquid ammonia (NH4OH, liq. NH3) concentration increased. Similar reports suggesting that an increase in ammonia concentration can lead to change in shape (particle to rod) and size (25-150 nm to 2 µm) of nanostructures have been reported elsewhere [51]. In our study, we had an unsuccessful attempt of capturing the CTAB micelles prior to addition of other additives such as TEOS; however, the earliest timepoint (180-210 seconds in reaction, 1.0 ml TEOS) showed relatively small-size seed silica NPs at lower concentration of ammonia (82.7mM, 2.0ml) as shown in Figure 6ii,iii. With an increasing amount of TEOS the (5.0 ml to 10.0 ml), there was an increase in size of NPs and nanorods for 82.7mM and 248.2mM liq. NH3 (NH4OH) respectively. The addition of strontium nitrate (9.5 g), collection of NSs by centrifugation, and calcination did not alter the nanostructure shape as captured by electron microscopy and shown in Figure 6. Increasing the amount of liq. NH3 had a significant effect on the shape, as evident in the aspect ratio (AR) data shown in Figure 6iv. At a lower concentration of liq. NH3 2.0ml (82.7mM), the aspect ratio remained around 1.2 (SD: 0.068), and tailoring the concentration of CTAB (1.1g – 1.22 and 2.2g – 1.18) did not significantly change the average aspect ratio (p=0.3591) suggesting no effect on nanostructure shape. In the case of Sr-dopped MPSrSiO2 NRs, the average aspect ratio was 2.56 (SD: 0.64), and it increased significantly from 2.12 to 3.01 (p=0.0197) with increasing CTAB concentration. The concentration of TEOS and Sr-nitrate had a minimal effect on the aspect ratio. As per the literature, the surfactant molecules in the aqueous phase attain a spherical shape above the critical micelle concentration. As a molecular simulation model, it is the hydrocarbon carbon chain of CATB that forms the core of micelle because of hydrocarbon density [52,53]. In addition, the molecular structure of surfactant ions predominately plays a critical role in sphere-to-rod transition, as longer carbon chain molecules provide bigger tail volume and increased hydrophobic interaction favoring rod-like micelle [54,55]. Other shapes such as nanorods or wormlike structures have been linked to surfactant concentration, salt species, salt characteristics, and reaction condition such as pH and ionic strength [38,41,56]. The NaBr concentration above 0.06 M resulted in rod formation along with spherical CTAB micelles [56]. Furthermore, the alkyl group chain length (Cn, n = 1 to 4) in the silane is known to control the rate of hydrolysis there by guiding the size (20 to 80 nm) of mesoporous silica nanoparticles [57].

3.6. Future Directions

The method or approach motivated us to synthesize nanoparticles and nanorods with other metals and metal combinations for potential application in other fields such as ion delivery, tissue regeneration, and catalysis. There is limited literature focusing on tailoring the shape of nanostructures. As per literature, the physicochemical properties include the shape of NSs are known to modulate materials biological response at molecular level [58,59,60,61]. After optimizing the conditions for MPSrSiO2 NPs, we synthesized mesoporous cerium silicate (MPCeSiO2) nanoparticles [62] and nanorods doped with other metals such as ruthenium and rhodium with the limitation of metal salt solubility (in water) in relation to amount of TEOS or silicon (metal:silicon molar ratio less than 1). Figure 7 shows mesoporous cerium silicate nanoparticles (MPCeSiO2 NPs) and MPCeSiO2 nanorods, along with ruthenium (Ru) or rhodium (Rh) dopped mesoporous NSs synthesized using 2.2g CATB and altered NH4OH concentration. The solubility of metal salt played a crucial role as with increasing concentration of metal (with respect to Si), there was an increase in aggregate formation for both type of nanostructures (particles and/or rods). Similar observation has been reported in literature [63]. However, the method gives an edge to control the molar ratios of metal:Si within the nanostructures there by tailoring the surface area, pore diameter, pore volume, loading/release profile of model drug/molecule/ion, catalytic, and other physico-chemical properties. In a study by J. Zhang, calcium (5-10%) ions were replaced with iron (Fe) within the mesoporous calcium silicate NSs with no cytotoxicity observed against osteoblast cell line and improved bone regeneration and localized magnetic hyperthermia application [64]. Literature has provided enough reports of dopped porous metal silicate NPs with metal such as copper, magnesium, silver, zinc and delivery of payloads such as antibiotic, therapeutic drugs, growth factors both in vitro and in vivo [9,65]. When modified with polymers, lipids, and biomolecules, such MP metal silicate NSs are known to show boosted stability, biological responses, and responsiveness [66]. Highlighting the catalytic properties of porous silica, nickel-zirconia based mesoporous NSs supported the conversion of methane (CH4) and carbon dioxide (CO2) into syngas [67]. The application of porous materials is not limited to delivery and catalysis, but as the porous structure allows regulatable porosity, electron, and ion transport, and high surface area; harnessing energy storage is a potential application as suggested in review article [68,69].

4. Conclusions

Strontium (Sr)-doped mesoporous silica nanoparticles (MP-SiO2-NPs) were synthesized in this study using a Design of Experiments approach, and the study’s conclusions were:
  • Liquid ammonia concentrations strongly influenced the shape of Sr-doped MP-SiO2. Increasing liquid ammonia concentrations shift MP-SiO2 nanostructures toward NR.
  • Sr-doped MP-SiO2-NP have higher nanostructure stability than NR and lower Sr release.
  • Future studies will examine the dynamic between stability and Sr release where specific applications may favor stability, such as restorative materials and implant coatings, whereas other applications may favor higher Sr release that can augment pulpal stem cell differentiation and regenerative bone growth.
  • Higher TEOS, lower Sr-nitrate, and lower CATB led to higher gentamicin loading. Future studies can examine the addition of different antimicrobial nanoadditives and their bioactivity.
  • Ability to dope other metals (Ru and or Rh, similar metals) can lead to application of mesoporous metal silicate NSs in catalysis as well.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org.

Author Contributions

Conceptualization, D.K. and I.M.; methodology, D.K., I.M., T.S., T.N., G.L., M.J., D.M., W.G.; software, D.K., I.M.; validation, D.K., I.M., T.N., G.L., M.J., D.M., W.G.; formal analysis, D.K., I.M., T.N., G.L., M.J., D.M., W.G.; investigation, D.K., I.M., T.N., G.L., M.J., D.M., W.G.; resources, D.K., I.M., T.N., G.L., M.J., D.M., W.G.; data curation, D.K., I.M., T.N., G.L., M.J., D.M., W.G.; writing—original draft preparation, D.K., I.M., T.S., R.S.J., C.A.; writing—review and editing, D.K., I.M., T.S., R.S.J., C.A.; visualization, D.K., I.M.; supervision, D.K., I.M.; project administration, D.K., I.M.; funding acquisition, D.K., I.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by University of Minnesota Start-up package, grant numbers, “1024-11462-20088-8008396”, “1026-11456-20088-8008421”, and “1026-11434-20088-8008421”.

Data Availability Statement

The raw data generated during the study and used for analysis and to plot the graphs can be downloaded from the DRUM repository by University of Minnesota and can be accessed using the link: “https://doi.org/10.13020/5F90-E843” or “https://hdl.handle.net/11299/281832”.

Acknowledgments

DK acknowledges financial support from the School of Dentistry start up fund. IM thanks the University of Minnesota’s Office of Academic Clinical Affairs Faculty Research Development Grant for providing the financial support. In memoriam, Prof. Isha Mutreja, whose hard work, vision, and generosity were influential in this study.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript
BET Brunauer-Emmett-Teller
CTAB Hexadecyltrimethylammonium bromide
DoE Design of Experiments
ICP-OES Inductively Coupled Plasma – Optical Emission Spectroscopy
MP mesoporous
TEM Transmission Electron Microscopy
TEOS Tetraethyl orthosilicate
NSs Nanostructures
NPs Nanoparticles
NRs Nanorods
Sr Strontium
SiO2 Silica/silicate
Ru Ruthenium
Ce Cerium
Rh Rhodium
NH4OH Liquid ammonia, ammonia hydroxide

References

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Figure 1. TEM image for Sr-doped MP silicate NSs (NPs – 1st row and NRs – 2nd row) synthesized using 8 reaction conditions with varying parameters. TEM for additional conditions (8 more) has been included in SI (total 16 conditions).
Figure 1. TEM image for Sr-doped MP silicate NSs (NPs – 1st row and NRs – 2nd row) synthesized using 8 reaction conditions with varying parameters. TEM for additional conditions (8 more) has been included in SI (total 16 conditions).
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Figure 2. Absorption and desorption graphs for each reaction (A to H) along with pore diameters (I and J) of prepared nanostructures, compared between nanoparticles and nanorods. The inset images in A to H showing the structure for respective NS for the isotherm graph.
Figure 2. Absorption and desorption graphs for each reaction (A to H) along with pore diameters (I and J) of prepared nanostructures, compared between nanoparticles and nanorods. The inset images in A to H showing the structure for respective NS for the isotherm graph.
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Figure 3. physico-chemical properties such as zeta potential (A), surface area (B), pore diameter (C), and pore volume (D) compared for NPs and NRs as nanostructure. Also, release of strontium metal (E,G) and silicon (F and H) release from each nanostructure type on degradation when exposed to acidic buffer up to day 112.
Figure 3. physico-chemical properties such as zeta potential (A), surface area (B), pore diameter (C), and pore volume (D) compared for NPs and NRs as nanostructure. Also, release of strontium metal (E,G) and silicon (F and H) release from each nanostructure type on degradation when exposed to acidic buffer up to day 112.
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Figure 4. Heatmap representation for Zeta potential (Surface charge, i), Surface Area (ii), Pore Diameter (iii), and Pore Volume (iv) data analysed using design of experiment software.
Figure 4. Heatmap representation for Zeta potential (Surface charge, i), Surface Area (ii), Pore Diameter (iii), and Pore Volume (iv) data analysed using design of experiment software.
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Figure 5. Heatmap representation for loading and release of Gentamicin drug from nanostructures using DoE software.
Figure 5. Heatmap representation for loading and release of Gentamicin drug from nanostructures using DoE software.
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Figure 6. Potential growth mechanism (i) for nanoparticles and nanorods with change in ionic strength of reaction solution assisted with liq. ammonia (NH4OH), and figure ii and iii shows the captured growth structrue using TEM imaging. While figure iv shows the change in D1/D2 ratio represented using 3D heatmap graphs using DoE analysis.
Figure 6. Potential growth mechanism (i) for nanoparticles and nanorods with change in ionic strength of reaction solution assisted with liq. ammonia (NH4OH), and figure ii and iii shows the captured growth structrue using TEM imaging. While figure iv shows the change in D1/D2 ratio represented using 3D heatmap graphs using DoE analysis.
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Figure 7. Synthesis of Ce (A and B) and mixed Cerium-Ruthenium (C) and Cerium-Rhodium (D) nanostructures using 2.2g CTAB and alternate concentration of liq. ammonia as supported in reaction conditions.
Figure 7. Synthesis of Ce (A and B) and mixed Cerium-Ruthenium (C) and Cerium-Rhodium (D) nanostructures using 2.2g CTAB and alternate concentration of liq. ammonia as supported in reaction conditions.
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Table 1. the factor conditions investigated using the design of experiment (DoE) software for runs (1 to 8).
Table 1. the factor conditions investigated using the design of experiment (DoE) software for runs (1 to 8).
Std Run Factor A: CTAB (g) Factor B: lid NH3 (ml) Factor C: TEOS (ml) Factor D: Sr(NO3) (g)
4 1 1.1 (15.1mM) 6 (248.2mM) 10 (224.2mM) 4.75 (106.3mM)
2 2 1.1 (15.1mM) 6 (248.2mM) 5 (112.1mM) 9.5 (224.4mM)
3 3 2.2 (30.2mM) 2(82.7mM) 5 (112.1mM) 9.5 (224.4mM)
6 4 2.2 (30.2mM) 6 (248.2mM) 5 (112.1mM) 4.75 (106.3mM)
1 5 1.1 (15.1mM) 2 (82.7mM) 5 (112.1mM) 4.75 (106.3mM)
8 6 2.2 (30.2mM) 2 (82.7mM) 10 (224.2mM) 4.75 (106.3mM)
7 7 1.1 (15.1mM) 2 (82.7mM) 10 (224.2mM) 9.5 (224.4mM)
5 8 2.2 (30.2mM) 6 (248.2mM) 10 (224.2mM) 9.5 (224.4mM)
Table 2. The surface charge values and BET analysis data in terms of surface area, pore diameter, and pore volume along with standard deviation for each run (1 to 8).
Table 2. The surface charge values and BET analysis data in terms of surface area, pore diameter, and pore volume along with standard deviation for each run (1 to 8).
Run Surface charge (unit: mV) (SD) Surface area (unit: m2/g) (SD) Pore diameter (unit: nm) (SD) Pore Volume (unit: nm3) (SD)
1 (-29.45) (1.48) 341.796 (33.81) 0.389 (0.07) 3.989 (0.49)
2 (-30.65) (0.78) 173.963 (23.23) 0.343 (0.08) 4.860 (0.19)
3 (-22.50) (0.99) 447.302 (48.07) 1.247 (0.15) 35.952 (0.08)
4 (-30.10) (1.41) 536.778 (109.94) 0.535 (0.09) 3.149 (0.55)
5 (-21.85) (0.78) 591.125 (12.74) 0.792 (0.03) 3.454 (0.03)
6 (-27.75) (2.19) 486.424 (40.22) 1.934 (0.18) 30.989 (8.67)
7 (-29.60) (2.12) 401.572 (28.01) 0.745 (0.09) 35.513 (0.84)
8 (-31.80) (2.40) 418.939 (32.84) 0.530 (0.11) 5.936 (0.66)
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