Fabrication, characterization, and optimization selection of ceramic particulate reinforced dental restorative composite materials

The objective of the article is to explore the fabrication of dental restorative composite materials and the ranking order using the preference selection index (PSI) as a multi criteria decision making (MCDM) technique under a set of conflict performance defining criteria (PDCs). The polymer matrix of the dental restorative composite was prepared using bisphenol a-glycidyl methacrylate (55 wt.%), triethylene glycol dimethacrylate (44 wt.%), camphorquinone (0.3 wt.%), and ethyl 4-(dimethylamino) benzoate (0.7 wt.%). Five different dental restorative composite material compositions were fabricated using hybrid nSiO2-TiO2 particulates with a variation of nSiO2 (0, 2, 4, 6, 8 wt.%) while TiO2 is constant (15 wt.%). The results revealed that an increasing trend has been found in compressive strength, flexural strength, Vickers hardness, etc., while a decreasing trend has been shown in depth of cure, polymerization shrinkage, degree of conversion etc. The performance analysis of five dental composite formulations via the PSI method shows the following ranking order: nS4 > nS6 > nS2 > nS0 > nS8. The obtained experimental results are associated with the ranking order of the different sets of dental composite formulations. Hence, the preference selection index approach is one of the best techniques among MCDM techniques for ranking under different PDCs.

Introduction environment, and management suppliers. Boran et al. 13 proposed the Fuzzy with TOPSIS technique to provide the right time, right quality, and the right price for the supplier selection problem in the group decision making environment. The Hybrid FAHP-FTOPSIS technique has been widely used in different applications for weight criterion and ranking. [14][15][16][17] Maniya and Bhatt 18 suggested the application of the preference selection index (PSI) approach for the selection of materials from a set of mechanical results. A study has been investigated into the PSI approach used in the parameter settings on the lesser cutting machine for quality and productivity. 19 Attri and Grover 20 applied the PSI technique to the decision making during the life cycle of the production system. The PSI approach has been applied to give the rank among a set of conflict criteria in alloy composite materials.
Based on the above literature, this paper investigates the design, fabrication, and evaluation of mechanical, physical, and chemical properties, and finally, the use of the hybrid PSI technique to predict the optimal formulation and ranking of the alternatives for dental restorative composite materials.

Experimental details and methodology
The polymeric matrix was fabricated by bisphenol a-glycidyl methacrylate (Bis-GMA) (Sigma Aldrich, US), triethylene glycol dimethacrylate (TEGDMA) (TCI, Japan), camphorquinone (CQ) (Spectrochem Pvt. Ltd, Mumbai, India), and ethyl 4-(dimethylamino) benzoate (EDMAB) (Sigma Aldrich, US). Nano silica (nSiO 2 ) (average particle size of 20-80 nm; Sigma Aldrich, US) and titanium oxide (TiO 2 ) (average particle size of 10-25 μm; Sigma Aldrich, US) were used as fillers. Five different formulations (nS0, nS2, nS4, nS6, and nS8) were designed with 15 wt.% as a constant fraction of titanium oxide, while 0-8 wt.% of nSiO 2 with a step of 2 wt.% and the rest of the resinous matrix. The fabrication procedure followed as (i) at first, the resinous matrix composed of Bis-GMA (55 wt.%), TEGDMA (44 wt.%), CQ (0.3 wt.%), and EDMAB (0.7 wt.%) was prepared. (ii) Thereafter, γ-MPS (3-(trimethoxysilyl) propyl methacrylate) (TCI, Japan) silane treated 21 nSiO 2 particles were mixed with the resinous matrix in the required proportion along with TiO 2 particles. (iii) The mixture was then filled into the glass drum tubes of different specimen sizes. It was cured using a light activation unit (LED light cure GX-240, Ahmedabad, India) for 40 s on each side (with light wavelength 420-480 nm, light intensity 1200-2000 mW/cm 2 ). After that, specimens were extracted from the glass molds for further characterization and experimentation of dental composites. The dental composite designations are shown in Table 1. The compressive strength of the dental composite specimens (Ø 5 × 6 mm) as per ASTM D695-08 standard 22 and the flexural strength and modulus of dental composite specimens (25 mm × 2 mm × 2 mm) as per ISO 4049 standard 23 were performed on the universal testing machine (UTM, INSTRON-5967). The Vickers micro-hardness (Walter UHL, Germany) of dental composite specimens (Ø 8 × 6 mm) was measured as per ASTM E384-11 × 10 1 . 24 The experimental density of the dental composite was calculated by Archimedes Principle, 25 while the theoretical density was determined by the rule of mixture. The experiments were repeated four times for each dental composite specimen and the average value was taken for the analysis. Depth of cure as determined by measuring the height of cured dental composite specimens (Ø4 × 8 mm) using a digital caliper (least count = 0.01 mm), water sorption (W SP ) and water solubility (W SL ) as determined with cylindrical glass mold (Ø4 × 8 mm) were calculated as per ISO 4049 standard. The polymerization shrinkage of the fabricated dental samples (Ø4 × 8 mm) was determined by the Archimedes Principle. The degree of conversion 9 is evaluated by the ratio of absorbance intensities of the aliphatic peak at 1634 cm À1 and the aromatic peak at 1608 cm À1 of cured/uncured dental composite samples.
Preference selection index method algorithm 18,20,[26][27][28][29][30] The PSI technique comprises the following steps: Step 1. Determine the objective of the given problem and identify the relevant selection criteria for the evaluation of the set of alternatives. As per previous literature, the hierarchy structure of the complex decision making problem clarifies the problem. The decisive objective must be at the upper portion, evaluating criteria at the middle part and alternatives/options lie at the lower portion of the hierarchy structure ( Figure 1).
Step 2. Formulate the initial decision matrix, A where A ij is the appraisal value of i th alternative in respect to j th criterion, m is the number of alternatives, and n is the number of criteria Step 3. Discover the normalized decision matrix (B ij ) in which the data of the given matrix are expressed using the following equations: · for larger-the-better (beneficial) criteria · for smaller-the-better (non-beneficial) criteria Step 4. Calculate the mean values of normalized performance for each criterion using the following equation Step 5. Compute the preference variation value for each criterion using the following equation Step 6. Calculate the deviations value of the preference for each criterion using the following equation For uniformity, the summation of overall preference value for all the criteria should be unity, that is, Σ ρ j = 1.
Step 7. Compute the criteria weights using the following equation Step 8. Express the preference selection index of alternatives using the following equation Step 9. According to the preference selection index values of the set of alternatives, compute the ranking of alternatives. The alternative which has the highest preference selection index indicates the best ranked alternative.

Results and discussion
Physical, chemical, and mechanical characterization The various characteristics of hybrid fillers (nSiO 2 -TiO 2 ) based dental restorative composite materials are shown and listed in    The strength that resists any material deformation along the axis, when subjected to externally applied compressive load under compression test PDC-2: Flexural strength (FS) (MPa); (higher-the-better) The strength that resists any material deformation against externally applied lateral load in flexure test PDC-3: Flexural modulus (FM) (GPa); (higher-the-better) It is the tendency for a material to resist bending forces

PDC-4: Vickers hardness (HV) (higher-the-better)
Its material resistance against plastic deformation usually by penetration/scratch. Higher hardness improves wear resistance PDC-5: Depth of cure (DC) (mm); (higher-the-better) It is the curing depth of the dental composite by LED curing unit It is determined from the ratio of absorbance intensities of aliphatic (C-C) peak and aromatic peak (C=C) peak to the extraction of un-reacted components from the resinous matrix, monomer shrinkage, loss of weight, and reduction in mechanical properties. 32 Higher filler filled dental composites show high water sorption and water solubility. It may be due to missing filler particles in the oral environment during mastication (saliva in the mouth). As per ISO 4049 standard, water sorption and water solubility should be less than 40 μg/mm 3 and 7 μg/mm 3 , respectively, for any suitable dental composite materials. 33 The depth of cure (7.71, 6.91, 6.76, 6.42, and 5.81 mm) of the different dental composites decreases with increasing nSiO 2 filler particles in the resinous matrix. As per ISO 4049 standard, 23 the depth of cure should be more than 5 mm for the sustainability of the dental composite. The decreasing trend in the depth of cure could be due to the decreasing permeability of LED blue light in the different dental composites. 9 A decreasing trend was observed in the polymerization shrinkage (2.58, 2.21, 1.96, 1.62, and 1.33%) of the dental composites with increasing nSiO 2 filler particles in the resinous matrix. It was investigated that polymerization shrinkage depends upon the curing time and curing intensity of LED blue light and it also partially depends on monomer conversions. 9 A decreasing trend was observed in the degree of conversion (77, 74, 70, 67, and 65%) of the respective dental composites. The decreasing trend of the degree of conversion depends on the permeability of LED blue light in the dental composite and the mobility of the molecules' in the resin matrix. 9 A similar study has been carried out by Yadav and Kumar 33 on nano-hydroxyapatite and zinc oxide reinforced dental restorative composite materials.

Determinations of PIS method
In this article, the PIS method is applied to the decisive matrix (D), having performance criteria discussed in Table 3, with five dental composite formulations as alternatives (nS0, nS2, nS4, nS6, and nS8). The hierarchy structure of dental restorative composite materials is illustrated in Figure 5. The formulation of the initial decision matrix was calculated by equation (1). The normalized decision matrix (Bij) was calculated by equations (2a) and (2b) for larger-the-better and smaller-the-better. The mean values of normalized performance were determined by equation (3). The preference variation value, deviation value, and criteria weights were expressed by equations (4)-(6), respectively. Preference variation value, deviation value, and criteria weights for different PDCs are shown in Table 4. Finally, the preference selection index of alternatives was obtained by equation (7). Computation of Preference Selection Index (Ii) Ranking of alternatives is shown in Table 5. The performance analysis of five dental composite formulations via the PSI method shows a ranking order of nS4 > nS6 > nS2 > nS0 > nS8 under investigation (shown in Table 5). This verifies the consistency of dental resin composites in-line with an objective evaluation of performance (shown in Table 3). Hence, the PSI method could be employed for ranking orders from a set of alternatives based on performance; this could be a tool in the hands of material scientists in decision making due to the conflicting nature of the different sets of materials.

Conclusion
The designed, fabricated, and experimental evaluation of physical, mechanical, and chemical characteristics of hybrid ceramic particulate (nSiO 2 -TiO 2 ) reinforced dental restorative composites are analyzed using the PSI MCDM technique that leads to the following specific conclusions: The formulations have a well disbursement of ceramic particles in the resinous matrix that show superior characteristics like higher compressive strength, lower void content, lower water sorption, a higher degree of conversion etc. The results revealed that an increasing trend has been found in compressive strength, flexural strength, Vickers hardness, etc., while a decreasing trend has been shown in depth of cure, polymerization shrinkage, degree of conversion etc. The performance analysis of five dental composite formulations via the PSI method shows the following ranking order: nS4 > nS6 > nS2 > nS0 > nS8. The PSI method could be employed for ranking orders from a set of alternatives based on performance. This could be a tool in the hands of material scientists in decision making due to the conflicting nature of the different sets of materials. The sensitivity analysis reveals a stable ranking order as PDC weights change from 10% to 30%.